GEOMATE 2020 MELBOURNE, AUSTRALIA 11-13 NOVEMBER, 2020
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENTiPROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE – GEOMATE 2020
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENT, MELBOURNE,
AUSTRALIA 11-13 NOVEMBER, 2020

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

Dr. John V. Smith
School of Civil and Infrastructure Engineering
RMIT University, Australia

THE GEOMATE INTERNATIONAL SOCIETYii

Copyright @ 2020 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: 978-4-909106049 C3051

iii

Tenth International Conference on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020 ISBN: 978-4-909106049 C3051

Table of Contents

Prefacexii
xiii

Organization

Technical Papers
ID

1

Geotechnique

2

GX128

ELASTIC BEHAVIOURS OF A CEMENTED LATERITIC SOIL FOR A ROAD BASE
Sawanya Dararat, Warat Kongkitkul, Thitikorn Posribink and Pornkasem Jongpradist

3

GX132

INFLUENCES OF SEEPAGE FLOW HISTORIES ON DETERIORATION WITHIN EMBANKMENTS
Koji Nakashima and Katsuyuki Kawai

9

GX134

RELATIONSHIP BETWEEN THE PRODUCTION CONDITIONS OF SOLIDIFIED CRUSHED SOIL AND
THE PHYSICAL PROPERTIES AND STRENGTH OF THE PRODUCTAtsuko Sato and Osamu Hatakeyama

15

GX141

PREDICTION METHOD OF SLIP SURFACE DEPTH FOR SLOPE HEALTH MONITORING BASED ON
VOLUMETRIC WATER CONTENT
Keigo Koizumi, Sota Tsukabe, Mitsuru Komatsu, Kazuhiro Oda and Shinichi Ito

21

GX142

FUNDAMENTAL STUDY ON INSTALLATION POSITION OF SENSORS FOR ON-SITE MONITORING
FOCUSING ON SLOPE FAILURE
Haruki Iwamoto, Keigo Koizumi, Mitsuru Komatsu and Hiroshi Tsutsumi

27

GX159

A METHOD FOR REAL-TIME EVALUATION OF THE PROPERTIES OF COMPACTED SOIL FROMACCELERATION RESPONSE OF COMPACTION ROLLER
Hiroyuki Nagai, Yoshiaki Kikuchi, Sou Ihara, Shohei Noda, Taichi Hyodo and Fumio Tatsuoka

33

GX162

MECHANICAL STRENGTH OF PEAT SOIL TREATED BY FIBER INCORPORATED MICROBIALCEMENTATION
Meiqi Chen, Sivakumar Gowthaman, Kazunori Nakashima and Satoru Kawasaki

41

GX168

HYDRAULIC CONDUCTIVITY CHARACTERISTICS OF A FINE-GRAINED SOIL POTENTIAL FOR
LANDFILL LINER APPLICATION
Jonathan M. Tiongson and Mary Ann Q. Adajar

47

Gx169

GEOTECHNICAL CHARACTERIZATION OF ALLUVIAL SOIL AS AN ALTERNATIVE ROADWAYCONSTRUCTION MATERIAL
Emerzon S. Torres and Mary Ann Q. Adajar

53

GX172

RESISTANCE FORCE AND GROUND BEHAVIOR IN THRUST PROTECTION FOR BURIED PIPES
USING GEOGRID GABION
Hiroyuki Araki and Daiki Hirakawa

59

iv

GX179A STUDY ON SOIL RESISTIVITY MEASUREMENT USING READILYINSTALLABLE ELECTRODES MOUNTED ON DRIVING-PIPES
Hidetake Matsui, Tadafumi Fujiwara, Hiroyasu Ishii and Yoh Takahata

65

GX181

THE CONTRIBUTION OF PILES TO THE AVOIDANCE OF THE RAINFALL-INDUCED SLOPE FAILURE
Yujia Wang, John V. Smith and Majid Nazem

71

GX183

SIMULATING EARTHQUAKE-INDUCED SLOPE FAILURES USING A SOLID-FLUID COUPLING
MODEL BASED ON THE SMOOTHED PARTICLE HYDRODYNAMICS FRAMEWORKYusuke Ono

77

GX184

EVALUATION ON THE PERFORMANCE OF MICP TREATED SLOPE SOIL UNDER ACID RAINENVIRONMENT
Sivakumar Gowthaman, Arash Mohsenzadeh, Kazunori Nakashima, Hiromi Nakamura andSatoru Kawasaki

82

GX185

ESTIMATION OF SUBSURFACE STRUCTURE AND GROUND MOTION CHARACTERISTICS IN THE
DAMAGED AREAS DUE TO THE 2016 CENTRAL TOTTORI PREFECTURE EARTHQUAKE, JAPANIsamu Nishimura, Tatsuya Noguchi and Takao Kagawa

88

GX186

EVALUATION OF SCENIC HEALING AT VIEWPOINTS IN PARKS CONSIDERING DIFFERENCES INMIGRATION BEHAVIOR
Kazuki Ito, Kazunari Tanaka

94

GX189

ANALYSIS OF EMBANKMENT STRESSES PRODUCED DURING CONSTRUCTION AND IN-SERVICEPHASES CONSIDERING EMBANKMENT GEOMETRIES
Shin-ichi KANAZAWA and Haruna IGARASHI

100

GX190

DEVELOPMENT OF THE GROUND INJECTION METHOD USING GEO-POLYMER MATERIAL
Satoshi Tashiro, Naoaki Suemasa, Takamitsu Sasaki and Koichi Nagao

106

GX192

EVALUATION OF EARTHQUAKE-INDUCED SLOPE FAILURE HAZARD USING THE ANALYTICHIERARCHY PROCESS METHOD
Masanori Kohno and Yusuke Ono

112

GX194

ESTIMATION OF SUBSURFACE STRUCTURE BASED ON MICROTREMOR AND SEISMICOBSERVATION IN THE DAMAGED AREA DUE TO 2018 HOKKAIDO EASTERN IBURIEARTHQUAKE, HOKKAIDO, JAPANTatsuya Noguchi, Isamu Nishimura, Yusuke Ono and Masanori Kohno

118

GX200

STUDY ON COLLAPSE MECHANISM OF RIVER LEVEE DUE TO EXTERNAL WATER LEVELFLUCTUATION
Hayato Suzuki, Shin-ichi Kanazawa, Yudai Tonoki

124

GX201

STRESS ANALYSIS OF RIVER EMBANKMENT CONSIDERING EXTERNAL WATER LEVELFLUCTUATION
Tsubasa Ichijo, Shin-ichi Kanazawa

130

GX202

ANALYTICAL STUDY ON QUALITY EVALUATION OF EMBANKMENT STRUCTURE WITH A VIEW
TO LONGER LIFE
Emika Nakamura, Shin-ichi Kanazawa

136

GX206

NUMERICAL INVESTIGATION ON PILE BEHAVIOR DUE TO THE RISING GROUNDWATER EFFECT
Harutus Phoban, Uma Seeboonruang and Prateep Lueprasert

142

GX223

COUPLED ANALYSIS CONSIDERING DENSITY CHANGE OF UNSATURATED SOIL
Daiki Yamashita and Shin-ichi Kanazawa

147

GX224

MUTUAL RELATIONSHIP BETWEEN SUFFUSION AND SHEAR BEHAVIOR IN TRIAXIALCOMPRESSION APPARATUS
Taichi Ishimaru, Motoyuki Suzuki and Tomoki Wakamatsu

153

GX227

INVESTIGATION OF TIME-SERIES EXTENSOMETER DATA IN A CONCRETE LINED MINE SHAFT
Jordan Tyler Kalyvas, John Victor Smith and Gang Ren

159v

GX229LIMIT ANALYSIS OF THE FUNDAO TAILINGS DAM SLOPE FAILURE IN BRAZIL 2015
Juan Hernandez Espinoza, John V. Smith and Majid Nazem

164

GX235

EXPERIMENT REGARDING MONTMORILLONITE CONTENT AND STRENGTH PROPERTIES IN
BENTONITE UNDER THE INFLUENCE OF HEAT
Rie Suzuki, Shin-ichi Kanazawa, Masaki Yanai and Yoshino Osamu

170

GX252

A VALIDATION OF SHALLOW SLOPE FAILURE MONITORING METHOD BASED ON THE FIELD SOIL
MOISTURE OBSERVATIONS
Mitsuru Komatsu and Keigo Koizumi

176

GX253

A STUDY ON MIGRATION OF KAOLIN CLAY SUSPENSION IN THE PORES USED FOR REDUCINGSAND LAYER PERMEABILITY
Keisuke Takahashi, Mitsuru Komatsu, Toru Iwata, and Kouji Takimoto

182

GX254

A REVIEW ON THE UNDERSTANDING OF THE PROPERTIES OF GRANULATED BENTONITEMIXTURES IN THE RADIOACTIVE WASTE DISPOSAL CONCEPTS
Mazhar Nazir, Ken Kawamoto and Toshihiro Sakaki

188

GX261

THE NEW ADVANCED PROTOTYPE OF AIRBORNE VISUAL CONTROL OF A GROUND ROBOT
Oleg Kupervasser, Hennadii Kutomanov, Vitalii Sarychev, and Roman Yavich

194

GX272

INVESTIGATION OF OPEN-PIT MINE DRAINAGE FEATURES
Cristhiana Perdigão, Ashley Dyson, Mohammadjavad Yaghoubi and Thomas Baumgartl

200

GX274

A COMPREHENSIVE LEACHATE STUDY OF FIRED-CLAY BRICKS INCORPORATING CIGARETTE
BUTTS
Halenur Kurmus and Abbas Mohajerani

205

GX277

EFFECT OF FINES CONTENT ON LIQUEFACTION CHARACTERISTICS
Wen LIU, Shoji KAMAO and Kei YOKOYAMA

211

GX288

STUDY ON ROTATION ANGLE ESTIMATION METHOD OF UPLIFT FOUNDATION CONSIDERING
PLASTICITY REGION
Yasuhide Mochida and Makoto Minemoto

217

GX298

THE EFFECT OF CEMENT ADDITIVE TO IMPROVE STRENGTH
OF GRATI SOFT SOIL
Yulvi Zaika, Harimurti, Adista Fahara, Nisa Safira, Wahid Darmawan

223

GX310

LABORATORY MODEL TEST ON CONTACT EROSION BETWEEN SAND AND SILTY CLAY LAYERS
Pradeep Pokhrel, Jiro Kuwano

229

GX314

UNIAXIAL COMPRESSION TEST OF BENTONITE UNDER VARIOUS CONDITIONS
Masaki Yanai, Shinichi Kanazawa, Rie Suzuki and Osamu Yoshino

235

GX324

STUDY ON SOIL-BENTONITE VERTICAL CUTOFF WALLS WITH EARTHQUAKE-PROOF
PERFORMANCE
Koji Watanabe, Shinya Matsushita, Daisuke Ueno and Masaaki Hasegawa

241

GX328

FUNDAMENTAL STUDY ON TRENCH STABILITY OF DIAPHRAGM WALL
Yuki Sasaki, Koji Watanabe, Makoto Arakawa and Tadahisa Yamamoto

247

GX331

A PRACTICAL APPROACH TO DETERMINE THE TEMPERATURE CORRECTION FOR EC-5
MOISTURE SENSORS EMBEDDED IN VERTOSOL
Rajitha Shehan Udukumburage, Chaminda Gallage and Les Dawes

253

GX344

EXISTENCE OF SILT SEAM LAYER TRIGGERNG FLOW LIQUEFACTION ON THE PALU MW 7.5
EARTHQUAKE 2018
Dandung Sri Harninto and Widjoyo A. Prakoso

259

vi

GX345EXPERIMENTAL RESEARCH ON THE INFLUENCE OF DISTANCE BETWEEN CFG PILES ON THE
FOUNDATION TREATMENT RESULT IN VIETNAM
Doanh Phu Bui, Tung Hoang, Phuong Viet Nguyen, Duc Viet Ngo and Cuong Phu Cao

267

GX347

DESIGNING GABION STRUCTURES UNDER MULTI CRITERIA OBJECTIVES WITH GOAL
PROGRAMMING
Vuttichai Chatpattananan, Tanadol Kongsomboon, and Vatanavongs Ratanavaraha

273

GX350

INTRODUCTION OF ARTIFICIAL INTELLIGENCE SYSTEM FOR SUPPORTING SOIL CLASSIFICATION
Shinya Inazumi, Kazuhiko Kojima,Hiroyuki Hashida and Ken-ichi Shishido

280

GX353

UNSATURATED SHEAR STRENGTH OF EXPANSIVE SOILS FROM NATURAL SLOPES IN
QUEENSLAND
Tharindu Abeykoon, Chaminda Gallage and Jessica Trofimovs

284

GX362

A LABORATORY METHOD FOR ACCURATE CALIBRATION OF STRAIN-GAUGE TYPE SOIL
PRESSURE TRANSDUCERS
Chamara Prasad Gunasekara Jayalath and Chaminda Gallage

290

GX383

NUMERICAL SIMULATION OF INHIBITORY EFFECT OF SUBSIDENCE FOR THE LEANING PAGODABY GROUND IMPROVEMENT
Yuko Ishida, Hajime Ito and Ryoichi Fukagawa

295

GX384

APPLICATION OF ABAQUS PROGRAM TO INVESTIGATE THE EFFECT OF VARIATION IN
SUBGRADE LAYER PROPERTIES ON THE DAMAGE OF FLEXIBLE PAVEMENT STRUCTURE
Saad Farhan Ibrahim AlAbdullah, Maryam Hussain Hassan, Suha Aldahwi

301

GX395

DEVELOPMENT OF A COMBINATION OF A ROTARY PENETRATION TYPE SAMPLER
Katsuya Iwai, Naoaki Suemasa and Tsuyoshi Tanaka

309

GX398

BASE-ROCK ELEVATION MEASUREMENT USING SINGLE AND ARRAY MICROTREMORINVESTIGATION
Windu Partono, Sukamta, Frida Kistiani, Undayani Cita Sari and Kukuh Cahya Adi Putra

315

GX416

OUTFLOW CHARACTERISTICS OF FINE PARTICLES IN TWO KINDS OF WATER-PASSING
EXPERIMENTS USING A COLUMN
Tomoki Wakamatsu, Motoyuki Suzuki and Taichi Ishimaru

321

GX429

APPLICATION OF COMPUTER VISION TECHNIQUE IN RAPID SOIL CLASSIFICATION
Aw, Y.J.E., Koh, J.W., Chew, S.H., Tan, S.E.D., Cheng, L.M., Yim, H.M.A., Ang, L.J.L. and Chua K.E.,

327

GX434

UTILISATION OF RECYCLED MATERIALS IN STABILISATION OF EXPANSIVE SOILS: A REVIEWJiasheng Zhu, Mohammad Saberian, Haiyun Luo, Junkai Gong, Manlin Liu, Gang Ren and Jie Li

333

GX435

OPTIMUM UTILIZATION OF RICE HUSK ASH WASTE FOR GROUND IMPROVEMENT
Najmun Nahar and Zakaria Hossain

339

Construction Materials

345

GX147

FIELD EXPERIMENTS TO INVESTIGATE PULL-OUT MECHANISM OF FLIP-TYPE GROUNDANCHORS INSTALLED IN CLAY SOIL
Shota Yoshida, Tatsunori Matsumoto, Mimu Yoshida

346

GX130

FINITE DIFFERENCE APPROXIMATION FOR SOLVING TRANSIENT HEAT CONDUCCTIONEQUATIONOF THE CAST IRON
Dalal Adnan Maturi

352

GX180

DEVELOPMENT OF ARTIFICIAL BEACHROCK USING WATERMELON SEEDS AS A SOURCE OF
UREASE
Md. Al Imran, Kazunori Nakashima, Niki Evelpidou and Satoru Kawasaki

356

ID

vii

GX220STUDY ON STRUCTURAL EFFECTIVENESS OF A BUILDINGS STRUCTURE BY USING A CFRTP
Yasuhide Mochida, Kouhei Kasahara

362

GX282

UTILIZATION GGBFS AS CEMENT SUBSTITUTION TO REDUCE PRODUCTION COST OF
CONSTRUCTION PROJECT
Ranti Hidayawanti, Yusnita Rahayu, Soetjipto Soewono, Fahdun Ibnu Wachid and Rony Panca
Adi Widodo

369

GX260

POSSIBLE RECYCLING OF WASTE GLASS IN SUSTAINABLE FIRED CLAY BRICKS: A REVIEW
Yuecheng Xin, Abbas Mohajerani, Halenur Kurmus, John V. Smith

376

GX263

DYNAMIC PROPERTIES OF RIGID POLYURETHANE FOAM IN CYCLIC TRIAXIAL TESTSAtsushi Koyama, Daisuke Suetsugu and Yoshinori Fukubayashi

382

GX264

EVALUATION OF FROST DAMAGE RESISTANCE OF LPC-FA COMBINED CONCRETE CURED FOR A
LONG TIME
Shogo KAWAMORITA, Kazuhito NIWASE

388

GX267

IMPROVEMENT OF INITIAL STRENGTH OF LPC-FA CONCRETE BY USING C-S-H-TYPE
ACCELERATOR
Takuya KUDO and Kazuhito NIWASE

394

GX279

POSSIBLE RECYCLING OPTIONS FOR WASTE MATERIALS IN CERAMIC TILES
Qibin Yuan, Abbas Mohajerani, Halenur Kurmus, John V. Smith

400

GX281

EFFECTS OF SIZE AND GRADING, TYPE AND QUALITY OF AGGREGATES ON MECHANICALPROPERTIES OF UNBOUND ROADBED MATERIALS: A LITERATURE REVIEW
Hong Nam Thai, Akira Kato, Hoang Giang Nguyen, Tien Dung Nguyen, Ton Kien Tong,

406

GX258

RECYCLING OF CIGARETTE BUTTS IN STONE MASTIC ASPHALT
Md Tareq Rahman, Abbas Mohajerani and Filippo Giustozzi

413

GX292

A STUDY ON COMPRESSIVE STRENGTH AND EFFECTIVE DIFFUSION COEFFICIENT OF
SOLIDIFIED BODY WITH HIGH ZEOLITE MIXING RATE
Ryo Takano and Kazuhito Niwase

419

GX375

INVESTIGATING COMPRESSIVE, PULLOUT AND TENSILE STRENGTH OF CONCRETE CONTAINING
POLYPROPYLENE
Md Jahidul Islam, Niaz Md Ashraful haque and Anika Tahsin

425

GX433

SOLAR PANEL FOUNDATIONS ON SLOPING LAND AND OFFSHORE ~ A REVIEWAlex Otieno Owino, Jamie Ure Ovia, Jim Shiau, Zakaria Hossain

432

Environment

443

GX126

THE DYNAMICS OF LAND COVER CHANGE AND CAUSAL FACTORS IN THE KURANJI WATERSHED
Teguh Haria Aditia Putra, Bambang Istijono, Aprisal, Bujang Rusman and Taufika Ophiyandri

444

GX133

CONVERSION OF WASTE STONE-FINE SLURRY INTO GEOPOLYMER CEMENT WITH ADDITION
OF COAL FLY ASH
Takaaki Wajima and Say Buntan

450

GX135

SALINITY PREDICTION USING DYNAMIC PARAMETER OF THAILAND’S BANG KRACHAOUma Seeboonruang, Pinit Tanachaichoksirikun and Uba Sirikaew

456

GX153

SOIL PARAMETERS IN DIFFERENT LAND USE TYPES IN CU LAO DUNG, VIETNAM
Duong Minh Truyen, Truong Hoang Dan, Duong Van Ni and Ly Van Loi

463

ID

viii

GX157

PREPARATION OF FLUORIDE ADSORBENT FROM ZIRCON SAND USING MECHANOCHEMICALTREATMENT, AND ITS APPLICATION FOR FLUORIDE REMOVAL
Nagisa Minami and Takaaki Wajima

467

GX174

COMPARISON OF THE NATIONAL PARK MANAGEMENT SYSTEM IN THE UNITED STATES AND
JAPAN
Takashi Kuchii, Atsushi Nakashima

473

GX175

DIVERSITY OF NATURE CONSERVATION SYSTEM IN EACH REGION AND CHALLENGE OF
CREATING GLOBAL NATURE CONSERVATION SYSTEM
Takashi Kuchii, Hiroko Kuchii and Atsushi Nakashima

480

GX188

THE ENVIRONMENTAL PARAMETERS CONTROLLING THE HABITAT OF INVASIVE ALIEN SPECIES,
SPARTINA ALTERNIFLORA IDENTIFIED BY PREDICTIVE MODELING
Michiko Masuda, Testuya Morioka and Fumitake Nishimura

486

GX197

PARADIGM SHIFT IN NATURAL RESOURCES AND ENERGY POLICIESHisatoshi Tanaka, Atsushi Nakashima and Nabil Maghrebi

492

GX199

TECHNOLOGICAL INNOVATION AND ESG INVESTMENT IN THE JAPANESE ENERGY SECTOR
Hisatoshi Tanaka, Atsushi Nakashima and Nabil Maghrebi

497

GX212

DESALINATION BEHAVIOR OF NATURAL ZEOLITE IN SEAWATER
Fumika Sekihata, and Takaaki Wajima

502

GX219

ESTUARY ZONATION BOUNDARY BASED ON SEA SURFACE SALINITY DISTRIBUTION IN CILETUH
BAY, WEST JAVA
S. Supriatna, Iqbal Putut Ash Shidiq,Dennisa M. Tunjung

508

GX239

EFFECTS OF RIPARIAN WORKS ON ICHTHYOFAUNA IN THE NACHI RIVER, JAPANTaiki Mizumachi, Kentarou Hirashima and Atsushi Nakashima

514

GX262

EARLY FOREST RESTORATION ON THE CLOSED SKI SLOPE IS ENCHANCED BY NATURAL
REGENERATION AND ARTIFICIAL THINNING
Tetsuoh Shirota, Chizuru Iwasaki, Tetsuo Okano, and Shinjiro Oya

520

GX266

THE ADEQUACY OF HOUSEHOLDS SANITATION LEVEL IN THE CITY OF JAKARTA: A MICRODATAANALYSIS FROM SUSENAS 2017
Noviyati Valentina Sidabutar and Chotib

525

GX280

SPATIAL PATTERN OF EARTHQUAKE PRONE AREAS OF BANTUL REGENCY, INDONESIA
Susiloningtyas, D, Della Ayu Lestari, Hengki Tasman, Tuty Handayani,Annacletus Ari Dartoyo

531

GX315

INFLUENCE OF LIGHT ON MICROBIAL RIVER WATER ECOSYSTEM AND ITS SELF-PURIFICATIONPOTENTIAL
Fumitake Nishimura, Yuki Koike and Michiko Masuda

537

GX319

ABILITY AND WILLINGNESS TO PAY FOR WASTE WATER MANAGEMENT MAINTENNACESERVICES (IPAL)
Marselina Djayasinga, Tsra Subianto, Tri Joko Prasetyo

543

GX330

LIFE CYCLE ASSESSMENT APPROACH FOR PREDICTING GREENHOUSE GAS EMISSION FROMCOLUMN, BEAM AND PLATE IN A REAL ESTATE CONSTRUCTION
Yatnanta Padma Devia, Indradi Wijatmiko, Bernadus Martino, and Annisa Nur Rakhmawati

550

GX335

TRENDS IN THE USE OF STREET TREES IN JAPANMasaaki Furuno, Taizo Uchida, Daisuke Hayasaka, Xue Jun Huan and Teruo Arase

557

GX361

THE RELATIONSHIP BETWEEN LAND USE AND TRANSPORT PERFORMANCE: A CASE STUDY OF
PADANG, INDONESIA
Aleksander Purba, Nikko Yulianda, Shahnaz Nabila Fuady

563

ix

GX388

THE UTILIZATION OF BIOGAS SLUDGE TO LEAD ZERO WASTE SYSTEM IN BIOGAS
IMPLEMENTATION: THE EFFECT OF VOLUME ON CARBON DIOXIDE AND METHANE CONTENT
Ambar Pertiwiningrum, Margaretha Arnita Wuri, Widyawati Luhur Pambudi and Lies Mira

570

GX404

ENVIRONMENTAL IMPACT AN ALYSIS IN HOTEL SECTOR: A CASE STUDY IN THAILAND
Jarotwan Koiwanit and Viachaslau Filimonau

574

GX425

TOWARD SUSTAINABLE COMMUNITIES: A COMPREHENSIVE ENVIRONMENTAL IMPACTASSESSMENT OF BUILDING
Nantamol Limphitakphong and Orathai Chavalparit

580

GX437

EXPERIMENTAL INVESTIGATION OF PHYSICAL PROPERTIES OF PEATS IN WESTERN PROVINCE,SRI LANKA
A.M.Z. Zimar, M.C.M. Nasvi, D. Robert, S.Jayakody, J.R.R.N.Jayarathne and John V. Smith

586

GX176

SPATIAL MODELLING OF LARVA GLASS EEL AT CILETUH BAY, WEST JAVA, INDONESIA
Illa Annisa, S. Supriatna, and Iqbal Putut Ash Shidiq

592

GX177

NUMERICAL SIMULATION OF THREE-DIMENSIONAL FLOW AND LOCAL SCOUR AROUND A
CYLINDERINDUCED BY TSUNAMI RUN-UP
Yuki Kajikawa and Masamitsu Kuroiwa

599

GX195

ON RELATIONSHIP BETWEEN MONUMENT AND THE STATION BUILDING
Kaho HASEGAWA, Kazunari TANAKA

605

GX198

STUDY ON THE MEANING OF “QUAINT” SPACE
Yoshiki Shirafuji, Kenta Aoki, Koji Inoue, Dr. Kazunari Tanaka

609

GX226

SPATIAL CHARACTERISTICS OF FACTORS INFLUENCING THE OCCURRENCE AND SCALE OF
MULTIPLE SLOPE FAILURES
Takashi Wada, Ryo Kodani, Hiroshi Miwa

612

GX268

SPATIAL ENTRAPMENT OF WOMEN WORKERS IN METROPOLITAN AREAS
Putri Halimah, Chotib

618

GX293

DAMAGE PREDICTION OF THE STEEL GIRDER ARCH BRIDGE MODEL BASED ON ARTIFICIALNEURAL NETWORK METHOD
Widya Apriani, Reni Suryanita, Yohannes Firzal, and Fadrizal Lubis

624

GX325

OPTIMUM LOCATION OF LAST-MINED STOPE IN SILL PILLAR RECOVERY WITH INFLUENCE OFBACKFILLING: A CASE STUDY
Derek B. Apel

630

GX341

MEASURING ACCESS TO SUSTAINABLE TRANSPORT OPTIONS FOR THE ELDERLY
Kaniz Fatima, Sara Moridpour, Chris De Gruyter and Tayebeh Saghapour

635

GX348

QUANTIFICATION OF SLOPE DEFORMATION USING LASER POINT CLOUD DATABY MOBILE MAPPING SYSTEM
Naoto Samori, Satoshi Nishiyama, Koki Sakita, Michinari Fuziki and Naoya Ono

641

GX354

MEASURING THE ACCESSIBILITY TO TRANSIT STATION: A CASE STUDY OF TANJUNG KARANG,LAMPUNG INDONESIA
Aleksander Purba Diti Artanti Utami Putri, Rahayu Sulistyorini, Shahnaz Nabila Fuady, andLusmeilia Afriani

647

GX370

SEISMIC RESPONSE OF ABUTMENTS IN A REAL BRIDGE MODEL
Desy Setyowulan, Toshitaka Yamao, Eko Andi Suryo Eva Arifi, and Devi Nuralinah

653

GX374

PREDICTION OF AERODYNAMIC COEFFICIENTS FOR OFFSHORE TURBINE BLADE UNDER THE
VARIATION OF WIND VELOCITIES
Eaindra Soe, Yasothorn Sapsathiarn and Jetsadaporn Priyadumkol

658

xGX409NUMERICAL ANALYSIS OF INVASION PATTERNS DURING DRAINAGE PROCESS IN A SIMPLIFIED
PORE NETWORK MODEL
Yuto Takeuchi, Junichiro Takeuchi and Masayuki Fujihara

664

GX410

A US$6.07 BILLION JAKARTA - BANDUNG HIGH - SPEED RAIL: CHALLENGES AMID COVID-19
PANDEMIC
Aleksander Purba

670

GX423

GX321

GX247

APPLYING DECENTRALIZED CONSENSUS: A CASE STUDY OF CARBON TRADING
Pasu Poonpakdee and Jarotwan Koiwanit
EVALUATING EFFECTS OF MIXING PROPORTION ON WATER RETENTION CURVE AND PORE SIZE
DISTRIBUTION OF RECYCLED CONCRETE AGGREGATES BLENDED WITH AUTOCLAVED AERATEDCONCRETE GRAINSPHAM Van Nam, Akira KATO, NGUYEN Hoang Giang, NGUYEN Van Tuan, PHAN Quang Minh,and Ken KAWAMOTO
A COMPARATIVE STUDY OF CONVENTIONAL EXTENDED AERATION AND MODERN MEMBRANE
BIOREACTOR METHODS OF SEWAGE TREATMENT FROM THE ENVIRONMENTAL AND
FINANCIAL PERSPECTIVES
Mohamed Fetouh, Rabee Rustum and Adebayo J. Adeloye

676

681

686

GX278

RESEARCH ON HOUSING RESTORATION COSTS AND DISASTER RISK MANAGEMENT FOR
URBAN EARTHQUAKES
Norikazu Sakaba and Harumi Yashiro

692

GX129

INVESTIGATION OF THE INFLUENCE OF RECYCLED RUBBER PARTICLES ON CEMENT PASTEPROPERTIES
Mohamed Atef, NahedA.Azab, Mohamed Hazem Abdellatif and Ghada Bassioni

700

GX436

IMPROVEMENT OF SOIL STRENGTH USING RICE HUSK ASH
Jamie Ure Ovia, Najmun Nahar, Alex Otieno Owino, Noma Tamaki and Zakaria Hossain

706

GX360

SURFACE SHAPE MEASUREMENT AND EVALUATION OF MECHANICAL STABILITY BY
DEM SIMULATION FOR DAMAGED TRADITIONAL STONEWALLSSatoshi Sugimoto, Maho Yamaguchi and Minoru Yamanaka

710

xi

Tenth International Conference on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020 ISBN: 978-4-909106049 C3051

PrefaceOn behalf of the GEOMATE 2020 Organizing Committee, we would like to welcome you in attending theInternational Conference on Geotechnique, Construction Materials and Environment held at the RMIT
University, Australia in conjunction with School of Civil and Infrastructure Engineering, RMIT University,
Australia, The GEOMATE International Society, Useful Plant Spread Society, Glorious International, AOIEngineering, 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 2020
conference covers three major themes with 17 specific themes including:
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Advances in Composite Materials
Computational Mechanics
Foundation and Retaining Walls
Slope Stability
Soil Dynamics
Soil-Structure Interaction
Pavement Technology
Tunnels and Anchors
Site Investigation and Rehabilitation

Ecology and Land Development
Water Resources Planning
Environmental Management
Public Health and Rehabilitation
Earthquake and Tsunami Issues
Safety and Reliability
Geo-Hazard Mitigation
Case History and Practical Experience

Due to COVID-19, this year we have received less number of paper submissions from different countries.
The participated countries are Australia, Bangladesh, Canada, Indonesia, Israel, Japan, Philippines, Saudi
Arabia, Singapore, Thailand, United Arab Amirates, and Vietnam. The technical papers were selected fromthe vast number of contributions submitted after a review of the abstracts. The final papers in the proceedings
have been peer reviewed rigorously and revised as necessary by the authors. It relies on the solid cooperationof numerous people to organize a conference of this size. Hence, we appreciate everyone who supports aswell as participate in this joint conferences.
Last but not least, we would like to express our gratitude to all the authors, session chairs, reviewers,
participants, institutions and companies for their contribution to GEOMATE 2020. We hope you enjoy theconference and find this experience inspiring and helpful in your professional field. We look forward toseeing you at our upcoming conference next year.
Best regards,

Prof. Zakaria Hossain,
Mie University, Japan
Chairman (General)

Dr. Dr. John V. Smith
RMIT University, Australia
Chairman (Program)xii

Tenth International Conference on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020 ISBN: 978-4-909106049 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:
A/Prof. Dr. John V. Smith, RMIT Univ. Australia (Program)
Prof. Dr. Zakaria Hossain, Mie University, Japan (General)
Conference Organizing Committee:
A/Prof. Dr. John V. Smith, RMIT Univ. Australia (Program)
Prof. Dr. Zakaria Hossain, Mie University, Japan (Chair)
A/Prof. Dr. Annan Zhou, RMIT University Australia (Co-Chair)Prof. Dr. M Ibn Ibrahimy, Prof., Int. Islamic Univ. (Co-Chair)Prof. Dr. Toshinori Sakai, Mie University, Japan (Co-Chair)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, 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, Prof., Angles Univ. Found., Philippines
Dr. A.S.M. Abdul Awal, Prof., UTHM, Malaysia
Dr. M. Ibn Ibrahimy, Prof., Int. Islamic Univ., Malaysia
Dr. Bujang B.K. Huat, Prof., Univ. Putra Malaysia
Dr. Nemy Banthia, Prof., Univ. of British Columbia, 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, Prof., University of Adelaide, Australia
Dr. Hj. Ramli Bin Hj. Nazir, A/Prof., UTM, Malaysia
Dr. M.S. Hossain, Prof., International Islamic Univ. BangladeshxiiiDr. Suksun Horpibulsuk, Prof. Suranaree Uni of Tech, Thailand
Dr. Muzamir Hasan, Director, CERRM, Univ. Malaysia Pahang
Dr. Afshin Asadi, S/Lecturer, Int. College of Auckland, New ZealandInternational Technical Program Committee:
Prof. Sai Vanapalli, University of Ottawa, Canada
Prof. Alaa Masoud, Tanta University, Egypt
Prof. Aly Ahmed, Beni-Suef University, Egypt
Prof. Aminaton Marto, Universiti Teknologi Malaysia, Malaysia
Prof. Bandari Shankar, Osmania University, India
Prof. Bashir Ahmed Mir, National Institute of Technology, India
Prof. Hamidi Abdul, Aziz, Universiti Sains Malaysia (USM), Malaysia
Prof. Hussein Elarabi, University of Khartoum, Sudan
Prof. M. Shohidullah Miah, International Univ. of Business Agriculture &Tech., Bangladesh
Prof. Michele, Casagrande, Pontifical University Catholic of Rio De Janeiro, Brazil
Prof. Radim Cajka, Technical University of Ostrava, Czech RepublicProf. Rajaraman Jambunathan, AMET University, India
Prof. Reshma Chandran T., Global Institute of Architecture, India
Prof. Roslan Hashim, University of Malaya, Malaysia
Prof. Seyed Naser Moghaddas Tafreshi, K.N. Toosi University of Technology, IranProf. Valeriy Perminov, Tomsk Polytechnic University, Russia
Prof. Quanmei Gong, Tongji University, China
A/ Prof. Abdoullah Namdar, Sichuan University, China
A/ Prof. Abdul Naser Abdul Ghani, Universiti Sains Malaysia, Malaysia
A/ Prof. Alaeddinne Eljamassi, Islamic University of Gaza, Palestine
A/ Prof. Alina Paranina, Herzen State Pedagogical University of Russia, Russia
A/ Prof. Ashraf Elmoustafa, Ain Shams Univeristy, Egypt
A/ Prof. Bindu C S, Cochin University of Science& Technology, India
A/ Prof. Chee-Ming Chan, Universiti Tun Hussein Onn Malaysia, Malaysia
A/ Prof. Chidanand Naik, Anjuman Instistute of Technology and Management, India
A/ Prof. Dahlia Hafez, Cairo University, Egypt
A/ Prof. Deepa G Nair, Isfahan University of Technology, India
A/ Prof. Dolrerdee Hormdee, Khon Kaen University, Thailand
A/ Prof. Farhad Behnamfar, Isfahan University of Technology, Iran
A/ Prof. Gabriela B. Cazacu, Geotech Dobrogea, Romania
A/ Prof. Hadi Khabbaz, University of Technology, Sydney (UTS), Australia
A/ Prof. Homayoon Ganji, Herat Univercity Badghis, Afghanistan
A/ Prof. Hudson Jackson, US Coast Guard Academy, United States
A/ Prof. Inazumi Shinya, National Institute of Technology, Akashi, Japan
A/ Prof. John Smith, RMIT University, Australia
A/ Prof. Kasinathan Muthukkumaran, National Institute of Technology, India
A/ Prof. Lindrianasari Lindrianasari, Universitas Lampung, Indonesia
A/ Prof. Mahdi Karkush, Baghdad University, Iraq
A/ Prof. Mohamed Redha Menani, Batna University, Algeria
A/ Prof. Navid Khayat, Ahvaz Branch, Islamic Azad University, Iran
A/ Prof. Salam, Bash AlMaliky, AlMustansiriya University, Iraq
A/ Prof. Shailesh Kumar Jha, Indian Institute of Technology, India
A/Prof. Teodor Lucian Grigorie, University of Craiova, Romania
A/Prof. Paresh Dalal, Shri Vitthalrao Shankarrao Naik Arts, Commerce and Science College, India
A/Prof. Muhammad Qasim, Abdul Wali Khan University Mardan, Pakistan
A/Prof. R. S. Ajin, GeoVin Solutions Pvt. Ltd., India
Dr. Abdul Karim M. Zein, University of Khartoum, Sudan
Dr. Abdull Halim Abdul, Universiti Teknologi MARA, Malaysia
Dr. Afshin Asadi, University Putra Malaysia, Malaysia
Dr. Ahmad Safuan A Rashid, Univeristi Teknologi Malaysia, Malaysia
Dr. Ahmad Safuan Bin A Rashid, Univeristi Teknologi Malaysia, Malaysia
Dr. Akindele Okewale, Federal University of Petroleum Resources, Nigeria

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

Technical Papers

1Geotechnique2

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ELASTIC BEHAVIOURS OF A CEMENTED LATERITIC SOIL FOR A
ROAD BASE
Sawanya Dararat1, Warat Kongkitkul2, Thitikorn Posribink3 and Pornkasem Jongpradist4
Department of Civil Engineering, Faculty of Engineering, King Mongkut’s University of Technology
Thonburi, Thailand; 3 Bureau of Maintenance, Department of Rural Roads, Ministry of Transport, Thailand
1,2,4

ABSTRACT
Cemented soils are wildly used as a base course material for flexible pavement structures in Thailand. In thecurrent design approach, the compressive strength of cemented soil cured for 7 days is taken into account as the
only design strength parameter. The strains mobilized in the layers of pavement structure are not taken account for
determination of the lifetime. In order to utilize the cemented soil more efficiently, its elastic (or resilient)behaviour should be investigated and understood. In this study, a series of triaxial compression (TC) tests, in which
the specimen’s axial deformation was measured locally so as to reliably determine the quasi-elastic Young’s
modulus (Eeq) at various stress states, were performed on a cemented lateritic soil cured for 7 days. It was foundthat the Eeq value exhibits a hypo-elastic stress state-dependent behaviour, which can be explained by the k-model as for the resilient modulus (Mr) used in the mechanistic-empirical design of a pavement structure. In
addition, the Eeq value also increases with increasing cement content (C). Thus, the Eeq value can be expressed asa function of k,  and C.
Keywords: Triaxial compression, Elastic modulus, Cement, Lateritic soil, Pavement structure

INTRODUCTION

Highways (DOH), Thailand and Department of Rural
Roads (DRR), Thailand specify the minimum valueof qu for soil cement base of not less than 1717 kPa.
The thickness of this soil cement base, typically 20
cm, is also specified. However, this design method
does not take the deformation behaviours responded
from the traffic loading into consideration.
Development of more rational design methods
becomes necessary. Analytical or mechanistic design
has been developed and is popular among a numberof pavement engineers since 1940 and 1960s,
respectively. This design uses fundamental material
behaviour (linear or nonlinear-elastic, plastic, viscoelastic, etc.), and theoretical model of each pavement
material to predict the response of stresses, strains anddeflections due to the traffic loads. Using resilient
modulus (Mr) as a key parameter in the design has
been widely recommended in many design guides[6]–[8]. Generally, the Mr is determined fromrepeated load triaxial apparatus for simulating wheelload [9]. Because the Mr is a stiffness of a material
responded after many cycles of traffic loading havebeen applied until there is no irrecoverable
deformation developed (therefore, resilient), it issignificantly the same as the elastic stiffness. On theother hand, triaxial compression tests, in which small
strain-amplitude cyclic loadings were applied, were
performed to determine the quasi-elastic Young’s
modulus (Eeq) of geomaterial [10], [11]. In addition,
it was shown that the determination of Eeq by triaxial
compression test can be alternatively used in place ofMr [12].
This study aims to analyse the elastic modulus of

For a pavement structure, most of base layermaterials are usually compacted crushed rock, which
is an unbound granular type. However, pavement
construction in Thailand encounters the problem ofmaterial deficiency for a long time, especiallycrushed rock products used for granular base layer. In
many cases, the material sources are located far awayfrom the construction site, which results in a highconstruction cost. In addition, the production of
crushed rock aggregate involves drilling, blasting,crushing and road haulage, all of which create dust,
causing environmental problems. Although thelateritic soil is local natural material abundantly foundin many areas in Thailand, it is poor in engineering
properties such as high plasticity, low strength, highpermeability, and a tendency to retain moisture
content [1]–[3]. Lateritic soils, especially finegrained lateritic soils, are not suitable for as the roadbase layer. Cement stabilization with the lateritic soil
has been widely employed to improve the mechanicalproperties [4], [5] such that it is strong enough toserve as a road base or subbase layer.
The conventional design method of pavement
structure in Thailand is based on empirical rules frombehaviour observed during service of the pavement
structure or of the experimental sections. For the
cement-treated soil, it must satisfy the minimum
requirement in terms of unconfined compressive
strength (qu). Moreover, in the construction practice,
the qu value is the only strength parameter of
cemented soil used for quality control. Department of3

GEOMATE – Melbourne, Australia, 11-13 November 2020

cemented lateritic soil and to report its dependencywith the stress state and cement content so that abetter understanding of the material can be achieved.

automated way, it becomes possible: i) to smoothly
switch between displacement and load control
loading phases and between sustained loading or
stress relaxation stage and a constant strain rateloading or unloading phases; ii) to apply monotonic
loading with a very precise controlled displacement
rate; and iii) to apply very small amplitudeunload/reload cycles to evaluate the elastic propertiesof test material during otherwise constant strain ratemonotonic loading. This gear loading system has acapacity of 50 kN.

MATERIAL AND APPARATUS
Test Materials
A lateritic soil, which did not satisfy therequirements (i.e., gradation, plastic index,
and %CBR) for use as a subbase material of ordinary
pavement structure specified in DOH and DRR
standards, was used in this study. This lateritic soil
contains the amount of fines more than the limit
indicated in the standards (finer than sieve No.200
over 20%) as shown in Fig. 1. A series of modified
Proctor compaction tests (ASTM D1557) wereconducted to determine the maximum dry density(MDD) and the optimum moisture content (OMC).
Then, CBR tests (ASTM D1883) on specimens
prepared with the water content at OMC were
performed to determine %CBR for various values ofdry density (ρd). Table 1 lists its physical andcompaction properties. The %CBR at 95% of MDD
is equal to 4.26%. This value is significantly lower
than the minimum value specified in the standards(DH-S 205/1989 and DRR-S 202/2014) for use as
subbase material (i.e., %CBR= 25%).

Table 1. Physical and index properties of the lateritic
soil
Properties
Lateritic soil
Specific gravity, Gs
2.89
Liquid limit, LL (%)
31.3
Plastic limit, PI (%)
14.7
Optimum water content, OMC (%)*
10.79
Maximum dry density, MMD
2.099(g/cm3) *CBR at 95% of MMD (%)
4.26*

Modified Procter compaction test (ASTM D1557)

TEST METHODSSpecimen Preparation

100

Upper Limit of DOH
and DRR

Percent finer, F (%)

80

The lateritic soil has been treated with Portland
cement type I to improve the mechanical performancefor using as a material for base in the pavement
structure. The cement content (C) was varied at %1,
2%, 3%, 4%, and 5% by dry weight of the lateritic
soil, and then after being compacting in mould, thespecimens were cured for 7 days. This curing period
is for verification in the construction process, also
specified in the DOH and DRR standards (DH-S
204/1990 and DRR-S 244/2013). The cement-treatedlateritic soil specimens were wrapped with the plastic
film in order to avoid loss of moisture after
dissembling the mould and were kept in an incubatorfor 7 days. For all the tests, the specimens arecylindrical. They are 150 mm high and 70 mm indiameter. Specimens were prepared to achieve the dry
density equal to maximum dry density (MDD). The
values of water content and wet density of a specimen
were controlled not to vary by more than ±1% of
OMC and ±3% of the target value, respectively.

60

40

Lateritic soil
20

Lower Limit of DOH
and DRR
0
0.1

1

10

Particle size, D (mm)Fig. 1

Gradation characteristics of the lateritic soil
in comparison with DOH and DRR
standards.

Apparatus
A compression machine, consisting of a reaction
frame and a precise gear loading system, was used in
the present study. The loading system is driven by acomputer-controlled servo-motor and is able toperform load reversal with practically no backlash,which is a very important feature for performing
precise cyclic loading test [13]. By controlling thedisplacement to accuracy of less than 1 m in an

Test Procedure
A specimen is set on the pedestal and a membrane
is put on it. An axial load cell used in triaxial
compression (TC) tests performed in the present
study was connected in series with the cap inside the4GEOMATE – Melbourne, Australia, 11-13 November 2020chamber so that its reading was free from friction that
may be mobilised at the bearing house on top of thechamber. Prior to mould disassembly, suction of -20
kPa was temporarily applied to the specimen via thedrainage lines connected to the cap and the pedestal
so that the membrane was adhered firmly with thespecimen’s side surface. Then, a pair of local
deformation transducers (LDTs) were installed on thepseudo-hinges firmly glued on the membrane at the
opposite diametrical sides, and three clip gauges(CGs) were placed at the height of 1/5, 1/2 and 4/5 ofthe specimen’s initial height, as shown in Fig. 2.
These LDTs and CGs were calibrated with amicrometre head mounted with a cross slide roller
table. In this paper, the axial strain and the radialstrain in TC tests are the averages of readings fromtwo LDTs and three CGs, respectively. In addition,
the specimen’s axial deformation was also measured
externally with a LVDT, which was necessary whenthe measuring range of LDT was exceeded.
The TC test was performed by applying small
strain-amplitude cyclic loadings (CLs) at various q:pstress states to determine quasi-elastic Young’s
modulus (Eeq). In these TC tests, the Eeq value were
determined from local axial deformation measured bya pair of local deformation transducers (LDTs, [14]),so the Eeq is free from bedding error. These q:p stressstates are in accordance with AASHTO T307-99
standard [9]. The loading pattern for the TC waspresented by Dararat et al. [12]. That is, the sample isfirst isotropically confined with different values ofcell pressure (σ3). Then it is continuous monotonic
loading (ML) sheared by axial compression to a target
deviator stress (q) value, while the cell pressure is
kept constant to achieve a target q:p stress state. After
that, sustained loading (SL) is performed for 30 min,holding the stress state at the target, while the sample
is allowed to deform (i.e., creep). Next, cyclicloadings (CL), of which the stress-amplitude is equal
to 30 kPa, are performed for 10 cycles for evaluating
the Eeq value. Then, ML shearing is performed to thenext target q value, at which 30-min SL and then CLs
for 10 cycles are repeated. After finishing CLs at thelargest target q value, q is reduced to zero, and thenthe cell pressure is increased to the next target value
under isotropic condition. Then, similar shearing
processes as of the first target cell pressure arerepeated. In this TC test, the specimen’s response wasmeasured at 15 q:p stress states by varying fivedifferent confining pressures and deviator stress.
More details of this TC apparatus and test procedures
can be found at Dararat et al. [12].

CG3
LDT2
CG2
CG1

Fig. 2

LDT1Installations of LDTs and CGs formeasurements of axial and radial
deformations free from any bedding errors.

400

Deviator stress, q (kPa)

350
300

Triaxial Compression Test
Cement-treated lateritic soil
Cement = 3%

d = 2.103 g/cm3

Curing time = 7 days

w = 9.30 %ameasured by LDT
ameasured by LVDT

250
200
150
100
50
0
0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Axial Strain, a (%)

Fig. 3

Relationships between deviator stress (q)and axial strain (εa) obtained from smallstrain amplitude cyclic loading test on
cement-treated lateritic soil with C = 3%.

level must be reliably measured, and therefore thelocal strain measurement was employed by using
LDTs. Figure 3 shows the relationship between
deviator stress (q) and axial strain (εa) from the TC
test employing continuous monotonic loading test,
intervened by 30-min sustained loadings, after which
ten small-strain amplitude unload-reload cycles were
performed, with a constant strain rate of 0.0277%/min, on lateritic soil treated with 3% cement. In
addition, Fig. 4 shows a zoomed-up portion of Fig. 3,
and only axial strain measured by using a pair of
LDTs (εa,LDT) is presented.
These figures show that at the same deviator stress(q) the value of axial strain (εa) measured by LVDTis always greater than that measured by a pair of
LDTs, which is due to the measuring errors consistingof system compliances and bedding errors. The axial
strain measured by using a pair of LDTs (εa,LDT), arefound to give a sound basis for axial strains
measurement at small strains. Moreover, from Fig. 4,

TEST METHODS
Stress-Strain RelationshipAs the elastic modulus is the parameter that is ofinterest in this study, the behaviours at small strain
5

GEOMATE – Melbourne, Australia, 11-13 November 2020

it can be readily seen that the q-εa,LDT loops during therespective unload-reload cycles are very small, which
are consistent with the fact that the residual axial
strain developed by these cycles is very small.
Therefore, the behaviour during small unload-reload
cycles is highly linear-elastic.

from a linear relation fit to the unloading branches
presented in Fig. 5. Each Eeq value can be definedwith a degree of confidence, as confirmed by thevalues of coefficient of determination (R2-value)
shown in the figure. For each sample, the Eeq value ismostly constant among loop nos. 6-10, implying thatthe behaviour during these unloading branches issignificantly linear-elastic. The Eeq at the other q:pstress states (shown in Fig. 4) were determined in thesame way as of Fig. 5. It is worth noting that as axial
strain value is measured locally, and hence free frombedding error, the Eeq value defined as shown in Fig.
5 is of the true value.
Figure 6 shows relationships between the average
Eeq and the bulk stress (θ) in a full-log plot for lateritic
soil treated with cement with cement contents of 1%,
2%, 3%, 4%, and 5%. The θ value is normalised by
the reference pressure (Pa) of 100 kPa. The Eeq value
increases significantly with an increase in the bulk
stress ratio (θ/ Pa) value. That is, the Eeq of cementtreated lateritic soil is also of hypo-elastic type.
Dependency of Eeq with θ can be mathematically
expressed by Eq. 1.

300

Deviator stress, q (kPa)

Triaxial Compression Test
Cement-treated lateritic soil
Cement = 3%
200

15

12

9

Curing time= 7 days3d = 2.103 g/cm
w = 9.30 %
8

100

11
10

2
1
0

13

7

5

3

0

14

6

4

0.00

0.01

0.02

0.03

0.04

Axial strain [LDT], a,LDT (%)

Fig. 4Close-up of q-εa,LDT relationship obtained
from small-strain amplitude cyclic loading
test on cement-treated lateritic soil with C =
3%.

 
Eeq = Eo   Pa 

m(1)

Quasi-Elastic Young’s Modulus (Eeq)
where E0 is the value of Eeq when θ = Pa =100 kPa;
and m is constant. The lines were best-fitted to the test
data points shown in Fig. 6(a). The values of E0 and
m for respective test samples are shown in Fig. 6(a).
The functional forms of Eeq in Eq. 1 is similar to thek-Theta (k-θ) model [15].
Considering at the m value, it could be seen thatthe m values obtained from test samples with different
cement contents are quite similar. This implies thatthe characteristics of increasing Eeq with the bulk
stress for different C values are very similar. For thisreason, averaging the m values was attempted and theaveraged m value (mavg) of 0.271 was obtained withan exemption that the value of sample for cement
content equal to 1% was excluded. Then, regression
analysis was re-performed for different cement
content values using Eq. 1, but with the fixed value ofm = mavg as shown in Fig. 6(b). The new values of E0
and m determined from the regression analysis withthe fixed value of m = mavg are also shown in Fig. 6(b).
Although the data are scattered to some extent,
especially for the sample with 1% cement as seenfrom Fig. 6(b), the Eeq value can be defined with adegree of confidence, as confirmed by the coefficientof determination (R2-value) value shown in the figure.
The E0 values by regression analysis with thecommon average m value are plotted against the Cvalue in full-logarithmic scale as shown in Fig. 7. It
seems that the E0 is rather a function of C.
Relationship between E0 and C can be fitted using themathematical form expressed in Eq. 2.

Figure 5 shows the unloading q-εa,LDT branches forthe loop nos. 6-10, by small unload-reload cycles atthe sequence no. 5, at which q = 68.9 kPa and p = 57.5
kPa, obtained from the TC tests on lateritic soil
treated with cements with the cement contents of 1%,
2%, 3%, 4%, and 5%, respectively. It is obvious thatthe q-εa,LDT branches exhibit highly linear-elastic
behaviour for the whole vertical deviator stressamplitude (30 kPa). Thus, the Eeq was determined

Deviator stress, q (kPa)

Triaxial compression test
100 Cement-treated lateritic soil
Cement = 3%

80

Sequence No.5: p= 57.5 kPa
q= 68.9 kPa

d = 2.103 g/cm3

Loops no. Eeq (kPa)
6
7
8
9
10

R

2

1421110 0.94393
1414779 0.94264
1414860 0.94017
1420302 0.93415
1418832 0.94452

w = 9.30 %60

Eeq,avg.= 1,417,977 kPa
40

0.003

0.004

0.005

0.006

Axial strain [LDT], a,LDT (%)

Fig. 5

Relationships between deviator stress (q)and axial strain measured by LDTs (εa,LDT)
during unloading branches to determine thequasi-elastic Young’s modulus (Eeq) on
lateritic soil treated with 3% cement.
6

GEOMATE – Melbourne, Australia, 11-13 November 2020

E0 = E0,C C

nCONCLUSIONS(2)

From the test results and analyses performed inthis study, the following conclusions can be derived:
1) The compressive strength and stiffness of
cement-treated lateritic soil significantly
increase with an increase in the cement content(C).
2) The triaxial compression test (TC) employing
by the small strain-amplitude cyclic loadings
can be used to evaluate the true quasi-elastic
Young’s modulus (Eeq) for cement treatedlateritic soil by using the local displacementtransducers (LDTs) to locally measure theaxial strain. The Eeq value of test samples
exhibited significant dependency on the bulk
stress (θ) and the C value.
3) The equation to estimate Eeq value from thisstudy can be used as the Mr in the solution to
design and analysis of pavement structure.

where E0,C is the value of E0 when C=1% (equal to764.309 MPa); n is constant (equal to 0.582). The
values of E0,C and n for the cement-treated lateritic
soil for different cement contents are shown in Fig. 7.
By combing Eq. 1 with Eq. 2, Eeq of cement-treatedlateritic soil can be mathematically expressed asfollows.

 
Eeq = E0, C C   Pa 

mn(3)

where E0,C is the value of E0 when C=1% (equal to716.519 MPa); n is constant (equal to 0.582); m isconstant (equal to 0.268); and Pa is the reference
pressure (equal to 100 kPa). Fig. 8 shows thecomparison between predicted (by substituting the θ
and C into Eq. 3) and the measured Eeq values for
cement-treated lateritic soil. It can be seen that Eq. 3
gives very satisfactory prediction results as with agood agreement shown in Fig. 8.

Quasi-elastic Young's
modulus, Eeq (MPa)

1% Cement
2% Cement
3% Cement
4% Cement
5% Cement

Eeq=1884.052(/Pa)

Triaxial compression test
Cement-treated lateritic soil
Curing time = 7 days

E0 (MPa)

10000

10000

0.246

2

R =0.97374
Eeq=1700.100(/Pa)

0.246

1000

0.582

E0 = 716.519C

2

2

R =0.98285

R = 0.96346

Eeq=1215.702(/Pa)

0.276

2

R =0.98970
Eeq=1119.372(/Pa)

1000

0.304

2

R =0.95423
Eeq=545.070(/Pa)

1

2

0.467

3

4

5

Cement content, C (%)

2

R =0.96436
1

10

100

Fig. 7

Bulk stress ratio, /Pa(a)
1% Cement
2% Cement
3% Cement
4% Cement
5% Cement

Eeq=1834.856(/Pa)

0.268

2

R =0.96678
Eeq=1654.901(/Pa)

4000

0.268

2

Eeq=1227.871(/Pa)

0.268

2

R =0.98891
Eeq=1168.997(/Pa)

1000

0.268

2

R =0.94241
Eeq=698.252(/Pa)

0.268

2

R =0.80722
1

10

100

(b)

Bulk stress ratio, /Pa

Fig. 6

Relationships
betweenquasi-elastic
Young’s modulus (Eeq) and bulk stress ratio(θ/ Pa) for lateritic soil treated with various
cement contents using: (a) respective m; and(b) common average m.

1:1

1% Cement
2% Cement
3% Cement
4% Cement
5% Cement

3500

R =0.97553

Predicted quasi-elastic Young's
modulus, Eeq,predicted (MPa)

Quasi-elastic Young's
modulus, Eeq (MPa)10000

Dependency of the E0 value with the cement
content (C).

3000
2500

Eeq=716.519C(%)

0.582(/Pa)

0.268

2000
1500
1000
500
0
0

500

1000

1500

2000

2500

3000

3500

4000

Measured quasi-elastic Young's
modulus, Eeq,measured (MPa)Fig. 87Comparison between the predicted andmeasured values of Eeq.

GEOMATE – Melbourne, Australia, 11-13 November 2020

ACKNOWLEDGMENTS[7] ARA, Guide for mechanistic-empirical design of
new and rehabilitated pavement structures, Finalreport, NCHRP Project 1-37A, Washington, D.C.,
Transportation Research Board of the National
Academies, 2004.
[8] Austroads, Pavement design - A guide to thestructural design of road pavements, Sydney,
Austroads, 2004.
[9] AASHTO T307-99, Standard method of test for
determining the resilient modulus of soil andaggregatematerials,
Washington,
D.C.,
AASHTO, 2012.
[10] Hoque, E. and Tatsuoka, F., Anisotropy in elastic
deformation of granular materials, Soils andFoundations, Vol. 38, No. 1, 1998, pp. 163–179.
[11] Kongkitkul, W., Musika, N., Tongnuapad, C.,
Jongpradist, P. and Youwai, S., Anisotropy incompressive strength and elastic stiffness ofnormal and polymer-modified asphalts, Soils andFoundations, Vol. 54, No. 2, 2014, pp. 94–108.
[12] Dararat, S., Kongkitkul, W., Arangjelovski, G.
and Ling, H.I., Estimation of stress statedependent elastic modulus of pavement structurematerials using one-dimensional loading test,
Road Materials and Pavement Design, 2019.
(Published online: 27 May 2019)[13] Santucci de Magistris, F., Koseki, J., Amaya, M.,
Hamaya, S., Sato, T. and Tatsuoka, F., A triaxial
testing system to evaluate stress-strain behaviourof soils for wide range of strain and strain rate,
Geotechnical Testing Journal, Vol. 22, No. 1,
1999, pp. 44–6.
[14] Goto, S., Tatsuoka, F., Shibuya, S., Kim, Y.-S.
and Sato, T., A simple gauge for local small strain
measurements in the laboratory, Soils andFoundations, Vol. 31, No. 1, 1991, pp. 169–180.
[15] Hicks, R.G. and Monismith, C.L., Factors
influencing the resilient response of granularmaterials, Highway Research Record, Vol. 345,
1971, pp. 15–31.

The authors are grateful to Thailand Research
Fund (TRF) and King Mongkut’s University ofTechnology Thonburi (KMUTT) for the financial
support granted to this research via the Royal Golden
Jubilee Ph.D. Program under the contract number
PHD/0187/2556.
REFERENCES[1] Gidigasu, M. (Ed.), Laterite soil engineering:pedogenesis and engineering principles, Vol.9,
Amsterdam, Netherlands, Elsevier, 2012.
[2] Eluozo, S. N. and Nwaobakata, C., Predictivemodels to determine the behavior of plastic andliquid limit of lateratic soil for raod construction
at Egbema: Imo state of Nigeria, International
Journal of Engineering & Technology, Vol. 2,
No. 1, 2013, p. 25.
[3] Lawane, A., Vinai, R., Pantet, A., Thomassin, J.
H. and Messan, A., Hygrothermal features oflaterite dimension stones for sub-Saharanresidential building construction, Journal of
Materials in Civil Engineering, Vol. 26, No. 7,
2014.
[4] Millogo, Y., Hajjaji, M., Ouedraogo, R. and
Gomina, M., Cement-lateritic gravels mixtures:
Microstructure and strength characteristics,
Construction and Building Materials, Vol. 22, No.
10, 2008, pp. 2078–2086.
[5] Oyediran, I. A. and Kalejaiye, M., Effect ofincreasing cement content on strength andcompaction parameters of some lateritic soils
from southwestern Nigeria, Electronic Journal of
Geotechnical Engineering, Vol. 16, 2011, pp.
1501–1514.
[6] AASHTO, Mechanistic-Empirical Pavement
design guide: A Manual of Practice, Washington,
D.C., AASHTO, 2008.

8

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INFLUENCES OF SEEPAGE FLOW HISTORIES ON DETERIORATIONWITHIN EMBANKMENTS
Koji Nakashima1 and Katsuyuki Kawai1
1

Department of Civil and Environmental Engineering, Kindai University, Japan
ABSTRACT

Embankment structures are important to protect against flooding damage. Suffusion, in which fine particles
within the soil are transported and washed away following the seepage flow, intensifies the instability ofembankment structures. Therefore, it is possible that some embankment structures that repeatedly experienced
flooding and rainfall penetration have been deteriorated. However, there is little research investigating the relation
between seepage flow histories and deterioration within the embankments. In this study, small-scale modelingtests duplicating a river levee were conducted under different seepage flow histories: (i) short term-critical groundwater level, (ii) continuous-high ground water level and (iii) repeated-high ground water level. The work in thispaper investigates changes in “drainage flow rate”, “height of ground water level” and “particle size distribution”
during the seepage tests, and evaluates the effects of seepage flow histories on them. Soils gradually showed lower
permeability under the first seepage experience in each cases. In the case of relatively longer flooding duration,
the drainage flow rate is gradually increased. Fine particles were eroded, regardless of the seepage flow histories;“the number of fluctuations” and “height” of ground water level could particularly be a trigger of suffusion.
Keywords: Seepage, Embankment, Suffusion, Particle size distribution
1.

various natural soil materials extracted from the local
area, and they are repeatedly elevated using
additional material. Therefore, the embankment isassumed to have uncertain homogeneity in terms ofsuch as particle properties, density, permeability andstrength. Suffusion is one of the causes of internal
erosion, in which fine particles within the soils aretransported and washed away following the seepage
flow from the surface of the embankment. Figure 1
illustrates the deterioration problems of concern for ariver levee under seepage. Once fine particles wash
away, the area could become a water path for
localized flow. Moreover, clogging due to transported
fine particles increases the possibility of water level
rises. High ground water levels raised during flooding
events due to conditions such as continuous rain or
torrential rain intensify the occurrence of suffusion,
whereby the deterioration of embankment may leadto devastating failure. Hence, it is thought that there
are many working embankment structures whichhave been progressively deteriorating and have been
having a potential for failure.
Various laboratory experiments and analyticalstudies have previously been performed regarding
internal erosion, and they have revealed the causes ofsoils instability, such as particle properties, hydraulicconditions and stress conditions (Horikoshi and
Takahashi, 2015). However, there is very little
research investigating the relation between seepage
flow history and deterioration within theembankments due to suffusion.
In this research, small-scale modeling tests
duplicating a river levee were conducted under
different seepage flow histories. For the test
conditions, different seepage histories were applied toinvestigate their influences on the deterioration of

INTRODUCTION

Soils are often used for the construction ofembankment structures, such as levees and earth
dams, and they protect neighborhoods from flooding.
In recent years in Japan, increased daily
precipitation tends to accompany global warming,
which frequently causes flooding events and an
embankment failure. For example, a river levee along
the Yabe River failed following heavy rains inNorthern Kyusyu, in 2012. A piping development
was assumed as a cause of the failure (Zhang and
Maeda, 2015; Saito et al., 2015; Ueno et al., 2017). In
2015, a river levee along the Kinu River failed due toovertopping and seepage of water during heavy rain
(Yasuda et al., 2016). Furthermore, in 2017, many
embankment structures were failed due to flooding,such as a river levee along the Oda River.
Most embankments are constructed from theFig. 1 Deterioration problems of concern fora river levee under seepage
9

GEOMATE – Melbourne, Australia, 11-13 November 2020

soils within the embankment. As an evaluation ofdeterioration, the work in this paper focuses onchanges in “drainage flow rate”, “height of groundwater level” and “particle size distribution” during theseepage flow tests.

Percent finer by weight (%)

2.

100

EXPERIMENTAL STUDY

2.1 Experimental Material

Silica No.8
Silica No.4
Mixture

80

60

40

20

0
0.01

The experimental material, which incorporated
fine and coarse grain fractions, was prepared bymixing No.4 and No.8 silica sands. The combined
particle size distribution curves are shown in Figure
2. Kenny and Lau (1985) proposed the criterion thatclassifies the stability of soils against internal erosion.
The criterion can be defined by two parameters, H
and F, which are obtained from particle size
distribution curve, as illustrated in Figure 3.
According to the criterion, the experimental material
has (H/F)min of 0.55 (<1.0), which can be interpreted
as having a potential for instability due to internal
erosion, as shown in Figure 4. The material properties
are also presented in Table 1.
Constant head permeability tests were conducted
for 3 days in advance. Figure 5 shows the fluctuationof the coefficient of permeability k during thepermeability test. For comparison, the result of astable soil mixture with a (H/F)min of 1.84 (>1.3) isalso shown in the figure. It is worth mentioning that
decreasing of the coefficient of permeability can beobserved.

0.1

1

10

Grain size (mm)Fig. 2 Particle size distribution curve

Fig. 3 Definition of parameters H and F

0.5

MixtureH/F=1.3
H/F=1.0

0.4

2.2 Model Preparation
0.3

H

Figure 6 illustrates details of the model
configuration. The embankment model wasconstructed by compaction. A 0.075mm mesh filter
was fixed in a perforated metal plate to allow thewashing away of fine particles. The plate was theninstalled into the test box. Water was added to the test
material to a moisture content of w=10%, andunsaturated soil material was prepared. Five layers ofthe material were gently compacted into the test box
at a relative density of Dr=80%. After compaction, theexcess materials were then excavated using palletknives, and the embankment model was sculpted. The
embankment model had the following geometry:height of 185mm, crest width of 60mm, base width of
330mm, and a slope of 1:2. Three manometers were
equipped to the bottom of the base materials throughthe test box. Figure 7 shows an overview of the test
equipment. An observation camera used to capture
the pictures for digital image correlation (DIC)
analysis was set in front of the model.

0.2

0.1

0.0
0.0

0.1

0.2

0.3

0.4

0.5

F

Fig. 4 H/F relation
Table. 1 Material properties

water from the back of the embankment, as shown in
Figure 6. Water circulation using a pump enabled aconstant water level at the upper tank and the back of
the embankment.
In this study, three seepage histories were set as thetest conditions, as shown in Figure 8. Case 1 is the

2.3 Test Procedures
The seepage tests were conducted by suppling10

Coefficient of permeability k (cm/s)

GEOMATE – Melbourne, Australia, 11-13 November 2020

10 -3

Mixture (unstable)
Mixture (stable)

10 -4

10 -5

0

10

20

30

40

50

60

70

80

Time (hour)Fig. 5 Fluctuation of coefficient of permeability

Fig. 8 Seepage test condition
seepage for 24 hours was previously carried out.
Leaking water from the toe of the embankment
model was collected, and the flow rates were
calculated. The water level within the embankment
was investigated by measuring the manometer’swater level.
After the seepage tests, the soil materials at
different locations were sampled, and sieve analysis
was then conducted.

Fig. 6 Model configuration

3.

TEST RESULTS

3.1 Ground Water Table and Flow Rate
Figure 9 shows the results of the ground waterlevel for each manometers and drainage flow rateduring the seepage tests. As seen in the figure, theresult of the first 24 hours duration, when the top
water level was 50% of the embankment height,
shows a gradual decrease of drainage flow rate ineach tests. A decreasing a magnitude of thecoefficient of permeability was confirmed from theconstant permeability test, as mentioned before. It is
more likely that soil structures easily change, and thatsome areas of the embankment could be clogged due
to fine particle transportation following seepage flowin the first seepage experience. The permeability ofthe soils, therefore, tends to decrease. The
embankment model was compacted in the samemanner, nevertheless, the decreasing trends of flowrate are different for each cases. Figure 10 shows thedisplacement contours for first 24 hours. Matlab and
GeoPIV (White et al., 2003) were used to computethe DIC analysis. For Case 2, larger displacement canbe seen compared with other cases. This result
especially exhibits compaction of the embankment at

Fig. 7 Overview of test equipment
condition which simulates short term-critical groundwater level (90% of the embankment height) causedby concentrated heavy rain. Case 2 simulates
continuous-high ground water level (70% of theembankment height), such as seen in the rainy season.
Case 3 simulates repeated-high ground water level
rises (70% of the embankment height). In each cases,a ground water level at 50% of the embankment
height is assumed as the usual groundwater, and
11

180

27
Water level (M1)
Water level (M2) 24
Water level (M3)
Flow rate
21

Water level (mm)160
Water level (top)

140
120

18

100

15

80

12

60

9

40

6

20

3

0

0
150

0

30

60

90

120

Flow rate (cm 3 /min)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Elapsed time (h)(a) Case 1
Water level (M1)
Water level (M2)
Water level (M3)
Flow rate

Water level (mm)

160
140

(a) Case 1
27

Water level (top)

24
21

120

18

100

15

80

12

60

9

40

6

20

3

0

0
150

0

30

60

90

120

Flow rate (cm 3 /min)

180

Elapsed time (h)(b) Case 2

Water level (mm)140

Water level (top)

24
21

120

18

100

15

80

12

60

9

40

6

20

3

0

0
150

0

30

60

(b) Case 2

27

Water level (M1)
Water level (M2)
Water level (M3)
Flow rate

90

120

Flow rate (cm 3 /min)

180
160

Elapsed time (h)(c) Case 3
Fig. 9 Time histories of ground water level anddrainage flow rate(c) Case 3
Fig. 10 Displacement contours for first 24h

the toe. It is likely that clogging caused by thecompression at the toe induced the decreasing
permeability. It is assumed that differences indeformation are caused by non-uniformity in themodel construction. However, this clearly implies
that non-uniformity during construction of anembankment structure greatly affects permeability
and deformation behavior.
The results of Case 2 and Case 3 show that the
manometer water levels respond in an accordancewith flooding; whereas, the drainage flow rate
gradually increases after flooding. This is probably
because a water path gradually formed, and water was
locally transported. After the second flooding event
for Case 2, the gradient of increasing drainage flowrate is flatter, compared with the first flooding. For
Case 1, it is assumed that a delay of increasing flowrate cannot be seen because the flooding duration isrelatively shorter than those of the other two cases.
Devastating failure was not observed in any of thecases.
The expended energy Eflow, which is the time
integration of the instantaneous power dissipated by
the water seepage for the test duration, is represented
as follows (Marot et al., 2016);t

E flow  t    Q w ht

(1)0where, Q (m3/s) is the flow rate of water; w (kN/m2)
is the unit weight of water; h is the difference ofwater level between the upstream section and thedownstream section; and t is the time for a certain
duration. In this study, h is defined as the difference
12GEOMATE – Melbourne, Australia, 11-13 November 2020

Cumulative expended
energy E flow (kJ)

0.14

Case1
Case2
Case3

0.12
0.10
0.08
0.06

Flooding point
(24h)

0.04
0.02
0.00

Fig. 12 Sampled location
0

30

60

90

120

150

Elapsed time (h)htoe  l 2  htop 2  l(2)where, htop is the top water level and l is the basewidth. Figure 11 represents the time histories ofcumulative expended energy during the seepage tests.
As shown by the result of the first 24 hours duration,
the expended energy of each test is almost the same.
The cumulative expended energy at the terminate
point of Case 1 agrees with the magnitude of Case 3
at that time. Larger cumulative expended energy at
the terminate point can be seen in Case 2 comparedwith the other cases.
3.2 Particle Size Distribution
The tested materials were extracted at different
locations in the embankment model, as illustrated in
Figure 12. Figure 13 shows the particle size
distribution curves after the seepage tests. It can beobserved that finer particles were washed away ineach case. A decreasing rate of 0.075mm percentage
by weight after the test is summarized in table 2.
Assuming that the decreasing rate corresponds to themagnitude of suffusion, in Case 1 and Case 3, it isconfirmed that greater fine particles are washed away
compared with Case 2. A correlation between thelocation where the materials were extracted and thedecreasing rate was not observed.
3.3 Discussion
It was expected that one of the reason why theflow rate gradually increases after flooding is due tothe formation water paths following thetransportation and washing away of fines. Especiallyin cases where the ground water level repeatedly
changes, such as in Case 3, structural changes are
possible due to the fluctuation of pore water pressure
within the soil, whereby the permeable area regularlychanges. As a result, suffusion is significantly
observed in Case 3. In addition, in cases where theOriginal
A1
A2
A3

80

60

40

20

0
0.01

0.1

1

10

1

10

1

10

Grain size (mm)(a) Case 1
100

Percent finer by weight (%)

between the top water level and the water level of thetoe htoe. htoe can be calculated by Eq.(2);

Original
A1
A2
A3

80

60

40

20

0
0.01

0.1

Grain size (mm)(b) Case 2
100

Percent finer by weight (%)

Fig. 11 Time histories of cumulative
expended energy

Percent finer by weight (%)

100

Original
A1
A2
A3

80

60

40

20

0
0.01

0.1

Grain size (mm)(c) Case 3
Fig. 13 Particle size distribution after test

13

GEOMATE – Melbourne, Australia, 11-13 November 2020

3) Loss of finer particles could be observed in eachcase, and the magnitude of the suffusion was
evaluated by a decreasing rate of 0.075mm particles.
The relation between the cumulative expended
energy during the seepage tests and the loss of fineparticles could not be confirmed.
The results indicate that “the number offluctuations” and “height” of ground water level
could certainly be a trigger of suffusion. However, therising of the ground water table raising due toclogging of the embankment toe has larger effects on
the possibility of deformation.

Table. 2 Decreasing rate of 0.075mm particles

ground water level rapidly rises, such as Case 1, it was
expected that the pore water pressure would vary
rapidly, and hence the possibility of suffusion would
increase.
According to the one-dimensional seepage flow
tests of the elemental specimens, the mass of fineparticles that wash away, generally increases inaccordance with the magnitude of expended energy(Marot et al., 2016; Le et al., 2018). As observed in
Fig.10 and Table.2, the results in this study do notfollow such trends. This is probably because thecompression of the embankment model occurred withthe first seepage, and the non-permeable area wasmaintained after flooding. Additionally, wherever
there is less fluctuation of ground water levels, suchas in Case 2, the flow of water becomes locallyconcentrated in the same area, whereby less fineparticle loss can be observed against greater drainage
flow rate.
It was expected that “the number of fluctuations”
and “height” of ground water level could certainly bea trigger of suffusion. However, the event of fineparticles washing away has less effects on the
deformation of the embankment. As seen in the DIC
results, the rising of the ground water table due to theclogging of the embankment toe intensifies thepossibility of deformation.
4.

REFERENCES[1] Horikoshi, K., Takahashi, A., Suffusion-inducedchange in spatial distribution of fine fractions inembankment subjected to seepage flow, Soils andFoundations, 55(5), 2015, pp.1293-1304.
[2] Kenny, T.C., Lau, D., Internal instability of
granular filters. Canadian Geotechnical Journal,
22(2), 1985, pp.215-225.
[3] Le, V.Z., Marot, D., Rochim, A., Bendahmane, F.,
Nguyen, H., Suffusion susceptibility investigation
by energy-based method and statistical analysis,
Canadian Geotechnical Journal, 55, 2018, pp.5768.
[4] Marot, D., Rochim, A., Nguyen, H.H.,
Bendahmane, F., Sibille, L., Assessing thesusceptibility of gap-graded soils to internal
erosion: proposition of a new experimental
methodology, Natural Hazards, 83, 2016, pp.365388.
[5] Saito, H., Maeda, K., Izumi, N., Li, Z., Water
leak-piping behaviors of river levees withdifferent foundation ground properties underloading duration of high water level, Advances inRiver Engineering, 21, 2015, pp.349-354 (inJapanese).
[6] Ueno, T., Sasaoka, S., Mori, H., Nakamura, K.,
Fukushima, M., Suwa, Y., Analysis of soil
condition related to piping development in riverlevee based on model experiment, Advances inRiver Engineering, 23, 2017, pp.405-410 (inJapanese).
[7] White, D., Take, W., Bolton, M., Soil
Deformation Measurement Using Particle Image
Velocimetry (PIV) and Photogrammetry,
Géotechnique, 53(7), 2003, pp.619-631.
[8] Yasuda, S., Shimizu, Y., Deguchi, K,
Investigation of the mechanism of the 2015 failureof a dike on Kinu River, Soils and Foundations,56(4), 2016, pp.581-592.
[9] Zhang, W. and Maeda, K., SPH simulations for
slope and levee failure under heavy rainfall
considering the effect of air phase, Computer
Methods and Recent Advances in Geomechanics,
2015, pp.1465-1470.

CONCLUSION

In this paper, small-scale modeling tests
duplicating a river levee were carried out under
different seepage flow histories. The test material for
constructing the embankment model was preparedbased on the Kenny’s criterion. The following
conclusions were drawn;
1) Whenever soils experience the first seepage,
permeability tends to decrease. It is interpreted thatthe soil structure easily changes and that some areas
could become clogged due to fine particle
transportation following seepage flow.
2) In the case of respectively longer flooding duration,
it is assumed that a water path could gradually form,
and water is locally transported, whereby the flowrate gradually increases.

14

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

RELATIONSHIP BETWEEN THE PRODUCTION CONDITIONS OF
SOLIDIFIED CRUSHED SOIL AND THE PHYSICAL PROPERTIESAND STRENGTH OF THE PRODUCTAtsuko Sato1 and Osamu Hatakeyama1
1

Civil Engineering Research Institute for Cold Region Public Works Research Institute
ABSTRACT

When surplus soil generated in construction is too soft for reuse as a construction material, one method for
improving it is to add a solidifier. By this method expect to achieve improvement; however, an embankment
constructed using such improved soil may have a strength that exceeds the required value. As a method forcontrolling the development of excess strength, mixing a solidifier into the soft soil and then crushing the mixture
during solidification and investigated the physical properties and strength of the resulting soil. Constructed a fullscale embankment using crushed solidified soil on soft ground and investigated the deformation of thatembankment. The result found that it was possible to control the increase in the strength of the solidified soil bycrushing it and that an embankment constructed on soft ground from crushed solidified soil deformed following
the deformation of the soft ground.
Keywords: Solidified crushed soil, Production conditions, Physical properties, Strength
INTRODUCTION

paper summarizes the investigation results.

Soil that is generated during road and river
excavation is too soft for reuse in embankment
construction can be improved by solidification using
solidifiers such as cement or lime [1]. Be able toimprove soft soil by using this method; however, thestrength of an embankment constructed using thistype of improved soil may exceed the required value.
If the strength of an embankment constructed using
the solidified soil is exceedingly high due tosolidification, it may be difficult to re-excavate theembankment when installing median strips or
guardrails. High strength embankments may failwhen they are constructed on soft ground and are notable to accommodate the behavior of the soft ground.
For these reasons, the crushed solidified soil wasinvestigated, which is produced by mixing a solidifierin soft soil and then crushing it in the middle of thesolidification, as a material whose strength
development is possible to control even when asolidifier is mixed [2] [3]. Also the strength andcompaction characteristics of the solidified crushed
soil affect the type, amount of the solidifier, and theleaving time from the mixing of the solidified
material to the crushing. Investigated the physicalproperties and strength of crushed solidified soil
produced using volcanic ash depending on theamount of solidifier, time for leaving still before
crushing, and the curing time. Furthermore, two fullscale embankments were constructed on soft groundand investigated their deformation. The original soil
for these embankments was clay. One embankment
was constructed using solidified soil, and the otherwas constructed using the crushed solidified soil. This

CRUSHED SOLIDIFIED SOIL
The crushed solidified soil is a soil material
produced by mixing a solidifier in an original soil that
requires solidification and crushing it after a certain
length of time. The crushed solidified soil can be usedin construction that involves compaction. The
strength characteristics of the compacted crushedsolidified soil differ depending on the time between
solidification and crushing. When the time before
crushing is short, the solidification process has notbeen completed, and the constructability is inferior
because the strength development is low atcompaction. The solidification ability of this type ofcrushed solidified soil is still maintained after
compaction. Therefore, the strength develops further
after curing for a certain length of time, during which
the solidification reaction progresses. When the time
before crushing is long, crushing is done for soil thathas already developed a certain degree of strength;
therefore, the constructability is good. The residual
ability to develop solidification in the crushed soil issmaller than that of the soil with a shorter time before
crushing, and the increase in the strength after
compaction is small.
The terms used in this report are defined as shownin Figure 1.
Solidified soil: A mixture of the solidifier and the soil
subject to improvement (original soil).
Leaving time: The time between the mixing of thesolidifier in the original soil and the crushing.
Crushed solidified soil: A soil material produced by
15

GEOMATE – Melbourne, Australia, 11-13 November 2020

cm in height and 1 m in crest width was constructed
for each specimen. The embankment was formed bypressing the soil with the backhoe bucket. Theseembankments were left at the open test field undernatural weather conditions. The test specimens foreach solidified soil was produced by using thebackhoe bucket and the backhoe breaker and breaking
the part of the embankment into crushed solidified
soil of about 10 mm in grain size on the 11th, 53rd,
109th, and 259th days. A compaction test was
conducted on solidified crushed soil. The tests for the
specimen of each age were done in a laboratory
immediately after preparing the specimens so as to
avoid further progress in the solidification of thespecimens.
To clarify the time-dependent changes in thestrength of the compacted crushed solidified soil
specimens, the specimens for the unconfinedcompression test (5 cm in diameter and 10 cm inheight) [5] and those for the impact acceleration test
(15 cm in diameter and 12.5 cm in height) [6] were
produced. The impact acceleration is measured using
a steel weight with a built-in accelerometer, which
was dropped in a free-fall condition. This method of
measurement is applied at construction sites as aquality control technique by the Hokkaido Regional
Development Bureau of the Ministry of Land,
Infrastructure, Transport and Tourism.
As a result of previous studies [7], it was foundthat the solidified crushed soil has different
compaction curves depending on the standing time
and the amount of solidifying material mixed. For thisreason, the density of the specimen was determinedby compacting with standard proctor energy for eachstanding time and each amount of the solidifying
material to be mixed, with reference to Cement
Association's specimen manufacturing method [8].
The unconfined compressive strength and impact
acceleration were measured according to the age ofcrushing the solidified soil.
Curing time: The time between the production of a
specimen and the strength test for solidified soil andcrushed solidified soil.
Mixing solidifier
and original soil
Solidified soil

Production （Curing time）
Strength testof specimen（Lraving time）
Crush
Crushed solidfied soil

Production （Curing time）
Strength testof specimen

Fig. 1 Defined of term.
SOIL TEST METHOD
Table-1 shows the basic physical properties oforiginal soil No.1, which was used in testing thephysical properties and strength of the crushedsolidified soil, and No. 2, which was used forconstructing full-scale embankments. Original soil
No. 1 used for the physical properties and strength
tests was volcanic ash sand and had a high cone index.
The original soil used for constructing full-scaleembankments was clay with a high liquid limit,
whose cone index was very low, and construction
machines were not able to travel on it [4].
Physical properties and strength tests
The tests for physical properties and strength weredone at the Tomakomai Construction Test Field of the
Civil Engineering Research Institute for Cold Region.
The wet weight of original soil No. 1, which was put
in a container of 1m3, was measured. Portland blastfurnace slag cement B was added to the original soil.
The ratios of the added soil improvement material forthe specimens were 5%, 10%, and 15% in the dryweight ratio. Each specimen was mixed for about 10
minutes in the backhoe bucket. An embankment of 50

Table 1 Basic physical properties of original soil (The Japanese Geotechnical Society, JGS)
Sample No.
Purpose of test
Soil particle density ρs (g/cm3)
Natural water content wn(%)
2mm-(%)
Grain size
75μm-2mm(%)
characteristics-75m(%)
Liquid limit wL(%)
Plastic limit wP(%)
Ground material classification
Cone index qc(kN/m2)
Maximum dry density ρdmax (g/cm3)
Optimum moisture content wopt(%)

1
physical properties/
Strength
2.693
55.97
3.6
64.9
31.5
90.5.
62.0
SV
1500
0.961
51.7
16

2
Full-scaleembankment
2.587
88.6
0.0
0.1
99.9
115.4
39.4
CH
90
-

Standard number
JGS 0111-2009
JGS 0121-2009
JGS 0131-2009
JGS 0141-2009
JGS 0051-2009
JGS 0716-2009
JGS 0711-2009(A-c)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Mixing solidifier
and original soil
Mixing solidifier
and original soil

Solidfied soil

Solidfied soil

Strength test
Unconfinedcompression
strength
Impact acceleration

Productionof specimen

Constructed embankment using solidfied soil
(Leaving time)
11th, 53th, 109th and 259th days (After freezing snd thawing)
Crush

Crushed solidfied soil
Physical properties test
Soil particle density
Natural water content
Grain size characteristics
Consistency limit
Compactibility(Curing time)
7th, 30th, 91th, 182th qnd 365th dayStrength test
Unconfinedcompression
strength
Strength test
Impact acceleration
Unconfined compression strength
Impact acceleration

Productionof specimen

Index work

Fig. 2 Flows of the test procedures.
investigated these two embankments regarding thedeformation characteristics caused by the settlementof the ground. The information on the ground aroundthe embankments is shown in Table-2. The ground
was of alternate strata of peat and clay. The watercontent ratios of the upper layers were high. In thepast, an embankment of 2.8 m in height wasconstructed and found that this embankment had
subsidence of 70 cm in one year.
The full-scale embankment construction test wasdone by constructing one embankment using thesolidified soil and the other using the crushedsolidified soil. The material was prepared using
original soil No. 2 and a cement solidifier, which isconsidered as a solidifier with early strength
development and a small increase in the strength
development after the initial strength development(hereinafter, this solidifier will be called “ETR3”).
The target strength value for the improvement ofsolidified soil and crushed solidified soil was a cone
index of qc = 400kN/m2 at the construction of theembankment. The test done before the construction of
the embankments were found, to obtain the target
strength for the solidified soil and the crushedsolidified soil, it was necessary to use 200kg/m3 and95kg/m3 of ETR3, respectively. The embankment of
the solidified soil was constructed during the period
from September 24 to October 2, 2018, and theembankment of the crushed solidified soil with aleaving time of 28 days was constructed during theperiod from October 16 to 25, 2018. As shown in
Figure-3, the dimensions of the embankments are 3 m
in height, 20 m in length, and 5 m in crest width. The

each specimen. Measurements were done on the dayof the specimen production, i.e., on the 7th, 30th, 90th,
180th, and 365th days. Three specimens were used
for the unconfined compressive strength test, and one
specimen was used for the impact acceleration test.
The unconfined compressive strength and thecompressive strength of the solidified soil producedby mixing the original soil and the solidifier, which
had a mixing ratio similar to that for the soil solidified
on site, were measured using the same curing
durations as those for the crushed solidified soil
specimens. The flows of the test procedures areshown in Figure-2.
The full-scale embankment test
The river embankment constructed on soft groundsettles after construction. Such embankment has todeform following the deformation of the ground. If
the deformation of the embankment on the soft
ground does not follow the deformation of the ground,gaps may be created between the embankment andthe ground or cracks may occur in the embankment,
which may result in water leakage. Therefore, thematerial of the embankment has to behave flexibly
following the deformation of the ground. This
characteristic is also required for the crushedsolidified soil when it is used as the material for an
embankment on soft ground. Two full-scaleembankments were constructed on soft ground using
solidified soil for one embankment and crushedsolidified soil for the other embankment andTable 2 Ground information around the full-scale embankment construction site
Soil layer symbol
Ap1
Acp
Ap2
Acs

Soil layer namePeat
Clay mixed with peat
Peat
Sandy clay

Thickness (m)
1.4
1.0
2.5
2.4
17

Ignition loss Li(%)
48
26
54
-

water content wn(%)
414
263
396
42

GEOMATE – Melbourne, Australia, 11-13 November 2020

5
2

20

3

marker(Slaked0.
1429

Unit： ma. Vertical section
Defined of term.

Dry density ρd(g/cm3)

11 days
109 days

1.1

1.0

0.9
Soildifying material
mixing ratio 5%

0.8
30

Fig. 4

40

50
60
70
Water content w(%)

80

1.1

Dry density ρd(g/cm3)

Original soil
53 days
259 days

1.1

Dry density ρd(g/cm3)

Fig. 3b. Cross section

1.0

0.9
Soildifying material
mixing ratio 10%

0.8
30

40

50
60
70
Water content w(%)

80

1.0

0.9
Soildifying material
mixing ratio 15%

0.8
30

40

50
60
70
Water content w(%)

80

Compaction curves for the crushed solidified soil.
leaving time; however, the maximum dry densitychanged considerably depending on the leaving time
for the specimens with solidifier mixing ratios of 10%
and 15%. By continue of investigation to collect moredata and clarify the tendency in the changes in the
maximum dry density according to the solidifier
mixing ratio and the leaving time.

slope gradient was 1:1.5. To clarify the deformationof the embankment when it is excavated, markers
were set in the embankment during the construction.
For the markers, 1-cm thick lime layers with 60cm
intervals in the depth direction were installed in anarea of 2 m in width along the longitudinal center ofthe embankment. The construction machine used was
a 4t-combined-roller which had a steel wheel (roller)
at either of the front or the rear side and tires on theother side. Four runs of rolling compaction were donefor each layer. In constructing embankments, thatwere worked in a short time to avoid damaging the
ground. Up to the height of 1.8 m, 3 layers in one day
were constructed, and after that, one layer per day wasconstructed. The two embankments were constructed
20 m apart between the toe ends to avoid interference
from the other embankment's settlement. On August29, 2019, which was about one year after theconstruction of the two embankments, theembankments were excavated in the longitudinal
direction and investigated the deformation.

The strength of the specimens immediately after
production
The relationship between the leaving time and theunconfined compressive strength of the specimens
immediately after production is shown in Figure-5.
The relationship between the leaving time and theimpact acceleration of the specimens immediately
after production is shown in Figure-6. The longer theleaving time, the lower the unconfined compressive
strength was. The impact acceleration was slightly
higher for the longer leaving time. From this result, it
was considered that the quality of the soil specimenchanges from that of cohesive soil to that of sandy soil,
and the quality of sandy soil became dominant withlonger leaving times. The cohesion of the unconfinedcrushed solidified soil specimen was lower withlonger leaving time, and the uniaxial compressive
strength of the specimen became lower with longer
leaving time. In the impact acceleration test for theconfined specimen, the angle of shear resistance ofthe specimen became greater with longer leaving time,
and the impact acceleration of the specimen became
greater with longer leaving time.

TEST RESULTS
The physical properties and strength
Compaction characteristics
The compaction curves for the crushed solidified
soil specimens with solidifier mixing ratios of 5%,
10%, and 15% are shown in Figure-4. All thecompaction curves for the crushed solidified soil
specimens are above that for the original soil
specimen. The maximum dry density became higher
for the crushed solidified soil than that for the originalsoil. For the crushed solidified soil specimens with asolidifier mixing ratio of 5%, the maximum dry
density changed very little with the changes in the

The curing time and the strength
The relationship between the unconfinedcompressive strength and the curing time is shown in
Figure-7. Excluding the crushed solidified soil with a
18

80
75
70
65
60
Mixing ratio 5%
Mixing ratio 10%
Mixing ratio 15%

50
45
40
0

100
200
Leaving time (days)

300

600

Solidfied soil
Leaving time 11days
Leaving time 53days
Leaving time 109days
Leaving time 259days
Soildifying material
mixing ratio 5%400

Unconfind compressive
strength qu(kN/m2)

Unconfind compressive
strength qu(kN/m2)

600

200

0
0

100
200
300
Curing time (days)

400

140
120
100
80
60
Mixing ratio 5%
Mixing ratio 10%
Mixing ratio 15%

40
20
0
0

100
200
Leaving time（days）

600

Soildifying material
mixing ratio 10%

500
400
300
200
100

Soildifying material
mixing ratio 15%

500
400
300
200
100
0

0
0

100
200
300
Curing time (days)

400

0

100
200
300
Curing time (days)

400

Relationship between curing time and unconfined compressive strength
the unconfined compressive strength of the crushedsolidified soil specimens with longer leaving times
tended to be smaller than those of the specimen withshorter leaving times. In light of this, it can beconcluded that the unconfined compressive strengthof the crushed solidified soil with a low solidifier
mixing ratio or a long leaving time does notconsiderably increase. The relationship between the
curing time and the impact acceleration showed asimilar tendency as that observed between the curing
time and the unconfined compressive strength.

solidifier mixing ratio of 5% and a leaving time of 11days, the unconfined compressive strengths of thecrushed solidified soil specimens were lower thanthose of the solidified soil, regardless of the solidifier
mixing ratio and curing time. The unconfinedcompressive strength increases very slightly after the
curing time of 84 days for the crushed solidified soil
with the solidifier mixing ratio of 5%. However, forthe specimens with the solidifier mixing ratios of 10%
and 15%, the unconfined compressive strength tended
to increase with the curing time. The degree ofincrease in the unconfined compressive strength ofthe crushed solidified soil for the specimen with aleaving time of 259 days was great. The increases in

The full-scale embankment test

29m
20m
Center of embankment
50cm
3m

Crushed solidified soil

Ground65cm
60cm

Solidified soil
Loosenese

Groun
70cm
CracksEmbankment immediately after construction
Embankment one year after construction

Fig. 8300

Fig. 6 Relationship between leaving time, andunconfined compressive strength ofimmediately after production

Unconfind compressive
strength qu(kN/m2)

55

Fig. 5 Relationship between leaving time, and impact
acceleration immediately after production

Fig. 7

Unconfined compressive strength ofimmediately after production qu (kN/m2)

供試体作製直後の衝撃加速度
Impactacceleration of immediately
after production I (G)GEOMATE – Melbourne, Australia, 11-13 November 2020

Deformation of embankment.
19

GEOMATE – Melbourne, Australia, 11-13 November 2020

greater with longer leaving times; however, theincrease in the impact acceleration became negligible
after a certain length of leaving time. The unconfinedcompressive strength and the impact acceleration of
the compacted crushed solidified soil increase withthe curing time. The unconfined compressive strengthof the crushed solidified soil with a large amount ofsolidifier became higher with the curing time, evenfor the material with the leaving time of 259 days. The
strength development of the crushed solidified soil
was smaller than that of the solidified soil. Excess
strength development was able to be controlled.
[3] When an embankment was constructed on soft
ground using the solidified soil, the embankment was
not able to accommodate the deformation of the soft
ground and cracks developed; however, cracks did
not develop in the embankment constructed using thecrushed solidified soil. The crushed solidified soil isable to be used for the material to constructembankments on soft ground was concluded.

The excavated cross-section of the embankment
constructed using the crushed solidified soil is shownin Figure-8. Deformation was observed in the entire
body of the embankment constructed using thesolidified soil and that of the embankment
constructed using the crushed solidified soil. The
deformation of the embankment on soft ground islarge at the center of the embankment, and it isreported that the entire embankment is deformed [9].
About 60 cm to 70 cm of settlement was seen at thecenter of both of the embankments. It was found thatthe embankment constructed using the crushedsolidified soil was entirely homogeneous in soil
quality and had no cracks. The embankment
constructed using the solidified soil had three cracked
areas and one loose area near the bottom surface. At
these three locations, 2.0 m to 2.5 m long and 1 cm to2 cm wide cracks developed upward from the bottomof the embankment were found. At the one location,
the embankment toe failed from the loosening of thesoil near the location of the installed settlement plate.
Based on these findings, the cracks had formed in theembankment constructed using the solidified soil
because the embankment was not able to follow the
deformation of the soft ground were clarified;
however, the embankment constructed using thecrushed solidified soil followed the deformation of
the soft ground similar to the embankments
constructed using common soil materials.

REFERENCES[1] Civil Engineering Research Institute for Cold
Region: The Manual of Measures against
Unsuitable Soil in Hokkaido, 2013.
[2] Atsuko Sato, Jun’ichi Nishikawa and Hatsuo
Kitano, Strength characteristics of solidifiedcrushed soil, Proceedings of the 32th Japan
National
Conferenceon
GeotechnicalEngineering, 1997, pp.2423-2424.
[3] Atsuko Sato, Osamu Hatakeyama, Hirotsugu
Kouoka, Yutaka Inazawa, Tomonori Nagata,
Hideaki Takahashi and Kiyome Hatakeyama,
Preparation conditions and geotechnicalcharacteristics of solidified crushed soil,
Japanese Geotechnical Society, Hokkaido
Branch, Technical Report No. 60, 2020, pp. 93100.
[4] Japan Road Association, Road Construction
Summary, 2009, .p.287[5] Japanese Geotechnical Society, JapaneseStandards and Explanations of Laboratory Testsof Geomaterials, 2009, pp. 226-258.
[6] Hokkaido Regional Development Bureau, Road
Design Guidelines, 2019.
[7] Atsuko Sato and Naoyuki Kuji, Leaving time andgeotechnical properties of crushed solidified soil,
Symposium of graund Improvment No.13, 2018,
pp.415-418.
[8] Japan Cement Association, Strength test method
for improved products by cement-basedsolidifying material, 2006.
[9] PWRI Cold Region PWRI, Peat Soft Ground
Countermeasure
Manual,Annnn
B.,
Unpublished Work but Accepted, Vol., Issue,Year, 2017.

SUMMARY
By using the original soil containing volcanic ash
and that containing clay with a high liquid limit, therelationship between the production conditions andthe physical properties and strength of the crushedsolidified soil and the settlement characteristics of theembankment constructed using the crushed solidified
soil on soft ground were investigated. the mix
proportion and leaving time for producing crushedsolidified soil which does not develop excess strength
were could not determine; however, the following on
the crushed solidified soil were found.
[1] The physical properties of the crushed solidified
soil, including compaction characteristics, differedfrom those of the original soil. The compaction curves
showed that the maximum dry density of the crushedsolidified soil was higher than that of the original soil
and that the higher the mixing ratio of the solidifier,the higher the maximum dry density became. When
constructing an embankment using the crushedsolidified soil, it is necessary to perform quality
control by obtaining the compaction curve for eachspecimen.
[2] The unconfined compressive strength of thecompacted crushed solidified soil immediately after
the specimen preparation tended to be smaller withlonger leaving times. The impact acceleration became2010th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

PREDICTION METHOD OF SLIP SURFACE DEPTH FOR SLOPEHEALTH MONITORING BASED ON VOLUMETRIC WATERCONTENT
Keigo Koizumi1, Sota Tsukabe2, Mitsuru Komatsu3, Kazuhiro Oda4 and Shinichi Ito5
Graduate School of Engineering, Osaka University, Japan; 2Faculty of Engineering, Osaka University,
Japan; 3Graduate School of Environmental and Life Science, Okayama University, Japan; 4Faculty ofEngineering, Osaka Sangyo University, Japan; 5Graduate School of Science and Engineering, Kagoshima
University, Japan
1

ABSTRACTThis research proposes a method to install a sensor at an appropriate position for monitoring slope soundness
against heavy rainfall. The frequency of natural disasters caused by climate change has increased in recent years.
In particular, rainfall-induced slope failures owing to localized heavy rain have increased; accordingly,
countermeasures have been implemented on an urgent issue in Japan. As current nonstructural measures, there arevarious regulation standards, such as hourly rainfall, continuous rainfall, and Soil Water Index; however, it isdifficult to evaluate the risk of slope failures individually. In contrast, studies are evaluating individual slope
stability by monitoring rainwater infiltration, pore water pressure, and other parameters in the slope or by detecting
slope deformation. These studies focused on sandy slopes, and the value of Nd can be used to estimate the depthof the slip surface, which is one of the main factors of slope failures. However, Nd is an index value that shows therigidity and hardness of the ground. Therefore, it is not possible to directly estimate whether the water level forms
or not only based on the information of Nd. Therefore, to accurately analyze the slip surface, a method forevaluating water permeability and water retention in the slope is required. Herein, we proposed a method to predictthe depth of the slip surface, considering the rate of decrease of the volumetric water content in the soil. Then, its
applicability was confirmed by using numerical analysis and verified on an actual slope. As a result, we indicatedthat the slip surface could be predicted using the proposed prediction method in the actual slope.
Keywords: Slope failure, Volumetric water content, Slip surface depth, Slope health monitoring

predict slope failure based on the pore water pressure
or groundwater level [5], [6] and to evaluate thehealth of slopes based on the volumetric watercontent [7]–[9] have been proposed. These studies
have focused on the shallow slope failure. When thesensors for monitoring the slope stability are installed,only the value of Nd which is the index obtained byhandy dynamic cone penetration test can be used toestimate the depth of the slip surface [10]. Nd is anindex value that represents the degree of rigidity andhardness of the ground; however, it cannot be used toestimate whether a saturated zone has formed at theslip surface. Therefore, for accurately estimating theslip surface, Nd and the index for evaluating
permeability and water retention of the ground are
required. The purpose of this study is to propose amethod to install a sensor at an appropriate position
for monitoring slope soundness against heavy rainfall.
In this study, the existence of the slip surface and its
depth were analyzed by evaluating permeability andwater retentivity in the slope based on the volumetricwater content, which we are currently monitoring.
The slip surface described in this study is definedas the boundary layer where a saturation zone isformed. Therefore, it has not been discussed whether
the defined slip surface is consistent with the actual
slip surface.

INTRODUCTION
In recent years, the frequency of natural disasters
associated with climate change in Japan has increased.
In particular, the annual frequency of slope disasters
is increasing, and implementing appropriate
countermeasures is a major problem. Based on
current nonstructural measures, criteria such ashourly rainfall, continuous rainfall, and Soil Water
Index are set using rainfall data as an index; however,these criteria cannot be considered for evaluating therisk of slope failures individually. This is becauseslope failure occurs due to the combination of factorsthat indicate topographical and geologicalcharacteristics and incentives such as rain andearthquakes. However, this warning criterion basedon rainfall information does not become a criterion
considering slope angle and physical properties ofsoil as a predisposing factor. In contrast, several
studies have evaluated the health of individual slopesby monitoring rainwater infiltration and deformation
behaviors in the slope. For example, by focusing on
slope deformation, methods to forecast the failure
time based on the creep-rupture life or velocity ofsurface displacement [1], [2] and to predict the prefailure stages based on the tilting rate of tilt sensors[3], [4] have been proposed. Further, methods to
21

GEOMATE – Melbourne, Australia, 11-13 November 2020

Rate of decrease in volumetricwater content

PREDICTION METHOD OF SLIP SURFACE
DEPTH
This study describes a method to estimate the slip
surface based on the temporal change of volumetricwater content in the slope of sandy soil. First, a casewhere a slip surface is formed in a shallow layer isconsidered. When there is a soil layer with poor water
permeability in the shallow layer, it is considered thatthe water level is formed by the stagnation of theinfiltration surface in the upper layer of the slip
surface because water cannot drain out to the lower
layer. Figure 1 shows the rate of decrease in thevolumetric water content at each depth after rainfall
to a certain time period with and without slip surfaces.
In the case of “with slip surface,” though the water ofshallow depth infiltrates downward owing to gravity,
saturated and capillary zones are formed near and justabove the slip surface. Therefore, the rate of decreasein the volumetric water content is expected to berelatively slow with depth. In other words, assumingthat the capillary zone of sandy soil forming the upper
layer of the slip surface is low in height, the slip
surface may exist at a depth where the rate of decreaseis zero. This can be obtained by connecting the rate ofdecrease of the volumetric water content at each
depth to a straight line. On the other hand, even for“without slip surface,” the decrease rate of volumetricwater content due to gravity drainage is expected tobe slower at deeper depths than at shallow depths.
However, the difference is smaller compared with the“with slip surface” case, and it is possible that thedifference between the two becomes clear over time.

“without slip surface”“with slip surface”

Depth

Fig.1

Depth of slip surface

Conceptual diagram of the rate of decrease in
the volumetric water content at each depth
after rainfall “with and without slip surface”
50cm

20cm

120cm
80cm
100cm

Rainfall boundary condition
Free drainage condition

40cm

Undrained condition
Observation point
Upper layer
Lower layer

Fig.2

One-dimensional analytical model

Table 1VERIFICATION OF THE VALIDITY OF THE
PREDICTION METHOD USING NUMERICALANALYSIS
To verify the validity of the hypothesis shown in
Fig.1, a one-dimensional analytical model simulating
the slip surface was designed and evaluated using thesaturated–unsaturated seepage flow program–
HYDRUS-2D. Water flow is calculated using the
Richards equation [11] and a finite element method.
The van Genuchten model [12] is used to express thesoil–water characteristic curve, and the Mualem
model [13] is used to estimate the unsaturated
hydraulic conductivity.

ks
θr
θs
α
n

Parameters in each case
Case1
Case2
Upper
Lower
Upper
Lower
(1) 0.57*100(1) 0.074*100(2) 0.57*10-1(2) 0.074*10-1
0.57-2 0.074(3) 0.57*10(3) 0.074*10-2(4) 0.57*10-3(4) 0.074*10-3
0.153
0.034
0.065
0.034
0.430
0.460
0.410
0.460
0.085
0.016
0.075
0.016
1.60
1.37
1.89
1.37

layer were set. The lower layer with lower
permeability is classified as “with slip surface.” Thehydraulic conductivity of the upper layer was set by
estimating the upper and lower limits of the hydraulicconductivity of decomposed granite soil. The otherwater characteristic parameters were set based on thereferences. The observational depths were set to 20,
80, and 100 cm, according to the position where thesensor was installed on the actual slope. The rainfall
intensity was 30 mm/h, and it stopped when theinfiltration surface reached the boundary layer. Whenthe volumetric water content at each observational

Analytical Method
Figure 2 shows the analytical model. In this study,
a boundary layer assumed to be a slip surface was set
at a depth of 120 cm, and the hydraulic conductivityof the upper and lower layers was changed to showwhether there is a slip surface or not. Concretely, theanalytical model for two cases listed in Table 1 was
prepared. For the lower layer, four patterns of 1, 1/10,
1/100, and 1/1000 times of permeability of the upper
22

GEOMATE – Melbourne, Australia, 11-13 November 202010depth started decreasing, the rate of decrease in thevolumetric water content at each elapsed time wascalculated by changing the time intervals in the orderof 12, 32, and 60 min. The calculated results wereused to evaluate the depth of the slip surface that wasestimated based on the conceptual diagram in Fig.1.
Hereinafter, it is referred to as the estimated slip
surface depth.

12min((cm3/cm3)/min*10-4)

Decrease rate of VWC

32min

Results and discussion

60min

8

120min180min240min6

360min

4
2
0
0

120
Depth(cm)Figure 3 shows the change in the rate of decreasein the volumetric water content over time in cases
1-(1)and 1-(4). If the decreased rate shows a negativevalue owing to the formation of a saturated zone in
the boundary layer, it is plotted as zero forconvenience. Based on these figures, it can be
confirmed that the depth of the slip surface changes
with time and is not uniquely determined, as shownin Fig.1. In case 1-(1), the estimated depth of the slip
surface after 60 min roughly agreed with theboundary layer of 120 cm, but it deepened (arrows in
the figure) with time. On the other hand, in case 1-(4),the estimated depth of the slip surface after 60 min
roughly agreed with the boundary layer of 120 cm,
but the depth slightly rose (arrows in the figure) withtime.
Figure 4 shows the change in the rate of decreasein the volumetric water content over time in cases 2(1) and 2-(4). As in Fig.3, if the rate of decrease showsa negative value, it is plotted as zero for convenience.
In case 2-(1), the estimated slip surface depth changes
to a deeper side with time, whereas in case 2-(4), itchanges to a shallower one. This tendency is similar
to cases 1-(1) and (4) in Fig.3. To clarify these
relationships, Figures 5 and 6 show the changes in theestimated slip surface depth with time in cases 1-(1)to (4) and cases 2-(1) to (4), respectively.
Based on Fig.5, it was confirmed that theestimated depth of the slip surface was almost thesame, regardless of the hydraulic conductivity of thelower layer till 60 min of the elapsed time and that thedepth tended to change with time. In addition, theestimated depth of the slip surface varies at 120–360
min. In cases 1-(1) and (2), the depth of the slip
surface tended to increase with time. On the other
hand, as in case 1-(3), when the hydraulicconductivity decreases, the slope of the graphbecomes gentler, and the estimated slip surface depth
tends to approach the boundary layer of 120 cm.
When the hydraulic conductivity is further reduced,
as in case 1-(4), compared with the boundary layer,
the estimated depth of the slip surface tends to beshallower with time. This is because a saturated zone
is formed by the infiltration surface reaching theboundary layer, and the decrease rate at a depth of 100
cm changed to a negative value owing to the influenceof the capillary zone. Based on the results in Fig.6,
the estimated depth of slip surface tended to increase

Case 1-(1)
10

12min

((cm3/cm3)/min*10-4)

Decrease rate of VWC

32min
60min

8

120min180min

6
4
2

0
0

120
Depth (cm)Fig.3

Case 1-(4)Change in the decrease rate of volumetricwater content over time in Case 1
10

12min

Decrease rate of VWC((cm3/cm3)/min*10-4)

32min
60min

8

120min180min240min6

360min

4
2
0
0

120
Depth(cm)Case 2-(1)
10

12min

((cm3/cm3)/min*10-4)

Decrease rate of VWC

32min
60min

8

120min180min240min6

360min

4

2
0

0

120
Depth(cm)Case 2-(4)Fig.4 Change in the decrease rate of volumetricwater content over time in Case 2
23

GEOMATE – Melbourne, Australia, 11-13 November 2020

Depth of estimated slip surface(cm)060Elapsed time（min）
120
180
240

300

Table 2 Outline of the study site
Ankokuji temple, Ayabe, Kyoto Pref.

360

40

Japan

12min
32min
60min

120

180min120min120min120minCase1-(1)(0.57×10⁰)

200

Ankoku-ji
TempleBoundary layer

180min240min360min180min240minSL-A

Slope failure in2014

360min240minCase1-(2)(0.57×10⁻¹)

SL-B

Ankoku-ji
Temple180minCase1-(3)(0.57×10⁻²)
360minCase1-(4)(0.57×10⁻³)050m©Geospatial Information Authority of Japan

Fig.5

Changes in the estimated slip surface
depth with time in Case 1

Soil Classification (JGS)
Slope type
Slope angle
Depth of soil moisturesensors
Depth of water sensor
Measurement period

Depth of estimated slip surface (cm)Elapsed time（min）
0

60

120

180

240

300

360

40

12min
32min 60min

120

Boundary layer

180min240min180min120min240min180min360minRain gauge

240min360min275cm

Case2-(1)(0.074×10⁰)

200

Case2-(3)(0.074×10⁻²)

SL-B

SL-B

80cm

Base station

IoT data logger

Line of Nd≦20

Case2-(2)(0.074×10⁻¹)

SL-A

210cm

Case2-(4)(0.074×10⁻³)

Fig.6

SCsG
Natural slope
40°
20cm,80cm &
100cm
110cm
2019/10/24-27

100cm

20cm

Changes in the estimated slip surface
depth with time in Case 2

SL-A
IoT data logger

80cm
100cm
Soil moisture sensors：Installation depth of soil moisture sensors

with time in cases 2-(1) and (2) with high hydraulicconductivity (Fig.5). On the contrary, in cases 2-(3)and (4) where the hydraulic conductivities are low,graphs with a convex downward line are seen, and thedepths at the lowest point of the graphs are almost
coincident with the boundary layer of 120 cm.
Considering the above results, it is possible todistinguish “with slip surface” and “without slip
surface” by analyzing the time change of theestimated slip surface depth. The slip surface depth isthe depth at which the slope of the graph approaches
zero with the elapsed time or the depth at the lowest
point of the downward convex graph.

：Installation point of water level gauge

Fig.7

270cm 175cm 50cm

0

5m

115cm
115cm

Installation information of each sensor in the
study site

ON-SITE VERIFICATION

Table 3 Physical properties
Dry density 𝜌𝑑 (g/cm3)
Wet density 𝜌𝑡 (g/cm3)
Density of soil particle 𝜌𝑠 (g/cm3)
Natural water content w (%)
Gravel (%)
Sand (%)
Silt (%)
Clay (%)
Maximum particle size (𝑚𝑚)Here, the validity of the slip surface depth
estimation method proposed in previous chapter isverified based on the field measurements. The outline
information of the study site is listed in Table 2. The
study site is located at a mountain slope behind the
Ankoku-ji temple in Ayabe City, Kyoto Prefecture. In
2014, rainfall-induced slope failures occurred in thissite. Therefore, the slopes with the weathered soil aremonitored to analyze their health. Soil moisturesensors and water level gauges were installed at thetoe of the slope (SL-A) and the middle part of theslope (SL-B) to analyze rainwater infiltration
behaviors. Based on the previous studies, three soil
moisture sensors were installed along the depth of theslope. The installation depth of the soil moisturesensors and water level gauge was determined basedon the values of Nd at each location. Here, Nd ≦ 20 is
set as the boundary layer. The installation informationof each sensor is shown in Fig.7. Moreover, a rain
gauge for observing rainfall was installed in the study
site. The data recorded in the IoT (Internet of things)
data logger can be monitored in real time on the Web
via the base station. The physical properties are listed
in Table 3.
Figures 8 and 9 show time-series variation in
rainfall, volumetric water content, and water level in
SL-A and SL-B. The measurement interval of rainfall
and water content is 10 min, and the measurement
24

1.46
1.87
2.911
27.7
16.8
43.3
17.6
22.3
19

GEOMATE – Melbourne, Australia, 11-13 November 2020

9
8
7
6
5
4
3
2
1
0

5
4
3
2

40
1
0

20

Decrease rate of VWC((cm3/cm3)/min*10-5)

60

Rain intensity (mm/10min)
Water level (m)Volumetric water content (%)

80

-1
0
2019/10/24
VWC_20cm

Fig. 8

2019/10/25
VWC_80cm

2019/10/26

VWC_100cm-2
2019/10/27

Rain intensity(mm)60min
120min180min240min480min050

Water level

Fig. 10

Time series variation in rainfall, volumetricwater content, and water level in SL-A.

100
150
Depth(cm)

Change over time in the decrease rate ofvolumetric water content in SL-A
7

560
Field saturated state

3
2

40
1
0

20
80cm

0
2019/10/24
VWC_20cm

Fig. 92019/10/25
VWC_80cm-1

2019/10/26

VWC_100cm

Water level

Decrease rate of VWC((cm3/cm3)/min*10-5)

4

Rain intensity (mm/10min)
Water level (m)Volumetric water content (%)

80

200

60min

6

120min5240min180min480min

4
3
2
1
0

-2
2019/10/27

0Rain intensity(mm)Fig. 1150
Depth(cm)100

Change over time in the decrease rate ofvolumetric water content in SL-B

Time series variation in rainfall, volumetricwater content, and water level in SL-B.

Elapsed time (min)interval of water level is 60 min. As the volumetricwater content is calculated using the calibration
formula of the manufacturer, only the relative change
is emphasized. As shown in Fig.8, it can be estimatedthat the water permeability in the soil is high because
the volumetric water content at each depth repeatedly
increases and decreases in response to rainfall.
Therefore, there was no tendency for the water level
to increase in the boundary layer during theobservation period. On the other hand, in Fig.9,
although the volumetric water content increases
orderly from the shallow part due to rainfall, thevolumetric water content at depths of 80 and 100 cm
is relatively larger than that at 20 cm. The timing
when the volumetric water content at a depth of 80
cm showed a peak value roughly corresponds with thetiming when the water level gauge was at 80 cm,
indicating that the depth of 80 cm is in the fieldsaturated state.
Based on the analytical method described inpervious chapter, the rate of decrease in volumetricwater content was calculated by changing the timeinterval from the time when the volumetric watercontent began to decrease at each observation depth
after rainfall. Figures 10 and 11 show the changes in
the rate of decrease in the volumetric water content

0

100

200

300

400

500

Depth of estimated slip surface (cm)

0
SL-A

SL-B

50

100

150

200

Fig.12 Changes in the estimated slip surface depthwith time in case 2.
over time in SL-A and SL-B. Figure 10 shows that theestimated depth of the slip surface in SL-A becomesdeeper with time, and the estimated depth also
becomes deep. On the other hand, Figure 11 showsthat the estimated depth of the slip surface at SL-B
varies slightly regardless of time, and the estimateddepth remains at 100–110 cm. To clarify these
relationships, Figures 12 shows the changes in theestimated slip surface depth with time in SL-A and
SL-B, respectively. The estimated depth of slip the25

GEOMATE – Melbourne, Australia, 11-13 November 2020

surface in SL-A becomes deeper with time. Thistendency is similar to case 1-(1) and (2) in Fig.5 andcase 2-(2) and (2) in Fig.6, implying that there is noslip surface in the shallow layer where slope failure isa matter of concern. On the other hand, the estimateddepth of the slip surface in SL-B is constant at 100–
110 cm regardless of the elapsed time. It suggests thatthe slip surface may exist at the depth of ~110 cm,
and this tendency is (roughly) similar to case 1-(3) inFig.5.

Yamaguchi H., Seko I., and Qiao J., Precaution
and Early Warning of Surface Failure of Slopes
Using Tilt Sensors. Soils and Foundations, Vol.
55, No. 5, 2015, pp. 1086-1099.
[4] Ishizawa T., Sakai N., Moroboshi T., andFukuzono T., Effectiveness of Underground
Displacement Measurement Using Inclinometer
for Prediction of Slope Failure. Landslides.
Journal of the Japan Landslide Society, Vol. 50,
No. 6, 2013, pp. 256-267. (in Japanese)[5] Morikawa H., Inokuchi T., Hattanji T., Sassa K.,
Ochiai H., and Wang G., Failure Processes in a
Full-Scale Landslide Experiment Using aRainfall Simulator. Landslides, Vol. 1, No. 4,
2004, pp. 277-288.
[6] Sasahara K. and Ishizawa T., Time Prediction ofan Onset of Shallow Landslides Based on the
Monitoring of the Groundwater Level and theSurface Displacement at Different Locations on
a Sandy Model Slope. Japanese Geotechnical
Journal, Vol. 11, No. 1, 2016, pp. 69-83.
[7] Koizumi K., Sakuradani K., Oda K., Komatsu M.,
and Ito S., Relationship Between Initial QuasiSaturated Volumetric Water Content andRainfall-Induced Slope Deformation Based on a
Model Slope Experiment. Journal of
GeoEngineering, Vol. 13, No. 4, 2018, pp. 179187.
[8] Chae B. G. and Kim M. I., Suggestion of a
Method for Landslide Early Warning Using theChange in the Volumetric Water Content
Gradient
due toRainfall Infiltration.
Environmental Earth Sciences, Vol. 66, 2012, pp.
1973-1986.
[9] Tohari A., Nishigaki M., and Komatsu M.,
Laboratory Rainfall-Induced Slope Failure with
Moisture Content Measurement. Journal of
GeotechnicalandGeoenvironmental
Engineering, Vol. 133, No. 5, 2007, pp. 575-578.
[10] Koizumi K., Oda K., Komatsu M., Ito S., andTsutumi H., Slope Structural Health Monitoring
Method Against Rainfall-Induced Shallow
Landside. IOP Conference Series: Materials
Science and Engineering, Vol. 615:012046, 2019,
pp. 1-8.
[11] Richards L. A., Capillary Conduction of Liquids
Through Porous Mediums. Physics, Vol. 1, 1931,
pp. 318-333.
[12] van Genuchten, M., A Closed-Form Equation for
Predicting the Hydraulic Conductivity of
Unsaturated Soils. Soil Science Society ofAmerica Journal, Vol. 44, No. 5, 1980, pp. 892898.
[13] Mualem, Y., A New Model for Predicting theHydraulic Conductivity of Unsaturated PorousMedia. Water Resources Research, Vol. 12, No.
3, pp. 513-522.

CONCLUSIONSThe purpose of this research proposes a method toinstall a sensor at an appropriate position formonitoring slope soundness against heavy rainfall. In
this study, a method to predict the existence and depthof slip surfaces based on the rate of decrease of thevolumetric water content in the soil after rainfall was
analytically examined. And its applicability isverified in the actual slopes. The research findings are
as follows.
From the numerical analysis, it is confirmed to
distinguish “with slip surface” and “without slip
surface” by analyzing the time change of theestimated slip surface depth. The slip surface depth isthe depth at which the slope of the changes in theestimated slip surface depth with time approaches
zero with the elapsed time. The validity of the slip
surface depth estimation
method proposed innumerical analysis is verified based on the fieldmeasurements. As a result, it was confirmed that thedepth of the slip surface estimated from theobservation data by using the water level gauge andthe soil moisture sensors agreed with the depth of theslip surface estimated by the proposed method.
From these results, it is suggested that the slip surface
can be predicted by applying the prediction method
obtained from the analysis to the measurement resultof the actual slope.
ACKNOWLEDGMENTS
This research was partly supported by Garant-inAid of Scientific Research (17K00615 and
19K04599). We express our gratitude.
REFERENCES[1] Saito M., Prediction of Slope Failure due to theThird Creep. Landslides. Journal of the Japan
Landslide Society, Vol. 4, No. 3, 1988, pp. 1-8.
(in Japanese)[2] Fukuzono T., A Method to Predict the Time of
Slope Failure Caused by Rainfall Using theInverse Number of Velocity of Surface
displacement. Landslides. Journal of the Japan
Landslide Society, Vol. 22, No. 2, 1985, pp. 813. (in Japanese)[3] Uchimur T., Towhata I., Wang L., Nishie S.,
26

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

FUNDAMENTAL STUDY ON INSTALLATION POSITION OF
SENSORS FOR ON-SITE MONITORING FOCUSING ON SLOPEFAILUREHaruki Iwamoto1, Keigo Koizumi2, Mitsuru Komatsu3 and Hiroshi Tsutsumi4
1,2

3

Graduate School of Engineering, Osaka University, Japan;
Graduate School of Environmental and Life Science, Okayama University, Japan;
4West Nippon Expressway Co. Ltd., Japan
ABSTRACT

Rainfall-induced slope failure occurs frequently owing to the effect of recent extreme weather. Our research is
aimed at the direct evaluation of slope stability by using a real-time monitoring system on the slope. However,
several problems remain to be solved to allow for the practical use of this monitoring method, for example,
determining how many sensors should be installed or where they should be installed on the slope. To address thesequestions, in this study, we determine the position that is more effective for the installation of a sensor for slope
monitoring based on laboratory experiments. In addition, we propose a method for estimating an appropriate
position. The results show that the proposed method for estimating the point at which the water level rises agrees
well with the critical point of slope failure obtained through the laboratory-based model slope experiments.
However, this result is only feasible under a constant rainfall condition, and its verification with respect to theeffects on actual slopes remains to be solved in the future.
Keywords: Volumetric Water Content, Shallow Landslide, Rain Intensity, Sensor Location, Shear Deformation

INTRODUCTION

LITERATURE REVIEWIn recent years, natural disasters caused by heavy
rainfall, especially those causing traffic blockage andhuman casualties have been frequent in Japan, where
70% of the land is made up of mountains. Currently,
highways management company use only a rainfall
information as a traffic-control standard in the eventof abnormal rainfall. Although this method is simple
and convenient, it does not consider the mechanismof slope failure. Our research group conducted fieldobservations and model slope experiments and foundthat when rainwater infiltrates slopes during heavy
rainfall, the volumetric water content of the surface
ground temporarily reaches an equilibrium state(“quasi-saturated state”) and displacement occurs
after the volumetric water content in equilibrium(“initial quasi-saturated volumetric water content(IQS)”) is exceeded. In other words, the prediction of
the slope deformation caused by rainfall is possible
by monitoring whether the volumetric water content
reaches the IQS or not. Based on these findings, theauthors considered that determining whether the
traffic control is executed is possible by using IQS.
However, the current works on slope monitoring have
not yet considered a method determining the locationof sensor installation position; such a method iscurrently being determined empirically. Therefore, inthis study, the authors attempted to determine theappropriate sensor location based on model slope
experiments to accurately detect slope hazards duringslope monitoring.

This section provides an overview of literature on
surface failure. Yagi, Yatabe, and Yamamoto [1]conducted rainfall model experiments to elucidate themechanism of slope failure during heavy rainfall, andthey found that excess pore water pressure at thebottom of the toe of the slope is one of the causes ofslope failure. Kohgo, Saito, Niide, and Hori [2]conducted a rainfall model experiment and reported
that negative pressure is released from the toe of slope,which is then is saturated, and the saturation
progresses gradually to the top of the slope, leading
to failure. Sako, Suda, Fukugawa, and Kitamura [3]conducted infiltration and failure tests using
decomposed granite soil and found that after
rainwater
infiltration,
groundwater
startedaccumulating at the bottom of the soil layer, andfailure occurred at the toe of the slope owing to theincreased seepage force associated with pore waterrunoff from the toe of slope. Koizumi, Sakuradani,Oda, Ito, Fukuda, Feng, and Takemoto [4] conducted
model slope experiments for surface failure underconstant rainfall intensity and clarified therelationship between volumetric water content anddisplacement, as shown in Fig. 1. The figure also
shows that (1) the volumetric water content initially
increases with rainwater infiltration, and then (2)reaches a quasi-saturated state, resulting in an
equilibrium state of volumetric water content. The
quasi-saturated state is that in which the inflow andoutflow volumes are equal in an unsaturated state
27

GEOMATE – Melbourne, Australia, 11-13 November 2020

during the infiltration process, and the volumetricwater content at this time is defined as the IQS. Withfurther watering, (3) unsaturated capillaries areformed with the increase in the groundwater level,
and the volumetric water content increases again from
IQS. Eventually, (4) the groundwater level rises to thesensor position and reaches a saturated state.
Displacement occurs after re-elevation from the IQS,
i.e., after the formation of the groundwater level.
Therefore, it is assumed that the displacement occurs
after the volumetric water content reaches IQS. As
shown in Fig. 2, the location of the seepage face andgroundwater level can be estimated by measuring thevolumetric water content at multiple depths in thevertical direction. For example, at time TC, thevolumetric water contents in the deeper levels (points
DU and DL) rose again and reached saturation, whilethe volumetric water content at the shallow point (S)remains in equilibrium; then, the position of thegroundwater level can be estimated to be between
points S and DU.
Thus, the mechanism of slope failure is largely
related to the water content in the soil, especially thegroundwater level. Therefore, monitoring the soil
water behavior before and after the formation of thegroundwater level is important, i.e., the water
behavior from rainwater seepage to groundwaterlevel rise must be monitored to predict the slope
failure.

PREDICTION AND VERIFICATION OF
POINTS OF WATER-LEVEL OCCURRENCE

Fig. 1 Relationship between volumetric water content
and displacement

This section explains the mechanism of slope
failure. Rainwater infiltrates into the vertical directionin the slope, and the water that reaches the slip surface
is concentrated at the toe of the slope as seepage flow,
creating a water level. Then, slope failure occurs
owing to the decrease in effective stress caused by theincreases in soil mass weight and pore pressure.
Therefore, if the point of occurrence of water levelcan be estimated and the rainwater seepage anddeformation behaviors can be measured near thispoint, the slope danger can be detected more quickly.
The current method for determining sensor
locations involves a handy dynamic cone-penetration
test to determine the Nd values at each depth along theslope, and then connecting the points with Nd = 20, as
shown in Fig. 3, to estimate the boundary between the
topsoil and bedrock layers, and select locations where
rainwater is likely to concentrate. However, as the Ndvalue obtained by the handy dynamic conepenetration test is an index of the ground hardness andnot of soil permeability.
Therefore, a new method is proposed for
estimating the point of water-level occurrence. Figure
4 shows a conceptual diagram depicting the behaviorof the volumetric water content in the case ofcontinuous rainfall of certain intensity measured
using soil-moisture meters installed at the same
depths at points A, B, and C, as shown in Fig. 3. Aseach meter was installed at the same depth, thevolumetric water content would theoretically begin to
increase at about the same time, resulting in a quasisaturated state. Subsequently, as the water level rises
from the toe of the slope, the volumetric water content
is expected to increase again in the order of A, B, andC. Let t be the time when the quasi-saturated statecontinues, then tA < tB < tC. If a soil-moisture meter isinstalled at point P, the quasi-saturated duration ofthis point is expected to be almost zero, because thewater level is formed shortly after the rainwaterreaches the soil-moisture meter. Therefore, Fig. 5
shows the horizontal distance from the point of water-

Fig. 2 Estimation of water state in the soil according
to the volumetric water content

Fig. 3 Conceptual diagram of the location of
installation of soil-moisture meters
28

GEOMATE – Melbourne, Australia, 11-13 November 2020

level occurrence plotted on the horizontal axis, andtimes tA, tB, and tC in Fig. 4 are plotted on the verticalaxis. Water-level occurrence point P can be estimatedby finding the intersection of the line connecting
these plots and the horizontal axis.

Fig. 6 Analysis model
Table 1 SWCC parameters

Fig. 4 Conceptual diagram of the change involumetric water content over time

SampleGranite Soil

Residual Volumetric Water Content(θr)
Saturation Volumetric Water Content(θs)
Hydraulic Conductivity (ks)Parameter Related to Air Ingress
Value (α)
Dimensionless Parameter (n)0Dimensionless Parameter (l)0.377
0.726 cm/min
0.0821/cm
3.33
0.5

Analysis Results
The change in volumetric water content over timeis plotted in Fig. 7. The volumetric water content at
observation points A and B at the same depth
increased simultaneously owing to water infiltrationfrom the ground surface because of rainfall.
Subsequently, the rate of change in the volumetricwater content decreased and reached quasi-saturated
state. Then, the volumetric water content increased
again in the order of A and B due to the water level
was formed.

Fig. 5 Conceptual diagram of quasi-saturation
duration time t
VALIDATION OF THE ESTIMATION METHOD
THROUGH SATURATED AND UNSATURATEDSEEPAGE-FLOW ANALYSIS
Summary of the Analysis
The saturated-unsaturated seepageflow analysis
was performed using the software, HYDRUS-2D,
which is a general-purpose program for predicting
two-dimensional soil moisture and salinity transport[5]. In this software, the water flow is based on the
Richards equation and the solute transfer is based on
the transfer–dispersion equation. Both are computed
using the finite element method.
Figure 6 shows the analysis model used in thisstudy, and Table. 1 lists the soil parameters. As shownin Fig. 6, the red dots indicate the observation points
for the volumetric water content (31-cm deep), andthe rainfall intensity was set to 96 mm/h.

Fig. 7 Analysis-based change in volumetric watercontent over time
Estimation of
Occurrence

the

Point

of

Water-Level

The quasi-saturated duration was calculated fromthe data of volumetric water content at eachobservation point, and the point of water-leveloccurrence was estimated. The time when the change
29

GEOMATE – Melbourne, Australia, 11-13 November 2020in volumetric water content was below 0.005(cm3/cm3)/min was defined as the quasi-saturation
start time. Furthermore, the time when the change
increased above 0.005 (cm3/cm3)/min, the volumetricwater content was judged to start rising again. The
quasi-saturated duration time, t, is calculated bysubtracting the start time of the re-rising of thevolumetric water content from the start time of quasisaturated at each observation point. Fig. 8 plots thehorizontal distance from the point of water-leveloccurrence to the quasi-saturated duration time; theresult shows an approximate straight line. If theintersection of the approximation line and thehorizontal axis is defined as the point of water-leveloccurrence, the error is 8 cm from the actual point ofwater-level occurrence, implying that the point ofwater-level occurrence could be estimated with highaccuracy.

intervals. An image-sensing technology [3] was used
to measure the ground-surface displacement. In
addition, a camera was set up at a position facing theslope model, and displacement measurement targets
were set up at three locations, as shown in Fig. 9. The
ground surface was opened to receive water that was
sprinkled to simulate rainfall and drainage conditions.
The watering intensity was set at 96 mm/h. To
mitigate the effect of collapse settlement on thedisplacement measurements as much as possible, theexperiment was started after the surface layer was
slightly wet.

Fig. 9 Overview of the model slope
Table 2 Physical properties of the sample
Fig. 8 Estimation of point of water-level occurrence
VERIFICATIONEXPERIMENTS
Summary andExperiments

THROUGH

MethodofSampleInitial Water Content (w)Soil Particle Density (ρs)
Setting Dry Density (ρd)
Maximum Dry Density (ρdmax)
Hydraulic Conductivity (ks)
Void Ratio (e)

MODEL

Model

Slope

Figure 9 provides an overview of the model slope,and the physical properties of the samples are shownin Table 2. In this experiment, a model slope of1500 mm (length) × 300 mm (width) × 890 mm(height) was constructed with a topsoil thickness of
450 mm and slope of 30°. The experimental
conditions are shown in Table 3. The base of the
topsoil layer was assumed to be a sliding surface, on
which clay was applied for the undrained condition.
To confirm water-seepage behavior in the depth andslope directions, soil-moisture meters were installedat seven locations, as shown in Fig. 9, and thevolumetric water content was measured at 1-min

Granite Soil
5.0%
2.607 g/cm3
1.629 g/cm3
1.766 g/cm3
1.21×10−2 cm/s
0.6

Table 3 Experimental conditions
Top Surface
Back Side
Side Surface
Bottom Surface
Slope Gradient
Water Sprinkling Strength

Boundary
Condition

Drainage
Undrained
Undrained
Undrained
30°
96 mm/hExperimental Results
The change in ground-surface displacement overtime is plotted in Fig. 10. Considering the time of30

GEOMATE – Melbourne, Australia, 11-13 November 2020

failure, surface displacement initiated at all points
about 27 min after the start of the experiment. At
about 41 min, a small failure was observed near thetoe of the slope, after which small failures occurred
continuously from the toe to the top of the slope.
Failure at point A occurred at around 48 min, andthose at points B and C occurred at around 52 min.
The change in volumetric water content over timeis plotted in Figs. 11(a), (b), and (c). Regarding the
behavior of volumetric water content at line B, thevolumetric water content increased in order from B1
to B2 to B3 owing to water infiltration from theground surface and reached quasi-saturated state.
Subsequently, the volumetric water content increased
again in the order of B3 to B2 to B1. The behavior ofline C is similar to that of line B. The results agreewith those of previous studies showing that
displacement occurs after the volumetric watercontent exceeds IQS.

(a) Line A(b) Line B(c) Line C

Fig. 10 Change of ground-surface displacement overtime
Estimation of
Occurrence

the

Point

of

Fig. 11 Change in volumetric water content overtime according to the line of measure

Water-Level

duration, and the approximate straight lines areshown in Fig. 12 (10-cm depth) and Fig. 13 (31-cm
depth) for each measurement depth. If the intersectionof the approximation line and the horizontal axis isdefined as the point of water-level occurrence, at ameasurement depth of 10 cm (A1, B1, C1), the error
was 16 cm from the actual point of water-leveloccurrence, while it was 10 cm at the measurement
depth of 31 cm (B2, C2); therefore, the actual pointof water-level occurrence could be estimated withreasonable accuracy.

The quasi-saturated duration is calculated using
the data of volumetric water content at eachmeasurement point, and the point of water-leveloccurrence is estimated. The time when the change involumetric water content was below 0.01
(cm3/cm3)/min was defined as the quasi-saturation
start time, and the time when the change increased
above 0.01 (cm3/cm3)/min was defined as the time
when the volumetric water content started to riseagain. However, if the change did not exceed 0.01
(cm3/cm3)/min again, the time at which the change
reached its maximum was considered to be the quasisaturation end time. The quasi-saturated duration is
calculated by subtracting the start time of the re-risingof volumetric water content from the start time ofquasi-saturation at each observation point. The
horizontal distance from the point of water-leveloccurrence is plotted against the quasi-saturated

CONCLUSION
The authors investigated a method for predicting
slope failure by focusing on soil water during rainfall.
In this study, the appropriate locations for
installations of sensors for slope monitoring were
examined based on model slope experiments to detect
slope hazards more accurately. A method for31

GEOMATE – Melbourne, Australia, 11-13 November 2020(a) Estimation of the point of water-level occurrenc(b) Estimated location of the water-level occurrence

Fig. 12 Estimated point of water-level occurrence from the measured depth of 10 cm(a) Estimation of the point of water-level occurrence(b) Estimated location of the water-level occurrence

Fig.13 Estimated point of water-level occurrence from the measured depth of 31 cm
estimating the point of water-level occurrence waspresented based on the relationship between the
distance between the sensors and the quasi-saturated
duration time calculated using the analysis ofsaturated-unsaturated seepage flow. The validity ofthe proposed method was verified through modelslope experiments. The results suggest that slope
hazards can be reasonably detected by installing
sensors at the estimated point of water-leveloccurrence. The proposed method will be applied toactual slope monitoring in the future.

Engineering, E-14, Issue 991, 2013, pp. 19811982.
[3] Sako K., Suda T., Fukugawa R., and Kitamura
R., Soil tank tests on seepage and failure behaviorof masa slope due to rainfall, Proceedings of the
4th Symposium on Sediment-Related Disasters,
2008, pp. 21-26.
[4] Koizumi K., Sakuradani K., Oda K., Ito S.,
Fukuda Y., Feng M.Q., and Takemoto M.,
Fundamental research on advancement ofregulation standard for expressway management
against rainfall-induced surface failure, Journalof JSCE Ser. C (Geo-engineering), Vol. 73,
Issue 1, 2017, pp. 93-105.
[5] Simunek, J., van Genuchten M.T., and Senja,
M., The HYDRUS Software Package forSimulating the Two- and Three-Dimensional
Movement of water, Heat, and Multiple Solutes
in Variably-Saturated Media, March 2006.

REFERENCES[1] Yagi N., Yatabe R., and Yamamoto K., Slope
failure due to seepage of rainwater. Journal of
JSCE, Vol. 1983, Issue 330, 1983, pp. 107-114.
[2] Kohgo Y., Saito H., Niide T., and Hori T., Slopesafety following rainfall with model slopes, Japan
National
Conferenceon
Geotechnical32

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A METHOD FOR REAL-TIME EVALUATION OF THE
PROPERTIES OF COMPACTED SOIL FROM ACCELERATIONRESPONSE OF COMPACTION ROLLER
Hiroyuki Nagai1, Yoshiaki Kikuchi2, Sou Ihara2, Shohei Noda2, Taichi Hyodo3 and Fumio Tatsuoka2,4
1

Hazama Ando Corp.; 2Tokyo University of Science; 3Toyama Prefectural University; 4Tokyo University
ABSTRACTIn recent years, a number of soil structures were damaged by frequently occurring severe earthquakes andheavy rainfalls. In relation the above, the Ministry of Land, Infrastructure, Transport and Tourism Japan isadvocating the “i-Construction” promoting the development of new technologies that can improve construction
efficiency and can extend the life time of infrastructures including soil structures. It was recently shown thatthe soil compaction and the properties of compacted soil are controlled by the dry density ρd and the degree ofsaturation Sr, which can be evaluated typically by RI tests as a fast method. Yet, this method is time-consuming
and can be performed only at a limited number of places. To improve the efficiency of soil compaction control,
real-time and continuous compaction control ensuring high quality of compacted soil is required. We studied
a method to evaluate the values of ρd and Sr based on the ground stiffness estimated from the acceleration
response of vibratory compaction roller in real time and continuously (called the CCV method) together withproper control of field compaction energy level. By analyzing a large amount of data from on-site compaction
tests, an empirical equation expressing the CCV value, as a soil stiffness index, as a function of ρd and Sr
irrespective of the number of pass was developed. The ρd and Sr values for a given CCV value can be estimatedbased on this equation either when the water content ratio can be reasonably assumed or when the compactionenergy level is controlled to be equal to a given specified value.
Keywords: CCV, Embankment, Compaction, Dry density, Degree of saturation
INTRODUCTION

2015). In this study, an empirical equation
expressing the response acceleration value (i.e.,
CCV value) of vibratory compaction roller as afunction of ρd and Sr was derived by analyzing datafrom full-scale field compaction tests. It is shown
the CCV value is representative of the stiffness ofcompacted soil in the field. This empirical equation
is used to draw the contour lines of CCV values theρd and w plane and the contour lines were comparedwith the compaction curve obtained by the standard
compaction tests (1Ec). It is shown that the dry
density during a given compaction process isaccurately evaluated based on the empiricalfunction for CCV when the water content ratio is
known.

In recent years, natural disasters, such as large
earthquakes and heavy rainfall, have occurred
frequently. In addition, the damage caused by suchdisasters has increased rapidly, e.g., the collapse ofembankments, such as roads and railways, and theconstruction of residential land [1]. Therefore,
construction methods and quality control methods
that can realize high-quality embankments are
required.
The Ministry of Land, Infrastructure, Transport
and Tourism has proposed i-Construction for
promoting the research and development of newtechnologies for improving work efficiency andsafety and prolonging service life [2]. Therefore, areal-time soil compaction control method over thearea of concerned earthwork is required to improvethe construction efficiency and the quality ofcompacted soil.
In conventional soil compaction control, sandreplacement tests or RI tests are performed after
compaction at a limited number of places over theconcerned area to obtain the dry density ρd and thewater content ratio w of soil. This method is,
however, time-consuming.
It is known that the on-site CBR is a function of
the dry density ρd and the degree of saturation Sr
irrespective of compaction energy level (Tatsuoka,

MATERIALS AND METHODS
Embankment Material
Gravel sand mixed with fine particles with amaximum particle size of Dmax = 75.0 mm was used
to perform a rolling compaction test. A picture of
the embankment material is shown in Fig 1, and its
physical properties are shown in Table 1. Fig. 2
shows the grain size distribution curve of the field
fill material used in the field full-scale compaction
test (Fig. 5) and those of the samples that were33

GEOMATE – Melbourne, Australia, 11-13 November 2020

prepared by removing particles larger than a particle
diameter either D = 37.5 mm or D = 9.50 mm.

Passing weight percentage （%）

100

60
40
20
0
0.001

Fig. 2
Fig. 1

Picture of embankment material

Table 1

Properties of field fill material

Parameter
Soil particle density (g/cm3)
Maximum grain size (mm)Fine fraction content (%)
Uniformity coefficient
Coefficient of curvature
Classification
SymbolLiquid limit wL (%)
Plastic limit wP (%)
Plasticity index IP
Compaction test
(Dmax = 37.5 mm)
Maximum dry density(Standard proctor)
ρdmax(g/cm3)
Optimum water content ratio(Standard proctor) wopt (%)
Optimum degree ofsaturation Sropt (%)

0.01

0.1
1
10
grain shape （mm）

100

Grain size distribution curve

Laboratory Test
A laboratory compaction test (JIS A 1210) was
carried out by employing the B-c method (Standard
Proctor: 1Ec) using the sample with Dmax = 37.5 mm(Fig. 5). The compaction curve for other Dmax
values shown in Figs. 6 and 7 were obtained by theWalker-Holtz method (Walker and Holtz, 1951).
To evaluate the internal friction angle ofcompacted soil, a series of drainage triaxial
compression tests (JGS 0524-2009) were conducted(hereinafter referred to as the CD test) on specimens
with dimensions of Φ5 cm × H10 cm produced byusing the material with Dmax =9.5 mm. The
compacted states of the specimens are shown in Fig.
6. The CD test was performed after by saturating
the prepared specimens.

Value
2.778
75.0
10.1
334
16.2
Gravel sand mixed
with fine particles
G-FS
29.6
19.7
9.9

Field Test

JIS A 1210

Fig. 3 shows the full-scale compaction test yard
and the zones in which quality tests were performed.
The compaction test yard was 20 m long, 6 m wide,
and 60 cm thick. The yard consisted of two layerswith a thickness of 30 cm each. The first layer wasa preliminary layer, which was constructed byapplying rolling compaction 8 times using an 11 ton

1.897
14.8
88.5

No.1

No.2

No.3

〜Usage guide〜：RI：LevelFig.3

Field fill material
Dmax=37.5mm
Dmax=19.0mm

80

No.4

No.5

No.6

：CCV（ Mesh size = 50 cm ×16 points）

Full-scale compaction test yard and quality test area
34

GEOMATE – Melbourne, Australia, 11-13 November 2020vibratory compaction roller to the embankment
material with an average water content ratio of wn =11%. The second layer was the test layer, which wasconstructed by applying rolling compaction 16
times to the fill material with an average watercontent ratio of wn = 11%. While constructing thesecond layer, CCV measurement was performed on
the entire area of the yard from the response
acceleration records of the vibrating roller. Aftercompaction passes N = 2, 4, 6, 8, and 16, at
measurement points 1 to 6 shown in Fig. 3, thesettlement was measured and the dry density of the
top 20 cm was evaluated by the RI method.
In CCV measurement method, an acceleration
sensor is attached to the vibration axis of the front
wheel of the vibratory compaction roller as shownin Fig 4, and the soil stiffness is evaluated from theresponse acceleration of the vibratory compaction
roller during compaction [3]. The index referred toas the CCV value is used to evaluate soil stiffness inreal time over the area of earthwork. The CCV value
can be calculated by attaching an acceleration
sensor to the iron wheel (front wheel) of thevibration roller and analyzing the acceleration
waveform of the iron wheel vibration obtained
during construction. The empirical equation for theCCV value is given by Eq. (1) [3].
CCV=(S1+S3+S4+S5+S6)／(S1+S2)×100

shown below. These meshes included the RI test
and level measurement zones.
TEST RESULTS
Laboratory Test
Fig. 5 shows the results of the laboratorycompaction test performed using the sample with
Dmax = 37.5 mm.
As shown in Fig.6 at the compacted states
Nos.1–3 of the specimens tested in the CD tests, theρd value was (Dc)1Ec = 95% of [(ρd)max]1Ec, while thewater content ratio was different.
When the degree of compaction , Dc (i.e., ρd),increases under the condition that saturation Sr isconstant, the internal friction angle tends to always
increase [5]. For an allowable lower bound of anangle of φ equal to 30°, it was examined whether
the φ values exceed this lower bound when (Dc)1Ec≥ 95% for the water content ratio at Nos. 1 - 3. Table
2 shows the conditions for the CD test and themeasured internal friction angle, φ. φ ≥ 30° for all
Sr when (Dc)1Ec ≥ 95% can be confirmed. It wasconsidered that φ ≥ 30° can be ensured for any Sr if(Dc)1Ec ≥ 95% with the field fill material with Dmax
= 75.0 mm [6].
In Fig. 7, the acceptable zone for compacted
states of the field fill material with Dmax = 75.0 mm
is indicated in relation to its compaction curve (1Ec)
inferred by the Walker-Holtz method from thecompaction curve (1 Ec) evaluated by the laboratorycompaction test (1 Ec) on the material with Dmax =37.5 mm (shown Fig. 3). The allowable lower
bound of ρd corresponding to (Dc)1Ec= 95 % for Dmax
= 75.0 mm is indicated.

(1)

where S2, S1, S3, S4, S5, and S6 are the amplitude
spectra at f0, 0.5f0, 1.5f0, 2f0, 2.5f0, and 3f0,
respectively, and f0 is the fundamental frequency ofthe vibrating roller. The CCV value can bemeasured at 0.2 s intervals. The construction area isarbitrarily divided into meshes, and the CCV values
acquired when the vibratory compaction roller
passes through the meshes are averaged. A GNSS
antenna was installed on the vibratory compaction
roller to obtain the position information during
construction. Thus, the locations of the CCV
measurement points could be accurately determined.
In this study, the rolling compaction yard was
divided into 50 cm meshes and CCV values wereobtained for each mesh, as shown in Fig. 3.
The CCV values obtained using 2 m meshes (bluehatching in Fig. 3) were used in the analysis

Dry density, ρd （g/cm3）

2.00
1.90
1.80
1.70

The B-c method：Dmax=37.5mm[(ρd)max]1Ec：1.897g/cm3(wopt )1Ec=14.8%(Sr)opt ：88.5%

1.60
1.50

0

10
20
Water content ratio, w （%）

30

Fig. 5 Compaction test
Table 2
tests
No.

Fig. 41
2
3

Picture of acceleration sensor
35

Results of drainage triaxial compression
Drydensityρd(g/cm3)
1.573
1.568
1.554

Water
contentw (%)

Degree ofsaturation
Sr (%)

15.7
21.0
27.6

58
72
95

Internal
frictionangle
Φd (°)
32.3
36.6
34.6

GEOMATE – Melbourne, Australia, 11-13 November 2020

1.6 (Dc)1Ec95%
No.1 No.2 No.3

1.4

1.2

0

10
20
30
40
Water content ratio, w (%)

50

2.4

Wet density, ρt (g/cm3)

Dry demsity, ρd （g/cm3 ）also be taken into account when evaluating local
water content ratio.
The values of d and Sr strongly also depend on
the measurement point, as shown in Figs. 10 and 11.
Yet it may be seen that the values of ρd and Sr tend
to increase significantly until the number ofcompaction passes N becomes 2. Then, as N
increases from 6 to 16, the values of ρd and Sr either
increase gradually or tend to converge.

Compaction curve
Dmax=9.5mm(The Walker-Holtz method）

1.8

Fig. 6 Compacted states of specimens for triaxial
compression tests

Field fill material
Dmax=75.0mm(The Walker-Holtz
method)

1.8

DL=(Dc)1Ec95%

1.90

8

13

18

①：
④：〇
0

2

②：
⑤：△

4
6
8
10
12
Number of compaction passes, N (times)

③：
⑥：☐
14

16

Fig. 8 Relationship between wet density ρt andnumber of compaction passes N
14

Water content ratio, w (%)

Dry density, ρd (g/cm3)

Compaction curve(The B-c method)
Dmax=37.5mm

2.10

3

2.0

1.6

Acceptable zone

1.70

2.2

23

Water content ratio, w (%)

Field Test

②：
⑤：△

③：
⑥：☐

12

10

8

Fig. 7 Acceptable zone in compaction control

①：
④：〇

0

2

4
6
8
10
12
14
Number of compaction passes, N (times)

16

Fig. 9 Relationship between water content ratio w
and number of compaction passes N

Relationship between the number of compaction
passes and wet density ρt, w, ρd, Sr, and CCV value

Dry density, ρd (g/cm3)

2.3

Figs. 8 to 12 show all the data measured at thesix points shown in Fig. 3. Figs. 8 to 11 show the
relationship between ρt, w, ρd, and Sr and the numberof compaction passes N obtained by the fieldcompaction tests, where the values of ρt and w were
those measured by the RI tests, while the values ρdand Sr are those calculated from these measured
values.
As shown in Figs. 8 and 9, the values ρt and w
noticeably differ depending on the measurement
point. In particular, the value of w measured at point
No.1 was lowest and the one at point No.2 washighest, different by 3–4% from each other. This
fact means that, to accurately evaluate the dry
density from a given CCV value, we cannot rely ona given fixed average water content ratio for thearea but the local water content ratio at the place
where this CCV value is obtained should be
evaluated. In addition, the values at the samemeasurement point slightly fluctuate as the numberof compaction passes increases. This fact should

2.1
1.9
1.7
1.5

①：
④：〇
0

2

②：
⑤：△

4
6
8
10
12
Number of compaction passes, N (times)

③：
⑥：☐
14

16

Degree of saturation, Sr (%)

Fig. 10 Relationship between dry density ρd andnumber of compaction passes N
90
70
50
30

①：
④：〇
0

2

②：
⑤：△

4
6
8
10
12
Number of compaction passes, N (times)

③：
⑥：☐
14

16

Fig. 11 Relationship between degree of saturation Srand number of compaction passes N
36

GEOMATE – Melbourne, Australia, 11-13 November 2020

against Sr. It may be seen from Fig. 13(a) that Eq.
(2) captures very well the following trends of
behaviour of the data: 1) for a fixed value of Sr, theCBR value consistently increases with an increasein ρd in the same way irrespective of the Sr value;and 2) for a fixed value of ρd, the CBR value
consistently decreases with an increase in Sr in thesame way irrespective of the ρd value.

Fig. 12 shows the relationship between the CCV
value and the number of compaction passes N.
Similar to ρt, w, Dc, and Sr, the CCV value strongly
depends on the measurement point. At eachmeasurement point, the CCV value increases until N
becomes 8, reaching a maximum at N = 8, and thenstarts noticeably decreasing as N increases from 8
to 16, unlike the values of ρd and Sr. This trend may
be due to that, as N increases from 8 to 16, the CCV
value increases with an increase in ρd, while itdecreases with an increase in Sr, while the effects of
the latter factor are generally stronger than those of
the former factor.

CBR (%)

Solid line group：
Empirical equation(Independent of N)

8

CCV value

6

4
①：
④：〇
2

0

2

②：
⑤：△

4
6
8
10
12
Number of compaction passes, N (times)

③：
⑥：☐
14

Dry density , ρ d (g/cm3)(a) Relationship with saturation Sr at the end ofcompaction as a parameter16

Fig.12 Relationship between CCV value andnumber of compaction passes N
f CBR(S r)

Optimum
degree of saturation(Sr ) o p t=81.3%

Empirical equation for estimating ρd and Sr
using CCV value
In the following, we propose an empiricalequation expressing the CCV values as a function of
Sr and ρd, in the same way as the field CBR data
analyzed by Tatsuoka et al. [7-9]. The field CBRdata are shown in Fig. 13a, which were obtained
from a long-term research by full-scale compaction
tests conducted at the Institute of Construction
Mechanization (1965–1990) using many different
compaction machines on the same type of sandy
loam soil prepared at different water content ratio
ratio. The proposed empirical equation for CBR isexpressed by Eqs. (2) and (3):
CBR = fCBR(Sr)・(ρd/ρw－b)C(2)

fCBR(Sr) = A2+(A1-A2) / [1+exp{ (Sr – x0)/dx}](3)

Degree of saturation when compacted , S r (%)(b) Sr function fCBR(Sr)Fig.13 Relationship between on-site CBR and dry
density obtained in the full-scale compaction test
conducted at the Institute of Construction
Mechanization [7,9]In this study, we analyzed the data obtained
from the field full-scale compaction test andexamined whether empirical equations for CCV
values in the same form as Eqs. (2) and (3) arerelevant. Here, the empirical relationship between
ρd, Sr, and CCV is given by Eqs. (4) and (5).

The constants in Eqs. (2) and (3) were found as b =0.4, c = 9.5, A1 = 1.26, A2 = 0.042, x0 = 64.8, and dx= 9.97. The unit of Sr in Eqs. (2) and (3) was %, andall the other constants were dimensionlessquantities.
The relationships between CBR and ρd for
different Sr values were fitted by Eq. (2): firstly byfinding relevant values of parameters b and c bywhich Eq. (2) can fit the whole data; and then byfinding the value of fCBR(Sr) by which Eq. (2) can fit
the relation for the respective values Sr. In Fig.13(b),the values of fCBR(Sr) obtained as above are plotted

𝑪𝑪𝑪𝑪𝑪𝑪 = 𝒇𝒇𝒄𝒄𝒄𝒄𝒄𝒄 (𝑺𝑺𝒓𝒓 )×(𝝆𝝆𝒅𝒅 /𝝆𝝆𝒘𝒘 − 𝒃𝒃)𝑪𝑪
𝒇𝒇𝒄𝒄𝒄𝒄𝒄𝒄 (𝑺𝑺𝒓𝒓 ) =𝑫𝑫

𝟏𝟏+𝒆𝒆𝒆𝒆𝒆𝒆(𝑬𝑬𝑺𝑺𝒓𝒓 )

(4)
(5)

Fig.14 shows all the measured CCV values fordifferent values of Sr are plotted against the ρd value.
The data are classified based an interval of Sr of 5%(i.e., 50% ≦ Sr < 55%, 55% ≦ Sr < 60%, 60% ≦Sr < 65%, 65% ≦ Sr < 70%, 70 % ≦ Sr < 75%,
75% ≦ Sr < 80%, 80% ≦ Sr < 85%, 85% ≦ Sr <90%, and 90% ≦ Sr < 95%). The solid curves
37

GEOMATE – Melbourne, Australia, 11-13 November 2020

The CCV value is expressed as a function of ρdand Sr by Eqs. (4) and (5): i.e., by Eq. (6). Eq. (7) is
obtained from Eq. (6) by using w instead of Sr.

shown in Fig. 14 represent the CCV- ρd relation for
the respective ranges of Sr obtained by Eq. (4) withb = 0.4 and c = 9.5 and the value of fCCV(Sr) obtainedby Eq. (5). Eq. (5) was defined so that Eq. (4) fits
the whole data. That is, in Fig. 15, the value of
fCCV(Sr) obtained as CCV / (ρd/ρw – b)c of each datais plotted against Sr. A rather unique relation is
obtained in a form very similar to the one for thefield CBR seen in Fig. 13(b). The average relation
represented by a broken curve was defined as Eq.
(5) with D = 2.0 and E = 0.04.
It may be seen from Fig. 14 that the fitted Eq.
(4) is generally consistent with the data points. It is
to be noted that, although the fitting is relatively
poor with the data for Sr = 65–70% ( in the figure)and 70–75% ( in the figure), the fitting is rather
acceptable for Sr = 75–95%, which covers the
optimum degree of saturation (Sr)opt = 88.5%. (Sr)opt
is the value of Sr where the maximum dry density(ρd)max is obtained in a laboratory compaction test.
As (Sr)opt is rather insensitive to variations of soil
type and compaction energy level (CEL) in a given
project, (Sr)opt could be defined as the target Sr valueirrespective of inevitable variations of soil type andCEL to optimize the soil compaction efforts (i.e., toobtain (ρd)max) for given soil type and CEL.

CCV Value

10

1.5

1.7

1.9

2.1

0.6

fccv(Sr)

Eq.(5)
0.4
0.2

60
80
Degree of Saturation, Sr（%）

𝟐𝟐.𝟎𝟎

𝝆𝝆 𝝆𝝆𝒔𝒔 𝒘𝒘�𝝆𝝆𝒔𝒔 −𝝆𝝆𝒅𝒅(6)� ×(𝝆𝝆𝒅𝒅 / 𝝆𝝆𝒘𝒘 − 𝟎𝟎. 𝟒𝟒)𝟗𝟗.𝟓𝟓 (7)If the target ρd is determined and w and ρs are known,
the target CCV value can be determined from Eq.
(7). Moreover, if ρs and w are known, ρd can becalculated when the CCV value is measured during
construction. Therefore, real-time compaction
management over the area of compaction, which isthe purpose of this work, can be realized by utilizing
Eq. (7).
In the following, the validity of Eqs. (6) and (7)in the estimation of ρd is examined. Fig.16 shows
the plots of the measured and estimated values of ρdand the measured value of w in relation of thecompaction curve of the field fill material with Dmax
= 75 mm. The estimated values of ρd were obtainedby substituting the respective measured sets of CCV
value and w into Eq. (7). The measured andestimated values of ρd are generally in good
agreement, although the estimation is rather
inaccurate for the data for Sr = 65–75% with theestimated values being lower than the measured
values.
Fig.17 shows the relationship between themeasured and estimated values of ρd. Shown in thisfigure are the lines for Y = X, Y = X-0.1 and Y =X+0.1 together with the approximate curve
obtained using the least squares method, for which
the correlation coefficient (R2) between themeasured and estimated values is 0.7023. The line
Y=X indicates that the measured and estimatedvalues are equal to each other. Nearly all the data
are close to the line Y=X, and all the data points arelocated between the lines for Y = X+0.1 and Y = X0.1. Furthermore, a relatively high value of R2 =0.7023 confirms that the estimation accuracy israther high. Based on this, we can conclude that theaccuracy of estimating ρd using Eqs. (6) and (7) is
generally good.
Next, the compaction management methodreferring to the contour lines of the CCV value
calculated from the estimation equation is examined.
Fig.18 shows a vertical line where w = 9% and thecontour lines for CCV values of 1, 5, 10, 11.2, and30 obtained from Eq. (6), together with thecompaction curve for the field fill material (Dmax=
75 mm). Along each contour line of the CCV values,as ρd increases, the w value increases when Sr < Sropt,reaches the maximum value slightly before Sr = Sropt
is attained, and decreases in the range of Sr > Sropt.

Fig.14 Relationship between CCV and ρd40

� ×(𝝆𝝆𝒅𝒅 / 𝝆𝝆𝒘𝒘 − 𝟎𝟎. 𝟒𝟒)𝟗𝟗.𝟓𝟓

𝟏𝟏+𝒆𝒆𝒆𝒆𝒆𝒆�𝟎𝟎.𝟎𝟎𝟎𝟎 𝒅𝒅

Dry density, ρd（g/cm3）

0

𝟐𝟐.𝟎𝟎

𝟏𝟏+𝒆𝒆𝒆𝒆𝒆𝒆(𝟎𝟎.𝟎𝟎𝟎𝟎𝑺𝑺𝒓𝒓 )𝑪𝑪𝑪𝑪𝑪𝑪 = �●：Sr(%)=50-55：55-60：60-65■：65-70：70-75：75-80▲：80-85：85-90：90-9550

𝑪𝑪𝑪𝑪𝑪𝑪 = �100

Fig.15 Relationship between fCCV(Sr) and Sr
COMPACTION MANAGEMENT METHODUSING EMPIRICAL EQUATION

38GEOMATE – Melbourne, Australia, 11-13 November 2020

ρd is estimated by taking into account this trend of
behaviour, as explained below.
The average w value of each clod of fill material
unloaded from a dump truck was obtained by
performing RI measurement, and this w value wasdefined as the representative value of the area where
the clod was spread. It is assumed that the watercontent ratio over the concerned area of compaction
is constant at w = 9%, which is equal to the averagevalue of respective clods. In the compaction process,ρd changes along the contour line of w = 9%. Point
A is the point where the contour lines of CCV value= 11.2 contacts the w = 9% line. This implies thatthe maximum CCV value at w = 9% is 11.2.
Based on this, if the maximum CCV value canbe obtained in advance by measuring the w of thefill material and using an empirical equation. If
CCV values higher than this maximum value, it islikely that the actual local water content ratio islower than the assumed value (equal to 9 % in thiscase), which requires the careful measurements oflocal water content ratio and proper adjustments ofwater content ratio in the subsequent compaction
work.
Next, the case when the measured CCV value
below the maximum value is obtained is analyzed.
That is, as shown in Fig.18, the contour lines of
CCV value = 10 and w = 9% intersect at two points
B and C. This implies that there are two possible
solutions for ρd. We must judge whether ρd is in therange of Sr ≤ Sropt (initial stage of compaction) or Sr> Sropt (late stage of compaction). On the other hand,
the CCV value changes during the compaction
process as shown in Fig. 12. That is, the CCV value
tends to increase in the early stage of compaction
and decrease in the late stage of compaction.
According to this trend, if the current compaction
stage can be determined by examining the trend ofchange in immediately preceding CCV values andtotal compaction time until the present time, it ispossible to accurately evaluate ρd.
Thus, when estimating ρd based on the CCV value
and w using the empirical equation, it is important
to consider not only the CCV value but also thehistory of number of compaction pass.

Dry density, ρd (g/cm3 )2.3This allows for real-time and compaction
management over the concerned area of compaction.
It is expected that a high-quality embankment
structure can be realized by employing a rational
construction method, such as the one proposed inthis paper.

Estimated value, ρd (g/cm3 )

2.2

18

1.9
1.8

Y=X-0.1

1.7

1.8

1.9

Dry density, ρd (g/cm3 )

2.3

2.1

2.0

2.2

B
A

2.1
CCV
=30

C

DL=95%

1.9

11.2
10

1.7

3

5

8

w=9%

13

1

18

23

Water content ratio, w (%)

Fig.18 Compaction control method using contour
lines of CCV values
CONCLUSION
A field full-scale compaction test was carriedout to develop a method for estimating ρd and Sr
based on CCV values to realize high-quality
embankment construction. The main findings ofthis study are as follows:(1) The results of the rolling compaction test showthat the local values of w, ρd, Sr, and CCV differ
considerably depending on the measurement
point. Moreover, the local value of w at eachmeasurement point slightly fluctuates as thenumber of compaction passes N increases.
These facts indicate that it is imperative toaccurate evaluate instantaneous local watercontent ratio at places where CCV values areevaluated.
(2) ρd and Sr increase significantly when N = 2,
gradually increase as N increases from 2 to 6,
and tend to converge as N increases from 6 to
16. The CCV values increase as N increases

DL=95%13

Y=X

Approximate
curve

Fig.17 Comparison of measured and estimatedvalues of ρd

1.9

8

2.0

Measured value, ρd (g/cm3 )：Measured value：Estimated value

3

2.1

1.7

2.1

1.7

Y=X+0.1

R² = 0.7023

23

Water content ratio, w (%)

Fig.16 Results of compaction test on ρd–w plot
39

GEOMATE – Melbourne, Australia, 11-13 November 2020

REFERENCES

from 2 to 8, reach a maximum when N = 8, anddecrease as N increases from 8 to 16. This factindicates that CCV is not a unique function of
ρd, but the effects of Sr are also important.
(3) The CCV value can be expressed by Eq. (6), in
which ρd and Sr are independent variables andthe number of passes and compaction energy
are not considered as variables. The accuracyof estimating ρd using the empirical equation
increases with the accuracy of Sr and fCCV(Sr).
(4) If the target ρd is determined and w and ρs are
known, the target CCV value can be determinedfrom Eq. (7). Moreover, if ρs and w are known,
ρd can be calculated when the CCV value ismeasured during construction. Therefore, realtime compaction management over theconcerned area of compaction, which is the
purpose of this work, can be realized.
(5) When the w of the fill material is assumed to beconstant, the CCV value reaches the maximum
immediately before Sr becomes equal to Sropt.
At the time of construction, the required highcompaction state can be achieved byconfirming that the CCV value has reached its
maximum after a predetermined number ofcompaction passes.
(6) When the CCV value is below the maximum
value, ρd can be accurately evaluated byconsidering the CCV value along with thenumber of compaction passes. Real-time
compaction management over the concernedarea can be realized by combining theprocedures (5) and (6).

[1] Watanabe K. Watanabe H., Burst Factors andRestoration Method of Construction of the
Fujinuma Dam, Water land and environmental
engineering, 2015, pp.866-867.
[2] Kondo K., Issues for the spread of ICTearthworks,
Construction
Management
Technology, 2017, pp.13-18．
[3] East Nippon Expressway Company Limited,Central Nippon Expressway Company Limited,West Nippon Expressway Company Limited.,
Quality control of embankments using theroller acceleration response method, Civil
Engineering and Construction Management
Procedures, 2017, pp.127-166．
[4] Walker F.C. and Holtz W.G., Control of
Embankment Material by Laboratory Testing.
Trans ASCE, Vo1. 77, No. 108, 1951, pp.1-25,[5] Ihara S. Okada M. Magara K. Noda S. Nagai H.
Sandanbata I. Kikuchi Y, and Tatsuoka F.,
Experimental analysis of the effect of gravel
content on compaction and strength propertiesof sand gravel, GEOKANTO, 2019.
[6] Uemoto Y. Shibuya S. Hashimoto Y. and
Kawajiri S., Influence of grain size distribution
characteristics
oncompaction
anddeformation-strengthcharacteristics
ofgravelly fill material, Japanese Geotechnical
Journal, Vol.6, No.2, 2011, pp181-190.
[7] Tatsuoka,F, Importance of the Control of theDegree of Saturation in Soil Compaction,
Engineering for dams No.354, 2016, pp.3-16.
[8] Tatsuoka F., Compaction Characteristics andPhysical Properties of Compacted Soil Controlled
by the Degree of Saturation. in Proc. of 15th PanAmerican Conf. on SMGE and 6th Int. Conf. on
Deformation Characteristics of Geomaterials,
Buenos Aires, 2015, pp.40-76.
[9] Tatsuoka et al., Geotechnical engineering
technical notes, Embankment compaction,
No,1～20, The foundation engineering, 2013～2015.

The results of this study show that thecompaction management using the empiricalequation for the CCV value based on ρd and Sr iseffective. This method can be applied to evaluate
and improve the real time quality control ofembankments. In the future, the authors will
investigate a method for the real-time measurementof w and a method of evaluating the quality ofembankments with a higher accuracy.

40

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

MECHANICAL STRENGTH OF PEAT SOIL TREATED BY FIBER
INCORPORATED MICROBIAL CEMENTATION

1

Meiqi Chen1, Sivakumar Gowthaman2, Kazunori Nakashima2 and Satoru Kawasaki2
Graduate School of Engineering, Hokkaido University, Japan; 2Faculty of Engineering, Hokkaido University,
Japan

ABSTRACTPeat soil is an accumulation of partially decayed vegetation, formed under the condition of nearly permanent
water saturation, which makes the high moisture and compressibility as two main features of peat. In recent years,
lack of construction lands diverts the attention of researchers to make use of marginal grounds, like peatland, aftersome improvements. The past decade has witnessed a growing interest towards microbial induced carbonate
precipitation (MICP) due to its reliability, wide application and potential contribution to sustainable and green
development. There are two primary aims of this study: (i) investigating the feasibility and effectiveness of MICP
in peat soil combined with bamboo fiber reinforcement, and (ii) ascertaining the mechanism of bamboo fiber
incorporated MICP. Bamboo fiber possesses some unparalleled advantages owing to its fast growth and ability tosurvive in diverse climates. This study differs from previous researches in the use of native bacteria isolated fromthe peat soil, while most of them were conducted using exogenous bacteria which might pose a threat regarding
adaption and microbial pollution. Different concentration of cementation resources (1-3 mol/L) and proportion offibers (0-50%) were studied, and each case was well designed. Treated samples were subjected to fall cone test at
certain time intervals to estimate the undrained shear strength. The results revealed that the samples with higher
fiber content gained higher strength than others did, whereas high initial cementation resources in soil could
contribute to the decline of strength. Microscale observations were also performed on treated samples to clarify
the mechanism of MICP incorporated with fiber.
Keywords: Microbial induced carbonate precipitation (MICP), Peat soil, Bamboo fiber, Fall cone test, Native
bacteria

disadvantaged for the harmful environmental effects.
In recent years, a considerable body of literatureis springing up around the theme of MICP (microbial
induced carbonate precipitation). The MICP is arelatively innovative technique developed via
biological processes, in which the production of
calcium carbonate bio-cement relies on theperformance of microbial urease [3], [7]. Up to now,
several studies have confirmed the effectiveness of
MICP on the application of removing contaminantsfrom the air and water, preventing liquefaction,
reducing
compressibility
andpermeability,
improving soil bearing capacity and so on [3], [4], [8].
The application of this technique could to some extent
reduce the dependence on cement products whereby
it can be an alternative of green development totraditional methods. In this research, anothersustainable material is the bamboo. As one type ofmost common plant fiber material, it has some
unparalleled merits due to its fast growth,survivability under diverse climates and excellent
engineering properties [9].
This study set out to investigate the usefulness ofbamboo fiber combined with MICP in soil
improvement: to assess the feasibility and efficiencyof MICP on bamboo fiber reinforced peat soil and to

INTRODUCTION
Peat is a type of soft soil with high content offibrous organic matters, which is produced by the
incomplete decomposition and disintegration ofsedges, trees, mosses, and other vegetation growing
in wetland and marshes in the anoxic condition [1].
Consequently, it is often referred as problematic soil
due to its low shear strength, high compressibility andhigh water content [2].
Methods to improve peat soils are typically soil
replacement, reinforcement to enhance strength andstiffness, stone columns, piles, chemical admixtures
etc. [3], [4]. Chemical stabilization like grouting andmixing using cement and lime, which is one of themost preferred methods to improve the soil for the
economical reason [3], [4]. In recent years, althoughknown as the most useful artificial material for
construction, cement has suffered criticisms related toits contribution to greenhouse gas emission.
According to the UNEP annual report of 2010, to
produce 1 ton of cement, 1 ton of CO2 will be released
into the environment, which means annually about 78% of overall CO2 emissions are coming from cement
industries [5], [6]. There is an increasing concern thatsome of these traditional methods are being
41

GEOMATE – Melbourne, Australia, 11-13 November 2020

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

reveal the mechanism in the solidification process.
For these purposes, ureolytic bacteria isolated fromlocal peat soil were examined before being utilized insolidification tests; different proportion of bamboo
fibers were mixed into the peat to clarify the effect onmoisture and strength change; the concentration of
cementation material was investigated as one of thesignificant factors governing the effectiveness of
MICP. Finally, fiber incorporated microbial
cementation experiments were conducted.

600

Moisture content (%)

500
400
R² = 0.9818

300
200

MATERIALS AND METHODS
100
0

Characteristics of Soil
The distribution of peat lands in Hokkaido, Japan
can be observed in Fig. 1. Peat soil used in thisresearch was obtained from Iwamizawa city
(43°18'17.9"N 141°40'23.9"E), Hokkaido (Fig. 1),
from the depth of 3 m underground. Collected peat
samples were then preserved in the sterile containers
under the temperature of 4 ℃ and then subjected tosome laboratory examinations. The intrinsic
characteristics of peat soil were obtained in thelaboratory, shown in Table 1. According to theAmerican Society for Testing and Materials (ASTM)
standard [10], the peat soil can generally be
categorized as one of the followings: (i) fibrous, (fiber
content ≥ 67%), (ii) hemic or semi-fibrous and or (iii)sapric or amorphous (fiber content < 33%). Based on
the analysis, the Iwamizawa peat soil was found to befalling under semi-fibrous category.

10
20
30
40
Bamboo fiber content, % (wf/wp)

50

Fig. 2 Variation of moisture absorption withbamboo fiber content
Characteristics of Fiber
The bamboo fiber utilized in this study was madeof bamboos with natural moisture content about 26%,
with a uniform grain size from 150 to 200 µm. Prior
to utilizing the bamboo fiber for solidification test on
peat soil, the water absorbing capacity of bamboo
fiber was investigated. Totally six cases with bamboo
fiber to peat soil ratio at 5%, 10%, 20%, 30%, 40%
and 50% were prepared, weighed and dried in theoven at 105℃ for more than 48 hours until the mass
variation was found to be negligible [11]. The results
are shown in Fig. 2, depicting a significant decreasein the water content of peat with increasing fiber
content. It is worth noting that the water content could
be reduced by half with 20% of fiber addition.

peat ground

Isolation and Characterization of Bacteria

Iwamizawa

Isolation of native bacteria
Isolation was conducted by a sequence of dilution(101-103) of peat soil and then being plated on the
NH4-YE agar medium [12], [13]. After 72 hours ofculture at 30℃, colonies were inoculated into newplates using a platinum loop to obtain a purified single
colony from groups of distinctive bacteria. Following
the purification process, the bacteria were cultured for
24 hours as the preparation of the urease activity test.

SapporoFig. 1 Distribution of peat ground in Hokkaido [1]Table 1 Basic characteristics of Iwamizawa peat

Identification of ureolytic bacteria
Parameters

ValuesWater content

711 - 824 %Density

1.821 g/cm3

Ignition loss

65.815 %pH

4.6 - 4.8

k10-4 - 10-5 cm/s

During the process of urea hydrolysis, the pH of
solution increases over time. Urease activity test
using cresol-red could realize the identification by asimply qualitative observation on the color change
from yellow to purple, indicating an increase in pH
from 7.2 to 8.8. Detailed experimental process could
be found in the following previous works [12], [13].
42GEOMATE – Melbourne, Australia, 11-13 November 2020

The bacteria were added into the testing solution,
shaken sufficiently, then incubated at 45℃ for 2
hours. Species changed the color into purple was
identified as urease activity positive.

bacteria (OD600=11, 2-day cultured) were used.
Different proportion (10%-50%) of bamboo fibers
were added to keep the water content of peat soil at a
relatively low level. Sufficient mixing was always
followed by adding process. Cases set in thisexperiment were depicted in the Table 2. Molded
samples were then put into an incubator with constanttemperature of 30℃ for curing. Examinations werecarried out after 48 hours and on day 7.

16S rRNA sequencing and analysis
According to the Japanese laws as to themicroorganism utilization, it is a must to examine thebio-safety level of bacteria, in case the unknown
bacteria would be harmful to human body. Finally,
the isolates were characterized by sequencing their
16S rDNA and comparing with the database of
Apollon DB-BA 9.0, Gen Bank, DDBJ (DNA bankof Japan) and EMBL (European Molecular Biology
Laboratory).

Fall cone test
Fall cone test was conducted according to the JGS
0142-2009 [15]. One advantage of this method is thatit could be applied on soft clay materials [16]. And
for peat, it avoids the problem of the organic matter.
The test is based on an approximate relation between
the undrained shear strength (τf) and the depth ofpenetration (h), as presented in Eq. 1. K is the fallcone factor which depends mainly on the cone angle,
and Q is the cone weight.

Urease activity and growth curve measurement
Quantitative measurement of bacterial population
and urease activity were realized by colorimetry,
which is used to test the concentration of a solution
by measuring its absorbance of a specific wavelengthof light. In case of the determination of bacterial
population, the optical density was scanned at the
wavelength of 600 nm (OD600). This parameter was
set as 630 when measuring the urease activity ofbacteria by spectrophotometric determination ofammonia as Indophenol [12], [14].

τf =KQ/h2
Table 2 Different cases in this experiment

A-1
A-2
A-3
B-1
B-2
Only fiber
B-3
B-4
C-1
MICP
C-2
incorporated
C-3
with fiber
C-4
Only MICP

Solidification test
Injection method and mixing method are currently
two of the most popular methods for investigating theefficiency of solidification using MICP. In this study,
the unique characteristics of peat soil made the choiceof mixing method for its reliability and validity. First,
NaCO3 was used to adjust the pH condition of peatsoil, followed by mixing of cementation resources:
CaCl2 (Ca2+), urea (CO32-) and ureolytic bacteria(urease). In each 150 g of peat soil, only 15 mL of

(a)
8
6
4
20°C
25°C
30°C

2
0
4

6
Time (days)

8

1 mol/L
2 mol/L
3 mol/LNone

1 mol/L

None
20%
30%
40%
50%
20%
30%
40%
50%

Note: Vs is sample volume; Wf is weight of fiber; Wp is weight ofpeat soil.

Urease activity (µmol/min/mL)

Bacteria growth (OD600)

102

Cementation Proportion of
resources/ Vs fiber (Wf/Wp)

Case

Treatment and Evaluation

0

(1)

0.8

(b)

0.7
0.6
0.5
0.4
0.3

20°C
25°C
30°C

0.2
0.1
0
0

10

43

2

4
6
Time (days)

8

10

GEOMATE – Melbourne, Australia, 11-13 November 2020

Estimated undrained shear strength (kPa)

Fig. 3 Performance of PS-1 under different temperatures: (a) growth curve and (b) urease activity
RESULTS
Fiber incorporated MICP on peat soil
Bacteria Performance
Figure 5(a) presents the experimental data on thestrengthof fiber reinforced peat soil. The
In total, 14 isolated species were tested, of which
improvementof peat soil with as small as 10% fiber
three species were finally identified as ureolytic
additionwasnegligible.
However, as the fiber content
bacteria. Two of them were sent to DNA Data Bank
increased to 50%, the shear strength grew by moreand characterized as Staphylococcus edaphicus (PSthan 40 times after 7 days of curing under the constant1) and Oceanobacillus profundus (PS-5). After thetemperature of 30℃. What stands out in Fig. 5(b),preliminary analysis, PS-1 was chosen for further
which describes the efficiency of MICP incorporated
experiments as per its higher relative performance,with fiber, is the dramatic growth of undrained shear
and its performance under different temperature were
strength in samples with 50% of fiber, reached 43 kPa,
confirmed by a series of tests. Figure 3 reveals that,
improved by more than 80 times of the untreated,
for the first few days, there has been a gradual rise in
twice of that of fiber reinforced. It also can be clearly
the population of bacteria (showed as OD600),
seen in this figure that no significant increase in cases
irrespective of the incubation temperature. However,with lower fiber content.
this number reached a peak at the temperature of 20℃
5after four days. As per the urease activity tests, thet=0bacteria, on the other hand, showed the highest
preference at high temperature. The activity undert = 2 days
4
30℃ peaked around 0.75 U/mL after 6 days oft = 7 days
culturing.
Strength Characteristics
MICP on peat soil (without fiber)

10Fig. 4 Effect of different initial concentration of
cementation resources

50

50

(a)
40
30

2

Control
12
3Concentration of cementation material (mol/L)

Estimated undrained shear strength (kPa)

Estimated undrained shear strength (kPa)

To study the effect of initial concentration of
resources, the peat soil was treated preliminary by
MICP. As shown in bar chart (Fig. 4), theimprovement of peat soil made by MICP seems to be
insignificant here, and the development of undrained
shear strength declined steadily along with theincrease of concentration of cementation material
added. Based on the observations, therefore, 1 mol/L
was chosen to be appropriate for further experiments.

3

t=0
t = 2 dayst = 7 days

20
10
0
20

30
40
Fiber content (%)

50

(b)

40

30

t=0
t = 2 dayst = 7 days

20

10

0
20

30
40
Fiber content (%)

Fig. 5 Fiber incorporated MICP with different concentrations of cementation material:
44

50

GEOMATE – Melbourne, Australia, 11-13 November 2020(a) only fiber and (b) 1 mol/L
(a)

(b)

(c)

Filled fibers

Open cellular structure

Coated fibers

Bonded fibers

Fig. 6 SEM images of peat soil: (a) untreated; (b-c) MICP- treatedconcentration of resources mixed significantly
governs the effectiveness of MICP. The results
showed that the increasing the initial concentration of
resources decrease the strength gaining of peat soil. A
possible explanation of this might be that highconcentration of MICP chemicals (particularly urea)
might affect the urease enzyme. During theexperimentation, the softening of peat soil was
experienced with the increase in resource
concentrations. Basically, peat soil is rich in colloids
which are charged nano∼micro particles, responsible
for most of the chemical responses of peat soil. Highconcentration of resources would induce the fierce
chemical reaction within a short time, leading to theincrease in ion-exchange and possibly softening thepeat. However, when the moisture is controlled by thefibers, a significant enhancement is achieved in MICP
treatment (as compared in Fig. 5).
The query that was not addressed in this study was
how this method could alter the consolidation
responses of peat soil, which is an importantparameter to evaluate. Another limitation may be thelimited supply of resources, and multiple mixing
phased might further improve the mechanicalresponses of peat soil. Despite of the possessed
limitations, the study suggests the potential value ofbamboo fiber in engineering field. Further
investigations are necessary to firmly establish thetechnique with a deeper understanding of theapproach.

SEM Observation
The results obtained from the SEM (scanningelectron microscope) analysis of untreated and treatedpeat soil are compared in Fig. 6. Fig. 6(a) shows the
microstructure of the untreated peat soil. As indicated,fibers were found to be at various sizes and shapes;
open cellular structure could also be seen in fibers ofuntreated soil, suggesting a high degree of decay.
When the MICP treatment was applied, the fibers
were bonded together by the precipitated calcium
carbonate (as depicted in Fig. 6(b)). Certain amountof calcium carbonate was also found to be randomly
precipitated on the surface of fibers, which might
contribute to the increase in surface roughness.
Moreover, the precipitated calcium carbonate tended
to crystalline within the open pore structure of the
decayed fibers, and a clear microstructure ofenhanced fiber material is presented in Fig. 6(c).
Overall, the microscopy analysis has provided
important insights to understand the mechanism how
the strength improvement is achieved in peat soil.
DISCUSSION
Previous studies [17], [18], [19] evaluated theeffectiveness of chemical stabilization on peat, ofwhich the results showed that the strength wassignificantly improved when the considerable amountof pozzolanic materials were mixed in-situ or ex-situ.
Therefore, traditional methods such as Portland
cement are firmly being demanded, although someadverse environmental effects are often reported.
MICP is a recent call, which is believed to have thepotential for stabilizing a wide range of soils. Amongthe studies performed to this date, only very little
were focused on improving peat material, and theimprovements were found to be relatively poor [20],which is essentially due to the high moisture content,
weak skeleton of peat and intrinsic chemical
conditions.
In this study, it has been found that the initial

CONCLUSIONS
The fall cone test, micro-scale observation and aseries of examinations were conducted for MICP
incorporated with different bamboo fiber ratios, inorder to clarify the feasibility and effectiveness offiber incorporated MICP on peat soil and to ascertain
its mechanism. Conclusions could be drawn as below:(i) The addition of bamboo fiber could greatly reduce
the water content of peat soil, resulting in an
improvement in strength.
45

GEOMATE – Melbourne, Australia, 11-13 November 2020

139-153.
[9] Gowthaman S., Nakashima K., Kawasaki S., A
state-of-the-art review on soil reinforcement
technology using natural plant fiber materials:
Past findings, present trends and future directions,
Materials (Basel), Vol. 11, 2018, 553.
[10]ASTM D1997-13: Standard test method forlaboratory determination of the fiber content ofpeat samples by dry mass, ASTM International
2013.
[11]ASTM D2216-19: Standard Test Methods forLaboratory Determination of Water (Moisture)
Content of Soil and Rock by Mass.
[12]Gowthaman S., Iki T., Nakashima K., Ebina K.,
Kawasaki S., Feasibility study for slope soil
stabilization by microbial induced carbonate
precipitation (MICP) using indigenous bacteria
isolated from cold subarctic region, SN Appl. Sci.,
Vol. 1, 2019, 1480.
[13]Gowthaman S., Mitsuyama S., Nakashima K.,
Komatsu M., Kawasaki S., Biogeotechnicalapproach for slope soil stabilization using locallyisolated bacteria and inexpensive low-grade
chemicals: A feasibility study on Hokkaido
expressway soil, Japan, Soils Found., Vol. 59,
Issue 2, 2019, pp. 484-499.
[14]Bolleter W. T., Bushman C. J., Tidwell P.W.,
Spectrophotometric Determination of Ammonia
as Indophenol, Anal. Chem., Vol. 33, Issue 4,
1961, pp. 592-594.
[15]JGS, 2009. Testing Methods and Their
Interpretation for Geotechnical Materials (inJapanese).
[16]Tanaka H., Hirabayashi H., Matsuoka T., Kaneko
H., Use of fall cone test as measurement of shear
strength for soft clay materials, Soils Found., Vol.
52, Issue 4, 2012, pp. 590-599.
[17]Kalantari B, Huat B. B. K, Load-bearing Capacity
Improvement for Peat Soil. European Journal of
Scientific Research, Vol. 32, Issue 2, 2009, pp.
252-259.
[18]Erdem O. Tastan, Tuncer B. Edil, F. ASCE, Craig
H. Benson, F. ASCE; and Ahmet H. Aydilek, M.
ASCE, Stabilization of Organic Soils with Fly
Ash, J. Geotech. Geoenviron. Eng., Vol. 137,
Issue 9, 2011, pp. 819-833.
[19]Hayashi H., Nishimoto S., Strength Characteristicof Stabilized Peat using Different Types of
Binders. International Conference on Deep
Mixing Best Practice and Recent Advances,
Sweden, CD-R, Vol. 1, 2005, pp. 55-62.
[20]Canakci H., Sidik W., Kilic I. H., Bacterail
Calcium Carbonate Precipitation in Peat. Arab J
Sci Eng., Vol. 40, 2015, pp. 2251-2260.

(ii) With the increase of initial concentration of
cementation material, the strength gain decreased.
(iii) The undrained shear strength of peat soil was
improved by more than 80 times using MICP
incorporated with 50% of bamboo fiber, twice asvalues obtained from cases of fiber only.
(iv) MICP filled the cellular structure, coated andbonded separate fibers together, increasing theroughness of the fiber surface to make them awhole.
ACKNOWLEDGMENTS
Authors express the gratitude to Civil Engineering
Research Institute for Cold Region (Sapporo, Japan)
for their fullest supports and technical assistance.
This work was partly supported by JSPS KAKENHI
Grant Number 19H02229.
REFERENCES[1] Huat B. B. K., Organic and Peat Soil Engineering.
1st edn. University Putra Malaysia Press, Serdang,
Malaysia, 2004, pp. 1-15.
[2] Kazemian S., Huat B. B. K., Prasad A., Barghchi
M., A state of art review of peat: Geotechnicalengineering perspective, Int. J. Phys. Sci., Vol. 6,
2011, pp. 1974-1981.
[3] DeJong J. T., Mortensen B. M., Martinez B. C.,
Nelson D. C., Bio-mediated soil improvement,
Ecol. Eng., Vol. 36, 2010, pp. 197-210.
[4] Whiffin V. S., van Paassen L. A., Harkes M. P.,
Microbial carbonate precipitation as a soil
improvement technique. Geomicrobiol. J., Vol.
24, 2007, pp. 417-423.
[5] UNEP Annual Report 2010: United Nations
Environment Programme; ISBN 9789280731491,
2011.
[6] Islam M. T., Chittoori B. C. S., Burbank M.,
Evaluating the Applicability of Biostimulated
Calcium Carbonate Precipitation to Stabilize
Clayey Soils, J. Mater. Civ. Eng., Vol. 32, 2020,
pp. 1-11.
[7] Portugal C. R. M. e., Fonyo C., Machado C. C.,
Meganck R., Jarvis T., Microbiologically Induced
Calcite Precipitation biocementation, green
alternative for roads – is this the breakthrough? A
critical review, J. Clean. Prod., Vol. 262, 2020,
121372.
[8] Ivanov V., Chu J., Applications of
microorganisms to geotechnical engineering forbioclogging and biocementation of soil in situ.
Rev. Environ. Sci. Biotechnol., Vol. 7, 2008, pp.

46

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

HYDRAULIC CONDUCTIVITY CHARACTERISTICS OF A FINEGRAINED SOIL POTENTIAL FOR LANDFILL LINER APPLICATION

1Jonathan M. Tiongson1 and Mary Ann Q. Adajar2
Faculty, Mindanao State University-Iligan Institute of Technology, Philippines;
2
Faculty, De La Salle University, Manila, Philippines

ABSTRACTSanitary landfills (SLFs) are usually employed as final waste disposal facility to protect public health and theenvironment. As a result of rapid population growth and urbanization, there is currently a great demand toconstruct SLFs in the Philippines. The hydraulic conductivity characteristics of remolded samples of a locallyabundant fine-grained soil compacted at different compaction energy level is investigated to determine thesuitability of the soil as landfill liner material. The hydraulic conductivity of lining system is one very salientfeature of the SLF to prevent contamination of nearby soil and water sources. The physical properties of the soil
are determined through a series of laboratory tests which includes the grain-size distribution, specific gravity,
Atterberg limits, soil classification, Cation Exchange Capacity (CEC), X-ray powder diffraction (XRD) andscanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). The falling head laboratory
test was conducted to determine the saturated coefficient of hydraulic conductivity. A numerical model wasformulated that can predict hydraulic conductivity as a function of the void ratio. The resulting coefficient ofhydraulic conductivity ranges from 1.98 x 10-6 to 1.0 x 10-7 cm/sec meet the Philippine standard requirement.
The soil being classified as clay loam can readily be used as top lining material. However, additional study on
unconfined compressive strength and volumetric shrinkage among other parameters is recommended prior to useof the fine-grained soil as bottom lining material as soil amendment maybe necessary.
Keywords: Hydraulic conductivity, Permeability, Landfill Liner, Sanitary Landfill

hydraulic conductivity and the ability to attenuate
pollutants migrating through.
In this study, the saturated hydraulic conductivityof a locally abundant fine-grained soil is explored aspart of a much bigger study on the possible use ofthat soil as compacted soil lining material for amunicipal SLF. The use of compacted soil lining
with or without admixture of clay is common due its
relatively low cost, accessibility, durability, highresistance to heat, and other factors among otherliner materials [1]. Some marginalized materials arealso studied as potential landfill liners such as
residual soil [2], dredged marine soils [3], compost[4], fly ash used as soil treatment in combination
with bentonite [5] among others. Published literatureon the use of compacted soil in the Philippines as
landfill liner is limited.
Compacted soil liner consists of a mineral layer
which satisfies hydraulic conductivity and thickness
requirements
along
with
other
technical
requirements such as cation exchange capacity(CEC) which varies for different countries. The
hydraulic conductivity is the principal factor that
affects hydraulic barrier performance. Typicalhydraulic conductivity value must be less than orequal to 1 x 10-7 cm/sec for soil liners and covers tocontain hazardous waste, industrial waste and

INTRODUCTION
Sanitary landfills (SLFs) are usually employed as
final waste disposal facility to protect public healthand the environment. As a result of rapid population
growth and urbanization, there is currently a great
demand to construct SLFs in the Philippines on topof the statutory requirement set forth by Republic
Act (RA) 9003 otherwise known as the “Ecological
Solid Waste Management Act of 2000”. This law
requires all local government units (LGUs) totransition from open dumpsite to controlled
dumpsite, and eventually to a sanitary landfill (SLF)
not later than February 2006. However, as February
2020, only 108 sanitary landfills or 6 percent of thetotal required number of 1700 landfills nationwide
as reported in the Philippine Environmental
Management Bureau website. The construction for
more SLFs gives rise to the need for sustainable
materials especially for the lining system. The lining
system in SLF is one of the most important featuresof the facility to provide containment against
environmental degradation that may cause adverse
effect to public health. Compacted clayey layer,
geomembrane, geosynthetic clay layer or acomposite of these may constitute the barriercomponent of the liner which should have a low
47

GEOMATE – Melbourne, Australia, 11-13 November 2020

possible utilization as soil liner without discounting
the need of an actual conduct of field hydraulicconductivity test as well as other requirements set
forth by the aforementioned law and its IRR. The
information can be utilized as a part of the basis forsoil modification whenever needed prior toutilization as a construction material.

municipal solid waste. Other considerations inchoosing the soil liner material include but notlimited to shear strength, swelling and volumetricshrinkage. Most soils that possess the properties
shown in Table 1 will meet these requirements [6].
Table 1 Specifications for Soil Liner Materials [6]Soil Property
Percentage of Fines (%)
Plasticity Index (%)
Percentage Gravel (%)
Maximum Particle Size (mm)

MATERIALS AND METHODS

Range of Values≥ 39-50≥ 7-10≤ 20-50
25-50 mm

Source of the Fine-Grained Soil Sample
The soil sample used was excavated from a
proposed sanitary landfill site in the municipality ofKauswagan, Lanao del Norte, Philippines. The
sample was taken at a depth of least 2 meters to
exclude organic matters. The soil looks to be
reddish-brown. The soil consistency is sticky, plastic
when moist and firm when dry. Figure 1 shows thesoil sample used in the study.

Hydraulic conductivity or permeability of soil isthe capacity of soil to allow water to pass through it.
Soils are permeable due to the existence ofinterconnected voids through which water flow frompoints of high energy to points of low energy.
Permeability of soils are affected by particle size,
void ratio, properties of pore fluid, shape of particles,
structure of soil mass, degree of saturation, absorbed
water, entrapped air and organic impurities in water,
temperature, and stratification of soil. Reduction in
void ratio will result to decrease in permeability. For
the same soil sample, only the void ratio will be themost significant factor. In compacted soils, thecompaction variables also greatly influence thehydraulic conductivity.
In this paper, the saturated hydrauliccharacteristics of a local fine-grained soil as
potential landfill liner material compacted at threedifferent compaction energy level were investigated.
The values obtained from the test is compared to therequirements of RA 9003 and its implementing rules
and regulations (IRR) as shown in Table 2. Relations
to estimate the saturated hydraulic conductivity interms of the corresponding void ratio at the different
compactive efforts were developed and validatedwith the previous related studies.

Fig. 1 Moist Soil Sample
Preliminary Experimental Program
The preliminary laboratory tests in the study issummarized in Table 3.
Table 3 Preliminary Experimental Program

Table 2.
RA 9003 and its IRR Hydraulic
Conductivity and Soil Thickness Requirements
Landfill
Category

Capacity(tons/day)

I

≤ 15

II

> 15 ≤ 75

III> 75 ≤ 200

IV

> 200

Test

Min. Requirement
for k (cm/sec)
and thickness (cm)1 x 10-5& 60
1 x 10-6& 75
1 x 10-7& 75 or 60*
1 x 10-7& 60*

Grain Size Analysis-Mechanical Method-Hydrometer Method
Specific Gravity Test
Liquid Limit Test
Plastic Limit Test
Shrinkage Limit Test
Min. Index Density Test
Max. Index Density Test
XRD
SEM-EDX
Compaction Test
-Reduced Proctor-Standard Proctor-Modified Proctor

Note: *Build together with synthetic liner of 1.5 mm thick

The findings will be used for initial assessment
and design purposes of the local fine-grained soil for48

Test Standard
ASTM D6913
ASTM D7928
ASTM D854
ASTM D4318
ASTM D4318
ASTM D427
ASTM D4254
ASTM D4253

Daniel & Benson [7]ASTM D698
ASTM D1557

GEOMATE – Melbourne, Australia, 11-13 November 2020

CEC

US EPA 9081*

Note: *United States Environmental Protection Agency

Fig. 2 Grain Size Distribution Curve

The physical properties were determined througha series of laboratory tests which are the grain-size
distribution including both the mechanical andhydrometer analysis, specific gravity, Atterberg
limits covering the plastic limit, liquid limit andshrinkage limit and soil classification as per Unified
Soil Classification System (USCS). Cation
Exchange Capacity (CEC) conducted through the
Sodium Acetate Distillation-Titration Method
employed to determine exchangeable ions. X-ray
powder diffraction (XRD) was performed on soil
sample to obtain a mineralogical analysis of thesample. The analysis used manually ground ovendried soil sample of fine powder passing sieve No.
200. The sample was scanned from 3° to 70° at ascanning rate of 1 degree per minute. Scanningelectron microscopy was done to provide a broad
character of the soil microfabric. Soil samples ovendried at 105oC of grains passing sieve No. 200 wereused to remove moisture, coarse and organic
material are used for this as well as in Energy
dispersive X-ray spectroscopy (EDX) analysis which
was used to determine the elemental composition of
the surface of the soil specimen. The laboratorycompaction test was carried out using Modified
Proctor Test, Standard Proctor Test and Reduced
Proctor Test [7] to represent varying compactionenergy of equipment in the field. Three runs of testfor each compactive efforts.

Soil Constants
The various laboratory tests resulted to the values
for soil physical properties as shown in Table 4. The
resulting experimental specific gravity value falls
within the range of 2.6 to 2.9 typical for clayey andsilty soils. The value of the Plasticity Index (PI) falls
within the range of 20-40 which is for the highly
plastic soils. The values for liquid limit andplasticity index are subsequently used as inputs forsoil classification in addition to the percentage fines
discussed in the previous subsection.
Table 4. Soil Constants [8]Soil Property
Value
Specific Gravity, Gs
2.66
Liquid Limit, LL,
88.49
Plastic Limit, PL
55.68
Plasticity Index, PI
32.81
Shrinkage Limit, SL
29.69
Min. Void Ratio, emin
1.07
Max. Void Ratio, emax
1.55
Min. Dry Unit weight, γdmin, kN/m3 10.24
Max. Dry Unit weight, γdmax, kN/m3 12.59
Soil Classification

RESULTSIn accordance with the Unified Soil
Classification System (USCS), the soil sample isclassified as elastic silt with designation MH. The
values of the soil properties being percent finerpassing No. 200 > 50%, liquid limit >50%,
plasticity index plots below the “A” line andpercentage passing of sand or gravel < 15 to 29%.
However, the said soil is classified as clay loam
according to the United States Department ofAgriculture and are good landfill cover or top lining
material [9].

Grain Size Distribution
The grain size distribution (GSD) is graphically
depicted in Fig. 2 with mechanical and hydrometer
test combined. The mechanical sieve analysis
yielded a small 2% of fine sand and majority 98% ofsilt and clay. The GSD curve exhibits a poorly
graded soil. Considering the percentage of soil
grains passing No. 200 sieve more than 50%, the soil
is said to be fine-grained and is expected to respondpoorly to compaction.

XRD Result

49

GEOMATE – Melbourne, Australia, 11-13 November 2020

weight percentage distribution is as shown in Table
5.
Table 5. EDX Elemental Distribution
Element Name Symbol
OxygenO
AluminumAl
SiliconSi
Iron
Fe
CarbonC
Titanium
Ti
CalciumCa
Compaction Tests

Fig. 3 X-ray Diffractogram of the Soil Sample
The resulting XRD pattern shows sharp peaks as
depicted in Fig. 3. The result suggests that the soil
sample comprises mainly of the crystal phases ofkaolinite, biotite, antigorite, and goethite.

Weight Percentage
52.4
16.6
16.6
6.9
6.7
0.7
0.1

A set of the typical compaction curves for thesoil sample compacted with the three compactive
efforts is shown in Fig. 5. It is observable that the
maximum dry unit weight increases with theincrease in compactive effort while the optimum
water content decreases with the increase incompactive effort. All the curves are well below thezero-air voids curve.

SEM-EDX
A micrograph for the soil sample at 100, 000x
magnification factor is exhibited by Fig. 4. The
image shows sub-angular and sub-rounded shapes
like those of granular soils. It also reveals large
intergranular voids. Also visible are flakey,
spheroidal, tubular morphological attributes andinter granular voids usually associated to clay
minerals. A flakey configuration in the soils iscommon in fine-grained varieties such as clay andsilt enabling those types of soil to have large surface
areas for moisture adsorption. The image gives animpression that the soil contains many components
such as mica, clay species (silicates) occurring inplaty, sheet, flake structure in the family ofphyllosilicates and few organic materials.

Fig. 5 Typical Compaction Curve
Cation Exchange Capacity
The resulting laboratory test for CEC yield avalue 7.76 cmol/kg which falls within typical value
for kaolinite of 5-15 cmol/kg.
Hydraulic Conductivity
The permeability test was carried out on
remolded samples with compaction energies
corresponding to the three compactive efforts in arigid wall permeameter with a cross-sectional area of
5.73 square centimeter and height of soil specimenof 7.60 centimeters using distilled water as permeant.
Further, the permeability test was performed under afalling head condition under a hydraulic gradient ofaround 20.

Fig. 4 SEM Micrograph at 100, 000x magnificationof the Soil Sample
The elemental analysis yields that oxygen,aluminum, and silicon comprised most of the soil
sample with traces of iron, carbon, titanium andcalcium which are among the most abundantelements in the Earth’s crust. These are the elements
which compose the family of phyllosilicates. The

50

Hydraulic Conductivity,k (cm/s

GEOMATE – Melbourne, Australia, 11-13 November 2020in a semi-log plot, the hydraulic conductivity in alogarithmic scale and the void ratio in arithmetic
scale as ordinate abscissa, respectively.
The equation can take form:

2.01E-06
1.51E-06
1.01E-06

𝒌𝒌 = 𝒄𝒄𝒇𝒇𝒆𝒆

5.10E-07(1)

where:
c = material constantk = hydraulic conductivity in cm/sec
fe = function describing the effect of void ratio

1.00E-08
30.00

40.00
50.00
60.00
Moisture Content, ω (%)
Reduced Proctor
Standard Proctor
Modified Proctor

The regression analysis yielded the quantities inTable 6 together with the coefficient ofdetermination R2. A high correlation coefficient, i.e.
close to +/-1.0 suggests a strong linear relationshipexist between the variables. This underpins thestrength of the linearity of the regression equation.

Fig. 6 Hydraulic Conductivity versus Moisture
Content
The hydraulic conductivity coefficient wascalculated using Darcy’s law.
The saturated
hydraulic conductivity result ranges for the threecompactive efforts from 1.98 x 10-6 to 1.0 x 10-7
cm/sec which can be said to be of low hydraulicconductivity. This range falls within the typicalvalues for elastic silt [9]. The values meet thePhilippine requirement depending on the targetedcategory of the SLF. Figure 6 shows a typical plot of
the hydraulic conductivity, k versus the moisture
content, ω at the three compaction efforts. It can beobserved that the values of the hydraulicconductivity decrease as the compactive effort isincreased. It is notable that the values of k is lowest
to the left of the optimum moisture content. Thisagrees to the earlier finding of a similar study thatsoils that are more plastic and have a great quantityof fines yield lower hydraulic conductivity whencompacted wet of the optimums [10].

Table 6 Coefficients for the Regression Model
Compactive
EffortReduced Proctor
Standard Proctor
Modified Proctor

c

fe

R2

3.0x10-11
1.0x10-11
3.0x10-12

exp6.00e
exp7.10eexp8.81e

0.92
0.93
0.92

The high values of the coefficient ofdetermination R2 indicates that the model fits thedata well. The values are comparable to similar
studies [13]-[14]. Figure 7 depicts the plots of thelog k versus the void ratio at the three compactive
efforts.
1.00E-05
log k

Predictive Models
Predicting the hydraulic conductivity, k of soils
accurately and dependably is among the lingering
problem for geotechnical engineering as laboratory
studies have shown that hydraulic conductivity is
influenced by a lot of parameters as discussed in the
introduction section. One equation widely used is the
Kozeny-Carman relation wherein k is a function as afunction of the void ratio of soils, shape constant,specific surface area of particles, particle density ofsoil, unit weight and viscosity of the fluid permeant.
However, the model is not appropriate for clayeysoils as it assumes no electrochemical reactions
between the solid particles and permeant. Researchstudies have been made on possible relationships
between hydraulic conductivity and void ratio, e offine-grained soils. One relationship established isthat a linear relation between the logarithm of thehydraulic conductivity and the void ratio for clays[11]-[12]. In this study, a linear regression analysis
on the void ratio and logarithm of the hydraulicconductivity per compactive effort to develop a
predictive model. The output of the test is presented

1.00E-06

1.00E-07
1.35

a.

1.55

1.75

Void Ratio, e

1.95

Reduced Proctor

log k

1.00E-05
1.00E-06
1.00E-07
1.00E-08
1.20

1.40

1.60

Void Ratio, eb.

51Standard Proctor

1.80

GEOMATE – Melbourne, Australia, 11-13 November 2020

1.00E-05

Malaysian Residual Soils for Utilization as ClayLiners in Engineered Landfills, International
Journal of GEOMATE, Vol.16, Issue 58, June
2019, pp.20 – 25.
[3] Anuar N. M., Chan C. and Johan S.F.S.M., An
Innovative Sanitary Landfill System with
Dredged Marine Soils (DMS) as Liner Material:
Geo-Environmental
Studies,International
Journal of GEOMATE, Vol.14, Issue 41, Jan.
2018, pp.181-185.
[4] Hermawansyah Y., Hadinata F., Dewi R. andHanafiah, Study of Compost Use as an
Alternative Daily Cover in Sukawinatan Landfill
Palembang, International Journal of GEOMATE,
Vol. 15, Issue 51, Nov. 2018, pp. 47-52.
[5] Nguyen L.C., Chu H. L. and Ho L.S., Soil
Treatment by Bentonite and Fly Ash for Linersof Waste Landfill: A Case Study in Vietnam,
International Journal of GEOMATE, Vol. 17,
Issue 63, Nov. 2019, pp. 315-322.
[6] Daniel D.E., Geotechnical Practice for Waste
Disposal, Chapman and Hall, London, 1993, p.
140.
[7] Daniel D. and Benson C., Water Content-Density
Criteria for Compacted Soil Liners, J. Geotech.
Engrg., ASCE, Vol. 116, Issue 12, 1990, pp.
1811-1830.
[8] Tiongson, J.M. and Adajar, M.A.Q., Compaction
Characteristics of a Fine-Grained Soil Potential
for Landfill Liner Application, International
Journal of GEOMATE, Vol. 19, Issue 71, July
2020, pp. 211-218.
[9] The Unified Soil Classification System,
Waterways Exp. Sta Tech. Mem., 1953, pp. 3357 (including Appendix A, 1953, and Appendix
B, 1957), US Army Corps of Engineers,
Vicksburg, Miss.
[10] Benson, C.H. and Trast, J., Hydraulic
Conductivity of Thirteen Compacted Clays, J.
Geotech Geoenviron. Eng., Vol. 43, Issue 6,
1995, pp. 669-681.
[11] Taylor, D. W., Fundamentals of Soil Mechanics,
John Wiley and Sons, Inc., N.Y., 1948, pp. 97123.
[12] Lambe, T. W. and Whitman, R.V., Soil
Mechanics, John Wile and Sons, Inc., N.Y., 1969,
pp. 281-294.
[13] Adajar, MA.Q. and Zarco, MA.H., An
Empirical Model for Predicting Hydraulic
Conductivity of Mine Tailings, International
Journal of GEOMATE, Vol. 7. No.2, 2014, pp.
1054-1061[14] Tiongson J.M. and Adajar M.A.Q., Viability ofKauswagan Soil as Sanitary Landfill Liner,
Conference Proceedings of DLSU Research
Congress, June 2018, Volume 6, Series 2, p.3.

log k

1.00E-06
1.00E-07
1.00E-08
1.10

c.

1.30

Void Ratio, e

1.50

Modified Proctor

Fig. 7 Plot of the log k versus e for the Three
Compactive Efforts
The models can be useful to soil similarly
situated and the strength lies on the simplicity with
only void ratio as the parameter. Those can be
helpful to specify field compaction.
CONCLUSIONS
Based on this study, the following conclusions
can be drawn:
The fined-grained soil as elastic silt based on
USCS or clay loam based on USDA. The soil beingclassified as clay loam can readily be used as top
lining material or cover. The soil is said to be of highplasticity. The soil sample composition consists
mainly of the crystal phases of kaolinite, biotite,
antigorite, and goethite from the XRD analysis andconsistent from the SEM-EDX results.
The
resultingcoefficientof hydraulicconductivity ranges from 1.98 x 10-6 to 1.0 x 10-7
cm/sec meet the Philippine standard requirement
depending on the SLF Category. The Strong
correlation was developed between the logarithm of
the hydraulic conductivity and the void ratio at thethree compactive efforts.
Further study is warranted on field hydraulicconductivity test, unconfined compressive strengthand volumetric shrinkage to provide baseline
information for design and quality assurance
purposes. Those are additional informationnecessary for soil modification as maybe desired forthe utilization of the soil as marginalized bottom
SLF lining material.
REFERENCES[1] Wang M. C. and Huang C.C., Soil Compaction
and Permeability Prediction Models, J. Environ
Engrg., ASCE Vol. 110, Issue 6, 1984, pp.1063–
1083.
[2] Yong L. L., Emmanuel E., Purwani R. andAnggraini V., Geotechnical Assessment of52

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

GEOTECHNICAL CHARACTERIZATION OF ALLUVIAL SOIL AS AN
ALTERNATIVE ROADWAY CONSTRUCTION MATERIAL

1

Emerzon S. Torres1 and Mary Ann Q. Adajar1
Department of Civil Engineering, Gokongwei College of Engineering, De La Salle University, Philippines

ABSTRACTThe rapid infrastructure development in the Philippines over the past years poses problem on construction cost
due to the limited supply of suitable road foundation materials within economic haul distances. An economic andsustainable solution is to use locally available but suitable materials. This study utilized the naturally-occurring
alluvial soil along the Angat Riverbanks as a potential road foundation material. Geotechnical characterization of
the alluvial soil was performed including direct shear and California bearing ratio (CBR) tests. The findings of theindex properties showed that the alluvial soil is 92% sand, non-plastic, and was classified as poorly-graded sand.
A CBR of 41% makes the river sand a suitable subbase course material. However, the sample failed on thegradation requirement for subbase application due to lacking of coarse aggregates. From the direct shear tests, thesoil showed brittle failure that yields peak shear stress and dilative volume change behavior. The critical state
friction angles for dry and saturated conditions are 37.52° and 36.61°, respectively. Morphological analyses werealso performed to further evaluate the material composition of the alluvial soil. The sedimentological analysis
found that the sample is both texturally and compositionally immature. Sediments are angular to subrounded andmoderately sorted. Correspondingly, the SEM-EDX revealed flocculation with flakey particles and small intergranular voids that justifies the shear strength of the sand. It is recommended to blend coarse aggregates with the
alluvial soil to meet the gradation requirements and possibly increase its bearing strength for base course
applications.
Keywords: Alluvial soil, Road foundation, CBR, Direct shear strength, Morphology

restricted from mining due to public perception andconservation efforts, and (3) higher quality standards
may further reduce the amount of usable aggregates
mined from specific sources [3].
Searching for alternative construction materials
is considered a sustainable way of development if
secondary material is free from hazardous
components and does not have any adverse long-term
effects [4]. Road construction costs can be very highbecause of the limited opportunity of locating suitableroad base material within economic haul distances [5].
It is preferable and economical to use and to stabilize,
if necessary, locally available but abundant materials
than to import conventional aggregates.
One thing that is abundant in the Philippines is
its alluvial deposits of soils. The alluvium occupies
approximately 15 percent of the land surface of thearchipelago [6]. The alluvium is the soil transported
by water and settled along riverbanks. It is usuallymost extensively developed in the lower part of thecourse of a river, forming floodplains and deltas, but
may be deposited at any point where the river
overflows its banks.
Alluvial soil was already researched for its
suitability as an alternative road foundation aggregate
in various countries [7] [8]. However, alluvial soils in
the Philippines are yet to be explored. Due to its
archipelagic characteristics, Philippine lands is

INTRODUCTION
The "Build, Build, Build" program of the
Department of Public Works and Highways (DPWH)
of the Philippines promises a significant construction
development in the Philippines. In the recent
Philippine Infrastructure Report of 2017, strong
growth in the construction and infrastructure industry
was predicted to occur over the next five years. The
report shows that the real growth will reach 12.5%
between 2017 and 2021, while the average annual
growth will reach 11.2% for the construction sector[1]. The Philippine Development Plan launched in2017, aims to change the Philippine income trend as
an upper-middle-income country by 2022 [1]. This
trend resulted in the urge to developing infrastructure
and therefore boost the construction industry in the
country [2].
It follows that there is a greater need forconstruction materials such as embankment and roadfoundation aggregates. However, there are three
emerging challenges on sourcing of aggregatematerials for road construction: (1) geographicaldistribution of natural deposits of high-quality
aggregates needed for road construction is uneven innature and not found in some areas, (2) in areas where
high-quality aggregates exist, gravel mines and rockquarries are being either lost to another land uses or

53

GEOMATE – Melbourne, Australia, 11-13 November 2020

composed of approximately 15% alluvium. The
country experiences the rise of rivers and streams
during the rainy season, which results in theformation of floodplains and thick alluvial deposits.
The province of Bulacan alone, the house of the
153-km Angat River with a catchment area of 1,085
km2, is composed mainly of alluvial orders of soil.
Alluvial soils along the rivers are considered part ofthe lowland soils, and these soils represent the heavy
use of land resources being easily accessible to thegeneral populace.
Alluvial soils are mainly used for agricultural
purposes. However, problems arise when some of thesoils possess high permeability that crops cannot
grow. This is attributed to the sandy soil types, suchas Quingua and Obando soil series, present along theAngat River [9]. With a thickness of 2 to 4 meters andcovers more than 810 hectares, this fine sandy soil isa potential source of road embankment materials [9].
The main objective of this study was tocharacterize and assess the potential of the naturally
occurring alluvial soils when used as a road subgrade,subbase course, and embankment materials. The
DPWH Standards was the major reference on theclassification of the alluvial soil on its suitability [10].
Table 1 shows the parameters and requirements basedon the DPWH specifications for subgrade, subbase
course, and for select granular backfill of
mechanically stabilized earth.

Friction angle

not less than 34%

MATERIALS AND METHODS
Source of Alluvial Soil
The alluvial soils were gathered from a vacantland in Brgy. Laog in the municipality of Angat,
Bulacan. The soils are located along the riverbanks.
Disturbed samples were used since the roadway
construction materials are actually blending ofdifferent materials with specific gradation. The ovendried and moist-tamped soil is shown in Fig. 1.

a. oven-dried soil

b. moist soil

Fig. 1 Soil sample used in this study
Experimental program
The laboratory tests performed on the samples
are enumerated in Table 1. These procedures werebased from either ASTM or AASHTO standards toestablish a reference in evaluating the index
properties and strength of the alluvial soil.
Morphology includes the SEM-EDX analyses,
texture, and mineral identification. These material
analyses supported the results of laboratory tests andexplained the soil behavior that is important for longterm applications.
CBR tests in accordance with AASHTO T193
were performed for the alluvial soil compacted at the
maximum dry density and under soaked conditions tosimulate the worst possible scenario in the field.
Moreover, specimens were soaked for 96 hours (4days). However, a shorter immersion period of notless than 24 hours was applied, as suggested by the
AASHTO standards, if the shorter period did notaffect the test results.
Direct shear tests in accordance with ASTM D
3080 were performed for the alluvial soil to complete
its geotechnical characterization. This study
considered fully saturated conditions to simulate theworst-case scenario and dry condition to see thestrength of the alluvial soil at the most possible
condition. The samples were subjected to varying
vertical stresses: 13.63 KPa, 27.75KPa, and 83.25
KPa. The first vertical stress would capture the lowstress condition while the second and third verticalstress will simulate the 1.5m and 4.5m overburdenpressure in the field. The relative compaction will be
60%, 80%, and 90% to represent the medium dense,

Table 1 DPWH Standards for road foundation [10]Parameter

Requirement
Subgrade course
GradationAll
not more than 15
particles
mass percent will
are finer
pass the 0.075 mm
than 75mm
sieve
Atterberg
PI not more LL not more thanlimitsthan 630
Unit weightgreater than 7.85 KN/m3
Subbase course
GradationAll
not more than 12
particles
mass percent will
are finer
pass the 0.075 mm
than 50mm
sieve
Soaked CBR
not less than 30% at MDD
condition
Dust ratioLess than or equal to 2/3
Atterberg
PI not more LL not more thanlimitsthan 1235
Select granular backfill
Gradation
all particles must be smaller than
100mm
particles finer than 0.0425mm
must be 0 – 60 percent by mass
at most 15% of the particles are
finer than 0.075mm

54GEOMATE – Melbourne, Australia, 11-13 November 2020

dense, and very dense conditions, respectively. Moist
tamping was done to achieve the desired density of
the reconstituted sample. For the saturated condition,
the specimen was soaked in distilled water before
transferring to the direct shear apparatus tank filledwith water. Unconsolidated undrained condition was
performed because of the non-plastic andcohesionless property of the soil sample. The test ran
at 1.25 mm/min rate of shearing.

The material constants is presented in Table 4.
The specific gravity of 2.60 is common to sandy soils[13]. The zero plasticity that is common to sand – silt
particles complies with the low plasticity requirement
for the road foundation materials. The modified effort
was employed during compaction to concur with the
DPWH standards for subbase/base course materials.
The alluvial soil reached a maximum dry density of
19.66 KN/m3 at an optimum moisture content of
10.65%. It absorbs water and produces a wide range
bell-shaped curve that falls on well-graded to finesands [12].

Table 2 Experimental program for alluvial soil
Test
Grain Size Analysis
Specific Gravity Test
Atterberg Limits Test
Compaction test
Max. Index Density Test
Min. Index Density Test
CBR Test
Direct Shear Test
SEM-EDX
Sedimentology

Test Standard
ASTM D6913
ASTM D854
ASTM D4318
ASTM D698
ASTM D4253
ASTM D4254
AASHTO T193
ASTM D3080-

Table 3 Soil Constants
Soil Property
Value
Specific Gravity, Gs
2.60
Liquid Limit, LL %Non-plastic
Plastic Limit, PL %Non-plastic
Min. Void Ratio, emin
0.48
Max. Void Ratio, emax
0.91
Max. Dry Unit Weight,γdmax kN/m3
19.66
Optimum Moisture Content, ωopt %10.65

RESULTS
Soil Classification
Particle Size Distribution
According to the Unified Soil Classification
System (USCS), the alluvial soil is a poorly-gradedsand. On the other hand, the AASHTO classified it
under A-1-b with group index of zero. Considered
granular material, the alluvial soil had a rating of
excellent to good subgrade material according to the
AASHTO classification system.

Figure 2 shows a typical particle size distribution
curve. Generally, a soil is referred to as well graded if
the uniformity coefficient (Cu) is larger than about 4–
6 and coefficient of curvature (Cc) is between 1 and3 [11]. The alluvial has a Cu of 5.11 and Cc of 1.2. It
has an effective grain size (D10) is 0.29. Based on its
D10, the soil falls on a medium size sand [12]. The
alluvial soil comprises of 92% sand, 6% gravel, and
1% silt. Based on the gradation requirements of
DPWH, the soil is not suitable for subbase course
application due to its insufficient amount of coarse
particles.

Soil Strength
California Bearing Ratio
Stipulated by the DPWH (2013) standard
specifications, the bearing strength is defined by the
CBR value obtained from AASHTO T93 provisions.
The alluvial soil obtained CBR values at 2.54mm and5.08mm penetrations of 30.77% and 41.03%,
respectively. These values correspond to the typical
CBR of poorly graded sand classified by the USCS
and of A-1-b soils identified by AASHTO [14]. Basedon these CBR values, the alluvial soil had a bearing
strength suitable for subbase course application as per
the DPWH specifications.

Percent passing

100%
80%
60%
40%
20%
0%
100

Direct Shear Strength
In regards with the direct shear test, for both dry
and saturated conditions, it was observed that as thenormal load increases the value of the critical state
shear stress or the shear stress at failure increases. The
shear strength or the critical state shear stress is theshear strength at which the volume change ceases.

10
1
0.1
0.01
Grain size diameter (mm)Fig. 2 Particle size distribution curve
Soil Constants

55

GEOMATE – Melbourne, Australia, 11-13 November 2020

For the dry condition, stress-strain curves follow
a similar pattern. As the shear strain starts to manifest,
the dry soil behaves elastically (linear curve) until
yielding occurs. At this point, the linear region starts
to form a curvature as it approach a peak as shown in
Fig. 3a. This phenomenon is called strain-hardening.
Furthermore, after the soil reach a peak shear stress,a strain-softening phenomenon happened before it
approach the critical state. This is attributed to theparticle crushing and reorientation [13]. It can also be
observed from the volumetric strain-shear strain plots
in Fig. 3b that during strain hardening the soilchanges its volumetric behavior from compression to
expansion. Moreover, the dry samples exhibit dilation
continuously until the graphs asymptoticallyapproach a critical region where volume change stops.
This is known as the critical state.
It is noticeable that the 13.63 kPa normal load
soil sample experiences the greater volumetric
expansion. Thus, it is concluded that the increasing
normal load directly affects the volumetric expansionof the soils.
Figure 4 displays the stress-strain and volume
change relationships of the saturated alluvial soil
subjected to direct shear test. Slight variations fromthe results of the dry condition can be observed. First,
the reduction of shear strength is very obvious sinceat saturated condition soil loses strength because of
the presence of water as shown in Table 4. Second,
the stress-strain curves for the 60% relative-density
soils show no peak stress under the highest normalload (83.25KPa). In addition, dilation were notobserved in this test sample. The reason for this is thata higher normal load can suppress the peak stress andthe dilatancy [13].
From Table 4, critical shear strength at different
initial relative densities are almost the same for aparticular effective normal stress. Secondly, theeffective normal stress varies directly with the critical
shear strength. Moreover, the densest soil specimens(Relative density = 90%) experience the largest
volumetric expansion. Negative values of volumetricstrain denotes soil expansion. The idea is that dense
soil has the greater chance to expand than to compress
upon shearing [13].
From the direct shear test, the critical state
friction angle for dry condition is 37.52° while thesaturated alluvial possess a 36.61° angle of internal
friction. These values fittingly fall on the typicalrange of friction angle for sand of 27° to 37° [13].
From the DPWH standards, the soil sample used inthis study is a suitable select granular backfill
material.

shear stress, τ (KPa)

100

Relative density = 90%

80
60
40
20
0
0

5
10
15
shear strain, γ (%)
a. Shear stress – shear strain curve20

volumetric strain, εv (%)<---- compression

-0.04
-0.03
-0.02
-0.01
0
0.01
0

5

10
15
shear strain, γ (%)

20

b. Volumetric strain – shear strain curve
Fig. 3 Stress-strain and volume change behavior of
alluvial soil for very dense dry conditions.
Table 4 Critical state values
Alluvial
soil

NormalDr
Dr
Dr
effectiv===e stress,
90%
80%
60%
KPa
Critical state shear strength (kPa)
Dry
13.63
15.58
15.58
15.63
condition
27.75
23.11
24.44
24.05
83.25
62.16
59.58
64.97
Saturated
13.63
13.61
12.28
11.55
condition
27.75
22.45
21.44
23.05
83.25
60.96
57.69
64.02
Critical state volumetric strain (mm/mm)
Dry
13.63
-0.035 -0.026 -0.018
condition 27.75-0.029 -0.024 -0.017
83.25-0.022 -0.021 -0.012
Saturated 13.63-0.013 -0.014 -0.009
condition 27.75-0.009 -0.008 -0.002
83.25-0.001 0.002
0.009

56

GEOMATE – Melbourne, Australia, 11-13 November 2020

shear stress, τ (KPa)70

and undergo weathering. The grain-size curve asshown in Fig. 2 validates the immaturity of the
alluvial soil grains [12].

Relative density = 60%

60
50
40
30
20
10
0
0

10
shear strain, γ (%)
a. Shear stress – shear strain curve20

volumetric strain, εv (%)<---- compression-0.01-0.005a. 500x magnification

0
0.005
0.01
0.015
0

10
shear strain, γ (%)

20

b. Volumetric strain – shear strain curve
Fig. 4 Stress-strain and volume change behavior of
alluvial soil for medium dense saturated conditions.

b. 5,000x magnification (larger than 0.075mm)

Morphology
SEM – EDX
Figure 6 depicts the micrographs of the alluvial
soil sample. The soil contains many components suchas blocks of minerals, clay, clay accessory minerals,
and organic materials. There are highly varied shapes
and size ranges as shown in Fig. 6a. Soil exhibits
small inter granular voids. It has flocculation withflakey particles usually associated with clay as well
as plagioclase laths and an indication of early stagesof weathering as shown in Fig. 6b. Lastly, Fig. 6crevealed inter granular voids with flakes and that iscommon to clay minerals in sedimentary soils [12].

c. 5,000x magnification (finer than 0.075mm)
Fig. 6 SEM micrographs of the soil sample

Sedimentology
The analysis revealed a texturally andcompositionally immature sedimentary soil. Texture
and composition are two components to describe
maturity. Texture provides a description of how
rounded and sorted the sample is. Composition
describes how much the soil composition is changing
towards more stable minerals and components. An
immature soil has the greater chance to breakdown

About the composition, the altered fragments arecomposed mainly of silicified, quartz, and chloritizedfragments. Lithic fragments are basaltic incomposition with varying textures. The sample havesparsely distributed broken chips of plagioclase withquartz, augite, epidote and hornblende The result of
EDX shows a soil that is made up mainly of oxygen,
57

GEOMATE – Melbourne, Australia, 11-13 November 2020

carbon, silicon, aluminum, iron, and magnesium withsmall amounts of calcium and titanium. All of theseelements corresponds with the mineralogical analysis.

[2] Philippines: Building Competitive, Sustainable
andInclusive
Cities,2017,
http://www.worldbank.org/en/news/pressrelease/2017/05/29/philippines-buildingcompetitive-sustainable-and-inclusive-cities[3] Xia, Y., & Tutemluer, E., Best value granularmaterial for road foundations, Minnesota
DepartmentofTransportation,2012,
http://www.dot.state.mn.us/research/TS/2012/20
1201.pdf.
[4] Gautamm, P. K., Kalla, P., Jethoo, A. S.,
Agrawal, R., & Singh H., Sustainable use ofwaste in flexible pavement: A review,Construction and Building Materials, 180, 2018,
pp. 239-253.
[5] Frempong, E. M., & Tsidzi, K. E. N., Blendingof marginally suitable tropical sub-base materials
for use in base course construction, Construction
and Building Materials, 13, 1999, pp. 129-141.
[6] Wernstedt, F. L., The Philippine island world,
University of California Press, 1983.
[7] Dano, C., Hicher, P. Y., & Tailliez, S.,
Engineering properties of grouted sands, Journalof Geotechnical andGeoenvironmental
Engineering, 130(3), 2004, pp. 328-338.
[8] Patell, Rashmi S., & Desai, M. D., CBR
predicted by index properties for alluvial soils ofSouth Gujarat, Indian Geotechnical Conference 2010, GEOtrendz, 2010.
[9] Japan International Cooperation Agency,
Feasibility study report on the improvement of
the operation and maintenance of national
irrigation systems (AMRIS) Volume II, National
Irrigation Administration, Philippines, 1984.
[10] Department of Public Works and Highways,
Standard specification for Highways, Bridges
and Airports, 2013.
[11] Das, B.M., Advanced Soil Mechanics (5th ed.),CRC Press, 2019.
[12] Terzaghi, K., Peck, R., & Gholamreza, M., Soil
Mechanics in Engineering Practice, Wiley, 1996.
[13] Budhu, M., Soil Mechanics and Foundations 3rdEd., Wiley, 2011.
[14] Liu, T. K., A review of engineering soil
classification systems, Highway Research Board
Record 156, 1967, pp. 1–22

CONCLUSION
Based on this study, the following conclusions
can be drawn:
The alluvial soil sample is a poorly graded sandwith textural and compositional immature grains ofquartz, basalt, chloritized, and altered fragments. It
has no plasticity and the alluvial soil reached amaximum dry density of 19.66 KN/m^3 at an
optimum moisture content of 10.65%.
The bearing strength based on the CBR value at
5.08mm penetration of 41.03% revealed that the
alluvial soil is a suitable subbase course aggregate
based on the DPWH Standards. However, the soil
failed on the gradation requirement for subbase
course because the amount of coarser particles isinsufficient.
For the direct shear strength, from the slope of the
failure lines critical state friction angle for dry
condition is 37.52° while the saturated alluvial
possess a 36.61° angle of internal friction. Both brittle
and ductile failure were observed from the test data
which allows the stress-strain behavior to experiencepeak and dilatancy for the volume change behavior.It
is a suitable select granular backfill material.
Further work on this research is recommended tofocus on the blending of alluvial soil with coarser
aggregates to comply with the gradation requirements
and to possibly promote its application to base course.
ACKNOWLEDGMENTS
The authors would like to acknowledge thesupport of DOST-ERDT for providing financial
assistance for this research, the laboratory testing
assistance of DPWH-BRS, and the help of Mr.
Michael Torres for the material acquisition. Also, thestudy would not be possible without the help of the
DLSU Civil Engineering Faculty.
REFERENCES[1] Philippines Infrastructure Report Q3 2017,https://www.giiresearch.com/report/bmi177867infrastructure.html.

58

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

RESISTANCE FORCE AND GROUND BEHAVIOR IN THRUSTPROTECTION FOR BURIED PIPES USING GEOGRID GABION
Hiroyuki Araki1 and Daiki Hirakawa2
Faculty of Engineering and Design, Kagawa University, Japan;
2
Faculty of Science and Engineering, Chuo University, Japan

ABSTRACTThe bend section of a buried water supply pipeline transfers a thrust force to the ground. Thus, a concrete block
is typically installed at the bend for protection. However, the concrete block may become unstable when the groundaround the concrete block liquefies during an earthquake. In a previous study, we proposed the use of a geogrid
gabion composed of a geogrid basket and gravel as a thrust-protection method. In this method, the geogrid gabionis placed on the ground in the direction of the thrust force. It is desirable for a geogrid gabion to be installed close
to the pipe. However, the pipe and gabion must be separated for the effective compaction of soil around the pipeduring construction. In this study, the effects of the horizontal distance on the thrust force protection performanceof the geogrid gabion were evaluated by applying a horizontal force on a buried pipe model in a saturated groundmodel. The results revealed that the resistance force acting on the pipe increased regardless of the horizontal
distance; the resistance force acting on the pipe was highest when the pipe moved close to the geogrid gabion.
Based on the relationship between the resistance and the ground behavior, the resistance force was modeled as thesum of the passive earth pressure acting on the pipe and the resistance increment due to the passive earth pressure
acting on the geogrid gabion.
Keywords: Buried pipe, Thrust force, Gabion, Resistance force, Strain distribution

INTRODUCTION

pressure acting on the entire welded section is larger
than that acting only on the bend, and a larger
resistance to the thrust force is estimated. However,some cases of pipe detachment at the boundary
between welded and non-welded sections during
earthquakes have been reported, and the inaccuracyof the calculation of the passive earth pressure acting
on the welded section has been pointed out [2].
Therefore, a new thrust-protection method isrequired. Kawabata et al. [3][4] proposed the use of ageogrid as a thrust-protection method. In this method,
the geogrid is connected to the pipe on the inside of
the bend to act as a lateral anchor. Several modelexperiments revealed that the lateral displacement ofa pipe protected with the geogrid is lower than that ofa pipe protected with a concrete block.
In [5], we proposed a new thrust-protection
method involving the use of a geogrid gabion
installed in the passive area of the buried pipe as apressure-receiving structure (Fig. 1b). It is assumed
that the passive earth pressure acting on the geogrid
gabion and the surface friction of the geogrid acted as
the resistance force against the thrust force. The
basket is made of a polymer geogrid to obtain longterm stability against environmental events. Gravelwith high permeability is used as the filling materialof the basket, which is expected to retain effective

A thrust force is generated in the outward
direction of the bend section of buried pipes, e.g.,water supply pipelines. The pipe is normally designed
to resist the passive earth pressure and circumferential
surface friction acting on the bend section. If thethrust force is estimated to exceed the resistance dueto the passive earth pressure and surface friction, aconcrete block is typically installed in the bend
section, as shown in Fig. 1a. The concrete block is
installed to increase the resistance by expanding thepassive earth pressure-receiving area and increasing
the friction at the bottom due to its heavyweight.
However, a concrete block is expected to have thefollowing disadvantages: generating a high inertial
force at the bend due to the heavyweight of theconcrete block during an earthquake, andsignificantly decreasing the resistance to thrust force
or the stabilization when the ground around the pipe
is liquefied. For example, it has been reported that thebend section detached when the surrounding groundliquefied [1].
If the proximity section of the bend is straight with
sufficient length, a certain section of a pipe, including
the bend, maybe welded instead of installing a
concrete block. In this method, the passive earth59GEOMATE – Melbourne, Australia, 11-13 November 2020

1100

[A-A’ section]
Concrete blockThrustforce

Concrete block
Buried pipe

Pipe displacementb)

Thrust force

350

Buried pipeA’
Water flow

Passive earth
Thrust forcepressure Frictional resistanceLoad cell and waterpressure meter

Geogridgabion[B-B’ section]

B

100

Buried pipe

Percentage passing [%]pressure

Water flow

Fig. 1

Schematic of the lateral load test set-up.

Geogrid gabion
Buried pipe
Passive earth
B’

Pipe

water

Fig. 2

Geogrid gabion
Thrustforce

Pipe

GroundWeight[Top view]

(mm)

50

A

17

[Top view]

100

a)

Thrust force
Frictional resistance

Schematics of the a) concrete-block and b)
proposed thrust-protection method [5][6].

6040Sand for ground
20
0
0.0

stress by dispersing excess pore water pressure during
an earthquake. The geogrid gabion is also expected toresist deformations due to the thrust force because thegravel is confined by the geogrid basket. In [5][6], wefound that the displacement of the pipe protected witha geogrid gabion was reduced and that the bottom partof the geogrid gabion should be buried deeper thanthe pipe owing to its high stability; the large width of
the geogrid gabion prevented its rotation in the
ground.
The geogrid gabion should be installed close to apipe but at a sufficient distance to allow the effective
compaction of the soil around the pipe during
construction. Therefore, the effects of the initial
distance between a pipe and a geogrid gabion should
be clarified. Moreover, the resistance force acting on
a pipe protected with a geogrid gabion has hardly
been discussed in the previous study. In this study, theresistance force acting on a pipe and ground behavior
are discussed to further clarify the effects of thrust
protection by conducting model experiments
reproducing the horizontal distance between a pipe
and a geogrid gabion.

Gravel forgabion

80

0.1

1.0

10.0

100.0

Particle size [mm]Fig. 3

Particle size distribution.

applied from the bend, it is difficult to evaluate thedeformation of the ground owing to the complexity ofthe boundary conditions. For simplification, we
modeled the model as a plane strain problem, with thecross-section B-B’, as shown in Fig. 1b [5][6]. Figure
2 shows a schematic of the lateral load test set-up. The
pipe model was loaded laterally inside the groundmodel constructed in a testing box. To evaluate theeffects of the initial horizontal distance between ageogrid gabion and a pipe, several tests with different
distances were conducted.
The ground model was constructed with Mikawa
silica sand No. 5 (soil particle density, ρs = 2.675
g/cm3) with the particle size distribution shown in Fig.
3. The maximum dry density and the optimum watercontent of the silica sand were 1.525 g/cm3 and 14.7%,
respectively. The ground model was constructed by
wet tamping using silica sand with a water content of
13% to achieve a compaction layer thickness of 50
mm and a dry density of 1.374 g/cm3.
The pipe model was designed as a straightcylinder with a diameter of 50 mm and a length of 390
mm. The pipe model had a smooth surface and wasburied in the ground model at a depth of 100 mm fromthe ground surface. A simulated thrust force wasapplied laterally to the pipe model through a stainlesssteel loading shaft, and the displacement of theloading shaft was measured as the pipe displacement
dp. A pore water pressure meter and a load cell with a

OUTLINE OF THE LATERAL LOAD TEST
Test Conditions
Lateral load tests were conducted to evaluate theresistance of a pipe and the behavior of the groundwhen a thrust force is applied laterally to the pipe.
Although the ground is assumed to be threedimensionally deformed when the thrust force is60

GEOMATE – Melbourne, Australia, 11-13 November 2020

17 mm width pressure plate were installed on the sideof the pipe model, as shown in Fig. 2. Under theloading of the simulated thrust force, the lateral earth
stress σp and pore water pressure up acting on the pipe
model were measured using a load cell and waterpressure meter, respectively. The effective value ofthe lateral earth stress acting on the pipe model σ'p isdefined as (σp − up).
The geogrid gabion model consisted of gravel (ρs= 2.807 g/cm3) and a polypropylene net, as shown in
Fig. 4. The geogrid gabion had a height of 100 mm
and a width of 50 mm. The particle size distributionof the gravel is shown in Fig. 3. The dry density of thewhole gabion was set to approximately 1.4 g/cm3.
The depth of the geogrid gabion model center fromthe ground surface was set to be the same as that ofthe pipe model. To prevent the migration of silica
sand into the geogrid gabions, a non-woven fabric
was laid on the upper surface. The non-woven fabric
was not laid on the side and bottom sections tosimplify the condition between the geogrid gabionand silica sand in the model test.

Width(50 mm)GravelHeight(100 mm)Polypropylene net

Fig. 4

Photograph of geogrid gabion model.

Table 1 Experimental cases considered.
Initial horizontal
Thrust force, F
distance, Dh[N]
Case N2－
149
Case G3
0.3D
149–254
Case G4
1.0D
149–277
Case G6
2.0D
149–301
D: pipe diameter of 50 mm.
(mm)

15

a)
45°

22.5°

GeogridgabionTable 1 shows the parameters of the test cases
considered. To reproduce the initial horizontal
distance between the geogrid gabion model and thepipe model Dh, three cases with Dh values of 15, 50,
and 100 mm were considered, as shown in Fig. 5. The
smallest value of Dh was set to 15 mm to ensure goodcompaction around the pipe (case G3). Considering apipe diameter D of 50 mm, the Dh values of 15, 50,
and 100 mm represented as 0.3D, 1.0D, and 2.0D,
respectively.
If the geogrid gabion is not installed, it ispresumed that the passive area of the ground incontact with the pipe mainly resists the thrust force.
The boundary of the passive area is assumed to belocated between Line A and Line B, as shown in Fig.
5, and varies according to the internal friction angleof the ground. In case G6, most of the geogrid gabion
was placed outside the ground passive area in contact
with the pipe. In this study, a total of four cases wereanalyzed, including the above three cases, and caseN2 without a geogrid gabion.
In the lateral load test, a simulated thrust force F
was applied to the pipe model after the ground model
was saturated with water. The simulated thrust force
was loaded until the changes in the pipe displacement
converged. The same procedure was repeated with a
stepwise increase in the value of F.

Pipe

b)
22.5°

50

50

Test Cases and Procedure

100

Thrust
force

50

45°

Geogridgabion

50

50

100

Thrust
force

Pipe

c)
45°

22.5°

100

GeogridgabionFig. 5

Pipe

50

50

100

Thrustforce

Schematic of the geogrid gabion layout; a)case G3, b) case G4, and c) case G6.

The values of σp, up, and dp were measured under
the loading of the simulated thrust force. The
experiment was finished when the value of σ'p began
to decrease. For Case 6, the experiment was finishedwhen the pipe displacement converged under a load
F of 301 N because the pipe displacement reached thelimit of the loading device.

61

GEOMATE – Melbourne, Australia, 11-13 November 2020

Aluminum markers with a diameter of 7 mm wereembedded in the side of the ground model beforehand,
and the displacement of the markers following thedeformation of the ground was observed by analyzing
the images taken from the side of the testing box. The
maximum shear strain γmax in the ground model was
calculated for each four-node rectangular element
using four markers.

dp before unloading [mm]120Case N2
Case G3
Case G4
Case G6

100
80
60
40
20
0
0

TEST RESULTS AND DISCUSSION

50

100

150

200

250

300

350

F [N]

Fig. 6

Pipe Displacement and ResistanceFig. 6 shows the relationships between F and dp
just before unloading in each step. It can be seen that,in case N2, the pipe displacement with a loading of
149 N increases to nearly 30 mm. In the other caseswith the geogrid gabion under 149 N loading, the pipe
displacements are lower than those in case N2. Underloading of 200 N or less, the dp value in case G3 issmall compared to those in cases G4 and G6, whichindicates that the pipe displacement is highly
restrained when the initial distance between the pipe
and the geogrid gabion is small. It should be notedthat the dp value in case G3 significantly increases
under loading of 254 N.
Figure 7 shows the relationships between σ'p and
dp during loading. Assuming that the surface frictionof the pipe model is small owing to the smoothsurface of the pipe, most of the resistance to the thrustforce is due to the passive earth pressure. In this study,
the value of σ'p is used as a representative indicator of
the resistance acting on the pipe model, although σ'p
was measured in a small area on the side of the pipe
model.
From Fig. 7, it can be seen that in case N2 without
a geogrid gabion, the σ'p value reaches its peak at
approximately 8 kPa and then gradually decreases. In
contrast, in the cases with a geogrid gabion, the peakvalues of σ'p are larger than that in case N2, and theslopes of the σ'p and dp relationships decrease as Dh
increases. It should be noted that the relationshipbetween σ'p and dp in case G6 might have trended
upward continuously if a thrust force larger than 301
N had been applied.
Figure 8 shows the relationships between the peakσ'p and dp at the peak values of σ'p. The peak valuesof σ'p in cases G3, G4, and G6 are at least twice thatin case N2. It is clear that the resistance acting on thepipe model is increased when the geogrid gabion is
installed.
Figure 9 shows the relationship between dp at thepeak values of σ'p and Dh. The dp value at the peak

Relationships between F and dp justbefore unloading in each step.
30.0
Case G3

25.0

σ'p [kPa]

20.0
15.0
Case G4

10.0

Case N2
Case G6

5.0
0.0
0

20

40

60

100

80

120

dp [mm]Fig. 7

Relationships between σ'p and dp.
30.0
Case G3

Peak of σ'p [kPa]

25.0

Case G4

20.0
Case G6

15.0
10.0
Case N2

5.0
0.0
0

20

40

60

80

100

120

dp at the peak of σ'p [mm]Fig. 8Relationships between the peak values of
σ'p and dp at the peak values of σ'p.

dp at the peak of σ'p [mm]120100Case G6

80
Case G4

60
40
20

Case G30

Case N2

0

20

40

60

80

100

120

Dh [mm]Fig. 962

Relationships between dp at the peakvalues of σ'p and Dh.

GEOMATE – Melbourne, Australia, 11-13 November 2020

values of σ'p linearly increases with Dh. This indicatesthat the resistance acting on the pipe is highest when
the pipe model approaches the geogrid gabion.

the thrust force is assumed to be hardly transmitted to
the geogrid gabion through the passive area of thepipe. The σ'p value in case G4 is larger than that in

Ground Deformationa)Fig. 10 shows the distributions of γmax in the
ground when the pipe displacement is approximately
20 mm. The thrust force was applied in the left
direction in all cases.
In case N2, the band of high shear strain is
distributed from the left side of the pipe model to theground surface, which indicates that the boundary of
the passive area in the ground (Fig. 10a). The band ofhigh shear strain on the right side of the pipe model isthe active area of the ground.
In case G3, the geogrid gabion moves byapproximately 15 mm horizontally, and the sharestrain increases from the bottom of the geogrid gabion
to the ground surface (Fig. 10b). This indicates thatthe geogrid gabion resists the thrust force received
from the pipe, and the passive area in the ground islarger than that in case N2. This geogrid gabion is
located across the boundary of the passive area of thepipe model (Fig. 5a), and the thrust force is probablytransmitted to the geogrid gabion smoothly.
In case G3, the effective value of the lateral earth
stress acting on the pipe reaches its peak value when
the pipe displacement is 19 mm (Fig. 8), which wasobserved under a loading F of 254 N (Fig. 6). Whenthe pipe displacement is 19 mm, a band of a highshear strain of approximately 30% formed in thepassive area in contact with the geogrid gabion (Fig.
10b), and the stress state in the band is assumed to bein the residual state. The thrust force of 254 N seems
to exceed the resistance force, and therefore, the pipe
displacement significantly increases from 9 to 73 mm
upon a loading F of 254 N.
In contrast, a high strain band appears in between
the pipe and geogrid gabion in case G6 (Fig. 10d).
Most of the geogrid gabion is located outside thepassive area in contact with the pipe model;
subsequently, the thrust force is assumed to be hardlytransmitted to the geogrid gabion. Hence, theresistance to the thrust force is mainly due to thepassive earth pressure acting on the pipe model. Thisis also supported by the fact that the relationshipbetween σ'p and dp in case G6 is similar to that in case
N2 at dp below approximately 20 mm (Fig. 7).
In case G4, although a high share strain band
appears between the pipe and geogrid gabion, theshear strain slightly increases in the passive area incontact with the geogrid gabion (Fig. 10c). Therefore,

b)

c)

d)

Case N2 (dp : Approx. 19 mm)

Case G3 (dp : Approx. 19 mm)

Case G4 (dp : Approx. 17 mm)

Case G6 (dp : Approx. 21 mm)

Fig. 10 Distribution of maximum shear strain at a dp
of approximately 20 mm; a) case N2, b) caseG3, c) case G4, and d) case G6.

case N2 and G6 when the pipe displacement isapproximately 20 mm, as shown in Fig. 7, probably
because the passive earth pressure acting on thegeogrid gabion also acts as a resistance.
Resistance by Geogrid Gabion
Figure 11 shows a schematic of the resistanceforce in the thrust-protection using a geogrid gabion
based on the experimental results. The resistanceforce to thrust force is modeled as the sum of the63

GEOMATE – Melbourne, Australia, 11-13 November 2020

Resistance force

passive earth pressure acting on the pipe and theresistance increment provided by the geogrid gabion.
When the value of Dh is as small as 0.3D, the3) When the initial distance was as small as 0.3D,
the resistance force rapidly increased even when
the pipe displacement was small.
4) As the pipe approached the geogrid gabion, thethrust force was transmitted to the geogrid
gabion; subsequently, the resistance due to thepassive earth pressure acting on the geogrid
gabion was gradually provided to the pipe.
5) The resistance force was modeled as the sum of
the passive earth pressure acting on the pipe andthe resistance increment provided by the geogrid
gabion.

Resistance increment
by gabion

Without geogrid gabion

Resistance due to passive
earth pressure acting a pipeO

ACKNOWLEDGMENTS

Pipe displacement

The authors would like to thank Mr. Nichiri
Kusunose and Mr. Kotaro Ngamori for their help withthe experiments. This study was supported by JSPS
KAKENHI Grant Number 19K15090.

Fig. 11 Schematics of the resistance force by using
geogrid gabion.

thrust force from a pipe is transferred quickly to ageogrid gabion. Therefore, the resistance force
increases even when the pipe displacement is small.
In contrast, when Dh is greater than 1.0D, theresistance is due to the passive earth pressure mainly
acting on the pipe. As the pipe approaches the geogrid
gabion, the thrust force is transmitted to the geogrid
gabion; subsequently, the resistance due to thepassive earth pressure acting on the geogrid gabion
increases gradually.
The increase in resistance is assumed to dependon the size of the pressure-receiving area of thegeogrid gabion. Under these experimental conditions,the resistance with the geogrid gabions is estimated to
be at least two times greater than that without thegeogrid gabions, regardless of the value of Dh.

REFERENCES[1] Mohri Y., Yasunaka M. and Tani S., Damage to
Buried Pipeline Due to Liquefaction Induced
Performance at the Ground by the Hokkaido
Nansei-Oki Earthquake in 1993, Proceedings ofFirst International Conference on Earthquake
Geotechnical Engineering, IS-Tokyo, 1995, pp.
31–36.
[2] Mohri Y., Ono S., Suzuki K.: Seismic damage tolarge-diameter pipelines, The Foundation
Engineering & Equipment, Vol.48, No.1, pp. 40–
43, 2020. (in Japanese)[3] Kawabata T., Sawada Y., Uchida K., Hirai T. andSaito K., Model Tests on Thrust Protecting
Method for Buried Bend with Geogrid,
Geosynthetics Engineering Journal, Japan
Chapter of International Geosynthetics Society,Vol. 19, 2004, pp. 59–64. (in Japanese)[4] Kawabata T., Sawada Y., Mohri Y. and Ling I.,
Dynamic Behavior of Buried Bend with Thrust
Restraint in Liquefying Ground, Journal ofJapanese Society of Civil Engineering, Vol.67,
No. 3, 2011, pp. 399–406. (in Japanese)[5] Araki H. and Hirakawa D., Model Tests on ThrustProtecting Method for Buried Pipe by Using
Geogrid Gabion, Journal of Japanese Society of
Civil Engineering, Vol. 74, No. 1, 2018, pp. 106–117. (in Japanese)[6] Araki H. and Hirakawa D., Effects of thrust
protecting method for buried pipe using geogrid
gabion of different sizes, International Journal of
GEOMATE, Vol. 16, No. 158, 2019, pp. 62–68.

CONCLUSIONSThe use of a geogrid gabion as a thrust-protection
method for buried pipes was tested on a model withdifferent initial distances between the geogrid gabion
and the pipe. The conclusions are summarized asfollows:
1) The resistance force of the pipe was higher when
the geogrid gabion was installed, regardless of
the initial horizontal distance between thegeogrid and the pipe.
2) In cases where the initial horizontal distance wasmore than 1.0D, the resistance force acting on thepipe gradually increased as the pipe
displacement increased and was highest when
the pipe approached the geogrid gabion.

64

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A STUDY ON SOIL RESISTIVITY MEASUREMENT USING READILYINSTALLABLE ELECTRODES MOUNTED ON DRIVING-PIPES
Hidetake Matsui1, Tadafumi Fujiwara1, Hiroyasu Ishii1 and Yoh Takahata1
1

Taisei Corporation, Japan
ABSTRACT

Soil resistivity measurement is one of the survey methods widely used for subsurface investigation. The method
has recently been studied as a useful tool to verify the range and quality of soil improvement. Although it has beensuggested that electrodes should be placed as close to target areas and as many in surrounding sections as possible,
it requires time and cost to drill boreholes to arrange many electrodes in the ground. This would be a main reason
why resistivity measurement has not been widely used in practice for soil improvement. Recently, however, wehave developed readily installable electrodes mounted on a driving pipe (called the measurement pipe here), which
could apparently lower the installation cost. This paper outlines the measurement pipes and shows somepreliminary test results to verify the driving performance of the measurement pipes and the applicability of theequipped electrodes for measurement of soil resistivity as well as ground resistance. The paper also presents thefield test results obtained from a jet grouting site. These results show that the measurement pipe could be driven
9m in an hour into the ground where SPT-N value was less than 5, and that the electrodes on the measurement
pipe could be applied for measurement of soil resistivity and ground electrical resistance. It is implied that soil
resistivity change caused by soil improvement could be detected by the measurement of ground resistance.
Keywords: Soil resistivity, Ground resistance, Driving-pipe, Soil improvement, Jet grouting
However, in the conventional electrodes placing, it
requires time and cost to drill boreholes to arrange
many electrodes in the ground. This would be a main
reason why resistivity measurement has not been
widely used in practice for soil improvement to date.
Recently, we have developed readily installable
electrodes mounted on a driving pipe (hereinafter,
called “the measurement pipe”), which could
apparently lower the installation cost [6], [7]. This
paper outlines the measurement pipes and showssome preliminary test results to verify the driving
performance of the measurement pipes and theapplicability of the equipped electrodes for themeasurement of soil resistivity and ground resistance.
The paper also presents the field test results obtained
from a jet grouting site.

INTRODUCTION
Soil improvement methods, which can improvesoil properties such as strength and water cut-off
performance, have been widely used as an essential
technique especially in the construction of structures
located on soft sediments. In the quality check of soil
improvement works, it is necessary to confirm thatthe soil has been improved to the required property,
and that the entire targeted area has been improved asplanned. However, the improved soils cannot be
observed directly, and are sampled only partiallyfrom a limited number of boreholes or tested with alimited number of specimens.
Soil resistivity measurement is one of the survey
methods widely used for subsurface investigation. It
has recently been studied as a useful tool to verify therange and quality of improved soils [1]. Focusing on
an electrical resistivity difference between groundwater and chemical grout, Komine et al. proposed amethod to visualize chemically grouted area withelectrical resistivity tomography [2]-[4]. On the other
hand, Fujii et al. investigated a relationship between
the coefficient variation of the resistivity of improved
soil and the rotation number of mixing blade, andsuggested that the efficiency of deep mixing method
could be evaluated by measuring soil resistivity [5].
It has been suggested that the accuracy ofresistivity measurements could be improved byplacing electrodes as close to a measurement target as
possible [4], and it is clear that the more electrodes
are placed, the higher the measurement accuracy.

DEVELOPMENT OF MEASUREMENT PIPE
Outlines
We have originally developed a steel driving-pipe
for rapid arrangement of air/chemicals injection wells
used for soil remediation [8]. Depending on how stiff
the soil is, the installation of the steel driving-pipe
may take from only several tens of seconds to several
minutes per meter. Newly developed measurement
pipes are based on this technique. Electrode parts ofthe steel pipe are exposed but the other surface iscovered with polyvinyl chloride plastic for electricalinsulation, while securing enough strength andrigidity for driving into ground. Although soil
65

GEOMATE – Melbourne, Australia, 11-13 November 2020

resistivity has generally been discussed in the soil
improvement evaluation, we also focus on the groundelectrical resistance. It is because ground resistance
implies the resistance between electrodes in groundand the point of zero potential, and it could beobtained more easily than obtaining soil resistivity.
Since ground resistance depends on soil resistivity
around an electrode, changes in soil resistivity causedby soil improvement might also be detected bymeasuring ground resistance.

PRELIMINARY TEST TO VERIFY DEVELOPED PIPES

Details of developed measurement pipes

Figure 2 shows soil profiles of the test field: Toplayer from the ground surface to a depth of 3.9 m
consists of backfilled sand and clay (SPT-N value <5); the second layer to a depth of 8.2 m consists of silt
and sandy silt (SPT-N value < 5); the third layer to adepth of 11.0 m consists of fine sand (SPT-N value <10); and groundwater level is about 2.9 m below
ground surface. Figure 2 also shows soil resistivities
measured in laboratory by using borehole samples
from several depths.

To verify the driving performance of the pipes andthe applicability of the equipped electrodes, themeasurement pipes mentioned above were driven into
ground and used for soil resistivity and groundresistance measurements.
Ground conditions of the test field

To verify driving performance of measurement
pipes and the applicability of the equipped electrodes
for soil resistivity and ground resistance measurements, four different measurement pipes weredeveloped and manufactured (see Fig. 1). Pipes No.1
and 2 have single electrode at the tip of the pipe andthe whole pipe surface is insulated by polyvinyl
chloride plastic. The difference in these two pipes is
the shape of the electrode: pipe No.1 has a conicalelectrode while No.2 has the same conical electrodewith a 50 mm high ring extension. In both pipes, theelectric current flows through the internal steel pipe
to electrode. Pipes No.3 and 4 have conical electrodeat the tip and several 20 mm high ring electrodes on
the side. The three electrodes on pipe No.3 areindividually connected to electric cables and thesethree cables are led to ground surface through theinside of the pipe. Therefore, electric current can beapplied to these three electrodes at the same time.
Pipe No.4 has a conical electrode at the tip of pipe andnine ring electrodes on the side, but these ten
electrodes are not connected to electric cables inadvance. To apply electric current to these electrodes,
an electric cable with a brush tip is inserted into theinternal steel pipe after the driving installation andconnected to a single selected electrode. Sinceelectric current flows through this movable cable,
multiple electrodes on pipe No.4 cannot be applied anelectrical current at the same time.

Fig. 10

Soil resistivity (Ω・m)
50
100
150
200

0
1
2

Depth (m)34
5
6

G.W.L.

Backfilled sandand clay
SPT-N < 5Silt andsandy silt
SPT-N < 5789Fine sand
SPT-N < 10

10

Fig. 2

Soil profiles of the test field.

Test conditions
Figure 3 shows the arrangement of the measurement pipes and auxiliary electrodes. The measurement pipes were arranged at 2 m interval and driven
to a depth of about 9 m from the ground surface toverify the driving performance by a self-propelledboring machine (ECO-3V, YBM Co., Ltd. Japan).
Table 1 shows 6 measurement cases. In four cases
from A-1 to A-4, ground resistance was measured by
the three-pole method using each measurement pipewith two auxiliary electrodes. In case B-1, soil
resistivity was measured by the Pole-Pole array
method using selected two of the three electrodes onpipe No.3 with two auxiliary electrodes. In case B-2,
soil resistivity was measured by the Wenner arraywith only the tip electrodes of the four pipes No.1 to
4. Here, apparent soil resistivity was calculated withthe results obtained from cases B-1 and B-2,
assuming that the target ground was uniform.

Schematic of developed measurement pipes.
66

GEOMATE – Melbourne, Australia, 11-13 November 2020

A

V

Case A

A-1

A-2

A-3

A-4

V

Case B-1

A

Case B-2

A

V
No.1

Auxiliaryelectrode

No.2

No.3

No.4

2m

2m

2m
5m

24.5m

Fig. 3

Auxiliaryelectrode

15.5m

Arrangement of measurement pipes and auxiliary electrodes with circuit in the preliminary test.

Table 1 Measurement cases in the preliminary test
Case
A-1
A-2

Pipe
No.1
No.2

Measured value
Ground resistance
Ground resistance

A-3

No.3

Ground resistance

A-4

No.4

Ground resistance

B-1

No.3

Soil resistivity

B-2

No.1-4

Soil resistivity

0

Case A-1

1

300

0

1

Case A-2

2

2

3

3

4

4

5
6

Ground resistance (Ω)
100
200

300

0

0
Case A-3

Depth (m)Depth (m)Ground resistance (Ω)
100
200

0
5 m

Case A-4
1

6 m
7 m

5
6

1

7 m

2

8 m

3

9 m

3

9 m

4
5
6

A-4tip
A-1

6

7

7

8

8

8

8

9

9

9

9
Red:Measured by a tip electrode

Red:Measured by a tip electrodeFig. 4A-3tip5710(a) Cases A-1, 2

A-4side(9m)

4

7

10

300

A-3side

5 m
6 m

2

8 m

Ground resistance (Ω)
100
200

0

Depth (m)Ground resistivity (Ω)
100
200
300

0

Depth (m)0Remarks
Using a tip electrode on No.1 and 2 pipe, every 1 m from a depthof 1 m to a depth of 9 m.
Using three electrodes on No.3 pipe, every 1 m from a depth of 5
m to a depth of 9 m.
Using ten electrodes on No.4 pipe, every 1 m from a depth of 5 m
to a depth of 9 m.
Using selected two of the three electrodes on No.3 pipe, every 1m from a depth of 5 m to a depth of 9 m (3 times at every 1 m).
Using tip electrodes of four driven pipes (only 1 measurement at a
depth of 9 m).

10(b) Case A-310(c) Case A-4(d) Sorted by electrode typeMeasurement results of ground resistance (Cases A-1 to A-4).
in pipe No.3 because it was required that electric
cables had also to be connected in the process; all inall, it took less than an hour to drive pipes 9m into
ground. It was confirmed through the test that theelectrodes could be arranged in the ground in a shortperiod of time when the developed measurement
pipes were used, compared to the conventional
electrode arrangement method in boreholes.

Test results
Driving performance of measurement pipe
Since the measurement pipes are divided into
pieces of 1 m long except the tip part, it was necessary
to alternate the pipe driving and pipe connecting
processes. In the test, it took approximately 30
seconds to drive a pipe 1 m into ground when the
SPT-N value was less than 5, regardless of the type of
pipe. On the other hand, time required to connect
pipes varied according to the type of pipe. In pipes
No.1, 2 and 4, pipe connecting process was necessary
to connect screw parts only, which took less than 2
minutes. On the contrary, it took more than 3 minutes

Applicability of equipped electrodes for soil
resistivity and ground resistance measurements
Figure 4 shows the measurement results of groundresistance. It may be seen that ground resistances
measured in cases A-1 and A-2 did not coincide with67

GEOMATE – Melbourne, Australia, 11-13 November 2020

each other, but their distribution trends were similar
at depths (see Fig. 4 (a)). It seems that the measured
resistivity was lower in case A-2 than in case A-1,
since the electrode used in case A-2 had a larger
surface area than that in case A-1. In cases A-3 andA-4, it was observed that the measured groundresistance was lower at a tip electrode than that at thering electrode on the side (see Fig. 4 (b), (c)). It is also
likely that this was caused by the difference inelectrode size at the pipe tip and on the side. On theother hand, the results measured by the same type ofelectrode showed almost the same value at each depth(see Fig. 4 (d)). However, the results of A-1 did notcoincide the values measured on side of A-3 and A-4
although those electrode size and shape were almost
same. This implied that the electricity supply method
in cases A-1 and A-2 using internal steel pipe might
have increased the measured values compared to thatin cases A-3 and A-4.
Figure 5 shows the measurement results of soil
resistivity. Soil resistivity at a depth of 8.9 m
measured in case B-2 was 46.8 Ω-m and it was close
to the measured value in case B-1 at 8.75m (43.7 Ωm). Although it was a limited comparison, the soil
resistivities obtained from in-situ and laboratory
testing could be seen almost same level from a depthof 6.5m to a depth of 7.5 m.
Through the test, it was confirmed that thedeveloped measurement pipes could be applied forthe measurement of ground resistance and soil
resistivity.
0

Soil resistivity (Ω・m)
20
40
60
80
100

0

0

Measurement conditions
Figure 6 shows the arrangement of measurement
pipes, auxiliary electrodes and soil-cement columns
created by jet grouting. Measurement apparatuses
were arranged a few hours before jet grouting.
Column in design has a diameter of 3.5 m and a heightof 4 m, that was created at depths between 2 m and 6
m. Two measurement pipes were arranged
horizontally 1.75 m away from the injection point(radius of the column in design). The distance
between the two pipes was 2m and the tip electrodewas placed at a depth of 4 m, which was the midheight of the column.
Previously created columns
TargetcolumnX

Case B-1

5 m
6 m

3

7 m

2

8 m

Pipe
No.1

X
2m

10m

Soil resistivity (Ω・m)
20
40
60
80
100

V

Case2

V

Case3

B-1
B-2
Borehole sample

4

GWL

A
V
A
2m
4m
Pipe
No.2

5
Depth (m)

4
5
6

10m

A

Case1

9 m

3

Auxiliaryelectrode

Pipe
No.2

(a) plan view

2

1

Depth (m)ground resistance as an easy index that might help
evaluate the changes in soil resistivity aroundelectrodes.

6m

6

Pipe
No.1

7

7

TargetcolumnAuxiliaryelectrode

Electrode8(b) cross-sectional view (X-X) with circuit

899(a) Case B-1
Fig. 5

Fig. 61010(b) Cases B-1, 2, BH sample

Measurement results of soil resistivity (cases
B-1, B-2 and borehole sample).

Measurement pipes and electrodes arrangement with circuit diagram of each case.

Table 2 shows measurement cases. In cases 1 and
2, ground resistance around the electrodes wasmeasured at pipes No.1 and 2 individually, while incase 3, total ground resistance around the twoelectrodes was measured using both pipes No.1 and 2.
In order to confirm the changes in ground resistancecaused by jet grouting each measurement was
repeated three times, before jet grouting, just after jet
grouting, and one day after jet grouting. In addition,
the resistivity of the soil discharged from jet grouting
work was also measured to confirm the actual
resistivity of improved area.

TRIAL MEASUREMENT OF GROUNDRESISTANCE BEFORE AND AFTER JET
GROUTING
Based on the results of the preliminary test
discussed above, a trial measurement of groundresistance was conducted before and after a cement
jet grouting in an actual construction site. The pipes
No. 1 and 2 (shown in Fig.1) were used, focusing on
68

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2 Measurement cases
Case
1
2
3

Pipe
No.1
No.2
No.1,2detected by the measurement of ground resistance.
On the other hand, the followings are theremaining issues which would be typical factors todecide the proposed method limitation:- As an issue relating to driving performance,
maximum driving lengths must be identified based on
ground conditions such as SPT-N value and soil
properties.
- As an issue relating to the soil resistivity
measurements, acceptable original ground resistivity
ranges must be clarified to secure evaluable
difference in soil resistivity of soil improved part.
Also, the optimization of electrodes arrangement
must be pursued to minimize installation costs. To
solve these issues, additional laboratory and fieldtesting are under planning by authors.
Through these efforts, we will make the proposed
method fit to practical use in near future. Furthermore,
we hope it will contribute to realization of “real-time”
management system of quality assurance for soil
improvement works, which is the authors image as anext-generation system.

Measured value
Ground resistance
Ground resistanceSum of ground resistances

Results of trial measurement
Table 3 shows measurement results of groundresistance in the site and the resistivity of discharged
soil. In case 1, three values of measured groundresistance were almost same, and the effect of jet
grouting was not observed. On the other hand, incases 2 and 3, lower ground resistance was observed
after, compared to before the jet grouting. Since theresistivity of the discharged soil shown in Table 3 was
obviously lower than the general range of natural soil
resistivity, it seemed that decreases in the groundresistance in cases 2 and 3 were caused by jet grouting.
In case 1, the ground resistance measured before thejet grouting was close to that measured just after thejet grouting in case 2. Therefore, measurement before
jet grouting in case 1 might have been affected bysurrounding columns created previously.
Through the trial measurement, it seemed that thechanges in ground resistivity caused by jet grouting
could be evaluated easily and readily using thedeveloped measurement pipes. How to visualize theimproved area and evaluate the improved soil quality
will be investigated in the next stage, using thedeveloped measurement pipes.

REFERENCES[1] Lin C.-P., Lin C. H., Wu P. L., Wang H. R. and
Liu H. C., Geophysics-assisted control andevaluation of ground improvement, Proceedingsof the 16th Asian Regional Conference on Soil
Mechanics and Geotechnical Engineering, Invited
Lecture Ⅲ, 2019.
[2] Komine H., Evaluation method of void filling ofchemical grouting by electrical resistivity (inJapanese), Journal of Japan Society of Civil
Engineers, No.463/Ⅲ-22, 1993, pp. 153-162.
[3] Komine H., Applicability of evaluation of
improved region by resistivity tomography inchemical grouting (in Japanese), Journal of Japan
Society of Civil Engineers, No.561/Ⅲ-38, 1997,
pp. 89-98.
[4] Komine H. and Goto K., Verification ofevaluation for chemical grouted region using
resistivity tomography by field test (in Japanese),
Journal of Japan Society of Civil Engineers,
No.603/Ⅲ-44, 1998, pp. 129-138.
[5] Fujii H., Kawamura M., Tamura M., Watanabe K.
and Mizoguchi E., Quality evaluation method forsoil-cement column by deep mixing method (inJapanese), Journal of the Society of Materials
Science, Vol.53, No.1, 2004, pp. 9-12.
[6] Fujiwara T., Matsui H. and Takahata Y., Field testof ground resistivity measurement using driving
type electrode pipe (in Japanese), Proceedings of
the Japan National Conference on GeotechnicalEngineering, 2020 (submitted).
[7] Fujiwara T., Matsui H., Ishii H. and Aoki T., Soil
resistance measurement of improved soil columns
using driving type pipe with electrodes (inTable 3 Measured ground resistance in the site anddischarged soil resistivity
Ground resistance (Ω)
BeforeJust after
Day after
1
22.4
22.0
21.6
2
55.8
25.4
24.6
3
183.0
48.0
47.0
Resistivity (Ω-m)
Discharged soil
1.23
1.49

Case

CONCLUDING REMARKS
To provide a rapid electrode arrangement method
for soil resistivity and ground resistance measurement,
the driving pipes were developed. Their effectivenesswas then examined through the preliminary test andthe trial measurement in the field.
The results obtained in the study show that themeasurement pipe could be driven 9 m in an hour into
the ground where the SPT-N value was less than 5,
and that the electrode of measurement pipe could beapplied for soil resistivity and ground resistancemeasurements. It was implied that the changes in soil
resistivity caused by soil improvement could be
69

GEOMATE – Melbourne, Australia, 11-13 November 2020

Japanese), Proceedings of the 75th Japan Societyof Civil Engineers Annual Meeting, 2020
(submitted).
[8] Takahata Y., Matsui H., Ishii H. and Horikoshi K.,

Development of driving-type injection pipe forsoil remediation and improvement (in Japanese),
Report of Taisei Technology Center, No.45, 2012,
pp. 52-1-52-6.

70

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE CONTRIBUTION OF PILES TO THE AVOIDANCE OF THE
RAINFALL-INDUCED SLOPE FAILURE
Yujia Wang1, John V. Smith1 and Majid Nazem1
1
RMIT University, Australia
ABSTRACT
Rainfall is the major cause of the slope failure in South Gippsland, Victoria, Australia. These slope failures
typically impact the rural road infrastructure in the form of requiring regular maintenance and continuous cost.
This issue thus is fixed by an integrated slope-stabilisation method that combines the gabion-faced geogridreinforced retaining-wall and the pile. This integrated reinforcement system has been investigated with a threedimensional non-linear finite element approach. An elastic-perfectly plastic model with Mohr-Coulomb criterion
is used to describe the behaviour of the soil and the gabion basket. The piles are shown to make a significant
contribution to the retaining system. The factor of safety was increased by approximately 30% and the horizontal
displacement at the retaining wall crest was reduced by 75%. Comparison between the model of the field pileconfiguration (diameter 0.4 m and spacing 1.4 m) and models of a range of pile diameters and spacings showedthat the field pile configuration is efficient with respect to the volume of concrete piles required.
Keywords: Slope stabilisation; Pile retaining structure; Geogrid-reinforced retaining-wall; Gabion basket; Finite
element.

Australia. The stability of the slopes in South
Gippsland has a major impact on the roadinfrastructure that is maintained by the local
government authority. An example of the slope
reinforcement system, which integrates piles into the
GF-GR-RW, used in this area to stabilise the slope isshown in Fig. 1.

INTRODUCTION
The failure of slopes traversed by the rural roadsof South Gippsland, Victoria, Australia frequently
requires stabilization by construction of retaining
walls [1]. The technique applied is geogrid-reinforced
retaining walls (GR-RW) as has been widely used for
local public sector retaining wall projects [2].
Roadside stabilisation with GR-RW has benefits
including cost-effectiveness, ability to tolerate
settlements, aesthetic appeal of the stone filled
gabions and good performance [3].
Piles are also used as a method of slope
stabilisation, and the effect of piled retaining wall inresisting lateral soil movement of a slope has been
investigated by many researchers [4]-[5]. In this study,
the method used is the finite element method (FEM)
which can provide coupled solutions in which the pile
response and slope stability are consideredsimultaneously.
The present research investigates the contributionof piles in increasing the stability of a slope when
integrating the pile retaining structure into theconventional
gabion-faced
geogrid-reinforced
retaining wall (GF-GR-RW). This study compares therepresentative indicators of the stability of the slope,the critical slip surface, the horizontal displacement,
and the FoS. The results demonstrate the significant
contribution of integrating the pile into GF-GR-RW.

Fig. 1. GF-GR-RW with piles used in South
Gippsland
Slope Configuration
The field conditions replicated in the model
include a GF-GR-RW installed to stabilise a roadcutting. Piles were installed at the toe of the retaining
wall (prior to its construction) with a diameter of 0.4
m and a centre-to-centre spacing of 1.4 m (a gap of 1
m between piles). For comparison, otherconfigurations were also modelled. These
configurations included GF-GR-RW without piles(Fig. 2(a)) and piles which are not embedded into a

STUDY AREA
Site Location
The study area is in South Gippsland, Victoria,

71

GEOMATE – Melbourne, Australia, 11-13 November 2020

case that is only stabilised by GR-RW without thepiles, (C1-C4) represent the cases that have the piles
but no weathered rock layer with the increase of the
S/D ratio, and (C5-C8) represent the cases that havethe piles and the pile is embedded in the weatheredrock layer with the increase of the S/D ratio.

weathered rock layer (Fig. 2(b)). For clarity, thesimpler cases will be presented prior to the more
complicated case (Fig. 2(c)).

Table 1 Pile configurations

C0
C1
C2
C3
C4
C5
C6
C7
C8

(a)

Centretocentre
spacing
(m)
1.4
1.4
4
4
1.4
1.4
4
4

Diameter(m)

S/D

Embeddedlength instable layer
(m)

1
0.4
1
0.4
1
0.4
1
0.4

1.4
3.5
4
10
1.4
3.5
4
10

2
2
2
2

NUMERICAL MODELLING
Material Properties(b)

The slope model is composed of two types ofmaterial, a natural soil layer underlined by a stablelayer of weathered rock. The material properties ofslope and reinforcement are summarised in Table 2.
The strength of the soil and the weathered rock is
determined based on the previous study [1]. The
strength of the pile and the steel is 𝐶𝐶30 and 𝑆𝑆500,
respectively. The geogrid is the typical polyester(PET) geogrid (GGW) and the strength is determinedby the standard. For the gabion basket, the strength
properties are determined based on the compression
test and the direct shear test of the gabion basket
group.

(c)

Table 2 Material properties

Fig. 2 Slope configurations: (a) GR-RW (b) GR-RW
with piles (c) GR-RW with piles embedded in thestable layer (dimension in meters)

Material
natural soil
weathered rockpile
gabion basket
steel
geogrid

The dimensions of the gabion baskets are 1 m
cubes filled with quarried rock. The facing of theretaining wall is composed of six layers of gabionbasket which are stacked with a stepped pattern toform a gradient of 9V:1H. In the model, the length ofthe pile is 6m, and the embedded length of the pile in
the weathered rock layer is 2m. The diameter (D),centre-to-centre spacing (S), and the presence orabsence of a weathered rock layer are varied for the
purpose of making the comparison between different
cases as summarised in Table 1. C0 represents the𝜸𝜸 (kN/m3)
20
23
23
17
79
2

𝝋𝝋′
(⁰)
20
35
45
-

𝒄𝒄′ (kPa)
10
200
560
-

Mesh Configurations
Regarding the mesh of the three-dimensionalmodel, the solid element including the pile, steel, thegabion basket, and the slope is discretised by eight72

GEOMATE – Melbourne, Australia, 11-13 November 2020

node brick elements (C3D8) without reduced
integration, and the geosynthetic is discretised byfour-node membrane elements (M3D4). A structured
mesh technique was used for the solid elements andmembrane elements. Fig. 3 shows the finite element
mesh of the reinforcement system and the finite
element mesh of the slope, consisting of 160,952
nodes and 130,926 elements, adopted in thecalculation. The computation time for the analysis ofeach case is about 1h, and the computations are
performed on the platform of National Computational
Infrastructure (NCI) using 32 core 2.6 GHz HighPerformance Computing (HPC) with 31 GB random
access memory. The mesh sensitivity andconvergence criteria were also investigated bycomparing simulation results due to the use ofdifferent shape or number of elements to make surethat the current shape and size of the mesh used forthis modelling is fine enough so that the results ofsimulations are not sensitive to mesh density.

𝒄𝒄′

𝒄𝒄𝒓𝒓 =𝐒𝐒𝐒𝐒𝐒𝐒

𝐭𝐭𝐭𝐭𝐭𝐭𝝋𝝋′𝝋𝝋𝒓𝒓 =arctan� 𝐒𝐒𝐒𝐒𝐒𝐒 �

(1)
(2)

where SRF is the strength reduction factor. The
FoS is equal to the value of SRF.
In strength reduction calculation, the factored
shear strength parameters 𝑐𝑐𝑟𝑟 and 𝜑𝜑𝑟𝑟 replace thecorresponding values of 𝑐𝑐 ′ and 𝜑𝜑′ by
𝒄𝒄′
(5)
𝒄𝒄 =
𝒓𝒓 𝒇𝒇(𝒕𝒕)𝐭𝐭𝐭𝐭𝐭𝐭𝝋𝝋′𝝋𝝋𝒓𝒓 =arctan� 𝒇𝒇(𝒕𝒕) �(6)where f (𝑡𝑡) =field variable [𝑓𝑓(𝑡𝑡) = (𝑎𝑎 − 𝑏𝑏)𝑡𝑡 +𝑏𝑏] ; t=time increment for every calculation step in
Abaqus (0 ≤ 𝑡𝑡 ≤ 1); a and b=adjustable factors (𝑎𝑎 >𝑏𝑏). The value of a and b can be determined by the user
based on the estimated range of the FoS. It can also
be found that the field variable is increasing with theincrement of the step time, which means during the
calculation, the value of c′ and 𝜑𝜑′ would be reduced
progressively until the failure happens. The nonconvergence of the solution is the indication of theslope failure, when it occurs, the value of FoS would
be obtained which is equal to the value of the field
variable of the final incremental step. Once the valueof the FoS is obtained, the graphical output of theindex ‘U1’ of Abaqus will be used to indicate thevalue of the horizontal displacement within the model[6], including the pile head deflection. From thehorizontal plane of the model, the soil arching effectbetween piles will be indicated by the relative
horizontal displacement between the moving and thestationary soil masses. From the vertical plane of themodel, the significant effect of piles in reducing thehorizontal soil displacement will be presented. In
addition, the reduction in the size of the critical slip
surface of the slope will also be represented by ‘U1’
because of the reduction in the horizontal soil
displacement. The accuracy and effectiveness of theStrength Reduction Finite Element Method (SRFEM) conducted within Abaqus have been validatedby [7]-[8], and it represents an effective tool toconduct this parametric study.

(a)

ANALYSIS OF RESULTS(b)

Lateral Soil Movement

Fig. 3 Meshed model: (a) Meshed reinforcement; (b)Meshed slope

To better understand the effect of the piles within
this system in reducing the lateral movement of soilin the slope, a comparison of three cases isrepresented by the vertical plane of the model. These
three cases are: (1) no piles (C0); (2) with piles (C2)and (3) with the piles embedded in the weathered rock

SHEAR STRENGTH REDUCTION FINITEELEMENT METHOD (SSR-FEM)
The reduced shear strength parameters 𝑐𝑐𝑟𝑟 and 𝜑𝜑𝑟𝑟
are given by:73

GEOMATE – Melbourne, Australia, 11-13 November 2020

layer (C6). The pile diameter and spacing in thesecases is the field site condition.
The lateral soil movement of the slope that is only
stabilised by the GR-RW (C0) is much larger than theother two cases. The length of the failure surface ofthe slope that without the piles (C0) extends almost to
the toe of the lower slope and with a large depth
which is defined as a deep-seated failure, the volumeof the unstable soil mass is also much larger than theother two cases (Fig. 4(a)). With the piles present at
the toe of the slope but not embedded in the stablelayer (C2), the depth of the lateral movement up to
the position of the piles is almost the same as
observed for no piles, but the magnitudes ofdisplacement are greatly reduced (Fig. 4(b)). With
piles embedded in a stable layer (C6), the depth andthe length of lateral movement has been reduced
significantly (Fig. 4(c)).

Fig. 4 Horizontal displacement: (a) Without piles(C0) (b) With piles (C2) (c) With piles embedded ina stable layer (C6) (cross section is located along thecentre line of the model; U, U1 represents thehorizontal soil movement in metre)
Comparison of four cases in the horizontal planeof the model illustrates the pile configurations
considered. In particular, the stress elevations
between piles is noted as the result of the soil archingeffect. These four cases are selected as follows: (1)
S/D=1.4 (C1); (2) S/D=3.5 (C2); (3) S/D=4 (C3); (4)S/D=10 (C4). These four cases are all without theembedment of piles into a weathered rock layer.
The different S/D ratio represents the different
magnitude of the soil arching effect (Fig. 5). When
the S/D ratio of the piles is equal to 1.4 (C1), the pile
deflection is the same as the horizontal displacementof the surrounding soil (Fig. 5(a)), which is a clear
manifestation of the strong arching effect. Comparedwith the S/D ratio which is equal to 10 (C4), the soilat the interval of the piles displaces much larger thanthe pile head deflects (Fig. 5(d)), which represents
that the soil at the interval of the piles is hardly
confined by the soil arching effect and flows between
the piles. This phenomenon indicates that the soil
arching effect within this reinforcement system isgradually diminishing with the increase of the S/Dratio. The distribution of the horizontal displacement
contours of the soil and the pile presents a similar
pattern when the S/D ratio of the piles is equal to 3.5(C3) and 4 (C4). The contour of the horizontal soil
displacement under these two conditions can beconsidered as effective soil arching (Fig. 5(b) (c)),
which could provide support to the soil mass andconfine the flow of the soil between the piles
effectively.

(a)

(b)

(a)
(c)
74

GEOMATE – Melbourne, Australia, 11-13 November 2020

The following result, expressing the horizontal
displacement at toe and crest of the slope, withrespect to the variation of the pile configurations is
obtained and shown in Fig. 6.
For the case of no piles (C0), the lateral
movements at toe and crest are much greater than all
the other cases with the piles (C1-C8). In the case of
no stable layer and with the largest S/D ratio (C4), thehorizontal displacements at toe and crest are smaller
than C0. Such a reduction in the horizontal
displacement is estimated at 17% of the crest to 29%of the toe of the slope. The existence of the piles
reduces the lateral soil movement significantly, i.e.,
the effect of including piles in reducing the horizontal
displacement of the slope has been demonstrated.
Regarding the lateral movement at the toe of theslope, the value of the horizontal displacement isreduced significantly if the pile is embedded in theweathered rock layer. Such a reduction is estimatedas 22% for the corresponding case when S/D ratio is
equal to 10 (C4&C8), for another corresponding casewhen S/D ratio equals to 1.4 (C1&C5), such a
reduction is estimated as 31%. The reduction inlateral soil movement is increasing with the decreaseof the ratio of S/D from 10 to 1.4. The advantages ofthe embedding piles into weathered rock layer in
reducing the horizontal displacement of the slope canalso be demonstrated.

(b)

(c)

Fig. 6 Horizontal displacement at toe and crest fordifferent cases (Table 1)
Factor of Safety(d)

To study the contribution of the existence of thepiles in influencing the FoS of the slope, the value of
FoS of all cases (C0-C8) is calculated and shown in
Fig. 7.
The FoS of the case without piles (C0) is 1.26,
while the lowest value of the FoS for the case that has
the piles is 1.43 (C4&C8). Such increment in the FoS
is estimated as 13%. The soil arching effect with alarge S/D ratio which equals to 10 (C4&C8) isrelatively weak, but the FoS is still increasing
compared with the case of without piles (C0), thus,
the contribution from the piles in increasing the FoS

Fig. 5 Lateral movement of soil in the horizontal
plane: (a) S/D=1.4 (C1) (b) S/D=3.5 (C2) (c) S/D=4(C3) (d) S/D=10 (C4) (the cross-section is locatedalong the surface 0.5m lower than road; two intervals
between three piles are selected; the movement of thesoil is from top to bottom in the picture; U, U1
represents the lateral soil movement in meter)
To investigate the contribution of the piles in
reducing the horizontal displacement of the slope, all
9 cases with varied pile configurations were studied.
75

GEOMATE – Melbourne, Australia, 11-13 November 2020

gabion facings and piling at the retaining wall toe has
been found to reduce the horizontal displacement at
the retaining wall crest by 75% from 51 mm to 13 mm
and increase the FoS of the slope by 31% from 1.26
to 1.65. Comparison with models of a range of pile
diameters and spacings found the field configuration
used (diameter 0.4 m and spacing 1.4 m) is efficientwith respect to the volume of piles required.
The focus of this study is the assessment of thecontribution of the embedded pile. The rainfall is the
major cause of the slope instability, therefore, theinfiltration and the surface runoff of the rainfall
deserve a deep investigation for the future study.

of the retaining structure can be demonstrated.
For cases without the weathered rock layer (C1C4), the FoS increases from 1.43 to 1.48 with thedecrease of the S/D ratio, whereas for the cases thathave a weathered rock layer (C5-C8), the FoS
increases significantly from 1.44 to 1.85 with thedecrease of the S/D ratio. The increase in the FoS is
approximately 28%.
In addition, based on the pile configuration (Table1), each value of the S/D ratio corresponds to two
different cases, and these two cases are defined ascorresponding cases, the difference between twocases that have the same S/D ratio is whether or not
the pile is embedded in a weathered rock layer. When
the S/D ratio equals to 1.4 (C1&C5), the contributionof the weathered rock layer in the increment of FoS is
estimated as 28%, it is 14% for the S/D ratio of 3.5(C2&C6), 8% for the S/D ratio equals to 4 (C3&C7),
and no increment in the FoS for the S/D ratio equals
to 10 (C4&C8) because of the relatively weak soil
arching effect. It can also be found that theeffectiveness of the weathered rock layer inincreasing the FoS of the slope will be influenced
significantly by the S/D ratio of the piles.

ACKNOWLEDGEMENT
The authors would like to thank the staff from theSouth Gippsland Shire Council for the assistance ofthe site visiting. Facilities of the National
Computational Infrastructure (NCI) were used for theprocessing of the numerical model.
REFERENCES[1] Smith J. Rapid and progressive deterioration of
local road assets caused by slope instability inregional Victoria, Australia. In: Proceedings of
1st International Conference on Infrastructure
Failures and Consequences (ICIFC2014). 2014.
[2] Gu M, Collin JG, Han J, Zhang Z, Tanyu BF,
Leshchinsky D, et al. Numerical analysis of
instrumented mechanically stabilized gabion
walls with large vertical reinforcement spacing.
Geotextiles and Geomembranes. 2017;45(4):294306.
[3] Yoo C, Kim S-B. Performance of a two-tiergeosynthetic reinforced segmental retaining wallunder a surcharge load: Full-scale load test and 3D
finite element analysis. Geotextiles andGeomembranes. 2008;26(6):460-72.
[4] Bathurst RJ, Vlachopoulos N, Walters DL,
Burgess PG, Allen TM. The influence of facing
stiffness on the performance of two geosynthetic
reinforced soil retaining walls. Canadian
Geotechnical Journal. 2006;43(12):1225-37.
[5] Hatami K, Bathurst RJ. Development andverification of a numerical model for the analysis
of geosynthetic-reinforced soil segmental walls
under working stress conditions. Canadian
Geotechnical Journal. 2005;42(4):1066-85.
[6] Abaqus. ABAQUS user’s manual. Version 6.14.
[7] Griffiths D, Lane P. Slope stability analysis byfinite elements. Geotechnique. 1999;49(3):387403.
[8] Dawson E, Roth W, Drescher A. Slope stability
analysis by strength reduction. Geotechnique.
1999;49(6):835-40.

Fig. 7 FoS for different cases (Table 1)
CONCLUSIONSIn South Gippsland, a specific reinforcement
system has been applied by integrating soil
reinforcement with geogrid, gabion facings and piling.
The effectiveness of this reinforcement system has
been demonstrated by investigating the lateral soil
movement and the FoS of the slope using threedimensional finite element analysis. For the slope
stabilised using only gabions and geogrid, the size ofthe slip surface is relatively large, and the slope
failure mode is deep-seated. However, for the slope
stabilised with the addition of piles, a smaller criticalslip surface developed through the toe of thereinforced slope. This reduction in the size of thecritical slip surface leads to an improvement in thestability of the slope.
The reinforcement method including geogrid and76

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SIMULATING EARTHQUAKE-INDUCED SLOPE FAILURES USING A
SOLID-FLUID COUPLING MODEL BASED ON THE SMOOTHEDPARTICLE HYDRODYNAMICS FRAMEWORKYusuke Ono1
Faculty of Engineering, Tottori University, Japan

1

ABSTRACT
Various numerical analysis methods have been developed to simulate earthquake-induced slope
failures. Simulations of earthquake-induced slope failures require capabilities to reproduce some factors,
including the trigger level of an input ground motion and a travel distance of debris. For reproducing a trigger
level of an input motion, the finite element method based on solid mechanics has been used. For reproducing
a travel distance of debris, simulation methods based on the fluid dynamics have been applied. This study
presents a new approach to simulate an earthquake-induced slope failure, coupling the solid mechanics and thefluid dynamics, based on the framework of the smoothed particle hydrodynamics (SPH). The presented
approach allows us to simulate an earthquake-induced slope failure from its triggering stage to its
accumulation stage. The presented approach demonstrated its capabilities to reproduce the trigger level ofground motion and a travel distance of debris through several simulation cases. The paper concluded that thepresented approach could be a promising method to simulate earthquake-induced slope failures.
Keywords: Earthquake-induced slope failure, Smoothed particle hydrodynamics, Earthquake response, Run-out
INTRODUCTION

with a Bingham fluid model was also applied toearthquake-induced landslides [9]–[11]. Thesestudies report that the SPH with a Bingham fluid
model can reproduce surface profile of post-failure
and run-out distance.
The aim of this paper is to develop a coupling
model of solid and fluid dynamics into the SPH
framework for simulating an earthquake-induced
slope failure or landslide. The developed model is
expected to simulate an entire process of anearthquake-induced slope failure: response to an
earthquake ground motion, occurrence of flow sliding,and accumulation of debris.

Once a strong earthquake strikes a mountainous
area, slope failures, landslides, and rockfalls arelikely to occur [1]. The 1994 Northridge earthquake(Mw 6.7) triggered more than 11,000 slope failuresthat killed 61 people [2]. The 2004 Chuetsu
earthquake (Mw 6.6) induced about 9200 slope
failures, and 68 people lost their lives [3] More than
200,000 slope failures were caused by the 2008
Wenchuan earthquake (Mw 7.9) and resulted in
87,633 casualties [4].
Numerical simulation techniques have beendeveloped to reproduce an earthquake-induced slope
failure. The sliding block model is used to examine
the occurrence possibility of slope failure for a certain
level of earthquake excitation [5]. The finite element(FE) analysis can be used to investigate slope stability
more precisely than the analysis using the sliding
block model [6]. Typically, a slope is modeled by anelastic or an elastoplastic solid material in the FE
analysis. However, the FE analysis has difficulties insimulating a flow-like slope failure.
For simulating a flow-like slope failure, thesmoothed particle hydrodynamics (SPH) method has
been applied recently. The SPH method was initiallyinvented in the field of astrophysics [7]. Afterward,
the application of the SPH method has extended to
fluid dynamics and solid dynamics. A slope failure
occurred in the municipal solid waste was simulatedby the SPH with a Bingham fluid model [8]. The SPH

SIMULATION MODELFirst, the framework of the SPH method is
introduced. Second, the SPH discretization ofequations of motion is given for a solid body and a
fluid, respectively. Third, a new coupling model ofsolid-fluid dynamics is proposed.
SPH framework
The SPH framework consists of two fundamental
approximation techniques: kernel approximation, andparticle approximation. The kernel approximation isbased on the following identical equation:

77

GEOMATE – Melbourne, Australia, 11-13 November 2020

f(𝒙𝒙) = � 𝑓𝑓 �𝒙𝒙′ � δ �𝒙𝒙 − 𝒙𝒙′ � 𝑑𝑑𝒙𝒙′𝐷𝐷s

σαβ = β(𝑡𝑡)�σαβ � + {1 − β(𝑡𝑡)}�σαβ �

(1)

𝐷𝐷

(2)

𝑙𝑙𝑙𝑙𝑙𝑙 𝑊𝑊(𝑥𝑥, ℎ) = δ(𝑥𝑥)

(4)

ℎ→∞

The particle approximation is introduced by thediscretized form of Eq. (2) as follows:𝑁𝑁f(𝑥𝑥𝑖𝑖 ) ≈ � 𝑚𝑚𝑗𝑗
𝑗𝑗=1

𝑓𝑓�𝑥𝑥𝑗𝑗 �W�xi − xj , h�
ρ𝑗𝑗

(5)

Solid-fluid coupling model�σαβ � = −pδαβ + ταβ

(12)

̇ +τ
ταβ = ηϵαβ
𝑦𝑦

(13)

ϕ𝑓𝑓 = 𝑆𝑆𝑅𝑅 ϕ𝑠𝑠(14)

𝑐𝑐𝑓𝑓 = 𝑆𝑆𝑅𝑅 𝑐𝑐𝑠𝑠

(15)

where ταβ is the shear stress tensor and is given by,̇ is the strain
where η is the viscosity coefficient; ϵαβrate tensor; τ𝑦𝑦 is the yield shear stress. To computethe isotropic pressure 𝑝𝑝 of fluid, Eq. (11) is also
applied.
The Mohr-Coulomb failure criterion is applied forboth solid and fluid states of a particle. Furthermore,
the strength reduction factor (𝑆𝑆𝑅𝑅 ) is introduced. The
relations between the internal friction angle of solidstate (ϕs ) and that of fluid state (ϕ𝑓𝑓 ) is defined bywhere α and β denote the coordinate directions; x α isthe position vector; 𝑣𝑣 α is the velocity vector; 𝑡𝑡 istime; ρ is density; σαβ is the stress tensor; 𝑏𝑏 α is the
body force. The SPH form of the equation of motion
is given by,

𝑗𝑗=1

(11)

𝑓𝑓

(6)

αβ

p = Kϵαβ𝑓𝑓

The equation of motion of a solid body or a fluid
is given by,

𝑁𝑁

(10)

The stress tensor of fluid-state �σαβ � is written by

Equation of motion

αβσ𝑗𝑗 ∂𝑊𝑊𝑖𝑖𝑖𝑖
dviασ= � 𝑚𝑚𝑖𝑖 � 𝑖𝑖2 + 2 �+ 𝑏𝑏𝑗𝑗dt
ρ𝑖𝑖ρ𝑗𝑗 ∂𝑥𝑥 β

�σαβ � = −pδαβ + 2Gϵαβwhere 𝐾𝐾 is the elastic bulk modulus.
On the other hand, the Bingham fluid model isapplied to calculate the stress tensor of fluid-state.

where 𝑚𝑚𝑗𝑗 denotes the mass of particle 𝑗𝑗.

𝑑𝑑𝑣𝑣 α 1 ∂σαβ=
+ 𝑏𝑏 α𝑑𝑑𝑑𝑑
ρ ∂𝑥𝑥 β(9)where 𝑝𝑝 is the isotropic pressure; δαβ is the
Kronecker delta; 𝐺𝐺 is the shear modulus; ϵ{αβ} is thestrain tensor. The isotropic pressure (𝑝𝑝) is calculated
by

(3)

𝐷𝐷

β(t) = exp�−γ�𝑡𝑡𝑓𝑓 − 𝑡𝑡��

where γ is a coupling control parameter; 𝑡𝑡𝑓𝑓 is thetime when the stress tensor reaches the failure surface
first time. For solid-state of a particle, the stress tensor
is calculated by the elastic constitutive model,

where ℎ is called the smoothing length that prescribes
the width of the function. A smoothing kernel
function 𝑊𝑊(𝒙𝒙) must be a differentiable even function
and must satisfy with the following conditions:� 𝑊𝑊(𝑥𝑥, ℎ)𝑑𝑑𝑑𝑑 = 1(8)where the superscript s and f denote solid and fluid
state, respectively; 𝛽𝛽(𝑡𝑡) is a weight function forcoupling the stresses of solid and fluid states and isdefined bywhere f(x) is a function of the position vector x; δ(𝑥𝑥)is the Dirac delta function; 𝐷𝐷 is the problem domain.
The kernel approximation in the SPH is defined byreplacing 𝛿𝛿(𝒙𝒙) by a smoothing kernel function
𝑊𝑊(𝒙𝒙):
f(𝒙𝒙) ≈ � 𝑓𝑓 �𝒙𝒙′ � 𝑊𝑊 �𝒙𝒙 − 𝒙𝒙′ , ℎ� 𝑑𝑑𝒙𝒙′

f

(7)

𝑗𝑗

The same (𝑆𝑆𝑅𝑅 ) is used for the cohesion:

In the present study, we propose that the stresstensor for a particle is computed by coupling solid andfluid states as follows:

where 𝑐𝑐𝑠𝑠 is the cohesion of solid-state of a particle; 𝑐𝑐𝑓𝑓
is the cohesion of fluid-state.

78

GEOMATE – Melbourne, Australia, 11-13 November 2020

Boundary condition

Internal friction angle (ϕ𝑠𝑠 )The dynamic boundary condition is applied tomodel the rigid box in the simulation. The positionsof the boundary particles are fixed, while the sameprocedure as for the other particles computes thestress tensors of the boundary particles.

20.0 (°)

Table 2 Material properties for fluid-state
Property
Density (ρ𝑓𝑓 )Viscosity coefficient (η)
Cohesion (𝑐𝑐 𝑓𝑓 )Internal friction angle (ϕ𝑓𝑓 )Rayleigh damping

Value (Unit)
1.964×103 (kg/m3)
2.00 (Pa·s)
150.0𝑆𝑆𝑅𝑅 (kPa)
20.0𝑆𝑆𝑅𝑅 (°)

Note: 𝑆𝑆𝑅𝑅 denotes strength reduction factor

For stabilizing a simulation and absorbing thevibration of a model, Rayleigh damping is applied inthis study. Rayleigh damping is commonly used inearthquake engineering. The application of Rayleighdamping to the SPH framework is appeared in thereference [13].

Input ground motion
The sinusoidal wave of the amplitude with 6.0 m/s/s
is used as the horizontal ground motion (Fig. 2). For
the vertical direction, the gravity load is applied.
From t=0 to 3 (s), no horizontal excitation is applied
to stabilize the slope model to the gravity. The
horizontal excitation starts at t=3 (s), and its
amplitude increases gradually. From t=9 (s), theamplitude decreases and becomes zero at t=10 (s).

Time integration
The Verlet time integration scheme is used to
update the velocities and the positions of the particles,
and the fixed time increment of 1.0 × 10−4 s isapplied.
NUMERICAL EXAMPLE
A slope model is considered as an example todemonstrate the performance of the developed
coupling model. The properties of the example slopemodel and other parameters required to execute thesimulation is presented in this chapter.
Slope model
The geometry of the example slope model isshown in Fig. 1. The slope angle of the model is 60°.
The material properties for solid and fluid states ofthe slope are listed in Tables 1 and 2.

Fig. 2The time history of the input acceleration.

Fig. 1

Fig. 3The initial arrangement of particles.

The geometry of the slope model.

Table 1 Material properties for solid-state

Results

Property
Density (ρ𝑠𝑠 )Young’s modulus (𝐸𝐸)
Poisson’s ratio (ν)
Cohesion (𝑐𝑐 𝑠𝑠 )Figure 3 shows the initial configuration of theparticles. The particles are placed homogeneouslywith 1.0 m intervals. The cubic spline type kernel
function is used, and its smoothing length of thekernel function is 2.3 m. In total, 2,404 particles areused, including the boundary particles. The colors ofValue (Unit)
1.964×103 (kg/m3)
1.675×106 (kPa)
0.346
150.0 (kPa)79

GEOMATE – Melbourne, Australia, 11-13 November 2020

the particles show their vertical positions, except theboundary particles colored in white.
Figure 4 compares the profiles of the slope model
after the horizontal excitation at t =15 (s) for various
strength reduction factors: (a)𝑆𝑆𝑅𝑅 =0.10; (b) 𝑆𝑆𝑅𝑅 =0.25;(c) 𝑆𝑆𝑅𝑅 =0.50. The colors indicate the initial height ofthe particles, except the boundary particles that arecolored in white. The same color-coding is commonlyused in Fig. 6. The coupling parameter (γ) is 100.0.
From these results, the effect of the strength reduction
factor is revealed. The smaller value of 𝑆𝑆𝑅𝑅 facilitates
the deformation of the slope. For the case of 𝑆𝑆𝑅𝑅 =0.10,
the slope model is fluidized and deforms significantly.
For the case of 𝑆𝑆𝑅𝑅 =0.5, the slope model is not
fluidized, although it deforms slightly.

larger value of the coupling parameter γ=100.0, theslope model collapses, while it deforms slightly forthe smaller value of 𝛾𝛾 = 1.0.
Figure 6 compares the vertical motion of particle
A, which is indicated in Fig. 2, for 𝑆𝑆𝑅𝑅 =0.10, 0.25, and
0.50. The effective time range of the horizontal
ground motion is indicated in the figure. The moment
when particle A starts moving is the same for all cases.
For 𝑆𝑆𝑅𝑅 =0.10, the slope model fluidizes, and particle
A starts moving earlier than for the other cases. For
𝑆𝑆𝑅𝑅 =0.50, the slope model fluidizes gradually, and theparticle moves slowly. For 𝑆𝑆𝑅𝑅 =0.50, the behavior of
the slope model is intermediate of the other two cases.

(a) 𝑆𝑆𝑅𝑅 =0.25, γ=100.0(a) 𝑆𝑆𝑅𝑅 =0.10, γ=100.0(b) 𝑆𝑆𝑅𝑅 =0.25, γ=100.0

Fig. 4(b) 𝑆𝑆𝑅𝑅 =0.25, γ=1.0

Fig. 5

Slope profiles after the horizontal excitation
at t=15 (s) for γ=100.0 and 1.0.

Fig. 6The vertical motions of particle
for γ=100.0, 𝑆𝑆𝑅𝑅 =0.10, 0.25, and 0.50.

(c) 𝑆𝑆𝑅𝑅 =0.50, γ=100.0

Slope profiles after the horizontal excitationfor 𝑆𝑆𝑅𝑅 =0.10, 0.25, and 0.50.

On the other hand, Fig. 5 compares the results for(a) γ=100.0 and (b) γ=1.0. The strength reduction
factor ( 𝑆𝑆𝑅𝑅 ). is the same and is 0.25. The result
suggests that the larger value of the coupling
parameter 𝛾𝛾 makes the slope model stable. For the80

A

The vertical motions of particle A for 𝑆𝑆𝑅𝑅 =0.25,
γ=100.0 and 1.0 are compared in Fig.7. The coupling

GEOMATE – Melbourne, Australia, 11-13 November 2020

parameter (γ) has the effect of delaying fluidization.
For the case of γ =1.0, the slope model deforms
gradually after the horizontal excitation ends. For
γ=100.0, the deformation of the slope model begins
and finishes during the horizontal excitation.

Fig. 7

1996.
[3] Sekiguchi H. and Sato H., Feature and distributionof landslides induced by the Mid Niigata
Prefecture earthquake in 2004, Japan, Landslides,
Vol. 43, pp.142–154, 2006.
[4] Dai F. C., Xu C., Yao X., Xu L., Tu X. B., andGong Q. M., Spatial distribution of landslides
triggered by the 2008 Ms 8.0 Wenchuan
earthquake, China, J. of Asian Earth Sci., Vol. 40,
pp.883–895, 2011.
[5] Jibson, R. W., Predicting earthquake-induced
landslide displacements using Newmark’s sliding
block analysis, Transportation Research Record,Vol. 1411, pp.9–17, 1993.
[6] Toki K., Miura F., and Oguni, Y., Dynamic slope
stability analysis with a non-linear finite element
method, Earthquake Engineering & Structural
Dynamics, Vol. 13, Issue 2, pp. 151–171, 1985.
[7] Gingold R. A. and Mohaghan J.J., Smoothed
particle hydrodynamics: Theory and application
to non-spherical stars, Monthly Notices of theRoyal Astronomical, Vol. 181, pp.375–389, 1977.
[8] Huang Y., Dai Z. L, Zhang W. J., and Huang M.S.,
SPH-based numerical simulations of flow slides
in municipal solid waste landfills, Waste
Management and Research, Vol. 31, Issue 3,
pp.256–264, 2013.
[9] Huang Y., Dai Z., Zhang W., and Chen Z., Visual
simulation of landslide fluidized movement basedon smoothed particle hydrodynamics, Natural
Hazards, Vol. 59, Issue 3, pp.1225–1238, 2011.
[10] Dai Z., Huang H., Cheng H., and Xu Q., 3D
numerical modeling using smoothed particle
hydrodynamics of flow-like landslide propagation
triggered by the 2008 Wenchuan earthquake,
Engineering Geology, Vol. 180, pp. 21–33, 2014.
[11] Dai Z., Wang F., Huang Y., Song K., and Iio A.,
SPH-based numerical modeling for the postfailure behavior of the landslides triggered by the2016 Kumamoto earthquake, Geoenvironmental
Disasters, Vol. 3, 2016.
[12] Ono Y., Nishida S., and Kiyono J., SPH
Simulation for Seismic Behavior of Earth
Structures, Proc. of 14th World Conference on
Earthquake Engineering, Paper No.14-0145,
2008.
[13] Ono Y., SPH Simulation of Earthquake-induced
Slope Failure, Proc. of 15th World Conference on
Earthquake Engineering, Paper No.22185, 2012.
[14] Bui H.H. and Fukagawa R., An improved SPH
method for saturated soils and its application toinvestigate the mechanisms of embankment
failure: Case of hydrostatic pore-water pressure,International Journal for Numerical andAnalytical Methods in Geomechanics, Vol.37,
pp.31–50, 2013..
[15] Chen W. and Qiu T., Simulation of earthquakeinduced slope deformation using SPH method,
International Journal for Numerical andAnalytical Methods in Geomechanics, Vol.38,
Issue 3, pp.297–330, 2014.

The vertical motions of particle A for
𝑆𝑆𝑅𝑅 =0.35, γ=100.0 and 1.0.

DISCUSSION AND CONCLUSIONSIn the application of the SPH method toearthquake-induced slope failures and landslides [8]–[11], fluid models such as Bingham fluid arecommonly used to reproduce the fluidization anddeposition process. In these studies, the occurrence
process of a slope failure has been ignored. On theother hand, previous studies [12]–[15] attempted tosimulate all processes of a slope failure or a landslide:occurrence, fluidization, and deposition. However,their studies are based on solid mechanics and havenot adequately reproduced fluidization anddecomposition processes.
In this study, we have developed a model couplingsolid and fluid models. The developed model allows
us to simulate the whole process of an earthquakeinduced slope failure or landslide: the occurrence offailure, fluidization, and sedimentation. As a further
study, an appropriate method for setting thesimulation parameters corresponding to the material
properties must be developed.
ACKNOWLEDGMENTS
This work was supported by JSPS KAKENHI
Grant Number 18H01523.
REFERENCES[1] Keefer D. K., Landslides caused by earthquakes,
Geological Society of America Bulletin, Vol. 95,
pp.406–421, 1984.
[2] Harp E.L. and Jibson R.W., Landslides triggeredby the 1994 Northridge, California, earthquake,
Bull. Seismol. Soc. Am., Vol. 86. pp. S319–S332,

81

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EVALUATION ON THE PERFORMANCE OF MICP TREATEDSLOPE SOIL UNDER ACID RAIN ENVIRONMENT
Sivakumar Gowthaman1, Arash Mohsenzadeh2, Kazunori Nakashima1, Hiromi Nakamura3 and Satoru Kawasaki1
1Faculty of Engineering, Hokkaido University, Japan; 2Department of Civil and Environmental Engineering,Amirkabir University of Technology, Iran; 3Hokkaido Office, East Nippon Expressway Company Limited, Japan
ABSTRACTMicrobial induced carbonate precipitation (MICP), through the field of bio-mediated geotechnical engineering,has gained significant momentum in research industry during recent past, which has promoted the strategy nearly
to field application stage. The aim of this study is to evaluate the durability of MICP specimens under the exposureof acidic environment. The representative soils from an erosion prone slope were treated to different cementationlevels on the basis of MICP. The acidic environment was enabled under different pH conditions (ranging 3.0-6.0)
through two different mechanisms: (i) infiltration and (ii) immersion, both of which represent acid rainfall andwater-logged environments, respectively. The influence of infiltration rate was studied under two potential rainfall
intensities, and in each case, specimens were infiltrated by acid rain volume equals fifteen-year rainfall volume.
The results indicate that decrease in environment pH increased the corrosion of CaCO 3, resulting considerable loss
in both mass and unconfined compressive strength (UCS). However, the increased cementation level showed highdurability of specimens. The results also revealed that the contact time of acid rain significantly governed the rateof corrosion, i.e. specimens subjected to lower infiltration rate showed higher loss of mass and UCS compared tothat of higher rate. Moreover, the immerged specimens showed relatively a similar response of those subjected to
low infiltration. Overall, this study addressed one of the prime environmental hurdles which might possibly be
experienced in the field, advancing the understanding on performance of slope near-surfaces conserved by MICP.
Keywords: Microbial induced carbonate precipitation (MICP), slope near-surface stabilization, durability, acidrain, cementation levelengineers’ proposal as a new sustainable alternative[3], [4]. The researches have already been initiatedand demonstrated the viability of the technique invarious standpoints, promoting the strategy nearly tofield application stage. Accordingly, Salifu et al. [5]showed that the MICP could improve the stability ofslope aggregates under repeated tidal currents,
concluding that relatively a shallow treatment would
be sufficient for coastal protection. The use of native
bacteria was another appeal from the sustainable
perspective, and that was proposed and demonstrated
for stabilizing expressway slope soils [6], [7]. In
another work, Gowthaman et al. [8] addressed theeffect of particle size distribution on MICP responses,
suggesting that presence of silt in small quantity tends
to filter more bacteria at near-surface zone and results
stable slope surfaces.
Despite of the considerable works, the studies
addressing the longevity of MICP treatment underclimatic factors are limited. Regarding the rainfallinduced impairments, treating the slope by 1 mol/L
cementation resources was found to have better
durability responses against erosion [9]. Considering
the effects from seasonal frosts (subarctic regions),
Gowthaman et al. [10] found that higher the
precipitated content of calcium carbonate the stronger
the inter-particle connections, leading to highaggregate stability and durability against freeze-thaw

INTRODUCTION
Microbial induced carbonate precipitation(MICP), is relatively a novel and environmentalfriendly soil stabilization technique and has gained
significant momentum in recent years. The MICP
treatment relies on urea hydrolysis; the urease
produced by supplied or enriched bacteria catalyzes
the urea hydrolysis, resulting the production ofammonium and carbonate ions (Eq. 1). The produced
carbonate ions then react to precipitate calcium
carbonate in the presence of calcium resources (Eq.
2) [1], [2]. This precipitated calcium carbonate
functions as bio-cement, bonding the adjacent soil
particles preferentially at particle-particle contacts
and enhancing the aggregate stability and strengthcharacteristics.
CO(NH2 )2 ＋ 2H2 O → 2NH4+ + CO2−
3
2+
CO2−
+
Ca
→
CaCO
(s)
3
3

(1)
(2)

Stabilization of slope near-surface against
aggregate instability is one of the serious concerns in
the field of Geotechnical Engineering. The widelyused traditional methods are primarily cement-based
and/or bitumen-based, and they would appear to be
ill-advised from a sustainability perspective. In recent
years, MICP is often one of the scientists’ and/or
82

GEOMATE – Melbourne, Australia, 11-13 November 2020

actions. Provided alkaline conditions persist, thelongevity of MICP treated soils can be expected to bestable for more than fifty years [11]. On the other
hand, CaCO3 material is chemically susceptible toacidic environment; therefore, longevity anddurability of MICP treatment under the exposure ofacid rainfall is often a question to be explored, andthat remains as a hurdle for applications to the fields
experiencing acid rainfalls. Therefore, the objectiveof this paper is to evaluate the durability of MICP
treated soils under the exposure of acid rainfall
environment.

supplies of cementation solution. Between the twophases, a period of 2-3 hours was allowed for bacteria
cells to be attached with the soil particles [13]. All thesolutions were supplied at the top of the columns at
relatively a constant flow rate of 4 mL/min.
Concentration of cementation solution used for thetreatment was 1 mol/L, and which was similar to
many previous works [14], [15]. The cementation
media consisted of 110.9 g/L calcium chloride, 60 g/L
urea and 6 g/L of nutrient broth.
In this study, three different levels of cementation(i.e., % of CaCO3) were considered (C-1, C-2 and C3). Different durations of MICP treatments were
performed to achieve different levels of cementation.
For C-1, around 7 number of cementation injections
were performed. Around 10 number of injections
were performed for C-2, and 14 injections for C-3. In
another terms, the C-1, C-2 and C-3 levels can bedemarcated as precipitated CaCO3 content ranging
between 12-14%, 16-18% and 21-23%, respectively.
Measurement of CaCO3 content was determinedusing the acid reaction method [8].

MATERIALS AND METHODS
Soil and preparation
An erosion-prone expressway slope located in
Onuma (Hokkaido, Japan) was selected for thecurrent study. The slope soil can be classified as
poorly graded (SP) fine sand according to the Unified
Soil Classification System (USCS). Results from XRay fluorescence (XRF) analysis revealed that thesilica is the dominant soil mineral, and the organic
content was found to be negligible. Table 1
summarizes the fundamental geotechnical propertiesof slope soil.

Acid rain (AR) simulation test
Accelerated test was applied in acid rain (AR)simulation. In real situations, AR could potentially bein contact with MICP treated slope near-surface at
two possible conditions: (i) during infiltration and/ or(ii) submerged, and both conditions were investigatedin this study. In order to reproduce the process of AR
interaction, an infiltration setup was arranged asshown in Fig. 1. Effects of various parameters such aslevel of cementation (C-1, C-2 and C-3), pH of AR
solution (3.0, 4.5 and 6.0) and intensity of AR (20 and
100 mm/h) were evaluated. The intensity of AR wascontrolled herein by the rate of delivery of the AR
solution at specimen top as shown in Fig. 1.

Table 1 Fundamental characteristics of slope soil
Property
Values

pH
6.997

D50
0.23

Cu
2.5

Cc
0.8

Gs
2.71

Note: D50 is the mean particle diameter (i.e., grain diameter at50%); Cu is the coefficient of uniformity; Cc is the coefficient ofcurvature; Gs is the specific gravity.

The soil columns (30 mm in diameter; 50 mm inheight) were prepared by tamping in three layers to
the average density of around 1.6  0.1 g/cm3,
followed by the MICP treatment.
Bacteria and MICP treatment
Lysinibacillus xylanilyticus were the bacteria usedherein to induce urea hydrolysis. The above ureolytic
bacteria were isolated from the same slope soil; thedetail on isolation process and characterization can befound in previous works [6], [8]. For the cultivationof bacteria, ammonium-yeast extract media (NH4-YE,
ATCC 1376) was used, which consisted of 20 g/L ofyeast extract, 15.7 g/L of tris buffer and 10 g/L ofammonium sulfate. The media was inoculated withthe bacteria pre-culture and subjected to shaking
incubation at 25C and 160 rpm. After 48-72 hours ofgrowth, the bacteria culture was used for the MICP
treatment (the optical density OD600 was around 4.0).
During the MICP treatment, a two-phase surface
percolation method [12] was incorporated: initialsupply of bacteria culture, followed by multiple

Infiltration
test setup

Columnspecimen
Effluent

Peristaltic pump
Gauze

60.0

Effluent
collection
Supply of acidrain solution

Fig. 1

83

Experimental set up of the AR infiltration

GEOMATE – Melbourne, Australia, 11-13 November 2020

AR solution was prepared by dropping a certain
volume of 1 mol/L nitric acid (HNO3) and sulfuric
acid (H2SO4) to deionized distilled water, and the pH
levels were adjusted to 3.0, 4.5 and 6.0. The
infiltration quantity of AR (Qinf) was estimated basedon the average annual precipitation in study location(Onuma, Hokkaido), as per the given equation (Eq. 3).
Similar to the previous works, it was assumed that
one-third of the total precipitation infiltrates in to theslope and two-third would be surface-runoff [16],[17].
𝑄𝑖𝑛𝑓 = 1⁄3 𝑃𝑎𝑣𝑔 𝐴

two rates, and the rates were chosen on the basis of
typical high and low rain fall intensities (around 20
and 100 mm/h, respectively).
For the submerged condition, specimens were
soakedcompletely
into
corrosion-resisting
cylindrical molds containing AR solution. As the pH
values of the AR solutions were changing due to theconsumption of protons (H+) by the reaction with
CaCO3, the solution was replaced every 24 hours by
newly prepared AR (to the total volume equivalent to
the precipitation of 15 years). At the same time, theconcentration of leached calcium ions was measured
in the reacted solution. In addition, with the different
immersion times, the weight loss was measured.
Finally, all the treated specimens were oven-dried for
48 hours, followed by the estimation of the UCS. The
UCS of the treated specimens were estimated byneedle penetration tests in accordance with JGS 34312012 [18].

(3)

where, the 𝑃𝑎𝑣𝑔 stands for the annual average
precipitation of study location (1210 mm), and 𝐴 isthe cross-sectional area of the specimen (7.06 cm2).
Accordingly, specimens were infiltrated by AR
solution equivalent to the precipitation of 15 years.
The AR solution was delivered to the specimens at

Corrosion of CaCO3 (g)

C-1

0.5
0.4

High intensity
Low intensity
Fully submerged

0.3
0.2

y = 1E-06x
R² = 0.9899

0.5

y = 3E-06x
R² = 0.9965

0.3

0.6

0.1
0

900

1800

2700

3600

0

4500

0.7

900

1800

y = 4E-05x
R² = 0.9911

y = 1E-06x
R² = 0.9952
High intensity
Low intensity
Fully submerged

0.4
0.3
0.2

y = 3E-06x
R² = 0.9967

0.5
0.4
0.3

900

1800

2700

3600

0

4500

0.7

y = 5E-05x
R² = 0.9722

0.6

900

1800

y = 3E-05x
R² = 0.933
2700 3600 4500

0.5High intensity
Low intensity
Fully submerged

0.4
0.3
0.2

y = 3E-06x
R² = 0.9994
y = 4E-06x
R² = 0.9937

0.1

0.5

High intensity
Low intensity
Fully submerged

0.4
0.3
0.2

0

900

1800

2700

3600

Acid rain volume (mL)

Fig. 2

4500

0

900

0.3

y = 0.0001x
R² = 0.9771

High intensity
Low intensity
Fully submerged

0

900

1800

2700

y = 5E-05x
R² = 0.9728

2700

y = 3E-05x
R² = 0.9772
3600 4500

Acid rain volume (mL)

3600

4500

y = 0.0002x
R² = 0.9996

C-3

y = 1E-04x
R² = 0.9769

0.4
y = 0.0001x
R² = 0.9778

0.3High intensity
Low intensity
Fully submerged

0.1

0
0

900

1800

2700

3600

Acid rain volume (mL)

The corrosion of calcium carbonate during the AR simulation test
84

4500

0

0.2

1800

3600

y = 9E-05x
R² = 0.9985

0.4

0.5

y = 5E-05x
R² = 0.9901

0.1

2700

y = 0.0001x
R² = 0.9959

C-2

0.6

0

0

1800

0.7

C-3

0.6
y = 2E-06x
R² = 0.9982

900

0.1

0.7

C-3

0

0.2

0
0

y = 1E-04x
R² = 0.9987
High intensity
Low intensity
Fully submerged

0.5

y = 5E-05x
R² = 0.988

0.1

0y = 0.0001x
R² = 0.9731

0

0.6High intensity
Low intensity
Fully submerged

0.2

y = 4E-06x
R² = 0.999

0.1

0.2

0.7

C-2

0.6

0.5

0.3

0.1

y = 3E-05x
R² = 0.9187
2700 3600 4500

0.7

C-2

0.6

y = 0.0001x
R² = 0.9923

0.4

y = 4E-05x
R² = 0.9567

0.2

C-1

0.5

High intensity
Low intensity
Fully submerged

0.4

y = 4E-06x
R² = 0.9973

0.1
0Corrosion of CaCO3 (g)

C-1

0.6

0

Corrosion of CaCO3 (g)

0.7

0.7

0.7
0.6

pH 3.0  0.1

pH 4.5  0.1

pH 6.0  0.1

4500

GEOMATE – Melbourne, Australia, 11-13 November 2020

pH 6.0  0.1

pH 4.5  0.1

Mass loss (%)

20

20

15

15

High intensity
Low intensity
Fully submerged

10
5

5
0

0
1000

2000

3000

4000

5000

0

1000

2000

3000

4000

High intensity
Low intensity
Fully submerged

5

5

0

1000

2000

3000

4000

5000

20

5

1000

2000

3000

4000

5

1000

2000

High intensity
Low intensity
Fully submerged

5

3000

4000

Acid rain volume (mL)

5000

1000

2000

3000

4000

5000

4000

5000

C-3

15

High intensity
Low intensity
Fully submerged

10
5

0
0

5000

20

10

0

0

5000

C-3

15

High intensity
Low intensity
Fully submerged

4000

0
0

20

10

3000

High intensity
Low intensity
Fully submerged

10

0

C-3

15

2000

C-2

15

High intensity
Low intensity
Fully submerged

10

0

1000

20

C-2

15

10

0

5000

20

C-2

15

High intensity
Low intensity
Fully submerged

10

5

0

C-1

15

High intensity
Low intensity
Fully submerged1020Mass loss (%)

20

C-1

C-1

0

Mass loss (%)

pH 3.0  0.1

0
0

1000

2000

3000

4000

5000

0

Acid rain volume (mL)

1000

2000

3000

Acid rain volume (mL)

Fig. 3 Mass loss of the specimens subjected to AR
RESULTS

was around 1.75 times higher than the corrosion
experienced under high intensity of AR. This could
be attributed to the contact time of AR within MICP
specimens. During the high intensity of AR, certain
hydraulic pressure was developed on the surface (dueto ponding), which tended to infiltrate the AR at highrates. The less contact time of AR with soil MICP
matrix resulted the less reaction with CaCO3. On theother hand, the low intensity facilitated relatively
slow infiltration, allowing sufficient time to thereaction with CaCO3 during the percolation.

Corrosion of CaCO3
The chemical reaction between AR and calcium
carbonate could result in the dissolution of CaCO 3
that precipitated in MICP specimens. During the test,
leached calcium ions was continuously measured
from the effluent of the specimens. From the Ca2+
concentrations, the corroded calcium carbonate wastheoretically estimated in each case, and the results
are plotted and compared in Fig. 2. From the plots, it
can be clearly observed that regardless of specimen
cementation level, the corrosion of CaCO3 was highly
reliant on the pH condition of AR solution. Minor
corrosion rates of calcium carbonate were observed tothe specimens exposed to AR of pH 6.0, and thecorrosion rate showed an increasing tendency withthe decrease in pH condition. For instance, when the
pH of AR decreased from 6.0 to 4.5, the corrosion rateincreased by around 10 times, and that rate further
increased at pH of 3.0.
The corrosion rate of calcium carbonate was also
considerably influenced by the intensity of AR (Fig.
2). The low intensity of AR (20 mm/h) resulted higher
corrosion rate compared to that under high intensity(100 mm/h). For example, by the end of the test (atpH 3.0), the specimens showed the corrosion ofaround 0.7 g CaCO3 under low intensity of AR, which

Mass Loss
Fig. 3 shows the mass loss of MICP samples as afunction of delivered volume of acid rain at different
pH conditions. As shown, the mass loss rate of MICP
treated specimens increased with increasing delivered
volume, and that increases with the increase in pH of
the AR. The tendency of mass loss rate (Fig. 2) is ina good agreement with the corrosion rate of calcium
carbonate (Fig. 3). However, the cementation level
highly governed the rate of mass loss. For example,
by the end of the test (at pH 3.0), all the specimens(C-1, C-2 and C-3, i.e. irrespective of the level ofcementation) showed the same corrosion of calcium
carbonate. The soil losses, on the other hand, were up
to around 20%, 9% and 5% for C-1, C-2 and C-3
specimens, respectively, suggesting that high85

GEOMATE – Melbourne, Australia, 11-13 November 2020

cementation level attributes to high durability of
MICP treatment against AR induced erosion.
From the physical observation, it could be seenthat the specimens treated to high cementation level(C-3) showed lower physical impairments than thoseof low cementation levels (C-1). The specimens
subjected to low intensity of AR delivery showed
higher surficial damage compared to that of highintensity of AR. As the AR was delivered to thespecimens from the top, the proton concentration of
AR was high and effective at the supply point,
resulted high dissolution of CaCO3 at surface. Whenthe solution reached specimen depths, the certain
quantity of protons has already been reacted andneutralized, which leads to the less reaction at bottom
specimens. At the same time, the specimens tested
under submerged conditions showed clear damage
structure on cylindrical surfaces as well, as the entire
surface of the specimens was directly exposed to AR
solution when submerged. The specimens treated by
AR of pH 3.0 showed higher observable damages
compared to those subjected to AR of pH 4.0. It also
should be noted that specimens subjected to AR of pH
6.0 did not show any observable changes.

DISCUSSION
The corrosion of calcium carbonate bio-cement isthe degradation mode of MICP under the exposure of
AR environment. Under the high intensity AR
delivery, the corrosion of calcium carbonate was low.
In contrast, when the AR is delivered by low rate of
infiltration or fully submerged conditions, the rate ofcorrosion was high, which is due to the sufficient
reaction time given. Regardless of the conditions
tested, the ultimate factor that determines the
responses of the specimens exposed to AR, is the
corroded quantity of calcium carbonate. Fig. 4
presents the compilation of mass loss data versus
corresponding corrosion of calcium carbonate. The
results suggest that the mass loss, with the corrosionof CaCO3, can be fairly evaluated by linear
relationships.
Fig. 5 presents the compilation of test data, i.e.,
UCS plotted against the corrosion of calcium
carbonate. The results indicate that the UCS of thespecimens exponentially decrease with the increase incorrosion of CaCO3 for all the levels of cementation.
The exponential tendency in decrease can be
explained by the decay of matrix support. During the
MICP treatment, the cementation begins at particle
contact, increasing the strength gradually(progression of contact cementation); with theincreasing treatment, the crystals starts to grow within
the pore spaces, sometimes bridging the particles [2],[6], leading to rapid increase in strength at latter stage.
The exponential progression of UCS in MICP soils
was evidenced in many previous studies [6], [19]. The
results presented herein (Fig. 5) demonstrate that the
UCS follows relatively the similar path for thedecrease during corrosion.
The results (Figs. 4 and 5) prove that the treatment
durability essentially depends on high cementationlevels. At high cementation level (e.g. C-3), particle
contact points are cemented stiffly by higher quantityof calcium carbonate compared that in lower levels(e.g. C-1). For the same supply of AR, the corrosionof calcium carbonate might be same, irrespective ofthe level of cementation; however, the connections
points could more quickly be debonded/ weakened in
low cementation levels, showing high susceptibility
to AR. On the other hand, for debonding/ weakening
the particle contacts in high cementation levels, morecalcium carbonates are required to be corroded,suggesting high longevity in performance.

20

Mass loss (%)

y = 5.5641x + 3.8789
R² = 0.5778

C-1
C-2
C-3

16
12

y = 2.6991x + 2.1626
R² = 0.7668

84

y = 1.673x + 1.0501
R² = 0.7694

0
0

0.5

1

1.5

2

2.5

Corrosion of CaCO3 (%)

Fig. 4 Relationships between corroded CaCO3 andmass loss at different cementation levels

12000

UCS (kPa)

C-1
C-2
C-3

y = -1821ln(x) + 3006.2
R² = 0.946

10000
8000
6000

y = -486.2ln(x) + 1952
R² = 0.9672

4000

y = -278.6ln(x) + 757.08
R² = 0.9761

2000
0
0

0.5

1

1.5

2

2.5

Corrosion of CaCO3 (%)

CONCLUSIONS

Fig. 5 Variation of UCS with corrosion of CaCO3
content at different cementation levels

Hypothetically, it was believed that the MICP
treated soils are susceptible to acid rain conditions.
However, this study has demonstrated that the
durability and longevity can be considerably86GEOMATE – Melbourne, Australia, 11-13 November 2020

enhanced by the levels of cementation. The tests
suggest that the corrosion rate of calcium carbonate
depends on the pH of acid rain and intensity.
However, the loss of soil particles and strength loss
due to corrosion of CaCO3 is highly governed by thelevel of cementation. In another words, for the same
quantity of corrosion, the higher the cementationlevel the lower the loss of mass and UCS, enhancing
the longevity of the treatment.

[9] Jiang N.J., Tang C.S., Yin L.Y., Xie Y.H., andShi B., Applicability of Microbial Calcification
Method for Sandy-Slope Surface Erosion
Control, J. Mater. Civ. Eng., Vol. 31, 2019.
[10] Gowthaman S., Nakashima K., and Kawasaki S.,
Freeze-Thaw Durability and Shear Responses of
Cemented Slope Soil Treated by Microbial
Induced Carbonate Precipitation, Soils Found.,2020 (in press).
[11] Dejong J.T., Soga K., Kavazanjian E., and et al.,Biogeochemical processes and geotechnicalapplications: Progress, opportunities andchallenges, Geotechnique, Vol. 63, 2013, pp.
287-301.
[12] Cheng L., and Cord-Ruwisch R., Upscaling
Effects of Soil Improvement by Microbially
Induced Calcite Precipitation by Surface
Percolation, Geomicrobiol. J., Vol. 31, 2014, pp.
396-406.
[13] Martinez B.C., DeJong J.T., Ginn T.R., Montoya
B.M., Barkouki T.H., Hunt C., Tanyu B., andMajor D., Experimental Optimization of
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[14] Omoregie A.I., Palombo E.A., Ong D.E.L.,
Nissom P.M., Biocementation of sand by
Sporosarcina pasteurii strain and technicalgrade cementation reagents through surface
percolation treatment method, Constr. Build.
Mater., Vol. 228, 2019, 116828.
[15] Cheng L., Shahin M.A., and Mujah D., Influenceof Key Environmental Conditions on
Microbially Induced Cementation for Soil
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[16] Bakhshipour Z., Asadi A., Huat B.B.K.,
Sridharan A., and Kawasaki S., Effect of acidrain on geotechnical properties of residual soils,
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[17] Kamon M., and Ying C., Effect of acid rain on
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No. 1, JGS Publication (in Japanese), Tokyo,
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[19] van Paassen L.A., Ghose R., van der Linden,
T.J.M., van der Star, W.R.L., and van
Loosdrecht, M.C.M., Quantifying BiomediatedGround Improvement by Ureolysis: Large-Scale
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Geoenvironmental Eng., Vol. 136, 2010, 17211728.

REFERENCES[1] Danjo T., and Kawasaki S., Microbially Induced
Sand
Cementation
MethodUsing
Pararhodobacter sp. Strain SO1, Inspired by
Beachrock Formation Mechanism, Mater.
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[2] DeJong J.T., Mortensen B.M., Martinez B.C.,
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Construction Biotechnology: a new area of
biotechnological research and applications,
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pp. 1303-1314.
[4] Naveed M., Duan, J., Uddin, S., Suleman, M.,
Hui, Y., and Li, H., Application of microbiallyinduced calcium carbonate precipitation withurea hydrolysis to improve the mechanicalproperties of soil, Ecol. Eng., Vol 153, 2020.
[5] Salifu E., MacLachlan E., Iyer K.R., Knapp
C.W., and Tarantino A., Application of
microbially induced calcite precipitation inerosion mitigation and stabilisation of sandy soil
foreshore slopes: A preliminary investigation,
Eng. Geol., Vol. 201, 2016, pp. 96-105.
[6] Gowthaman S., Mitsuyama S., Nakashima K.,
Komatsu M., and Kawasaki S., Biogeotechnicalapproach for slope soil stabilization using locallyisolated bacteria and inexpensive low-grade
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expressway soil, Japan, Soils Found., Vol. 59,
Issue 2, 2019, pp. 484-499.
[7] Gowthaman S., Mitsuyama S., Nakashima K.,
Komatsu M., and Kawasaki S., Microbial
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and Kawasaki S., Feasibility study for slope soil
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87

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ESTIMATION OF SUBSURFACE STRUCTURE AND GROUNDMOTION CHARACTERISTICS IN THE DAMAGED AREAS DUE TO
THE 2016 CENTRAL TOTTORI PREFECTURE EARTHQUAKE,
JAPAN
Isamu Nishimura1, Tatsuya Noguchi1 and Takao Kagawa1
1
Engineering, Tottori University, Japan

ABSTRACTOn October 21, 2016, an earthquake with MJMA 6.6 occurred in the central part of Tottori Prefecture, Japan.
Observed JMA seismic intensity were 6 lower at Kurayoshi city, Yurihama and Hokuei towns. Housing damages
due to the earthquake were concentrated in limited areas. In this study, we carried out microtremor observations
around damaged areas, and estimated subsurface structures and ground motion characteristics with previously
conducted studies. We consider that the relationship between ground motion characteristics and damage, and foundthat the damage occurred at the parts where the layer thicknesses change steeply. We suspect that the damages
concern to two- or three- dimensional effects of subsurface structures.
Keywords: Microtremor observation, Seismic observation, Subsurface structure, the 2016 central Tottori
prefecture Earthquake

INTRODUCTIONOBSERVATION
An earthquake with MJMA 6.6 occurred in thecentral part of Tottori Prefecture, Japan on October
21, 2016. Recorded JMA seismic intensity were 6
lower at Kurayoshi city, Yurihama and Hokuei towns(Fig.1) [1]. Housing damages were concentrated inlimited areas at the flatland in Hokuei town, and at theedge of mountain in Yurihama town [2], [3]. We
consider the damages might be caused by site
amplification due to subsurface structures.
The mainshock and aftershocks had been
observed at NIED’s K-NET and KiK-net, andobservation sites of local governments in Tottori
Prefecture and temporary sites of Tottori University.
From the analysis of those strong motion records,
variation of site amplifications due to differences insubsurface structure were suggested. Also, nonlinear
soil response due to the strong ground motions weresuspected at Ryuto (TGAR) and Hisadome (YRAR)
observation sites in Yurihama town, and Hashita(HJA) site in Hokuei town [3].
In order to evaluate strong motions, nonlinear soil
responses, and the relationship between groundmotion and damage, it is necessary to understanddetailed subsurface structure and ground motion
characteristics. In this study, we carried out
microtremor observations around the strong motion
observation sites and damaged areas, and estimatedsubsurface structures andgroundmotioncharacteristics.
Additionally,
deepergroundstructuture was estimated from aftershock records.

Housing damages and previous study of the target
area
Housing damages due to the mainshock of the2016 earthquake was concentrated in the limited areasat the flatland in Hokuei town, and at themountainside in Yurihama town. In the damaged
areas are shown in Fig. 1, roof tile damage wasrecognized based on blue sheet coverage from thesatellite image [4], and confirmed by site visits andinterviews. In this study, an area with 50m radiusfrom the damaged houses was considered as the
housing damage area.
According to the previous study [3], [5],predominant period of microtremor in Yurihama
town is about 0.5-2.0 seconds, and about 0.8-1.5
seconds in Hokuei town. It is considered that theperiods are correspond to thickness of soft layer with
S-wave velocities less than 400 m/s.
Summary of observation
Single-point observations were carried out at 294
sites in Yurihama town and 162 sites in Hokuei town,
mainly in residential areas and strong motion
observation sites. In areas where roof tile damages areconcentrated, we made observations at 50 m intervals.
The equipment used was JU410, a three-component
accelerometerwith
24bitsrecorder.
The
specifications with wave amplification factor 100,
sampling frequency 100 Hz or 200 Hz, and recording
88GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig 1. Epicenter and fault line [1], damaged area and seismic observation sites
sections. From the Fourier spectra, horizontal tovertical spectral ratios (H/V) were calculated, andpredominant periods were visually estimated. Many
H/Vs were unimodal with a single distinct peak. In
some areas such as mountainside where multiple
peaks were observed, the predominant period wasestimated in consideration of continuity with adjacentunimodal sites. Fig. 2 shows the entire predominantperiod distribution in the central Tottori Prefecture.
In analysis of array observation, reanalyses
including the past research data [3], [5] were
performed in order to unify the velocity structure of
the lowermost surface. The analysis procedure was asfollows. Using the open analysing tool [7], phasevelocity dispersion curves of array observation record
were estimated based on the CCA method [8]. At
least five sections were selected by automatic
extraction using RMS values of microtremor
recordings with 10.24 seconds segments. Next, power
spectra in those sections were smoothed with 0.3 Hz
bandwidth Parzen window, and averaged. Finallyphase velocity dispersion curves were determined.
Phase velocity dispersion curves obtained at eachradius were integrated each observation site
considering their continuity.
Using the phase velocity dispersion curves andmicrotremor H/Vs obtained from single-point
microtremor recording at the center of the array,
subsurface structure models were estimated fromforward modeling based on the fundamental mode ofRayleigh waves. S-wave velocities were determinedwith reference to previous studies [3], [5] andborehole data around the site [9]. Then modeling was
performed by changing the layer thickness. Densities
were set referring previous researches, and P-wave

time about 10-15 minutes wave used.
Array observations were conducted at 7 sites: 4strong motion observation sites (YRAR, TGAR, HJA,
YURA) and 3 temporary aftershock observation sites(OSK, TZAR, ARNSZ) (Fig.2). Four JU410s
synchronized by GPS clocks were used for themeasurement. One seismometer was placed at thecenter of a circle and other three were placed aroundcircumference to form an equilateral triangle.
Sampling frequency was used 200 Hz, amplification
factor was the same as for single-point observation,and recording time was about 15 minutes. The arrayradius was set within the range of 0.6 to 30 m,
depending on the observation site, to evaluate S-wave
velocity structures of Quaternary sediments.
Aftershock observations were placed at
Oshikadani and Takatsuji (OSK, TZAR) where roof
tiles damages were dominant. Oshikadani site had
been maintained from July 24, 2017 to July 2, 2018,
and Takatsuji site has been working from October 20,
2015 to present. The observation equipment used wasa sensor integrated recorder CV-374A, and thesampling frequency was 100 Hz. The seismometers
were installed indoors on a solid foundation, such asat the entrance of a building where external power canbe secured.
ANALYSIS
Microtremor records
From a single-point observation record, Fourier
spectra were calculated, and smoothed using a Log
window with a coefficient of 20 [6]. Average spectra
were calculated from at least 10 stable 20.48 second
89GEOMATE – Melbourne, Australia, 11-13 November 2020

velocities were set from S-wave velocities [10]. Table
1 shows the resulting subsurface structure model.
Aftershock records

30, number of generations was 300, crossoverprobability was 0.7, mutation probability was 0.01,
and dynamic mutation and elite selection wereconsidered. For SA, temperature drop function was
equation (1), with coefficients a=0.5, c=1.0, initial
temperature T0=100, and number of temperature
updates was 10.

In analysing aftershock records, the previous
research data [3], [5] were involved with nearly
employed data to make analyses by same standard.
The analysis procedure of aftershock records was asfollows. Time windows of 10.24 seconds with cosine
tapers of 5% of both ends were applied from S-wave
arrival. Analysis record length was prolonged to
20.48 seconds with adding zero data. Fourier
spectrum was calculated and smoothed with 0.2 Hzbandwidth Parzen window. From the Fourier
spectrum of each component, the spectral ratio ofhorizontal to vertical (strong ground motion H/V) was
evaluated. In this study, about 10 aftershocks wereanalysed and then averaged to obtain H/V at each site.
Subsurface structure models, were at firstestimated by forward modeling, with modifying themodel by comparing the observed and theoretical
H/V based on the diffuse wave field theory [11] ofstrong ground motion H/V. As an initial model,
shallow structures were estimated based on the arrayobservation models described above, and deep
structures were assumed from previous research [5],[12]. Based on the model, final structure model was
estimated from inversion analysis by a hybrid
heuristic search method [13] with genetic algorithm(GA) and simulated annealing (SA). Damping
constant h is assumed to be 0.03 for S-wave velocitiesof 100 m/s or less, 0.02 for 100-400 m/s, and 0.01 for
400 m/s or more. The inversion code was created by
Yasui et al. [14].
Other inversion parameters were as follows. For
GA, number of trials was 10, number of samples was

𝑇𝑇 = 𝑇𝑇0 exp(−𝑐𝑐𝑐𝑐 𝑎𝑎 )(1)

S-wave velocity and layer thickness of the first layer
and the bottom layer were fixed, and value of theother layers were searched within a range of ± 25%
from the initial value. The search targets were S-wave
and P-wave velocities and layer thicknesses.
Densities were set from an empirical formula [15]from S-wave velocity. Fig. 3 shows the resulting Swave velocity column diagrams from shallow to deep
layers.
DISCUSSIONS
Predominant period of microtremor H/V
Predominant periods in Fig.2 are 0.8-1.6 seconds
around YRAR and 0.4-0.8 seconds around TGAR,OSKAR and TZAR. Predominant periods around
ARNSZ and YURA are 1.0-1.6 seconds, and HJA at
northern Kurayoshi Plain are 0.4-1.0 seconds. Many
microtremor H/Vs with a single peak were observed
at northern Kurayoshi Plain and around Togo Pond,
and many H/Vs with multiple peaks were observed at
Nishizono and Yurashuku regions (Fig.4). It probably

Fig 2. Predominant period distribution map in the central Tottori Prefecture90

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1. Subsurface structures based on array observation
Yurihama Town
Site
YRAR
(Hisadome)

TGAR(Ryuto)OSKAR(Oshika
dani)

TZAR
(Taka
tsuji)

Hokuei Town

Thickness(m)

ρ(t/m3)Vp(m/s)Vs(m/s)Thickness(m)

ρ(t/m3)Vp(m/s)

33

1.6

1430

75

1.8

1730

140

9

1.6

1380

80

400

5

1.7

1460

150

100

2.1

2070

700

10

1.8

1510

200

∞

2.2

2620

1200

12

1.9

1730

400

9

1.6

1390

90

∞

2.2

2620

1200

Site

HJA
(Hashita)

Vs(m/s)

8

1.7

1510

200

10

1.7

1430

130

50

1.8

1730

400

10

1.7

1510

200

100

2.1

2070

700

70

1.8

1730

400

∞

2.2

2620

1200

60

2.1

2070

700

8

1.6

1400

100

∞

2.2

2620

1200

11

1.7

1490

180

65

1.8

1730

400

80

2.1

2070

700

∞

2.2

2620

1200

3

1.7

1510

200

30

1.8

1730

400

100

2.1

2070

700

∞

2.2

2620

1200

4

1.6

1390

100

4

1.7

1510

200

100

2.1

2070

700

∞

2.2

2620

1200

ARNSZ
(Nishizo
no)

YURA
(Yura
shuku)

Yurihama

HokueiFig 3. S-wave velocity structure column diagrams(HJA). It is suggested that the silt dominant layers
caused nonlinear response reported in the areas.
Deeper subsurface structures were estimated fromaftershock observation records. The numbers in Fig.
3 indicate the depth from surface layer to engineering
seismic base layer. From the subsurface structure
model, the basement depth tends to be deeper to thecoastal plain. This is consistent with the tendency forthe predominant periods become longer to the plain.
It is found that the thicknesses to deeper subsurface
structure with S-wave velocity more than 700 m/s
were different between Yurihama town and Hokuei
town.

reflects the difference of velocity structures in surface
layers.
Estimation of subsurface structure
From the borehole data around observation sites[9], S-wave velocities of 100 m/s or less in Table 1
are considered to be clay or silt layers, and S-wave
velocity of 200 m/s is considered to be sand layer.
Table 1 shows that the thickness of soft layer with Swave velocity of 80-200 m/s is 6-33m at each site andit tends to be thicker to coastal plain and thinner toinland. It was found that there were silt layers with Swave velocities of 80-140 m/s in the surface layer at
Hisadome (YRAR), Ryuto (TGAR) and Hashita

91

GEOMATE – Melbourne, Australia, 11-13 November 2020

thicknesses from Tp2 are deep in limited area.
Comparing damaged area and the layer thicknesses,
they overlap along the boundary where the layer
thickness of Tp2 change. This suggests effects of 2-D
or 3-D shapes of engineering seismic base on housing
damage.

Fig 4. Microtremor H/V at center of array observation
sites
Consideration on housing damage
Amplification at each site was evaluated using
AVS30 and ARV that are simple indicators of groundamplification [16]. ARV was calculated by Equation(2). AVS30 is the average S-wave velocity down to
30m underground. Table 2 shows AVS30 and ARV
at each site. ARV at YRAR and HJA where housing
damages were not dominant are around 2.5, and
OSKAR and ARNSZ where damages are widespreadare around 2.0. Unfortunately, clear relationship is
not found between housing damage and ARV.
𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 = 1.83 − 0.66𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙30

Fig 5. Layer thickness at Nishizono and Yurashuku
regions (upper map: Tp1, lower map: Tp2)

(2)

CONCLUSIONS

Table 2. AVS30 and ARV at array sites
Yurihama

YRAR

TGAR

OSKAR

In this study, we carried out microtremor andearthquake observations around damaged area due tothe 2016 Central Tottori Prefecture earthquake. We
estimated subsurface structure and evaluated groundmotion characteristics to consider the relationshipbetween those and damages. As a result, thefollowings were found.
H/Vs obtained from the single-point observationof microtremor tend to have a longer predominantperiod in coastal plain than inland area.
Phase velocity dispersion curves were obtained
from microtremor array observation data, and the Swave velocity structure was estimated from them. In
Hisadome, Ryuto and Hashita where nonlinear
responses were observed, low velocity silt layers
were found in surface layer, and it is highly suspectedthat the layers were related to the nonlinear responses
in the area.
Deeper subsurface structures were estimatedfrom aftershock observation records. As a result, it
was found that the thicknesses to deeper subsurface
structure with S-wave velocity more than 700 m/s
were different between Yurihama town and Hokuei
town.

TZAR

AVS30

140

174

220

328

ARV

2.59

2.25

1.98

1.48

Hokuei

HJA

ARNSZ

YURA

AVS30

142

197

276

ARV

2.56

2.07

1.65

In areas where housing damage is concentrated at
Nishizono and Yurashuku regions, surface layer
thicknesses were estimated and their special changes
were examined. S-wave velocities and predominantperiods (Tp) were used to estimate the layer
thicknesses (H) from equation (3). In both areas, shortpredominant period Tp1 and long predominant period
Tp2 are estimated, and the layer thicknesses were
estimated by the peaks from the equation (3).
𝐻𝐻𝑖𝑖 =

𝑉𝑉𝑠𝑠 ∙𝑇𝑇𝑝𝑝𝑝𝑝
4(3)Fig. 5 shows that the layer thicknesses from Tp1
are deeper from south to northwest and the layer
92

GEOMATE – Melbourne, Australia, 11-13 November 2020

its application, Transactions of the Japan Societyof Civil Engineers, No525/I-33, pp247-259. (inJapanese with English abstract)[7] Cho, I., Tada, T. and Shinozaki Y. (2009):
Possibility of microtremor array opened up by
general theory :Release of analysis tool BIDO,
Seismological Society of Japan Fall Meeting in2009. (in Japanese)[8] Cho, I., Tada T. and Shinozaki Y. (2006):
Centerless circular array method: Inferring phasevelocities of Rayleigh waves in broad wavelength
ranges using microtremor records, J. Geophys.
Res., 111, B09315.
[9] National Research Institute for Earth Science andDisasterResilience:GeoStation,<https://www.geo
stn.bosai.go.jp/>, [Viewed on October 9, 2019].
[10] Kitsunezaki, C., Goto, N., Kobayashi, Y., Ikawa,T., Horike, M., Saito, T., Kurota, T., Yamane, K.,
Okuzumi, K. (1990): Estimation of P- and Swave velocities in deep soil deposits forevaluating ground vibrations in earthquake,
Natural disaster science, 9-3, 1-17. (in Japanesewith English abstract)[11] Kawase, H., Sanchez-Seama, F.J. andMatsushima, S. (2011): The optimal use ofhorizontal-to-vertiacal
spectralratiosofearthquake motions for plane waves, Bull. Seism.
Soc. Am. Vol.101, No.5, pp.2001-2014.
[12] Noguchi, T., Nishikawa, H., Yoshida, S. andKagawa, T. (2016): Subsurface structure andevaluation of site effects at strong ground motion
observation sites in Tottori Prefecture, JSCE
Journal of Earthquake Engineering, Vol.72, Issue
35, pp.646-658. (in Japanese with English
abstract)[13] Yamanaka, H. (2007): Inversion of surface-wave
phase velocity using hybrid heuristic searchmethod, Geophysical Exploration, Vol. 60, Issue
3, pp-265-275. (in Japanese with English abstract)[14] Yasui, Y., Nishikawa, H., Maeda, T., Cho, I.,
Kojima, K., Koketsu, K. and Miyajima, M.
(2014): Applicability of an inversion ofearthquake H/V spectral ratios for soil structures– a case study using records in Fukui Prefecture,
JSCE Journal of Earthquake Engineering, Vol.70,
Issue 4, pp.628-643. (in Japanese with English
abstract)[15] Ota, T., Emori, K. and Kasai, Y. (2001): Building
foundation earthquake resistance / vibration /control, Kyoritsu Shuppan, pp-339.
[16] Midorikawa, S., Matsuoka, M. and Sakugawa, K.
(1994): Site Effect of Strong- Motion Records
Observed during the 1987-Chiba-kentoho-oki,
Japan Earthq. Eng. Sympo,Vol.3, pp.85-90.

Comparing ground amplification indexes
estimated from our models with housing damage, therelevance was found to be weak. From layer
thicknesses estimated from predominant periods andsurface S-wave velocities, it was found that thedamage occurred at the part where the layer thickness
changed steeply. Therefore, it is suspected that thedamage caused by irregular subsurface structureswith 2-D or 3-D effects. We need to continue ourstudy about the effects.
ACKNOWLEDGMENTS
For inversion analysis, we used the code by Dr.
Yuzuru Yasui. Mr. Ichihashi, a member of volunteer
fire department in the local government, and Mr.
Yamamoto, a technical staff member of the Facultyof Engineering, Tottori University, cooperated insetting up observation sites at Oshikadani and
Takatsuji, collecting data, and reporting the local
damages. We appreciate to everybody who supported
our research.
REFERENCES[1] Japan Meteorological Agency: Evaluation of theearthquake on October 21, 2016 in central Tottori
Prefecture,<https://www.static.jishin.go.jp/resour
ce/monthly/2016/20161021_tottori.pdf>,[Viewed
on June 11, 2020].
[2] Kagawa, T. (2017): 2016 Central Tottori
Earthquake (M6.6), Earthquake Journal, No.63,
pp.14-22. (in Japanese)[3] Kagawa, T., Noguchi, T., Yoshida, S. andYamamoto, S. (2017): Effect of the surface
geology on strong ground motions due to the 2016
Central Tottori Earthquake, Japan, Earth, Planets
and Space, 69:106 DOI 10.1186/s40623-0170689-0.
[4] Honda, Y., Mochizuki, K., Shimomura, H. andMonma, N. (2017): Automatic extraction of blue
sheet-covered housing from satellite images of2016 Central Tottori Earthquake, Japan Society
for Disaster Information Studies, Proceedings ofthe 19th conference, pp.244-245. (in Japanese)[5] Noguchi, T., Nishikawa, H., Yoshida, S. andKagawa, T. (2019): Estimation of subsurface
structure and characteristics of ground vibration
based on microtremor and seismic observation in
the central Tottori Prefecture, Japan, Journal ofJapan Association for Earthquake Engineering,Vol. 19, Issue 6, pp 258-271. (in Japanese withEnglish abstract)[6] Konno, K., Ohmachi, T. (1995): A smoothing
function suitable for estimation of amplification
factor of the surface ground from microtremor and93

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EVALUATION OF SCENIC HEALING AT VIEWPOINTS IN PARKSCONSIDERING DIFFERENCES IN MIGRATION BEHAVIOR

1

Kazuki Ito1, Kazunari Tanaka1
Civil Engineering department, Osaka Institute of Technology, Japan

ABSTRACTThis paper contributes to a set of prescriptive principles for planners and others to use in planning andregulating the design and management of green landscape formation. The research background is that it
originates in the mineralization of the city by optimizing the efficiency of the city based on the national land
creation plan of the Ministry of Land, Infrastructure, Transport and Tourism. The research method was aquantitative analysis based on the results of the questionnaire surveys. As a result, the quantitative standard
based on the healing point in the green landscape formation became clear. This suggests that the harmony ofthe landscape ratio between natural and artificial landscape formation may reduce the stress inherent in
humans.
Keywords: Landscape, Healing, Migratory Behavior, Urban Design
for a city and to build an attractive city (in terms ofhealing), we must pay attention to the migratory
behavior in urban green spaces. Focusing on
migratory behavior based on the idea that elucidating
the factors that cause behavior, and exploring the lawof behavior contributes to the development of thisresearch, in other words, focusing on basic humanbehavior in behavior analysis. This leads us to thegoals of grasping the change in psychologicalpotential associated with the transition of the visuallandscape during the migratory behavior of a green
space, clarifying quantitatively and qualitatively the
extraction of the healing factors and the structure, anddemonstrating how to evaluate healing in terms of aplace.
Most of the past studies on healing attempt tosystematically grasp the psychological and verbal
healing of the subject's landscape from the scenic
landscape (no distinction between real space andvirtual space). [1] [2] [3] Comparison of theconventional method landscape evaluation method
and the proposed new method here to further improve
the accuracy of identifying the aspect of the landscapesuch as the unique structures and colors, theimpression, and factors affecting the impression etc.
[4] [5] [6] In general, with landscapes such as scenic
landscapes and sequence landscapes, we identify the
incentives and values, and the occurrence ofexperience that can be obtained from them. Thesestudies capture landscapes with an emphasis on
versatility, not just healing. [7] [8] This study
attempts to clarify the relationship between the target
public space that does not depend on the landscapeand the surrounding environment from a
psychological aspect through a questionnaire survey.
[9] Unlike the previous research, the emphasis here ison recognizing landscape recognition as a place, that

INTRODUCTION
The Ministry of Land, Infrastructure, Transport
and Tourism is promoting compact cities and location
optimization plans as upstream policies for land
creation in response to the current urban structure
where population decline is occurring.
Amid the trend of shrinking cities, a variety ofstructures and people will be concentrated in thecentral city area in the future. Let’s not forget that itis beneficial to cities to be centralized, streamlined
and optimized. Optimization and efficiency areimportant for the development and conservation of
the city, but they are always detrimental to thematerial properties and functions of the city. It isnecessary to create an attractive city, not just city
planning. In addition, in the revision of the Urban
Green Space Law (2018), it is feared that theattractiveness of park stocks will decline as aqualitative issue toward the creation of green towns. I
think this suggests that the park, which is a public
space, is a place for people to relax and to be a tool tosolve the weakening of communication with families
and society.
The “attractive” city in this study is meant to be acity that provides healing. Healing has various effects
such as psychological relief, euphoria, andproductivity improvement. Healing is said to comefrom landscape experiences. From the “Worker
Health Condition Survey” conducted by the Ministryof Health, Labor and Welfare every five years, theimpact of stress is increasing year by year, and it isconsistent with both men and women by age group.
In city planning consideration, it is necessary toapproach it from the perspective of solving or
mitigating social problems.
In order to promote optimization and efficiency
94

GEOMATE – Melbourne, Australia, 11-13 November 2020

performed from the viewpoint height of 1.6m along
the route. Patterns were shot in two ways, clockwise
and counterclockwise. A questionnaire survey on thetwo patterns of sequence landscape will be conductedbased on the photographs taken.

landscape analysis is being attempted as a viewpoint
field, and that discussions on semantic recognition
seem to have a limit. [10]Although research on healing and impressions ofstreetscapes has been performed from numerous paststudies, it is hard to say that there are much sufficient
discussions on “healing”. Although there are paststudies that analyze healing from one angle of thelandscape, no previous studies have grasped healing
as a place. This study attempts to quantitatively
analyze healing as a place by grasping the routes inthe target landscape from different migratory
behaviors.
RESEARCH METHODS
The analysis method used a descriptive andquantitative analysis based on a questionnaire survey
using materials obtained by field observation. The
location of this research from Nakanoshima Park in
Chuo Ward, Osaka City, Osaka Prefecture - Japan.

Fig. 2 How to collect local materials

Outline of Target Area

Second, the details of the landscape analysis aredescribed. The landscape analysis will be conducted
on 8 points that feel healing and 3 points that do not
feel healing, for a total of 11 points. The landscapeconstituent elements of each landscape image are
extracted mainly using Photoshop. The landscapecomponents are roughly divided into “natural
resources”, “artificial resources”, and others. In
addition, natural resources will be subdivided into 5
items such as trees, sky, rivers, roses, and others; andartificial resources will be subdivided into 4 items
such as roads, humans, structures inside and outside
the premises, and others. In selecting these items,
Hayashi et al. (3) referred to the selection of theelements that make up the urban streetscape.

It was the first designated scenic park in Osaka,
established in 1884. The surrounding area is linedwith historic buildings such as the Central Public Hall
and Nakanoshima Library, and is easily accessible bypublic transportation from the Keihan and Midosuji
Lines, and in recent years has attracted attention as a
tourist destination. The main pedestrian flow routes
are limited due to the structure of the park, and there
are many park users who walk around according tothese routes. The landscape of healing is captured byfocusing on the limited flow routes.

Fig. 1

Geographical location of Nakanoshima Park

Analysis Techniques
The analysis techniques used in this study include:a. Questionnaire survey
b. Landscape analysisc. Correlation analysis
First, we will cover the details of the questionnaire
survey. The starting point is the starting point of thetarget park route. Shooting points are set at 10m
intervals from each starting point, and shooting is

Fig. 3
95

Example of simple extraction of landscapeGEOMATE – Melbourne, Australia, 11-13 November 2020

As mentioned before, the ratio of the landscape fromeach observation point was calculated for eachlandscape element. Figures 5 and 6 show thelandscape-only components that are expected to beaffected by the unique landscape from the survey
results. An example of a place where you can easily
feel healing is a landscape that contains many natural
resources such as trees, sky, low-rise trees and lawns.
In addition, the scenery with a good view is divided
into two patterns at the observation points. These are,1) a landscape that is rich in greenery (natural
resource) and 2) a landscape where natural resources
and artificial resources exist at the same ratio. On theother hand, the place where it is hard to feel healing
is a landscape with a very high proportion of roads
and structures inside and outside the site. Regarding
the relationship between healing and landscape, theabove-mentioned questionnaire survey hypothesized
the importance of natural resources and artificial
resources, but landscape analysis suggested theexistence of landscape components that can be judged
to be effective.

elements
Finally, we'll discuss the details of correlation
analysis. Correlation analysis is performed using thehealing assessment value and the ratio of landscapecomponents, which were analyzed using Microsoft
Excel.
RESULTS AND DISCCUSION
Results and discussion of the questionnaire survey
The questionnaire survey was conducted on a totalof 20 students, mainly belonging to the same
university. Since it is between clockwise andcounterclockwise, the points are labeled as clockwise
and counterclockwise. Eight points (points 24, 25, 26,
27, 28, 63, 64, 65) are considered the points of viewthat are particularly easy to see the landscape andhealing effects. Point 28 has the highest score of 45
points. A total of 54 points were evaluated as positive.
On the other hand, there are 3 spots (20,34,49) where
no healing was felt. Overall, point 34 is significantly
lower than other points, at -47 points. A total of 14
points were evaluated as negative. For Nakanoshima
Park (which was surveyed this time) it is consideredfrom the results that there are a mixture of local
healing points and non-healing points. Moreover, theresults suggest that the landscape distribution ofnatural resources and artificial resources has a greater
relationship than the observed data.

Fig. 5 Percentage of landscape components at points
where healing is felt

Fig. 6 Percentage of landscape components at points
where healing is not really felt
Result discussion of the correlation analysis
The correlation with the healing evaluation from thecorrelation coefficient is shown in Tab. 1 and Fig.815. Following multicollinearity, the level with asignificance probability of 0.01% or less will be
adopted. From the results, differences were observed

Fig. 4 Changes in questionnaire results at eachobservation position
Results and discussion of the landscape analysis

96

GEOMATE – Melbourne, Australia, 11-13 November 2020

males at point 20 and female girls at point 65 areaffected by gender and age group. It is assumed thatthese factors are the cause of error. Counterclockwise
63 has a high positive healing rating despite a low
ratio of natural resources-other. This indicates that thelandscape before point 63 is relevant. Since the bridge
and the east side of the park can be seen from thelandscape, and the details can be clearly seen at point
63, the visibility of the landscape affects the healingevaluation regardless of the increase / decrease inother ratios of natural resources.
Next, we will look at “population resources-roads”.
From the results, it is considered that the higher the
proportion of road area in the landscape, the morenegative the effect on healing. At the points 20,34,49
where healing is not felt, the ratio exceeds about 30%.
On the other hand, all healing points are less than 30%.
I think this will be an index in the composition of thelandscape that is considered healing.
Finally, we will consider the “population resourcesstructures inside and outside the site”. Mainly theseon-site and off-site structures include streetlights,
benches, signs, etc. inside the park, and buildings
outside the park. Healing is divided into positive andnegative, with the ratio of structures inside andoutside the site comprising about 30%. In theclockwise direction, points 24 to 28 form the highest
scoring group; and in the counterclockwise direction,
points 63 to 65 form the highest scoring group. From
this, it is not necessary that the proportion of thestructures inside and outside the site in the landscapeis low, and it can be inferred that there are conditions
such as the traits and the number of things that makeup the structure themselves. From the above, we wereable to understand the landscape components related
to healing evaluation and their criteria.

from the analysis for natural resources-trees and otherstructures, artificial resources-roads, and structuresinside and outside the site. A statistically significant
difference was observed for all items except for“natural resources-trees” which had a slight positive
correlation. For “natural resources-others”, positive
correlations were found, and highly accurate analysis
results were obtained. “Man-made resources-Roads”
showed a negative correlation, and a certain degree of
accuracy was also found. It can be seen that the“artificial resources-structures inside and outside thesite” also had a negative correlation as with roads, andthe accuracy of results also holds a certain high level.
Considering the correlation analysis, from theresults, it can be seen that there is a positive effect onhealing when trees exceed 40% of the landscapecomposition. At points 20, 34, 49 where theproportion of trees is low, the healing score is very
low regardless of whether the rotation is clockwise orcounterclockwise. However, although the percentageof trees was low at points 63 to 65, the averagehealing score was higher than that at points 24 to 28
in the counterclockwise direction. From this, thearrangement of trees in the landscape is considered.
The composition of the landscape differs dependingon whether the trees are in the near view or distantview from the viewpoint. In other words, although thetree itself has a positive function in healing, it isspeculated that its arrangement is important.
Next, we will look at “natural resources-other items”.
Similar effects can be seen in the effects of otheritems in the clockwise and counterclockwise
directions. Natural resources-Other items mainly
include lawns and bushes in the landscape. From theresults, if the composition ratio is 25% or more, it has
a positive effect on healing. Focusing on points 20, 65
in the clockwise direction and point 63 in the
counterclockwise direction, the outliers are those witha large error from the regression line. Clockwise point
20 makes up 20% of natural resources-others, andthere is a good possibility that a positive evaluation
will be obtained. However, healing results is very low.
It is considered that this is related to the public toilet,
which is a man-made structure in the near view ofpoint 20. Public toilets belong to the category of“structures inside and outside the premises”, and it isconsidered that the higher the proportion of structures,the more negatively impacted the healing is.
Therefore, it is considered that the reason why point
20 is calculated as an outlier with a large error is the
public toilet in the landscape. Clockwise point 65 hasa similar natural resource-other landscape ratio tolocation 20, but receives a positive healing rating. We
think that this is related to the roses and children in
the foreground. The proportion of roses at point 65 is
0.68%, which is higher than other points. The
percentage of people are high, at 1.61%. From theresults, the higher the percentage of people, the lower
the healing evaluation, but it is considered that adult

Table.1 Correlation coefficient of each landscapecomponent

97

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig.12 Correlation of Clockwise-Artificial resourcesRoad
CONCLUSIONSIn this study, we conducted a survey analysis overthree phases regarding healing evaluation considering
the difference in migratory behavior, and clarified thecriteria of landscape constituent elements that provide
healing as a place. The results are summarized below.

Fig.8 Correlation of Clockwise-Natural resourcesTree

Fig.9

Correlation of
resources-Tree

Counterclockwise-NaturalFig.13 Correlation of Counterclockwise-Artificial
resources-Road

Fig.10 Correlation of Clockwise-Artificial resourcesOthers

Fig.14 Correlation of Clockwise-Artificial resourcesOn-site and off-site structures

Fig.11 Correlation of Counterclockwise-Artificial
resources-Others

Fig.15

Correlation of Clockwise-Artificial
resources-On-site and off-site structures

Tab.2 Impact criteria for components
LandscapePositive
Negative
components
influences
influences
Natural resourcesX≧40%
40%≧X
Trees
Natural resourcesX≧25%
25%≧X
Others98

GEOMATE – Melbourne, Australia, 11-13 November 2020

Artificial resourcesRoad

30%≧X

X≧30%

Artificial resources- On-

30%≧X

X≧30%

[5] Taro Kagawa, Ayako Taniguti,Satoshi Fuji,
Quantitativeevaluationoflandscapeimprovement measures using simple evaluation
model for streetscape, Journal of the City
Planning Institute of Japan, No.6 pp.31-41, 2009[6] Saya Mori, Tomohide Okada, A study on thecharacteristics of landscape that promotes the
migratory behavior of the narrow streets in thecity block, Conference for Architecture andInfrastructure Environment journal, No.13,
pp.556-564, 2017[7] Masahiro
Hayashi,
HiroshiNakahara,
Revaluation of the Urban Landscape Using theTime Axis -Characterization of Sequential
Landscape for the Improvement of Value in the
Metropolitan Suburbs, BULLETIN OF JSSD,
Vol.64, No.1, pp.59-68, 2017[8] Kazuki Nakamura, Masatsu Kii, Evaluation of thequality of walking space by pedestrian attributes
based on perceptual factors, Conference forArchitecture and Infrastructure Environment
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Perceptions and Pedestrian Walking Behavior in
City Centers -A Case Study of Monami Ikebukuro
Park-,Journal of the City Planning Institute ofJapan , Vol.53 No.3 , 2018[10] Hiroyasu Shirayanagi, Katsuya Hirano, Limits offield semantic recognition, ournal of the City
Planning Institute of Japan, No.6 pp.160-165,201

site and off-site structures

ACKNOWLEDGMENTS
This paper was supported by the Osaka Institute ofTechnology and its affiliated professor Kazunari
Tanaka's equipment. We would like to express our
gratitude.
REFERENCES[1] Kazuko KOGA, Yutaka IWASAKI, Exploring
Emotional Experiences of the Healing Effects ofGreenery: A Text Mining Analysis,
Environmental Information Science Academic
Research Paper Presentation 32 (2018) pp.269274[2] Hikaru Nagata , Effects of Healing in Urban
Environment-Impact of Landscape-, Laboratoryof urban planning and management, Tokyo City
University[3] Fusayo Asano, Yoshihide Takaesu, Noriko
Yamamoto, Survey on Images of “Healing
Landscape”, Japanese Society of People-Plant
Relationships journal 5(2) pp.25-30,2006[4] Yuichi SAKAI, Yoshio FUJII, An Analytical
Method of Streetscape Evaluation, J.JILA 65(5)pp.833-836, 200299

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ANALYSIS OF EMBANKMENT STRESSES PRODUCED DURING
CONSTRUCTION AND IN-SERVICE PHASES CONSIDERINGEMBANKMENT GEOMETRIES

1

Shin-ichi KANAZAWA1 and Haruna IGARASHI2
National Institute of Technology, Fukushima College, Japan; 2OYO Corporation

ABSTRACT
Soil compaction tests are carried out to improve embankment construction engineering characteristics, such asstability and deformation. For construction, enclosed embankment structures perform total strengthening and ameasure of drainage functions. This provides stability, improves deformation properties, and prevents thedegradation or rot of objects placed in the embankment fill. Nevertheless, chances are that inadequate compaction
will cause material, such as rocks or stones, to fall from the slope surface, as well as erosion. Therefore, thestandards for embankment slope steepness set forth the use of a 1:1.8 ratio, permissible on an as-needed basis toallow for mechanical rolling for compaction. When constructing steep embankment slopes, it is necessary to
conduct embankment stability checks and fully consider the provision of slope protection to prevent erosion.
However, differences in stress conditions attributable to varying slope steepness have not been elucidated. In thisstudy analyzes embankment construction in terms of different construction seasons and slope change, using theunsaturated soil-water-air coupled FEM analysis program. In this way, more accurate qualitative evaluation could
be achieved.
Keywords: Embankment Geometries, Unsaturated soil, Compaction, Finite element analysis

embankment intermittently after the commencementof construction and during the in-service phase.
Accordingly, rainfall during construction may change
stress conditions inside the embankment. However,embankments are constructed in no specific season
and are subject to various levels of rainfall during
construction. Generally, embankments are notconstructed in the rainy season; although,occasionally, this happens for unavoidable reasons. In
such cases, attention is paid to weather, and drainage
measures are implemented. Moreover, embankments
are subject to climate conditions, such as repeateddrying and wetting cycles. Therefore, the effects ofstress behavior occurring during transitions between
unsaturated and saturated conditions are notnegligible. Hence, to estimate the initial stressconditions taking place after embankment
construction, it is important to consider the effects ofdry and wet climate conditions. This leads to the needfor analysis techniques that consider climaticconditions, such as rainfall and evaporation.
Moreover, differences in the steepness ofembankment slopes are an important factor.
Standards are in place that specify embankment slope
steepness for different embankment materials andheights. In general, low embankments are regarded as
not at risk for a major collapse if their slope steepness
is 1:1.5 and their construction is sound. This does not
apply to those embankment materials that are
problematic, specifically in terms of quality.
However, at the slope steepness of 1:1.5, the slope

INTRODUCTION
In recent years, torrential rain and other extreme
weather events have caused frequent disasters acrossthe world. It is urgently necessary to establish an
analysis technique to continuously evaluate andexamine the quality of embankment structures during
construction and in-service phases. In this regard, it isimportant to determine the initial stress. Normally,
embankment structures are made up of compacted
earth in order to improve stability and deformation
properties. However, existing reports present manycases of collapsed embankment structures that faildue to torrential rains.
Currently, to evaluate embankment stability
against heavy rains, Embankment relies heavily onfour factors: treatment of foundation ground, qualityof embankment materials, degree of compaction, andhandling of water. Specifically, the guidelines place
importance on drainage. In addition, engineers rely on
rules of thumb in carrying out embankment work,
maintenance, and management. Considering these
conditions, it is highly probable that, in the future,importance will be placed on embankment
maintenance and management against torrential rains.
Another set of important factors includes
embankment drainage measures and when theembankment is subjected to the effects of rainfall.
Drainage measures taken against rainfall during
embankment construction differ from those taken
during the in-service phase. Rainfall affects the100

GEOMATE – Melbourne, Australia, 11-13 November 2020

face is unlikely to receive adequate compaction.
Chances are that inadequate compaction will causematerial, such as rocks or stones, to fall from the slope
surface, as well as erosion. Therefore, the standards
for embankment slope steepness set forth the use of a
1:1.8 ratio, permissible on an as-needed basis to allow
for mechanical rolling for compaction. When
constructing steep embankment slopes, it is necessary
to conduct embankment stability checks and fully
consider the provision of slope protection to prevent
erosion. However, differences in stress conditions
attributable to varying slope steepness have not been
elucidated.
To meet this challenge, this study analyzes
embankment construction in terms of different
construction seasons and slope change, using theunsaturated soil-water-air coupled FEM analysis
program (DACSAR-MP)[1].

1
3
p ' = σ' : 1, q =
s:s
3
2

(6)

1
s =σ' − p ' 1 =A : σ', A =I − 1 ⊗ 1
3(7)Here, nE is the shape parameter; εν is the plastic
pvolume strain; M is the q/p’ in the limit state; D is'the dilatancy coefficient; psat is the yield stress at
saturation; a and ns are the parameters representing

the increase in yield stress due to unsaturation; λ isthe compression index; and κ is the expansion index.
Equation (8) shows pore water velocity. Equation (9)shows air velocity. Pore water and air flow follow
Darcy’s law.

vw =−k w ⋅ gradh

RESEARCH METHODp−k a ⋅ gradha , ha =a
va =

(9)

γw

Soil/Water/Air Coupled Finite Analysis CodeThe finite element analysis code used in this study
formulates the unsaturated soil constitutive model
proposed by Ohno et al [2] . This model is framed as
the soil/water/air coupled problem using the threephase mixture theory. Equation (1) shows theeffective stress. Equation (2) shows the base stresstensor and suction stress. Equation (3) shows suction.
σ' = σ net + ps 1

(1)

σ net =
σ − pa 1, ps =
Se s

(2)

S r − S rcs =−pa pw , Se =1 − S rc

(8)Here, vw is the pore water velocity; va is the air
velocity; k w is the hydraulic conductivity; k a is thecoefficient of air permeability; h is the total head;γ w is the unit weight of water; and ha is thepneumatic head. Equations (10)-(11) show hydraulicconductivity and the coefficient of air permeability by
way of Mualem's [3] formula and the Van Genuchten[4] formula.

)

(

2

m
1
1 

k=
krwk wsat= Se 2 1 − 1 − Se m  k wsat
w



(3)

(

ka =
kra k ares =(1 − Se ) 2 1 − Se m
1

Here, σ' is the effective stress tensor; σ is thebase stress tensor; 1 is the second order unit tensor;
σ is the total stress tensor; s is the suction; ps is the

1

) k

(10)

2m

ares

(11)

net

Here, krw is the ratio of hydraulic conductivity;

kra is the ratio of coefficient of air permeability; mis the Mualem constant; k wsat is the hydraulicconductivity at saturation; k ares is the coefficient ofsuction stress; pa is the pore air pressure; pw is thepore water pressure; S r is the degree of saturation;

Se is the effective degree of saturation; and S rc is the

air permeability in dry conditions. Equations (12)(13) show the continuous formula of pore water and
air using three-phase mixture theory.

degree of saturation at s→∞. Equations (4), (5), (6)and (7) provide the yield function.
=f ( σ',
ζ , ενp ) MD lnnEp'
MD  q +− ενp 0='nE  Mp ' ζ psatλ −κζ =exp (1 − Se ) ln a  , MD =


ns



1 + e0nSr − S r εv + divvw =0(4)(1 − Sr ) εv + nSr − n (1 − Sr )

(5)

(12)

p a
0 (13)− divva =pa + p0

Here, n is porosity; εv is volumetric strain; and101

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table.1 Material Parameter
λ
0.18
n
1.0

ANALYSIS CONDITIONS
Degree of saturation Sr (-)

ka
1.00
kw
0.01

500

Drying
AD=34.7,BD=5.9

0.6
0.4
Wetting
0.2
WW
A =24.0,B =4.6
00
200 400 600
Suction s (kPa)

450

降雨量
Rainfall400

Evaporation
蒸発散量

350
300
250
200
150
100
50
0

80

1 2 3 4 5 6 7 8 9 10 11 12

Month

Fig.1 Moisture characteristic Fig.2 Rainfall condition
curve
embankment construction season1, and an analysis ofstresses in embankments in service for 10 years, this
6m

9m

3m

6m

Table 1 and Fig. 1 show analysis material
parameters used for both foundation ground andembankment and moisture characteristic curves. The
value was obtained by experiment. Figure 3 shows theembankment used for analysis and its analysis regions.
Regarding the size of the analysis region, thefoundation ground was set to 15 m long and 45 m
wide and the embankment was set to 3 m and 15 mlong at the top and bottom, respectively, and 6 m high.
The foundation ground was set as saturated between
the ground level and 3 m above ground level. Slopes
were set to three levels: 1:1, 1:1.5, and 1:2. The
construction period was set to one month. After beinglaid down, each individual 30 cm layer was subjected
to loading and unloading pressures of 500 kPa torepresent compaction. For displacement boundaries,
the lower end of the foundation ground was fixed in
the vertical and horizontal directions. These
conditions were used to conduct a plane strain
analysis(Ignore depth). The water level boundaries ofthe foundation ground were set to be drained at the
top and bottom boundaries and undrained at the rightand left boundaries. Regarding drainage measures, adrainage layer (3 m long and 0.6 m high) was
provided at the toe of the slope, and the entire slope
face was covered with vegetation mats as a root
element (to consider only evaporation effects). The
water permeability coefficient of the drainage layer
was set to 500 times that of the embankment, with the
air permeability coefficient being 100 times the water
permeability coefficient. Other conditions specified
in the Embankment Earthwork Guidelines [5] werealso fulfilled. Figure 2 shows rainfall conditions [6]used for the analysis. Amounts of rainfall observed in2016 in Utsunomiya, Tochigi Prefecture were used as
rainfall conditions to simulate the average amount of
rainfall in Japan (approx. 1,750 mm). The analysis
selected spring, summer, fall, and winter seasons, as
shown in the figure. For the analysis, the embankment
construction process was assigned a seasonallyaveraged daily amount of rainfall. Evaporation rates
were also matched with rainfall seasons.
Subsequent to an analysis of initial stresses inside
embankment, observations of differences with

1
0.8

Sr0
0.15
Gs
2.7

m
0.8
v
0.33

Vegitation mat
Drainage layer

15m

Analysis Conditions

M
1.33
a
10.0

κ
0.037
nE
1.3

Amounts of rainfall(mm)p0 is atmospheric pressure. The elasto-plastic
constitutive model obtained from Equation (4) andthe equilibrium equation [Equations (12) - (13)] areformulated as the soil/water/air coupled problem.

45m

Fig. 3 Analysis meshstudy evaluated embankment failure, assigningembankment treatments of 10 mm to 50 mm per hour
rainfall. These were applied as torrential rainfall for 1
h and continuous rainfall for 5 h.
3.2 Stress Change inside Embankments in Service for
10 Years, Noting Contributions of Differences in
Slope Face Angle
Figure 4 summarizes analysis results forembankments in service for 10 years in terms of meaneffective principal stress, void ratio, deviatoric stress,
suction, shear strain, and critical state determination
ratio.
First, the foundation ground will be examined.
The values of mean effective principal stress are highwith respect to all angles (I). The values represented
in the void ratio distributions are small for all seasons(II). These results are interpreted as fully
demonstrating compaction effects according to therelationships between void ratio and effective stress.
Next, the mean effective principal stress values
102

GEOMATE – Melbourne, Australia, 11-13 November 2020

0.98

140.0

夏（3m）

夏（6m）

夏（9m）

夏（3m）
summer(3m)夏（6m）summer(6m)夏（9m）summer(9m)

冬（3m）

冬（6m）

冬（9m）

冬（3m）
winter(3m)冬（6m）winter(6m)

冬（9m）winter(9m)(I) Mean effective principal stress(II)Void ratio
139.0

1.0

夏（3m）
summer(3m)winter(3m)夏（6m）summer(6m)夏（9m）summer(9m)夏（3m）summer(3m)winter(6m)winter(9m)winter(3m)(Ⅲ) Deviator stress

夏（9m）summer(9m)winter(6m)winter(9m)(Ⅳ) Suction

0.018

0.000

夏（6m）
summer(6m)

0.000

夏（3m）
Summer(3m)

夏（6m）Summer(6m)

夏（9m）Summer(9m)Winter(3m)Winter(6m)Winter(9m)夏（3m）(V) Shear strain
Fig. 4 Summarizes analysis results

夏（6m）

夏（9m）

(VI) Critical state determination ratiosof embankment having a 9 m long top than withembankment having a 3 m long top. Additionally,
according to II, void ratio values at the toe of the slope
are small. This is interpreted to mean that at higher
slope angles, compressive forces are applied more
intensively to the toe of the slope. Meanwhile, shear
strain values (V) at the toe of the slope are higher forembankment with a 9 m long top than forembankment with a 3 m long top. The critical state
determination ratios (VI) indicate no risk of failure
under the current conditions. However, at steeper
slope angles, the risk of failure is likely high, notably
at the toe of the slope.
Figure 5 presents matrices showing results that
represent the relationship between the amount andduration of rainfall(in Summer). The analysis
assumed that risk of failure was present at levels of
3% or higher shear strain and 1.00 or higher criticalstate determination ratios. Cases with no concern
about failure are indicated with a circle (Blue) andthose with a concern about failure are indicated witha cross (Yellow). Generally, concern about failure
was present with increasing amount and duration of
rainfall. The matrices reveal no difference between
varying slope steepness.
To understand rainwater infiltration conditions at

inside embankment will be compared, noting
seasonal differences. According to I, values are highin winter when the amount of rainfall is small. In
contrast, values are low in summer when the amountof rainfall is large. Meanwhile, according to II, thevoid ratio values are lower in winter than in summer.
In summer, values are high in the slope area. Probablecauses of these results are volumetric expansion due
to rainfall and the loss of suction in summer, as
represented by the suction distribution (IV).
Moreover, strength and deformation properties
improve with increasing suction (decreasing degreeof saturation). Consequently, the internal strength ofthe embankment is likely to be high in the low rainfall
seasons. Shear strain values are low in winter as
revealed by shear strain distributions (V), while insummer, a slight sign of rotational slip is present. The
maximum shear strain value is approximately 1.8%,
at which a failure can occur if torrential rain or otherextreme weather conditions occur.
A comparison considering angle differences
reveals that according to I, mean effective principal
stress values at the toe of the slope are larger for cases

103

GEOMATE – Melbourne, Australia, 11-13 November 2020

August(9m)3m

center of slope

2 hours 3 hours 4 hours

center of slope

Insidethe embankment
toe of slope

Insidethe embankment
toe of slope

toe of slope(c) 9m(b) 6mRainfall

2 hours 3 hours 4 hours(a) 3m(c) 9m(b) 6mRainfall duration
2 hours 3 hours 4 hours(a) 3mdegree of saturation

degree of saturation

degree of saturation(I) Mean effective principal stress×

1 houreffective mean stress(kPa)Rainfall duration
1 houreffective mean stress(kPa)effective mean stress(kPa)(a) check point

Rainfall

August(3m)9m

center of slope

Insidethe embankment(a) 3mAugust(6m)6m

top of slope

top of slope

top of slope

Rainfall duration
1 hour(c) 9m(b) 6m(II) Degree of saturation values

Rainfall×

Fig. 5 Matrices showing results
average rainfall intensity, Fig. 6 shows stress
distributions rendered thorough an analysis ofcontinued rainfall at 30 mm/h. Top contour diagrams
represent stress conditions inside the embankment
immediately following 5 h of 30 mm/h rainfall.
Bottom graphs show the relationships between the analysis period and individual stresses, revealingchanges ove r time in each stress. The colors of thecurves correspond to the colors of embankmentelements shown in the middle.
The mean effective principal stress values (I) are
low at the top and slope face, which were areas withassigned rainfall. The values at the toe of the slope
increase with increasing slope steepness. The graphs
also reveal that the mean effective principal stressvalues at the toe of the slope are higher withincreasing steepness of embankment, and that as
rainfall continues, values inside the embankment
increase while values at the slope face decrease.
The degree of saturation values (II) at the toe ofthe slope are low. Therefore, the drainage layer issatisfactorily functional. The values are high at the
top and slo pe face, which were assigned rainfall, withno sign of rainwater infiltration into the embankment
interior. This ascertains that moisture infiltrates theembankment along the surface layer. This isinterpreted to mean that because of the rapid rate ofrainfall assigned, the moisture had little time to enter
the inner part of the embankment and thus flowed
through the surface layer. Specifically, the analysis(a) 3m(c) 9m(b) 6m(III) Void ratio(a) 3m(c) 9m(b) 6m(IV) Suction(a) 3m(c) 9m(b) 6m(V )Shear strain
0(a) 3m(b) 6m((VI) Critical state determination ratios
Fig. 6 Summarizes analysis results
techniques used in this study accounted for groundinfiltration capacity. The computation took groundinfiltration capacity into account so as not to allow
rainfall to cause a positive pore pressure at the groundlevel. Therefore, the surface layer water flow was
104

GEOMATE – Melbourne, Australia, 11-13 November 2020

CONCLUSION

rendered, and this simulation suggested, as a result,the formation of a surface layer slip. Moreover, thedegree-of-saturation values inside the embankment
and at the toe of the slope are virtually constantagainst the passage of time, as shown by the graphs.
The values at the toe of slope are high due to rainfall
after embankment construction.
Void ratio values (III) at the toe of the slope arelow, with smaller values occurring more intensively
at the toe of the slope with increasing slope steepness.
The graphs also reveal that the void ratio values arehigher, although slightly, at the center of the slope
face, while the values are of lower intensity at the toeof the slope with increasing slope steepness. The
values decrease substantially along the slope face due
to rainfall.
Suction values (IV) are low at the top and slope
face of the embankment, which are areas assigned
rainfall. Because strength and deformation properties
increase with increasing suction, it is highly probablethat the strength of the slope is low. Moreover, thegraphs reveal virtually constant suction values inside
the embankment and at the toe of the slope where thedrainage layer is present. Suction values at the slope
face are largely affected by the amount of rainfall andsubstantially decrease due to rainfall.
Shear strain values (V) are high at the top andslope face of the embankment, which are areas
assigned rainfall. The values at the toe of the slope arehigher with increasing slope steepness. The graphs
also show that shear strain values at the slope face
increase due to rainfall and that the values at the toeof slope are higher with increasing slope steepness.
This suggests the presence of vulnerable regions at
the top and along the slope face, the emergence ofvulnerable regions at the toe of the slope withincreasing slope steepness, and resultant higher risksof embankment failure.
The critical state determination ratios (VI) prove
no risk of failure under the current conditions.
Nonetheless, at higher slope steepness, failure risks
are thought to be high at the toe of the slope.
These findings indicate the occurrence ofvulnerable regions at the top and along the slope, theemergence of vulnerable regions at the toe of theslope with increasing slope steepness, and higher
risks of embankment failure as a result of torrential
rains on an embankment in service for 10 years.
Meanwhile, neither shear strain nor critical state
determination ratios exceeded the set limits.
Therefore, under the current conditions, there is noconcern about failure.

Using the unsaturated soil-water-air coupled FEM
analysis program (DACSAR-MP, dissolved-air
model), this study provided an analyticalrepresentation of stress changes in embankment
caused by rainfall during construction and internalmechanical behaviors of embankment assigned
torrential rains during the in-service phase.
Although matrices and contour diagrams showed
little differences in stress conditions between varying
slope steepness, graphs drawn to describe changes inindividual stress conditions against the passage oftime made differences in stress conditions visuallyclear between varying levels of slope steepness.
Future challenges include simulating theembankment construction phase using the properties
of soil materials from actual ground. In this way,
more accurate qualitative evaluation could beachieved. The conclusions in this study were reachedusing limited conditions, with the analysis results
obtained under ideal conditions. It is necessary to
conduct analyses simulating actual torrential rains in
the future, using analysis conditions better
representing the natural environment.
REFERENCES[1] Kanazawa, S., Toyoshima, K., Kawai, K.,
Tachibana, S. and Iizuka, A.: Analysis ofmechanical behavior of compacted soil with F.E.
method, journal of JSCE, No.68 (2), pp.291-298,
2012.
[2] Ohno, S., Kawai, K. and Tachibana, S.: Elastoplastic constitutive model for unsaturated soil
applied effective degree of saturation as aparameter expressing stiffness, Journal of JSCE,
Vol.63/No.4, pp.1132-1141,2007.
[3] Mualem, Y.: A new model for predicting thehydraulic conductivity of unsaturated porous
media, Water Re-sources Research, Vol.12, No.3,
pp.514-522, 1976.
[4] Van Genuchten: A closed-form equation forpredicting hydraulic of unsaturated soils, Soil
Science Society American Journal, Vol.44,
pp.892-898, 1980.
[5] Japan Road Association,: Roadway Workers - Fill
Construction Guidelines, 2010.
[6] Japan
Meteorological
Agency:http://www.jma.go.jp/jma/index.html.

105

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DEVELOPMENT OF THE GROUND INJECTION METHOD USING
GEO-POLYMER MATERIAL
1*Satoshi Tashiro1, Naoaki Suemasa1, Takamitsu Sasaki2 and Koichi Nagao3
Tokyo City University, Japan ;2 Kyokado Engineering co. Ltd, Japan ;3 Sato kogyo co. Ltd, Japan
ABSTRACT

Severe liquefaction damage has frequently occurred due to earthquakes in the soft grounds in Japan. At
present, although various liquefaction countermeasure methods have been proposed and applied, it has beenrequired to develop a new countermeasure method applicable for narrow spaces such as residential areas at an
inexpensive cost. In this study, we focused on a fine particles grouting method and adopted fine geo-polymer
as a grouting material. The fine particles grouting method is a kind of soil stabilization technique in which fineparticles are injected into sand layers to fill the voids of sand so that the soil is solidified and reinforced. Geopolymer (GP) is well known as a construction material used for ancient Roman buildings and Egyptianpyramids, and its high strength has been maintained even after more than 2000 years have passed. In order tomake this method more effective, we consider the following three factors are necessary; firstly to choose an
optimum "combination" of materials containing GP, secondly to develop "micronization" to pulverize thematerials to be very fine enough to be injected into ground, and finally to establish a "permeation" method todeliver the fine particles into the voids of sand in ground. In this report, the first factor, the "combination" wasfocused on and several patterns of combinations of fine particles were selected and their solidification andcompressive strength were investigated.
Keywords: Liquefaction, The fine particles grouting method, Geo-polymer, combination
is solidified and reinforced. Geo-polymer (GP) is
well known as a construction material used forancient Roman buildings and the Egyptianpyramids, and its high durability has been
maintained even after more than 2000 years havepassed. In order to make this method using GP
more effective, a research process was made asshown in Fig. 2. It indicates the following threefactors are necessary; firstly to choose an optimum"combination" of materials containing GP,
secondly to develop "micronization" to pulverize
the materials to be very fine enough to be injected
into ground, and finally to establish a "permeation"
method to deliver the fine particles into the voids ofsand in ground. In this report, the first factor, the"combination" was focused on and several patternsof combinations of fine particles were selected andtheir solidification and compressive strength were
investigated.

INTRODUCTION
Severe liquefaction damage has frequently
occurred due to earthquakes in soft grounds in Japan.
Figure 1 shows the relationship between
construction area and the expense for liquefaction
measures [1]. At present, although various
liquefaction countermeasure methods have been
proposed and applied, there is hardly any suitablecountermeasure methods applicable for small
spaces such as residential areas at an affordable cost,
as shown in the red frame in Fig. 1.
Fig.1 Relations of construction areas and expense
for liquefaction measures

In this study, we focused on a fine particles grouting
method and adopted fine geo-polymer as a grouting
material. This method uses a soil stabilization
technique in which fine particles are injected into
sand layers to fill the voids of sand so that the soil

Fig. 2 Research process

106GEOMATE – Melbourne, Australia, 11-13 November 2020

OF

suppressed in a process of manufacturing and its
durability can be maintained longer thanconventional concrete. In addition, as a result of

At first, an understanding of a solidification
principle of GP is important to use the material
properly. GP [2] is made by mixing water withalkaline silica solution (active material in asolidification process), and an active filler(amorphous substance particles containing acomponent active in an alkaline region) instead of
cement. Alkaline silica solution is substituted withsodium silicate, and an active filler is substituted
with industrial by-products such as fly ash (FA) or
blast furnace slag fine particles (BS). Fig. 3 shows
the solidification images of cement and GP. As for
cement, the water added to cement is absorbed andreacted with a alkaline component to become
hydrate crystal, as cement particles solidify to
become an aggregate, in which a large number ofthorns on the surface of cement particles areconnected with each other as shown in Fig. 3 (a). As
for GP, on the other hand, when metal ions eluted
from an active filler are contacted with alkaline
silica solution (silicate), crosslinked polymer isgenerated during crosslinking a silicic acidcompound, so that the particles of active filler arecompacted with mineral indeterminate form gel asshown in Fig. 3(b).

having examined fine particles grouting materials
of a non-cement system, Uemura et al. [4]SOLIDIFICATION
PRINCIPLEGROUTING MATERIALS

confirmed that they are effective enough forliquefaction
countermeasures.
From
these
perspectives, GP is expected as a new promisinggrouting material.

Fig. 4 Solidification principle of GP

COMBINATION EXAMINATIONPLURAL FINE PARTICLESUSINGExperiment summary
In this study, the followings were performed byusing fine particles mixtures: confirmation ofsolidification of chemical solutions, pH
measurement, and unconfined compression tests.
The fine particles were put in a beaker in a room
where the temperature was set at 25±3 ℃ and thestates of solidification and the pH were measured in
1, 3, and 7 days after the mixing. The solidification
was measured by observing the states andconfirming the resistance when penetrating a 3mmdiameter metal bar. In addition, four cases among
all the combinations in Table 1, which showedenough solidification, were selected for unconfinedcompression tests. The test specimens were formed
at a relative density Dr=60% with the mixture ofgrouting materials and the quartz sand No. 6, andthen wet-cured at a normal temperature. Then
unconfined compression tests were carried out on
the improved test specimens after 3, 7, and 28 daysof curing. The method of preparing the test
specimen is shown in Fig. 5. In order to facilitate
unmolding of the specimen, grease was applied on
the plastic mold inside and OHP sheet was spreadin the bottom of the test specimen beforehand. In aprocess of preparing the specimen, the sand and thegrout materials were injected in the mold to keep
having flooding parts and the density was adjusted
by tapping the side of the mold with a wooden
hammer. After injection was completed, a porous

Fig. 3 Solidification images of cement and GP
Fig. 4 shows a solidification principle of GP.
According to Ikeda [3], the alkaline silica solution
forming GP consists of active silica and alkaline
sources, and the fillers are classified in two kinds ofan activity filler and the inert filler. The active silica
contains silica such as water glass, and the alkaline
source, such as sodium and calcium hydroxide,
contains a component which react with the active
silica. By mixing these materials, silicate is
generated while alkaline silica solution is generated.
It is said that the fillers are selected in terms ofmineral characteristics and activity, and, inparticular, FA and BS have high activity and affectsilicate crosslinking, whereas the inert filler such as
quartz sand does not react to silicate by itself but
plays a role in aggregating and reinforcing thestrength of GP. Since GP does not use cement, there
are advantages that the expense can be reduced, aquantity of carbon dioxide emission can be107

GEOMATE – Melbourne, Australia, 11-13 November 2020

plate was installed in the upper end of the test
specimen to level the upper part, and the relative
density was confirmed at Dr=60±3%.
The combination conditions for this experiment(Table 1) were arranged, in which a weight ratio ofwater to particles was 0.1, and the grout materials
used for the experiment were as follows: second
crushed SiO2 as main material, active silica,magnesium hydroxide (Mg(OH)2), calcium
hydroxide (Ca(OH)2), aluminium hydroxide(Al(OH)3) and magnesium oxide (MgO) as alkaline
sources to activate, and FA and BS which containamorphous silica as active filler. Fig. 6 shows thegrain size accumulation curves of the grout
materials used in the experiment. All the materials
were absolutely dried and powdered.

Fig. 6 Grain size accumulation curve of grout
materials
Laboratory findings and consideration
The solidification and the pH obtained from thetests are shown in Table 1 and the changes in pH ofeach case in Fig. 7. In Table 1, the marks represent
as follows. ○: solidified, ×: unsolidified, and △:the state between ○ and × (satisfying theconditions that the surface of chemical solution
inclined when a beaker inclined, and a hole made byinserting a metal bar to a mixed specimen did not
close). Due to the demolding failures at D5 and D6,
their pHs were unmeasurable. Comparisons weremade from Table 1 and Fig. 7, focusing on active
materials. First, in the cases with Mg (OH)2, the pHs
were approximately 9 and the changes over time
were likely to be small, and less solidifications wereconfirmed. Next, in Ca (OH) 2, the initial pHs wereapproximately 12 and decreased with solidification.
In Al (OH)3, the pHs were approximately 8 andthere were two patterns that the pHs decreased in
the early days of curing (C3, C4) and remained
unchanged over time (C1, C2). Finally, in MgO, theinitial pHs, approximately 10, remained unchangedin the early days of curing with FA, however, thepHs increased to approximately 12 with BS, andthen decreased with solidification in both cases.

Fig. 5 Method of preparing test specimen
Table 1. Combination conditions
Case
A1
A2
A3
A4
B1

pH(Day)
0
9.0
9.2
9.2
9.2

1
9.1
9.1
9.3
9.3

3
8.7
9.0
9.1
9.1

Solidification(Day)
7
8.6
8.8
8.8
8.8

12.2 10.3 10.0 9.9

0
×
×
×
×
△

1
×
×
×
×
〇

3
×
×
×
×
〇

7
○
△
×

SiO₂
4.490
SiO₂

Ca(OH)₂
1.010

BS
4.490

Ca(OH)₂
1.010
Al(OH)₃
1.010

FA
2.990

Al(OH)₃FA

1.010
Al(OH)₃
1.010

4.490
BS
4.490

Al(OH)₃
1.010

FA
2.990

MgO
8.999
MgO
8.999
MgO
8.999
MgO

FA
1.010
BS
1.010
FA
0.505
FA

1.010
MgO
1.010
MgO

8.999
BS
8.999
FA

BS

1.010

4.500

4.500

12.3 11.7 11.3 10.8

×

〇

〇

〇

B4

12.5 12.3 11.1 10.9

×

〇

〇

〇

C1

8.3

×

〇

〇

〇

C3
C4

7.7
8.3

7.2
8.2

6.9
8.2

7.2
8.1

×
×
×

〇
×
×

〇
〇
〇

〇
〇
〇

D1

10.5 12.4 10.1 9.9

×

△

〇

〇

D2

10.5 12.4 12.2 11.5

×

△

〇

〇

D3

10.4 12.2 11.9 11.2

×

△

〇

〇

D4

10.5 12.2 11.9 9.8

×

〇

〇

〇

D5

10.5 12.2 11.7

×

〇

〇

〇

D6

10.5 11.9 10.9

-

×

〇

〇

0.1

FA
4.490

B3

7.9

0.1

Ca(OH)₂
1.010

〇

6.9

BS
4.490

SiO₂
4.490

〇

7.2

Mg(OH)₂
1.010
Mg(OH)₂

〇

〇

7.7

FA
4.490

1.010
Ca(OH)₂
1.010

×

C2

0.1

Mg(OH)₂
1.010

SiO₂
8.999

9.4

7.9

4.490
SiO₂
4.490

P/W

△

12.5 10.1 9.8

6.9

8.999
SiO₂

Combination(g)Mg(OH)₂
1.010

SiO₂
2.990

B2

6.3

SiO₂

〇

2.990
SiO₂
8.999
SiO₂
4.490
SiO₂
4.490
SiO₂
2.990

FA
2.990

BS
2.990

0.1
0.1
0.1
0.1

BS
2.990

0.1
0.1
0.1
0.1

BS
2.990

0.1
0.1
0.1

BS
0.505

0.1
0.1

Fig. 7 Changes in pH of each case

0.1
0.1

108

GEOMATE – Melbourne, Australia, 11-13 November 2020

Among all the cases, Fig. 8 shows the fullysolidified four cases, B2, B3, C2, and D5. There wasa tendency that the alkaline component neutralized
during solidification so that the pH significantly
decreased.

Fig. 8 Cases of full solidification
Unconfined compression tests were carried out on
the above cases. The states of the test specimens
after the tests are shown in Fig. 9 and the results ofapplying maximum compressive stresses on thespecimens in 3, 7 and 28 days of curing are shownin Fig. 10.

Fig. 10 Maximum compressive stressComparison was made focusing on amorphoussilicas in the cases where SiO2 was used as a mainmaterial. In the combination of Al (OH)3, C2, since
the pH was neutral and the change with the elapseof time was smaller than those of Ca (OH)2 andMgO, the durability tended to easily deteriorate.
Next, in the cases where Ca (OH)2 was used as
active material, the pH had a tendency to decrease
close to be neutral in the early days and the strength
was reinforced more with BS (B3) than FA (B2). As
known from Table 2, FA contains much SiO2
whereas BS contains much CaO. It is thought thatthe durability changed depending on the differencein characteristics and activity of mineral. On theother hand, when MgO was used as active material
and BS was added, the pH changed not to be neutral
but to be alkaline in the early days, so that thechemical solution was temporarily solidified but did
not lead to strength increase.

Fig. 9 States of test specimens after unconfinedcompression tests

Table 2. Chemical components (FA, BS)
In Fig. 9, all the improved test specimens became
independent in 3-day curing, which indicates all the
combinations of chemical solutions were solidified
because only quartz sand No. 6 is not capable to be
independent. Among them, split failure wasconfirmed in B2 and D5, while shear failure wasconfirmed in B3. In 7-day curing, although C2 andD5 were temporarily independent, but unable tomaintain enough durability for unconfinedcompression tests so that the maximum
compressive stresses were set at 0 kN/m2 as seen in
Fig. 10. On the other hand, the changes in color of
specimens were confirmed in some cases where
unconfined compression tests were carried out. For
example, compared 7-day to 1-day curing in B3 in
Fig 9, the color changed to be blue. From this, there
is a possibility that color changes by a reaction ofchemical solution.

SiO₂
Al₂O₃
CaO
MgO
FA 40.1%-74.4% 15.7%-35.2% 0.3%-10.1% 0.2%-7.4%
BS 33.8%
13.4%
41.7%
7.4%

T-Fe
0.4%

Fe₂O₃
1.4%-17.5%-

The strength of all the test specimens decreased
from 3-day to 7-day curing, however, the strengthof B3 in 28-day curing exceeded that of 3-day. We
consider the reason of the decrease after 3-day
curing was caused by errors resulting from themixing method to manufacture specimens.
COMBINATION EXAMINATION USINGTWO KINDS OF BLAST FURNACE SLAG
Experiment summary
From the findings from Chapter 3 that the109

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 11 Grain size accumulation curves of grout
materials
Laboratory findings and consideration
The changes in pH in these cases are shown in
Fig.12. There was no significant difference in thepHs between the cases of BS with gypsum and thatwithout gypsum, and a similar tendency was seen in
Fig. 7. In addition, due to failures at demolding, thepHs were unmeasurable in Case 1, 2, 5, and 6 in 7day curing.
Fig. 12 Changes in pH of each casestrength was reinforced in the cases of Ca (OH)
2+BS and MgO+BS combinations, the following
materials were used in this chapter. SiO2: as mainmaterial, BS with gypsum and without gypsum:
active fillers, and Ca (OH)2 and MgO: active
materials.
Theexamination
items
were
solidificationofchemicalsolution,pHmeasurement, and unconfined compression test.
Table 3 shows solidification and the pHs. The

Since solidification was confirmed in all
combinations except for Case 5 and 6, where thestates were △ in 1-day curing, unconfinedcompression tests were carried out only on Case 1,
2, 3, and 4. Fig. 13 and Fig. 15 show the stress-strain
curves of 28-day cured specimens and the results ofunconfined compressive strength of 3, 7, and 28day cured specimens in each case, respectively.
Regarding the behavior before failure for 28-day
cured specimens, the compressive stresses gently
increased with strain in Case1 and 2, whereas
sharply increased in Case 3. In contrast, the postpeak compressive stresses gently decreased in Case
1 and 2, whereas suddenly decreased in Case 3.
From these, it was confirmed that Case3 was highin initial rigidity, but low in ductility.

specimens were made using the selected 4 kinds of
combinations
which
showedsufficientsolidification in Chapter 3 and then cured under thesame conditions. Unconfined compression tests
were carried out on these specimens. Grain size
accumulation curves of grout materials are shownin Fig. 11. SiO2 and MgO used in this experimentwere same as those in Chapter 3 and BS with
gypsum and without gypsum were absolutely dried
and powered conditions.

Fig. 13 Stress-strain relations of 28-day curing
The state of each test specimen is shown in Fig. 14.
The color changed into light blue in Case 1 and 2 in
1-day curing, but no color variation was observed inCase 3 and 4. In 7-day of curing, the color changed
into light blue in Case1, 2 and 3, but dark blue inCase 4. Thus, it is thought that the color changed
according as the chemical solutions reacted with

Table 3. Solidification and pH

110

GEOMATE – Melbourne, Australia, 11-13 November 2020

each other and solidified. In Case 4, however,
although the color changed, the test specimen
collapsed. As a result, similar to D5 (MgO+BS
combination) in Chapter 3, the change of color was
related to solidification of the chemical solutions
but it did not necessarily lead to strength increase.

Based on Chapter 3, according as pH changed,
the solidification strength was reinforced more in
the case of BS than that of FA in the early days ofcuring. In addition, when compared the cases of BSwith and without gypsum, there was a possibility
that gypsum could promote solidifying from thefindings in Chapter 4. As seen from B3 of Chapter
3 and Case 1 of Chapter 4, the combinations thatshowed great decrease in pH in the early period ofcuring, had a tendency of high strength due to active
alkali consumption. With the goal of obtaining highstrength and long-term durability of the fineparticles grouting materials, the effects on asolidification mechanism will be examined inrelation to combinations of fine particles, changes
in pH, and crystal structures.

ACKNOWLEDGEMENT
Have Taiheiyo Cement Corp. Sano, Ishimoto
provide high-purity silica to Suzuki Kogyo
Chikaishi in Cal technical center (CA(OH)2) to JFE
Steel Corporation Oka in BS on performing thisstudy, thank you very much for your help. I write it
down here and express gratitude.

Fig. 14 State of each test specimen
Since the maximum compressive stress of Case 3(with gypsum) was bigger than that of Case 2(without gypsum) in 28-day of curing as seen from
Fig. 15, it was revealed that the effect of gypsum
was low in the case of SiO2+Ca(OH)2+BS, but thestrength increased in the case of MgO+BS. Gypsum(CaSO4, 2H2O) [7], which is composed of sulfate
and calcium, solidifies when mixed with water. It
appears that solidification was promoted in theprocess that the contained calcium was consumed
and the pH changed from alkaline to neutral.

REFERENCES[1]Study on earthquake-resistant measures
Committee of the creation residential land: Guide- to protect a single-family house from study onearthquake-resistant measures committee's report- liquefaction of the creation residential land,
Japanese Geotechnical Society Kanto Branch,p209, 2014[2]Koji Harada: Study on durability of geo-polymer
mortar, concrete engineering annual memoirs,
Vol.33,No.1,pp1937-1942, 2011[3]Ko Ikeda: Become normal temperaturesolidification and the materials of the mineralparticles with the geo-polymer binder, resources
and material, the Mining and Materials
Processing Institute of Japan, Vol.114,p.497-500,
1998[4]Kentaro Uemura: Study on ground improvement
technology by the penetration infusion of mixed
fine particles infusion materials, Tokyo City
University doctoral dissertation, pp.59-80, 2018
[5]Association of Japanese fly ash: Science,physical property, www.japan-flyash.com of the
fly ash[6]JFE Steel Corporation: Steel slag productgeneral catalog[7]Fuji gypsum: Material, www.fujisekkou.com
called the gypsum

Fig. 15 Maximum compressive stress
CONCLUSION

111

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EVALUATION OF EARTHQUAKE-INDUCED SLOPE FAILURE
HAZARD USING THE ANALYTIC HIERARCHY PROCESS METHOD
Masanori Kohno1 and Yusuke Ono1
Faculty of Engineering, Tottori University, Japan

1

ABSTRACT
Landslides or slope failures are often caused by an earthquake. The 2018 Hokkaido Eastern Iburi Earthquake
and the 2016 Kumamoto Earthquake in Japan caused many slope failures widely and densely. This study
suggested an earthquake-induced slope failure hazard mapping method using the analytic hierarchy process(AHP) and a geographic information system (GIS) for to specific districts: Atsuma district in Hokkaido andMinamiaso district in Kumamoto. The earthquake-induced slope failure hazard assessment system used here isbased on the landslide distribution (NIED catalog, Japan) including tens of thousands of places in Atuma andMinamiaso districts. We considered relationships between landslide distribution and factors of landslide hazard(elevation, slope angle, slope type, catchment degree, geology and vegetation). These relations were introduced
to pairwise comparisons of factors in the AHP method. The AHP method was applied to evaluate slope failure
hazard rank. An earthquake-induced slope failure hazard map was created based on the slope failure hazard rankof I-V. Two earthquake-induced slope failure distributions are almost consistent with the slope failure hazard
rank. These results provide a practical method useful for evaluating earthquake-induced slope failure hazards.
Keywords: Earthquake-induced slope failure, AHP method, GIS, Landslide distribution
landslide topography, the method used in this study
mainly considers re-active type landslides. Thismethod was developed by several skilled engineers,
who have conducted landslide surveys over many
years. However, it largely depends on the subjective
and empirical judgments of the evaluators, and it
requires time and a high level of skill because theevaluation factors are subdivided.
This study suggested an earthquake-induced
slope failure hazard mapping method using the AHP
method and a GIS for to specific areas: Atsuma
district in Hokkaido and Minamiaso district inKumamoto. The Atsuma and Minamiaso districts arewithin the inner Kamuikotan metamorphic belt andRyoke belt [19], respectively. Hence, the AHP and
GIS analytical range in this study are these
geological belt districts (Fig. 1).

INTRODUCTION
Alteration by volcanic hydrothermal systems andthe formation of crushing and weathering zones overgeologic time have occurred widely throughout the
Pacific region. As a result of these geologic
processes, fragile geological features and steep
topography can be found on the surface of the Earth.
Landslides or slope failures are often caused by anearthquake. The 2018 Hokkaido Eastern Iburi
Earthquake and the 2016 Kumamoto Earthquake inJapan caused many slope failures widely anddensely (Fig. 1, Plots of slope failure distributions
are based on GSI [1] and NIED [2]). The predictionof the risk of slope failure and establishment ofappropriate measures is extremely important.
However, the implementation of preventive
measures in all locations that are at risk of slope
failure is financially challenging. This necessitates
the identification of slopes with a high risk ofcollapse and the implementation of investigative
countermeasures based on the level of priority.
Previous studies have tried the slope failure hazard
mapping based on various techniques. More recently,
many researchers and engineers studied the landslide
hazard assessment using geographic information
system (GIS) [3]–[9]. Furthermore, some researchersreported that method of evaluation of slope failure
hazard using the analytic hierarchy process (AHP)
method [6], [9]–[18]. This method can be eliminatedsubjective/empirical judgment of researchers/
engineers. However, as the methods that use the
AHP model are limited to evaluation within the

METHODOLOGYThe AHP is a decision-making method
developed by Saaty [20] and is also an analyticalmethod that determines the importance weight ofevaluation factors based on paired comparison. First,
the relationship between the landslide topography
area and the evaluation factors/elements area (i.e.,
the area ratio of the two) was statistically clarified
based on the landslide distribution map [21] (Fig. 1)
of Kamuikotan metamorphic belt and Ryoke belt. As
shown in Fig. 2, this study is used raster data oflandslide body. In this study, there are six evaluation
factors related to the level of risk of landslide basedon the AHP as follows: elevation, slope angle, slope
112

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 1

Location of the study area (Kamuikotan metamorphic belt and Ryoke belt districts), and distribution
map of the landslides in Atsuma and Minamiaso districts.

Fig. 2

Conversion of landslides topography toraster data (20 m × 20 m cell).

type, catchment degree, geology, and vegetation.
The topographic, geological and vegetation data
used in this study are Digital Elevation Model(DEM10B) by GSI [22], Seamless Digital
Geological Map of Japan (1:200,000) by Geological
Survey of Japan [23] and The Natural
Environmental Information GIS by Biodiversity
Center of Japan [24]. The slope type can be divided
into 9 specific types (Fig. 3), based on Suzuki [25].
In the calculation of catchment degree, Terrain
Analysis Using Digital Elevation Models [26] isincorporated into GIS.
A hierarchical system for landslide risk
assessment was constructed based on these set of
evaluation factors. From this hierarchical structure,
the importance weight of each element is first calcu-

Fig. 3

Basic classification of slope type (Based on
Suzuki [25]).

lated, and then the importance weight between thehigher evaluation factors is calculated. Saaty [20]gave a nine points scale. Equal importance is 1;
moderate importance is 3; strong importance is 5;
113

GEOMATE – Melbourne, Australia, 11-13 November 2020very strong importance is 7; and absolute importanceis 9. 2, 4, 6 and 8 are assigned for compromises
between the above. That is the subjective andempirical judgment of the engineer is indispensable
when using the AHP standard nine points scale.
Therefore, in this study, the importance weights
were calculated by introducing the statistical survey
results of the relationship between the landslide
topography distribution and the factors in the paired
comparison. Landslide hazard mapping is conductedby ranking the landslide risk for each evaluation
factor based on the estimated importance weight ofeach hierarchy and totaling the landslide risk scores
applicable to each evaluation factor on each slope
using GIS. Finally, evaluation factors withmaximum impact are identified and their suitability
is examined by comparing the composed landslide
hazard map with the slope failure distribution due toearthquake.

Xr
Vr
Xs
Vs
Xv
Rv
Vv
0

20

40

60

80

100

Landslide topography area [%]Fig. 4

As an example, relationship between landslide
distribution and factor (slope type), and the pairwise
comparisons of factor (slope type) in the hierarchy
level 2 are shown in Figs. 4 and 5. Slope failures aredensely distributed in convex-ridge, concave-ridge,
convex-valley and concave-valley slope type area.
According to these results, it is thought that slope
failure linked to earthquake is closely related toslope type. The weight of hierarchy level 1 (Fig. 6)
is calculated based on Kohno et al [18]. The
landslide hazard score p is calculated using the AHP
hierarchy weight, and is given by Eq. (1) [18]:One example of relationship between
landslide distribution and evaluation factors(e.g. slope type). Abbreviations in thisfigure correspond to Fig. 3.

Level 2

Xr

Rr

Vv

weightXr

1

17.00 1.02 23.91 22.14 16.59 1.09 20.36 1.04

24.29

Rr

0.06

Vr

0.98 16.69

Pairwise comparisons of factors in the hierarchy
level 1 and 2

Rs

Vs

Xv

Rv

0.06 1.41 1.30 0.98 0.06 1.20 0.06

1.43

1

23.85

23.47 21.74 16.28 1.07 19.98 1.02
10.93 0.69 0.05 0.85 0.04

1.02

1

1.10

0.04 0.71 0.04

0.05 0.77 0.05 1.08

Vs

0.06 1.02 0.06 1.44 1.34

Xv

0.92 15.59 0.93 21.94 20.32 15.22

Rv

0.05 0.83 0.05 1.17 1.09 0.81 0.05

Vv

0.96 16.35 0.98 23.00 21.30 15.95 1.05 19.58

0.75 0.05 0.92 0.05
1

0.07 1.23 0.06

1.46

1

22.29

18.68 0.95
1

0.05

1.19

1

23.37

One example of pairwise comparisons ofevaluation factors (e.g. slope type) in thehierarchy level 2. Abbreviations in thisfigure correspond to Fig. 3.

Hierarchy level 1

A

B

F

weightA

Elevation

1

0.67 0.60 1.80 0.71 0.83

13.64

B

Slope angle

1.50

1

0.89 2.70 1.07 1.25

20.43

C

Slope type1.68 1.12

1

22.87

D

Catchment degree

E

Geology

1.40 0.93 0.83 2.52

F

Vegetation

AC

0.40 0.46

7.57

1

1.16

19.09

1.20 0.80 0.72 2.17 0.86

1

16.40

B 20m

ラスターデータ
Raster data

ベクターデータの属性

This study’ s data
ラスターデータの属性

4E

3.02 1.20 1.39

0.55 0.37 0.33

ベクターデータ
Vector data

Landslide hazard mapping based on 6 factors

D1

One example of pairwise comparisons ofevaluation factors in the hierarchy level 1.

1

3

3
2

Fig. 7
114

1

Xs

Xs

Fig. 6One example of landslide hazard map in
Kamuikotan metamorphic belt and Ryoke belt distri-

Vr

Rs

Fig. 5(1)

where W1 is the weight of hierarchy level 1, W2 is
The weight of hierarchy level 2, W2MAX is thehighest weight of hierarchy level 2. Landslide
hazard mapping is performed on the basis of thehazard rank classified from I to V, with the totaled
landslide risk score corresponding to each evaluation
factor (overlaid using GIS; Fig. 7) for a certain slope(raster data: 20 m × 20 m cell), set as the totallandslide risk score P. If the total score P is large,
the risk of landslide is high, and if the score is small,the risk is low.

Ryoke belt
districtRs

EARTHQUAKE-INDUCED SLOPE FAILURE
HAZARD MAPPINGWW
p  1 2  100
W2MAX

Kamuikotan
metamorphic
belt district

Rr

2

4
4
4
4
4
4
3
3
3
2

4
4
4
4
4
4
4
3
3
3

3
3
3
3
3
3
4
4
3
3

3
2
2
2
2
3
4
4
4
4

2
2
2
2
2
3
4
4
4
4

2
1
1
2
2
3
3
3
3
4

2
1
1
1
2
2
2
2
3
3

2
1
1
1
1
2
2
2
2
3

2
2
1
1
1
2
2
2
3
4

3
2
2
2
2
2
2
3
4
4

標高
Elevation
斜面傾
Slope angle
斜面型
Slope type
集水度
Catchment
degree
地質
Geology
植生
Vegetation

ハザード

Slope failure
マップ
hazard map

GIS model’s raster data overlaying.

GEOMATE – Melbourne, Australia, 11-13 November 2020

Cumulative relative frequency
of slope failure area [%]

100
100

80
80

60
60

40
40

20
20

00

Fig. 8One example of landslide hazard map in
Kamuikotan metamorphic belt and Ryoke
belt districts.
0.50000.5

Slope failure area [%]Cumulative relative frequency
of slope failure area [%]

100
100

0.5

1.5

2.5

3.5

4.5

II

2.5

III

3.5

IV

4.5

V

5.5

Landslide hazard rank

0.5

1.5

2.5

3.5

4.5

5.5

60
60

40
40

20
20

B
0.5

I

1.5

II

2.5

III

3.5

IV

4.5

V

5.5

Landslide hazard rank

5.5

Elevation
Slope type
Geology

Ryoke belt
district0.30000.3

Slope angle
Catchment degree
Vegetation

Fig. 10 Relationship between landslide hazard rank
and cumulative relative frequency. Eachweight of the hierarchy level is 99.9%. A:
Kamuikotan metamorphic belt district, B:
Ryoke belt district.

0.20000.2

0.10000.1

0.0000
0.0

0.5

I

1.5

II

2.5

III

3.5

IV

4.5

V

not necessarily represent the
distribution caused by earthquakes.

5.5

Landslide hazard rank

Fig. 9

I

1.5

80
80

00

Kamuikotan
metamorphic
belt district

0.40000.4

A
0.5

Landslidehazardtopographical factors

Relationship between landslide hazard rank
and slope failure distributions in thelandslide hazard map of Fig. 8.

mapping

slope
based

failureonThus, we investigated the factors among theevaluation factors that had maximum effect on slope
failure. This method calculates the landslide risk
score by positioning the importance of evaluation
factors in hierarchy level 1 as 99.9 and theimportance of other evaluation factors as 0.2 andperforms landslide hazard mapping accordingly. As
a result, relationship between landslide hazard rank
and cumulative relative frequency is shown in Fig.
10. Slope angle, slope type, and catchment degree

cts, and relationship between landslide hazard rank
and slope failure area based on these maps areshown in Figs. 8 and 9.
The composed landslide hazard map clearlyshows that landslides did not necessarily occur inareas with high landslide risk. Therefore, thelandslide hazard map created using this method does
115

GEOMATE – Melbourne, Australia, 11-13 November 2020

were found to exert maximum effect on slope failure
distribution due to earthquake in both districts.
Therefore, we performed landslide hazard mappingby employing only slope angle, slope type, andcatchment degree as evaluation factors andinvestigated the relationship between the composed
hazard map and slope failure distribution.
Relationship between landslide hazard rank andslope failure distributions in the landslide hazard
map based on slope angle, slope type and catchment
degree is shown in Fig. 11. With the increasinglandslide hazard rank, the slope failure area shows
the trend of increase. There was significant
correspondence between all case hazard ranks andslope failure distribution.

Slope failure area [%]0.50000.50.40000.4

0.30000.3

Kamuikotan
metamorphic
belt district

0.20000.2

Ryoke belt
district0.10000.1

0.0000
0.0

0.5

I

1.5

II

2.5

III

3.5

IV

4.5

V

5.5

Landslide hazard rank

Fig. 11 Relationship between landslide hazard rank
and slope failure distributions in thelandslide hazard map based on slope angle,
slope type and catchment degree in the
Kamuikotan metamorphic belt and Ryoke
belt districts.

CONCLUSION
This study suggested an earthquake-induced
slope failure hazard mapping method using the AHP
and a GIS for to specific districts: Kamuikotan
metamorphic belt and Ryoke belt district.
In this study, the importance weights were
calculated by introducing the statistical survey
results of the relationship between the landslide
topography distribution and the factors in the paired
comparison in AHP. The AHP method was applied
to evaluate slope failure hazard rank. An earthquakeinduced slope failure hazard map was created basedon the slope failure hazard rank of I-V. As a result,slope angle, slope type, and catchment degree were
found to exert maximum effect on slope failure
distribution due to earthquake in both districts:
Atsuma district in Hokkaido and Minamiaso districtin Kumamoto. There was significant correspondence
between all case hazard ranks and two earthquakeinduced slope failure distributions.
This study is used to identify the factors that are
related to landslides, establish a relation between thefactors and landslides, to predict the landslide hazard
in the future based on such a relationship. Our
methods provide a practical method useful forevaluating earthquake-induced slope failure hazards.
Aiming at further development of evaluation oflandslide hazard in the future, we will apply our
method to various districts.

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[5] Wang L., Sawada K. and Moriguchi S.,
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[6] Phukon P., Chetia D. and Das P., Landslide
susceptibility assessment in the Guwahati City,
Assam using Analytic Hierarchy Process

ACKNOWLEDGMENTS
This work was partly supported by research grant(Principal Investigator: Masanori Kohno) of Japan
Society of Erosion Control Engineering, research
grant (Principal Investigator: Masanori Kohno) of
Chugoku Construction Public utility Association,
and Grants-in-Aid for Scientific Research“KAKENHI” (Grant Number 18H01523, Principal
Investigator: Yusuke Ono) of Japanese Society for
the Promotion of Sciences (JSPS). These supports
are gratefully acknowledged.
116

GEOMATE – Melbourne, Australia, 11-13 November 2020

(AHP) and Geographic Information System(GIS), International Journal of Computer
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V.N., Risk analysis of landslide impacts on
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[10] Miyagi T., Prasad G.B., Tanavud C., Potichan
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[13] Hasekioğullari G.D. and Ercanoglu M., A new
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[14] Kayastha P., Dhital M.R. and Smedt F.D.,
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case study from the Tinau watershed, west
Nepal, Computers & Geosciences, Vol. 52,
2013, pp.398-408.
[15] Kohno M. and Maeda H., Experiment on
landslide hazard mapping, based on AHP
method, in consideration of point load strengthof hydrothermally altered rock: -Example in
the Ohekisawa-Shikerebenbetsugawa landslidearea, Japan-, Landslides – Journal of the Japan
Landslide Society, Vol. 50, Issue 3, 2013,
pp.121-129 (in Japanese).
[16] Kil S.-H., Lee D.K., Kim J.-H., Li M.-H. andNewman G., Utilizing the analytic hierarchy
process to establish weighted values forevaluation the stability of slope revegetation
based on hydroseeding applications in South
Korea, Sustainability, Vol. 8, Issue 1, 2016, 58.
[17] Zhang J. He P., Xiao J. and Xu F., Risk
assessment model of expansive soil slope
stability based on Fuzzy-AHP method and its
engineering application, Geomatics, Natural
Hazards and Risk, Vol. 9, Issue 1, 2018, pp.
389-402.
[18] Kohno M., Noguchi T. and Nishimura T.,
Landslide hazard mapping in the Chugoku
Region using AHP method and GIS, Japan-,Landslides – Journal of the Japan LandslideSociety, Vol. 57, Issue 1, 2020, pp.3-11 (inJapanese).
[19] Isozaki Y., Maruyama S., Aoki K., Nakama T.,
Miyashita A. and Otoh S., Geotectonic
subdivision of the Japanese Islands revisited:
Categorization and definition of elements andboundaries of Pacific-type（Miyashiro-type）
Orogen, Journal of Geography, Vol. 119, Issue
6, 2010, pp.999-1053 (in Japanese).
[20] Satty T.L., The Analytic Hierarchy Process,
McGraw-Hill Int. Book Co., New York, 1980,
pp.1-287.
[21] National Research Institute for Earth Scienceand Disaster Resilience (NIED), Digitalarchive for Landslide Distribution Maps,
http://dil-opac.bosai.go.jp/publication/nied_
tech_note/landslidemap/gis.html (in Japanese),
Accessed April 2020.
[22] Geospatial Information Authority of Japan(GSI), Digital Elevation Model (DEM10B),
https://fgd.gsi.go.jp/download/menu.php(inJapanese), Accessed April 2020.
[23] Geological Survey of Japan, National Instituteof Advanced Industrial Science andTechnology(AIST),
Seamless
DigitalGeological Map of Japan (1:200,000),
https://gbank.gsj.jp/ seamless/ (Basic version,
data update date: 29 May, 2015) (in Japanese),
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[24] Biodiversity Center of Japan, The Natural
Environmental Information GIS, https://www.biodic.go.jp/trialSystem/top_en.html (inJapanese), Accessed April 2020.
[25] Suzuki T., Introduction to Map Reading for
Civil Engineers Volume l -Geomorphological
Basis for Map Reading-, Kokon Shoin,
Publishers, Tokyo, 1997, pp.1-200.
[26] Tarboton, D., Terrain Analysis Using Digital
Elevation Models, http://hydrology.usu.edu/
taudem/taudem5/, Accessed April 2020.
117

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ESTIMATION OF SUBSURFACE STRUCTURE BASED ON
MICROTREMOR AND SEISMIC OBSERVATION IN THE DAMAGEDAREA DUE TO 2018 HOKKAIDO EASTERN IBURI EARTHQUAKE,
HOKKAIDO, JAPANTatsuya Noguchi1, Isamu Nishimura1, Yusuke Ono1 and Masanori Kohno1
1Faculty of Engineering, Tottori University, Japan
ABSTRACT

To investigate a cause of serious damages of the 2018 Hokkaido Eastern Iburi Earthquake, we observed
microtremors and aftershocks at a landslide area and around strong ground motion observation stations. Subsurface
velocity structures were determined by using a heuristic approach based on forward calculation by using phasevelocities and H/V spectra. As a result, S-wave velocity structures were estimated, and predominant frequency of
H/V spectra were obtained. The predominant frequencies of H/V spectra of the microtremor and seismic are 0.8Hz
to 5Hz. The S-wave velocity of the uppermost layer is 75 to 130 m/s, and its thickness is around 10 m. Especially,
it is suggested that the uppermost layer of volcanic ash had caused a landslide in the Atsuma Town sites. The
predominant frequency corresponds to the thickness of alluvial layers with S-wave velocities of 75 to 300 m/s. It
was also found that the predominant direction of the horizontal earthquake ground motion of the landslide wasaffected by the topography of the mountain.
Keywords: microtremor, S-wave velocity, the 2018 Hokkaido Eastern Iburi Earthquake, landslide(OIW) where seismic intensity 6+ was recorded. The
location of the observation sites is shown in Fig. 1.
Microtremor array observation was conducted at
all sites. A three-component acceleration typeseismometer (JU410) was used for the observation.
JU410 is a device that can record microtremors witha period of 0.05 to 10 seconds. In the arrayobservation, four seismometers were used, one on thecenter of the circle and three on the circumference atequal intervals. Each device was synchronized with
the GPS clock. The sampling frequency formeasurement was 200 Hz, and the observation timewas 15 minutes. The array radii were 1 m for ATM,
MKW1, MKW2 and OIW, and 1 and 4 m for SKN.
At the landslide site, one seismometer was installed

INTRODUCTION
The 2018 Hokkaido Eastern Iburi Earthquake
occurred in the eastern Hokkaido Iburi district, andseismic intensity 6+ was recorded at several locations
such as Atsuma, Abira, and Mukawa Town [1].
According to the seismic intensity distribution map
estimated by the Japan Meteorological Agency, aseismic intensity area of 6+ or 7 was estimated over awide area in the eastern region of Hokkaido Iburi [2].
This earthquake caused widespread landslides aroundAtsuma Town near the epicenter. In addition,buildings were locally damaged in Mukawa Town
and Abira Town [3]. To study the causes of thesedamages, it is necessary to investigate the subsurface
structure and strong ground motion characteristics.
Therefore, in this study, we conducted microtremor
observations at the site of landslide, the site ofbuilding damage, and the site where seismic intensityof 6+ or more was observed. In addition, temporary
aftershock observations were also conducted at thesite of landslide.
OBSERVATION
Microtremor observation
In this study, the following 5 points were targeted.
These are the landslide site in Atsuma Town(ATM),the building damage site in Mukawa Town (MKW1) ,JMA observation point ’Shikanuma’ where seismic
intensity 7 was recorded (SKN), and K-NET
observation point site HKD126 (MKW2) , HKD127

Fig.1 Location of the observation sites (Red line
area is display area of Fig.5)

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig.2 H/V spectra of microtremors (NS: North-South, EW: East-West, Comp: Composition, reddashed lines are the theoretical value of the Rayleigh wave by subsurface structure model)

Fig.3 Dispersion curves of phase velocity (Solid lines are curves obtained for each radius. Red dashed lines
are the theoretical value of the Rayleigh wave by subsurface structure model)
Table 1

Specifications of observed earthquake

Date and time

LatitudeLongitude

Depth

Magnitude

11/08/2018 0:54:32.2

42°33.7′N

141°56.2′E

24km

Mj 3.2

11/08/2018 11:15:13.4

42°13.7′N

141°23.5′E

104km

Mj 3.7

11/08/2018 11:34:24.8

42°47.1′N

142°0.5′E

37km

Mj 2.7at the mountainside (ATMT) and one at the foot
(ATMB)of the mountain. At this place, the sampling
frequency for measurement was 100 Hz, and theobservation time was 10 minutes. The ATMB was
installed at almost the same position as the arrayobservation point (ATM), and the ATMT was
installed near the seismograph used in the seismic
observation described later.

Temporary seismic observation
Temporary seismic observation was carried out
from November 7th to November 8th, 2018 at thelandslide site of Atsuma Town. For the seismic
observation, we used a system in which a threecomponent velocity seismometer (KVS300) wasconnected to the data recorder (HKS9700) by a cable.

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2 Subsurface structure models
MKW1

ATM
Thickness
ρ(t/m3)
(m)
4
1.5
3
1.6
3
1.7
60
1.9
250
2.1
∞
2.2

Vp(m/s)Vs(m/s)

1370
1460
1620
1900
2070
2620

75
150
300
550
700
1200

MKW2

Thickness
Thickness
ρ(t/m3) Vp(m/s) Vs(m/s)ρ(t/m3)
(m)
(m)
9
1.2
1380
80
6
1.2
35
1.7
1570
250
15
1.5
80
1.9
1900
550
40
1.7
∞
2.2
2620
1200
40
1.9
∞
2.2

Vs(m/s)

1380
1460
1570
1900
2620

80
150
250
550
1200

SKN

OIW
Thickness
ρ(t/m3)
(m)
2
1.5
5
1.6
30
1.9
60
2.1
∞
2.2

Vp(m/s)Vp(m/s)Vs(m/s)

1430
1470
1900
2070
2620

130
160
550
700
1200

Thickness
ρ(t/m3) Vp(m/s) Vs(m/s)
(m)
3
1.5
1380
80
4
1.6
1460
150
6
1.7
1620
300
30
1.9
1900
550
200
2.1
2070
700
∞
2.2
2620
1200

Fig.4 S-wave velocity structures (Black numbers are S-wave velocity value. Red numbers are
depth to layer of Vs=550ms)
The seismograph was buried near the ATMT where
the mountainside and no landslide occurred. The
observation specifications are continuous recording
at a sampling frequency of 200 Hz, time calibration
with GPS clock, and battery drive. Three earthquakes
could be recorded during the temporary observation
during the observation period. The specifications of
the observed earthquakes are shown in Table 1. The
observation points are shown in described later Fig.5.

dispersion curves were estimated based on the CCA
method [5]. The setting conditions for the analysis are
as follows. At least 5 sections were selected byautomatic extraction using the RMS value of the
microtremor record of each seismograph with asegment length of 10.24 seconds. The power
spectrum of these sections was smoothed by a Parzen
window with a bandwidth of 0.3 Hz and thenaveraged. Using the power spectrum of eachseismograph, the phase velocity dispersion curve wasobtained based on the CCA method [5]. Dispersion
curves of phase velocity are shown in Fig.3.
The velocity structures were determined on
forward calculation based on borehole data of PSlogging and geological situation. We determined thesubsurface structures to satisfy the H/V spectra andthe dispersion curves of phase velocity by using
fundamental mode of Rayleigh wave in the frequency
range of 2 - 40Hz. The parameters of the subsurface
structure models are number of layers, density, Pwave velocity; Vp, S-wave velocity; Vs and layers
thickness. The physical property values of thesubsurface structure models are shown in Table 2.
The S-wave velocity structures are shown in Fig.4.

ANALYSIS
Microtremor observation data
For the analysis of the 3-componet single-site
observation data, portions of 20.48s without artificial
noise were selected from the array observation
records. A horizontal-to-vertical spectral ratio (H/V
spectrum) was calculated by using the averaged eachcomponent Fourier spectra of the selected data.
Horizontal components were composed. Spectra weresmoothed by a log-window [4] with coefficient of 20.
H/V spectra of each sites are shown in Fig.2.
For array observation data, the phase velocity
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig.5 Location of observation and topographic map
at landslide site (Orange area is landslide area)

Fig.8 Velocity waveforms

Fig.6 H/V spectra of microtremor in ATM site

Fig.7 Horizontal spectral ratio HT/HB in ATM site

Fig.9 Particle motions of horizontal components

121GEOMATE – Melbourne, Australia, 11-13 November 2020

Seismic observation data
The data processing is as follows. 10.24 secondsof the S wave part of each component was cut out
from the seismic record, and after applying a cosine
taper with a half cycle of 0.5 seconds to both ends,
zero data was added to 20.48 seconds. The Fourier
spectrum of each component was calculated bysmoothing with a Parzen window with a bandwidthof 0.2 Hz. The H/V spectrum was calculated from theFourier spectra of these three components.
RESULTS
Microtremor H/V spectra
As a feature of the H/V spectra, there is clear peakat all points (Fig.2). The predominant frequencies ofthe H/V spectra are ATM: 3.96Hz, MKW1: 0.78Hz,
MKW2: 0.93Hz, SKN: 3.86Hz, and OIW: 4.79Hz.
The detailed situation of the H/V shape is as follows.
There is a small peak at 1.14 Hz in SKN, and a small
peak near 1.5Hz in MKW1, and there is aphenomenon that the shape near the peak is different
in OIW.

Fig.10 H/V spectra of earthquakes at ATM
and 1200m/s layers to tuff or Neogene sedimentaryrocks. The estimation accuracy is lower than that of
the shallow structure model because the dispersion
curves of phase velocity were not obtained
sufficiently in the frequency range required foranalysis. However, the Vs=550 m/s and 700 m/s layer
thicknesses are reasonable results because they arealmost consistent with the previous estimation results[7] of microtremor observation at HKD126.

S-wave velocity structures
The S wave velocity structure at each point is asfollows (Table 2, Fig.4). The layer thickness of thesoft soil layer (Vs=80 to 300m/s) is described. The
layer thickness of the low-velocity layer is Vs=80m/s,
3-9m (OIW is none). The thickness of Vs=150m/s,
300m/s under the first layer is max 60m in Mukawatown (MKW1, MKW2), and the thickness of theother sites is 7-10m. The geological condition of thisarea is that the ash layers of Tarumae volcanoes arewidely distributed [6]. Therefore, it is considered thatthe first layer of the landslide site (ATM) corresponds
to the volcanic ash layer of Tarumae Volcano, andthat the first layer of the ground model corresponds tothe collapsed layer. Layers with S-wave velocities
less than 300m/s at other points correspond to
alluvium or volcanic ash layers. As for the soil typeof the alluvium, the layers of clay, gravel, and silt arealternated in Mukawa town, according to the PS
logging information of K-NET (HKD126). In
addition, it was reported that a non-linear groundresponse occurred at the earthquake observation point(HKD126) in Mukawa Town [7], which may berelated to building damage. In the future, it isnecessary to study the non-linear ground response
based on the ground structure model obtained in thisstudy and to clear the factors causing the damage.
The deep structure is as follows. The maximum
layer thickness of the Vs=550m/s and Vs=700m/s
layers is 80m and 250m, respectively. The
Vs=550m/s layer is considered to correspond to
diluvial or volcanic sediments, and the Vs=700m/s

Microtremors at landslide site
Location of observation and topographic map atlandslide site is shown in Fig.5. Fig.5 is a map inwhich the area where the landslide occurred (orangearea) is overlaid on the topographic map, and also
shows the tremor observation points in this study.
H/V spectra of the mountainside (ATMT) and foot
(ATMB) of mountain are shown in Fig.6. The NScomponent of the horizontal motion is the blue line,
the EW component is the orange line, and thecomposited value is the black line in Fig. 6. A clear
peak can be confirmed around 5Hz at both points.
There is a peak near 10Hz, which is higher than
ATMB in the high frequency band of 4Hz or more at
ATMT. Also, although the EW component is larger
than the NS component in ATMT, there is nodifference in each component in ATMB (Fig.6). The
result of calculating the spectral ratio of the horizontal
component HT/HB (combined values in NS and EW
directions) to the spectrum between both points isshown in Fig.7. The HT/HB gradually increases in thefrequency band from 4Hz to 30Hz (Fig.7).
Seismic ground motions at landslide site
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GEOMATE – Melbourne, Australia, 11-13 November 2020

motion based on the information obtained from theobservation. Furthermore, based on the results, weplan to examine the factors causing the strong groundmotion and the factors causing the landslide causedby the earthquake.

The velocity waveforms of the three seismic
records observed are shown in Fig.8. The particle
motions of the velocity record of the horizontal
component with the NS direction as the vertical axis
and the EW direction as the horizontal axis are shownin Fig.9. The H/V spectra are shown in Fig.10 (Theblack line shows the average of the three earthquake
values). The amplitude of the EW component is larger
than that of the NS component for all earthquakes(Fig.8). From the particle motions of horizontal
component, the EW direction is predominant, and theaxis of the predominant direction is slightly rotatedclockwise (Fig.9). Furthermore, from the topographic
map (Fig.5) near the observation point, the directionof the slope is almost the same as the predominantdirection of the particle motion. The characteristic of
H/V is that the EW component is larger than the NScomponent near the peak of 5Hz (Fig.10). It is
possible that such seismic motion characteristics
contributed to the landslide. In the future, it isnecessary to study the factors that cause these
phenomena in more detail and clear the mechanismof landslide caused by this earthquake.

ACKNOWLEDGMENTS
We used seismic intensity information from theJapan Meteorological Agency, and the borehole
information on K-NET and KiK-net of the National
Research Institute for Earth Science and Disaster
Prevention. We used the basic map information of the
Geospatial Information Authority of Japan as the basemap for the location of the observation points.
REFERENCES[1] JMA website “Seismic intensity database”,(https://www.data.jma.go.jp/svd/eqdb/data/shindo/Event.php?ID=9901267), (in Japanese).
[2] JMA website “Estimated seismic intensity
distribution map, 03:08 on September 06, 2018
Iburi district Middle East M6.7”, (https://www.
data.jma.go.jp/svd/eew/data/suikei/2018090603
08_146/201809060308_146_1.html), (inJapanese).
[3] Cabinet Office Japan website “Disastermanagement in Japan”, (http://www.bousai.go.
jp/updates/h30jishin_hokkaido/pdf/310128_jishin_hokkaido.pdf, (in Japanese).
[4] 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.524/133, 1995, pp.247-259 (in Japanese with English
abstract).
[5] Cho I., Tada T., and Shinozaki Y., Centerless
circular array method: Inferring phase velocities
of Rayleigh waves in broad wavelength ranges
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[6] Hirose W., Kawakami G. Kase Y., Ishimaru S.,
Koshimizu K., Koyasu H. and Takahashi R.,
Preliminary report of slope movements at
Atsuma Town and its surrounding areas causedby the 2018 Hokkaido Eastern Iburi Earthquake,
Report of the geological survey of Hokkaido,
Vo.90, 2018, pp.33-44 (in Japanese).
[7] Takai N., Shigefuji M., Horita J., Nomoto S.,
Maeda T., Ichiyanagi M., Takahashi H.,
Yamanaka H., Chimoto K., Tsuno S., Korenaga
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during the 2018 Mw 6.6 Hokkaido eastern Iburi
earthquake, Earth, Planets and Space, Vol. 71,
2019, 10.1186/s40623-019-1044-4.

CONCLUSIONS
1) In Mukawa Town, the predominant frequency of
microtremor H/V was 0.8 - 0.9Hz, and it was
found that the soft soil layers with Vs=80 - 300m/s
was thickly deposited in the ground structure. It isconsidered that this layer amplified the groundmotion, and the possibility of nonlinear response
was also suggested.
2) The predominant frequency of microtremor H/Vat other seismic stations was 4 Hz at Shikanuma,
the JMA site of Atsuma Town, and 5 Hz at K-NETsite HKD127, Abira Town. From the groundstructure model, it was found that there were soft
soil layers at those points and the layer thickness
was thinner than that of K-NET site HKD126 in
Mukawa Town.
3) At the landslide site in Atsuma Town, thepredominant frequency of microtremor H/V is
5Hz, and the subsurface structure has a volcanic
ash layer with Vs=75 - 80m/s. It is possible that
this layer collapsed on the slope near this point.
4) As for the characteristics of microtremors andseismic motions at the landslide site, it was foundthat the amplitude of the EW component seismic
motion is larger at the mountainside than at thefoot of mountain. In addition, it was found that thecharacteristics of the H/V spectra were that the
spectral ratio was larger in the frequency band
higher than 4Hz and that the EW component waslarger near the peak of 5Hz in the seismic groundmotion.
In the future, we plan to evaluate the earthquake123

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STUDY ON COLLAPSE MECHANISM OF RIVER LEVEE
DUE TO EXTERNAL WATER LEVEL FLUCTUATION
Hayato Suzuki1 , Shin-ichi Kanazawa2 , Yudai Tonoki1
1

Social Environmental Systems Engineering Course, National Institute of Technology, Fukushima College,
Advanced Course Program, Japan
2
Civil and Environmental Engineering, National Institute of Technology, Fukushima College, Japan
ABSTRACTIn recent years, extreme weather events related to global climate change have caused enormous damage inJapan. Catastrophically, riverbanks have been repeatedly destroyed by heavy rain in various parts of Japan.
Among them, the damage caused by Typhoon No. 19 in 2019 was the most serious. Fundamentally, a riverembankment is a very important disaster prevention structure to protect riverside residents from damage, such asthat caused by flooding. For this reason, we must ensure sufficient safety. However, design methods based on thecurrent guidelines are not based in well-established technical knowledge. In order to prevent collapse, it isurgently necessary to elucidate the collapse mechanism of riverbanks. Therefore, in this study, we investigatedthe collapse mechanism of the river embankment through model experiments, and we analyzed the factors
contributing to collapse. We aimed to understand the difference in permeation surface formation due to thedifference in the speed of water level rise and layer thickness. In addition, a soil moisture meter was used tocheck the change in saturation inside the model. The experiments were performed with a total of 4 treatments at
layer thicknesses of 20 mm and 40 mm and rates of water level rise of 3.5 cm / min and 7.0 cm / min.
Keyword: River Embankment , External water level fluctuation , Model experiment , Unsaturated soil
Therefore, it is necessary to secure sufficient
safety. External factors related to inundation ofriverbanks include overflow, scouring, andseepage. These three factors are caused byrainfall. To date, when we glimpse the current
situation, where regulations on design andmaintenance of river levees have not beenclarified, the role of these structures as disasterprevention for floods caused by recent local
heavy rainfall have become extremely important.
However, design methods based on the current
guidelines are not based on well-establishedtechnical knowledge. Moreover, because collapse
mechanisms are not fully understood, it isurgently necessary to clarify the collapse
mechanisms of river embankment.
Therefore, in this study, we conducted anexperiment that focus on the piping phenomenon,
for which the cause is more complicated. This
was attempted because we thought it necessary toreview the system of embankment design, which
required knowing the structure and content ofriver embankment. Therefore, we preparedvarious treatments to confirm what kind of
influences are exerted in each collapse mode. To
achieve this purpose, we conducted an infiltration
experiment using a model embankment. A soil
moisture meter (EC-5) was installed inside themodel to measure saturation. A model
embankment was prepared by setting the rate ofwater level rise at 1.4 cm / min and 7.0 cm / minand the layer thickness at 20 mm and 40 mm.

RESEARCH BACKGROUND AND PURPOSE
In recent years, global climate change has caused
enormous damage in Japan. As one of the worst
outcomes, riverbanks have been repeatedly
destroyed by heavy rains in various parts of Japan.
The Kinugawa embankment collapse during
heavy rain in the Kanto and Tohoku regions in
September 2015 and Typhoon No. 19, whichcaused damage to all parts of Japan, in October
2019 describe just some examples. In recent
years, severe damage has also been caused by
local heavy rainfall. Fourteen people died inheavy rains in the Kanto and Tohoku regions andmany houses floated away due to river dikecollapse （ totally destroyed: 81 buildings, half
destroyed: 7045 buildings, floor flooding: 2495
buildings, underfloor flooding: 13159 buildings.
[1] In typhoon 19, 66 died, 13 went missing and
398 were seriously injured. In addition, theembankment collapsed into 71 rivers, including
first-class rivers such as the Chikuma River andAbukuma River. Housing and welfare facilities
were also severely damaged,（totally destroyed:
85 buildings, half destroyed: 324 buildings, floor
flooding: 24266 buildings, underfloor flooding:21474 buildings）, resulting in a major disaster.
[2] In Japan, such disastrous events haveoccurred one after another in recent years. A riverembankment is an extremely important disasterprevention structure to protect the lives ofriverside residents from floods and other disasters.
124

GEOMATE – Melbourne, Australia, 11-13 November 2020

From this, the difference in the collapse form andthe transition of saturation were examined. A
video of the formation of the infiltration surface
was also recorded.

Fig.1

infiltration of river water or rainfall at the time offlood into the levee body and the foundation ground.
This reduces the apparent strength of the levee body.
Rainfall not only weakens the surface layer of thedam or levee, but it also increases the saturation
within the soil of the embankment. This internal
water flow can move soil particles leading tointernal erosion or piping, a major cause ofembankment weakening.
In addition, when a large earthquake occurs,
the levee is largely submerged and deformed dueto the liquefaction of the foundation ground. Insuch cases, river water constantly flows into theinside of the levee in the zero meter area. It isalso assumed that a disaster will occur in these
instances. [3]Chikuma River at Typhoon No. 19

THE PROCESS OF RIVER EMBANKMENTCOLLAPSE

PHYSICAL PROPERTIES OF SOIL
Prior to the model experiment, the properties ofsilica sand Nos. 6, 7, and 8 mixed sand (mixing ratio
5: 2: 5) used for the embankment were set to thefollowing physical properties, according to theguidelines of the Geotechnical Society. A descriptionof each test procedure to obtain the physicalproperties is omitted, and the test results aredescribed below.
① Compaction test
Optimum water content：8.2%
Optimum dry density：1.570g/cm3② Soil particle test
ρs=2.61g/cm3③ Relative density test
Relative density ： 15.03%(Therefore loosesand)④ Particle size test
Effective particle size：2.10
Uniformity coefficient：10.36
Curvature coefficient：2.05
Fine grain content rate：12.7%
From the results of these tests, it was found thatthe model sample was classified as "sand containing
SF fine particles" using triangular coordinates.
It was found that this sample had a poor
composition and was prone to penetration anddisintegration. In this experiment, the infiltration
phenomenon and the collapse form of the riverembankment are examined. As the samples
exhibiting the above-described measured physicalproperties can remarkably demonstrate test results,
they are therefore considered suitable for this test.

According to the current guidelines, dikes madeof soil can easily cause floods and overtopped levees.
This occurs even if the flood level is below theplanned water level, as the levee may be damaged
due to infiltration and erosion, may lead to bankdamage.
The process of bank breakage due to each cause isshown below.

Fig. 2

Levee overtopping process

Fig. 3

Osmotic collapse process

Fig.4

Washbori process

Levee damage due to erosion and overflow iscaused by the movement of soil particles that makeup the dam body. This occurs by eroding the dam
surface or the dam skirt by the scavenging force ofrunning water or overflow water during flooding. It
is speculated that weakening of the surface of thebank due to the infiltration of rainfall is closely
related. On the other hand, levee damage due toinfiltration increases pore water pressure due to the

EXPERIMENT OUTLINEAs shown in Fig. 5, a 0.1m foundation ground andembankment were prepared in a 1.4m × 0.7m ×0.3m acrylic soil layer. Silica sand Nos. 3 and 5
were used at the foundation ground of 0.08 m and125

GEOMATE – Melbourne, Australia, 11-13 November 2020

0.02 m, respectively. In addition, silica sand Nos. 6,
7, and 8 were mixed at 5: 2: 5 for the embankment.
The dimensions of the embankment were 200 mm
in height, 1: 2 slope and 100 mm width at the top.
When preparing the embankment, a soil moisture
meter (EC-5) was inserted at each point 1 to 8, asshown in Fig. 5, to measure the degree of saturation.
In this case, each number from 1 to 8 is each CH
number. In the experimental procedure, the flow rate
is set from the right side of the soil layer to raise thewater level. Regarding the water level rise, the rateof rise in water level was increased by 1.4 cm / minor by 7.0 cm / min by referencing the rainfall data of
the Chikuma River during Typhoon No. 19 in
October 2019. If the water level rises directly into
the foundation ground, it may be eroded by waterpressure, so a space was provided between thefoundation ground and the water inlet. It can be said
that the provision of this space improved the
reproducibility of the experiment by approaching thephenomenon of an actual river embankment.
Moreover, two treatments were used for the layer
thickness of the embankment, 20 mm and 40 mm.
Combining each rate of water level rise with eachlayer thickness, the experiment was conducted witha total of four treatments, or cases. Each case was set
as shown in Table 1 below. The vertical is the layer
thickness, and the horizontal is the speed of waterlevel rise. In addition, the change in saturation ateach measurement point until the embankment
collapse was recorded with data collection software,and the appearance of the infiltration surface wasrecorded by video.

Rate Of Water Level Rise
The speed of water level rise was calculated byreferring to the rainfall data of the Chikuma Riverduring Typhoon No. 19, which occurred in October
2019, with reference to the fast and slow rates of rise.
We set 7.0 cm / min for the fast case and 1.4 cm /min for the slow case, converted them to L / min,and set the flow rate to achieve these fast and slow
water level rises. In order to maintain a constant rateof water level rise from the start of water level rise
to overflow, the space for increasing the transitionon the side of the embankment was divided into
three equal parts, and the flow rate in each section
was calculated using the volume. The experiment
was conducted by adjusting the flow rate for each.

Saturation Measurement Method
A soil moisture meter "EC-5", a data logger
and data collection software were used tomeasure the degree of saturation inside theembankment. EC-5 outputs soil moisture as a
voltage from the permittivity of soil. A
calibration formula for converting the output
voltage value into soil moisture was prepared inadvance, but the embankment material used in theexperiment was recalibrated to improve theaccuracy. Since each EC-5 had individual
differences, we calibrated each of the eight EC-5
used.
EXPERIMENTAL PROCEDURE

100mm

If the layer thickness was 20 mm, it was divided
10 times, and if the layer thickness was 40 mm, it
was divided 5 times. Using this process, theembankment was prepared.
The moisture content was set to 6.2% in order tofocus on the formation of the infiltrated surface andthe morphology of fracture. This is because ± 2% of
the optimum water content is allowed according toactual construction guidelines. In addition, thereason why -2% was used is that 6.2% wasconsidered to be capable of producing anembankment with a smaller dry density and aweaker state than the compaction test.
The experimental procedure was as follows.
① Fabrication of foundation ground
The required amount of silica sand No. 3 wasplaced in the soil layer and tamped to a height of
80 mm. The necessary amount of silica sand No.
5 was added and tamped to a height of 20 mm.
② Making the model embankment
The soil amount calculated for each layer to a
specified density (dry density: 1.545) was mixed
with water to a water content ratio of 6.2% andcompacted until it reached a height of 20 mm or 40
mm (after tamping). After completion of compaction,

200mm

3
Water
level
rise

4
1
6

5

2

7

8

1400mmFig. 5
Table 1

Manufacturing embankment map
Case settings
Water
level
rising speed
1.4cm/min

Layer
thickness
20 ㎜
Layer
thickness
40 ㎜

Water level
rising speed
7.0cm/min

Case1

Case2

Case3

Case4

126

GEOMATE – Melbourne, Australia, 11-13 November 2020

the model was cured for 1 day and excess soil was
removed.
The slope surface was made smooth．
③ Recording
A video camera was used to take a picture of
the formation of the infiltrated surface and thestate of the bank breakage inside the dam. The
EC-5 were used to record the saturation
monitoring inside the dam．
④ Water level rise
The water level was provided at the set discharge,
with reference to the rainfall data of the Chikuma
River.

rose, saturation reached 100% in the order of thearea outside the embankment (2, 8) → the lower partof the embankment (5, 7) → the central part of theembankment (4) and the area near the embankment(1, 6) → the upper part of the embankment (3).
About 18 minutes after the water level started
rising, the saturation level of the entire levee reached100%.
In addition, the transitions of saturation in case 2
are shown in Figs. 7 and 9. After the water level rose,
the saturation level increased from the outer levee tothe outer basin (2,8) to the lower part of the levee
body (5,7), and about 8 minutes after the water levelrose, the levee broke due to overflow. At this point,
the saturation level of the levee (1 and 6) and theupper part of the levee (4 and 3) increased. From this,it was confirmed that when the water level rises
quickly, the levee breaks due to overflow before thewater penetrates the entire levee body. It was also
confirmed that when the water level rises slowly, it
penetrates into the entire levee body and the bottomof the dike inside the levee becomes weak andbreaks. These results demonstrate that water
penetration rate in cases 2 and 4 was faster than incases 1 and 3.
Fig.6-9 shows the saturated boundary formed in
the in the infiltration. Also, Fig.14-17 shows the
final collapse morphology in each case.

EXPERIMENTAL RESULT
Results shown in Fig. 5 confirm that in case 1 theinfiltration surface was formed up to the inner sideof the embankment as the water level increased, andover time the infiltration surface was formed fromthe slope on the inner side of the embankment to theupper part of the embankment. Results also
confirmed that the degree of saturation increased ateach point, corresponding to the timing of theinfiltrated surface formation. It is highly probablethat a landslide created at the bottom of the sediment
was caused by lateral infiltration pressure andbuoyancy from below due to the inundation from theground and the lower part of the dam body. The dike
weakened and collapsed. Next, results shown in Fig.
6 confirm that in case 2, the infiltrated surface wasformed in the shape of an arc from the outside of theembankment, and the inundation was caused by
overflow before the infiltration reached the inside ofthe embankment. However, the water content
increased at the point where no infiltrated surface
was formed (CH1, 6). From this, it can be inferredthat water channels were formed. In case 2, unlike
case 1, the saturation value remained low as a whole.
Therefore, the infiltration phenomenon likely has
little effect on the collapse form because thepermeated flow rate is small. Next, the experimental
results for a layer thickness of 40 mm are discussed.
As shown in Fig.7 for case 3, when the layer
thickness is 20 mm, water permeates the entire
embankment area and enters from the foundation
ground and the lower part of the dam body. Due tothe buoyancy from below, the toe of theembankment became weak and the embankment
broke at this point. In case 4, as shown by Fig. 8, thelevee was breached by overflow before water
penetrated the entire embankment. The saturation
change and the process of infiltrated surface
formation are shown below. As described above,since there was no difference in the morphology due
to a change in layer thickness, we consider thedifference in morphology to be caused by the rate ofwater level rise. Figures 6 and 8 show the saturationtransitions for cases 1 and 3. After the water level
127

Fig.6

case1 (Layer thickness 20 mm: 1.4 cm / min)Fig.7

case2 (Layer thickness 20 mm: 7.0 cm / min)Fig.8

case3 (Layer thickness 40 mm: 1.4 cm / min)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig.9

case4 (Layer thickness 40 mm: 7.0 cm / min)
100
90

CH1

80

CH2

Saturation(%)

70
60

CH3

50

CH4

40

CH5

30

CH6

20

CH7

10

CH8

Fig.14

case1

Collapse form

Fig.15

case2

Collapse form

Fig.16

case3

Collapse form

Fig.17

case4

Collapse form

0

0

100

200

300

400

500

600

700

Elapsed time(s)Fig.10Transition of saturation (case1)

100

Saturation(%)

90
80

CH1

70

CH2

60

CH3

50

CH4

40

CH5

30

CH6

20

CH7

10

CH8

0
0

500

1000

1500

2000

Elapsed time(s)Fig.11

Transition of saturation (case2)

100

Saturation(%)

90
80

CH1

70

CH2

60

CH3

50

CH4

40

CH5

30

CH6

20

CH7

10

CH8

0

0

100

200

300

400

500

600

700

Elapsed time (s)Fig.12

Transition of saturation (case3)

100
90
CH1

Saturation(%)

80
70

CH2

60

CH3

50

CH4

40

CH5

30

CH6

20

CH7

10

CH8

0
0

500

1000

1500

2000

Elapsed time(s)Fig.13

Transition of saturation (case4)

128GEOMATE – Melbourne, Australia, 11-13 November 2020

destruction of the formation of the terrain on the step,
resulting in the promotion of collapse.
It is necessary to confirm not only the layer
thickness and the rate of water level rise, but also thesoil mix, the type of silica sand used, and thecompaction strength to confirm changes in thecollapse mode of the embankment in various states.
It is also important to note that there is nofundamental difference in the degree of basic
bonding between soil particles and the effect ofadhesive force within the bank. Understanding theseneeds, this experiment provides useful informationand knowledge for considering the failure
mechanism of actual embankment.
In addition, Mr. Fukuhara and Mr. Mori, Mr.
Hattori [6] have conducted experiments using plants
collected from actual embankments, and the erosion
resistance of the high-stem herbaceous plants thatgrow on the river embankment is mainly due to theresistance of leaves and stems in the upper part ofthe vegetation. This resistance is exhibited when theflow velocity is reduced. In the future, it may bepossible to obtain more useful experimental resultsby adding not only silica sand but also plants.

CONCLUSION
The experimental results confirmed that layer
thickness did not influence the collapse form, whichlargely depended on the degree of water level rise in
the tank. In case 1 and case 3, the infiltrated surface
was formed from the area outside the embankment
to the area near the embankment, but in CH1, 3, 6
the saturation level started to rise at almost the sametime. Figures 6 and 8 confirm that CH1 and CH6
reached the saturation level of 100% a little faster.
The reason for this is likely that the infiltrated
surface was formed earlier from the foundation
ground / lower part of the levee body, and buoyancy
was generated inside the levee, causing saturation.
This suggests that collapse was caused by osmoticpressure from the lateral direction and the buoyancy
force from the downward direction on the bottom of
the dike in the embankment, which is likely a weak
area. The structure inside the levee should be
reviewed with a slow water level rise. Also, case 2
and case 4 confirm that the infiltrated surface wasformed vertically from the outer side of theembankment and about half of the embankment was
infiltrated and destroyed by overflow. In this case,the structure inside the levee body was not very
relevant, and there was no evidence of collapse on
the outside of the levee surface. Therefore, theheight of the levee body itself or reinforcement of
the inside levee can prevent disasters.
Since the change due to the layer thickness was
small, the results of the rate of water level rise aresummarized as follows.
(1) Case1 and case3
The infiltrated surface was found to form up to theinside of the embankment and then infiltrated into
the crown as the water level rose.
It was found that the infiltrated surface penetrated
into the entire levee body and gradually collapsed in
the vicinity of the underground levee.
(2) Case2 and case4
The infiltrated surface was found to form
vertically and then penetrated into the embankment
side in this way.
It was found that before the water penetrated theentire bank, the levee collapsed due to overflow.
Mr. Tsuneoka and Mr. Furumoto, Ms. Obata, Mr,
Wakasa [4] stated that the entire embankment willreach a nearly fully saturated state when theinfiltration surface rises without any measures.
This also confirms the validity of this experiment.
In addition, Mr. Sekine and Mr. Sato, Mr. Kan [5]stated that the movement of debris leads to the

REFERENCES[1] Fire Department “About the damage situation
related to heavy rain by 2015 typhoon No.
18” (December 16, 2016)[2]Weathernews
https://jp.weathernews.com/news/29409/[3]National Technical Center: Guide for structural
examination of river embankments (revised
version), 2012 ,pp.8-9.
[4]Nobuyuki Tsuneoka, Kazushi Furumoto, Toshiko
Obata, Satoshi Wakasa："Large-scale model test
for measures to strengthen the infiltration ofriverbanks" ； 37th Geotechnical Research
Conference (2002)[5]Masato Sekine , Kosuke Sato, Toshiki Kan ："Study on the effect of gravel and clay containedon the failure process of simulated riverembankment" ； JSCE ProceedingsB1 （ Water
engineering）Vol.74,No.5,2018,I_1099-I_1104.
[6]Naoki Fukuhara , Hirotoshi Mori, Atsushi
Hattori ： "Experimental Study on Erosion
Resistance of High Stem Grass and Plants Using
Large Specimens Collected from River
Embankment" ； JSCE ProceedingsB1 （ Water
engineering ） Vol.70,No.4,2014,I_1351-I_1356.

129

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STRESS ANALYSIS OF RIVER EMBANKMENT CONSIDERING
EXTERNAL WATER LEVEL FLUCTUATION

1Tsubasa Ichijo1, Shin-ichi Kanazawa2
Social Environmental Systems Engineering Course, National Institute of Technology, Fukushima College,
Advanced Course Program, Japan;
2
Civil and Environmental Engineering, National Institute of Technology, Fukushima College, Japan

ABSTRACTIn recent years, local downpours frequently occur in various parts of Japan, and embankments are destroyedby these downpours. Many embankments are historical products of flood control that have been reinforced as they
age by raising their height. However, the structure of the river embankment was developed based on informationfrom disasters that actually occurred, and analysis of the destruction of the structure was not considered in design.
Furthermore, until now, mechanisms of levee collapse have been discussed, but they have not been fully elucidated.
Therefore, in this study, embankment analysis considering difference of layer thickness was performed by using
the unsaturated soil / water / air coupled element finite method analysis program (DACSAR-MP). Stress behavior
in the embankment during the rise of the river water level due to rainfall was analytically expressed, and the effectof the stress was considered to analytically investigate the collapse mechanism of the river embankment. From
this work, it was analytically confirmed that embankment failure mode differed depending on differences in the
speed of water level rise.
Keywords: River embankment, Unsaturated soil, Penetration, Collapse

and earthquakes are the causes of riverbank breakage
due to changes in the outside water level. Of these, all
except for earthquakes are caused by rainfall, and therisk of riverbank breakage is increasing due to therecent local heavy rainfall. Currently, as an evaluation
method for embankment structures, arc slip analysis
is performed using the infiltration analysis results,
such as rainfall, in the embankment design method
Nonetheless, deformation analysis is not performed,
and infiltration analysis and stability analysis are
performed individually. However, the infiltration
problem and the deformation problem are coupled,
and it is hard to say that the current embankment
evaluation method is sufficient.
It is thought that local torrential rain will increasein the future, and it can be said that the risk of bankbreakage will increases alongside this. Since thedamage caused by river embankment is enormous, it
is urgent to take measures against embankment
failure, and for that purpose, it is important toelucidate the destruction mechanism of the riverembankment.
In this study, using the unsaturated soil / water / aircoupled element finite method analysis program(DACSAR-MP) [2], we focused on embankment
reinforcement and conducted the embankment
analysis considering differences in layer thickness.
The purpose of this study is to analytically elucidate
river levee collapse mechanisms by analytically
expressing the stress behavior in the levee body

INTRODUCTION
Due to global climate change, there have beenfrequent reports of local torrential rains in various
parts of Japan in recent years, with associated riverdike collapse. Among these, the breach of theembankment of the Chikuma River caused by
Typhoon No.19 in October 2019 stands out. An
embankment is an extremely important disasterprevention structure that protects the lives of riverside
residents from floods, and it is necessary to ensure thesafety of these structures.
Regarding water infiltration resistance, erosion
resistance and seismic resistance ratings required forriver levees, the existing rules for satisfying the earth
levee principle and cross-sectional shape have beenrevised, and the magnitude of the external force
applied to the levees before the earth structure isconstructed has been stipulated. As a general rule, thedesign of levees for each river should be optimized
considering topography, weather, and river shape.
However, the mechanism of destabilization anddeformation of levees due to floods or earthquakes
has not yet been fully elucidated [1], and the current
guidelines do not necessarily represent wellestablished technical knowledge.
On the other hand, the difficulty of elucidating themechanisms of river levee infiltration and stability
problems is because these problems deal with
unsaturated soil, intermediate soil, compacted soil
and unsteady phenomena. Overflow, erosion, seepage,130

GEOMATE – Melbourne, Australia, 11-13 November 2020

Darcy’s law.

during river water level rise due to and considering
the effect of rainfall.

vw =−k w ⋅ gradh

RESEARCH METHODp−k a ⋅ gradha , ha =ava =Here, vw is the pore water velocity; va is the air

The finite element analysis code (DACSAR-MP)[2] used in this study formulates the unsaturated soil
constitutive model proposed by Ohno et al [3]. Thismodel is framed as the soil/water/air coupled problem
using the three-phase mixture theory. Equation (1)shows the effective stress. Equation (2) shows thebase stress tensor and suction stress. Equation (3)shows suction.
σ' = σ net + ps 1

(1)

σ net =
σ − pa 1, ps =
Se s

(2)

S r − S rcs =−pa pw , Se =1 − S rc(3)

velocity; k w is the hydraulic conductivity; k a is thecoefficient of air permeability; h is the total head;γ w is the unit weight of water; and ha is thepneumatic head. Equations (10)-(11) show hydraulicconductivity and the coefficient of air permeability by
way of Mualem's [4] formula and the Van Genuchten[5] formula.

1

degree of saturation at s→∞. Equations (4), (5), (6)and (7) provide the yield function.

(5)

1
3
p ' = σ' : 1, q =
s:s
3
2

(6)

1
s =σ' − p ' 1 =A : σ', A =I − 1 ⊗ 1
3

(7)



s



1 + e0(1 − Sr ) εv + nSr − n (1 − Sr )

(11)

(12)

p a
0 (13)− divva =pa + p0

Here, n is porosity; εv is volumetric strain; andp0 is atmospheric pressure. The elasto-plastic
constitutive model obtained from Equation (4) andthe equilibrium equation [Equations (12) - (13)] areformulated as the soil/water/air coupled problem.
Moisture Characteristic Curve Used In The
Analysis

Here, nE is the shape parameter; εν is the plastic
pvolume strain; M is the q/p’ in the limit state; D isthe dilatancy coefficient; pares

nSr − S r εv + divvw =
0

(4)

λ −κ
n
ζ =
exp (1 − Se ) ln a  , MD =2m

air permeability in dry conditions. Equations (12)(13) show the continuous formula of pore water and
air using three-phase mixture theory.

Se is the effective degree of saturation; and S rc is the

MD  q − ενp 0=nE  Mp ' ) kkra is the ratio of coefficient of air permeability; mis the Mualem constant; k wsat is the hydraulicconductivity at saturation; k ares is the coefficient ofpore water pressure; S r is the degree of saturation;

+

1

(10)

Here, krw is the ratio of hydraulic conductivity;

suction stress; pa is the pore air pressure; pw is thep'(

kra k ares =ka =(1 − Se ) 2 1 − Se mnet'ζ psat

)

(

2

m
1
1 

m
2
k=
k
=
−
−
k
S
S
1
1
w
rw wsatee k wsatHere, σ' is the effective stress tensor; σ is thebase stress tensor; 1 is the second order unit tensor;
σ is the total stress tensor; s is the suction; ps is the

nE

(9)

γw

Soil/Water/Air Coupled Finite Analysis Code=f ( σ',
ζ , ενp ) MD ln(8)

For a soil-water characteristic curve model, amodel capable of hysteresis expression, as proposedby Kawai et al. [6], is used. In addition, to determine
the logistic curve in the case of DRY and WET,
derived from arbitrary suction and the degree ofsaturation, the logistic curve equation of Sugii andUno [7] was used. This makes it possible to grasp the
moisture conditions of sloped ground where complex
water balance occurs. Figure 1 exhibits the soil-water'
sat is the yield stress at

saturation; a and ns are the parameters representing

the increase in yield stress due to unsaturation; λ isthe compression index; and κ is the expansion index.
Equation (8) shows pore water velocity. Equation (9)shows air velocity. Pore water and air flow follow
131

GEOMATE – Melbourne, Australia, 11-13 November 2020

characteristic curve used in this study.

Soil water characteristic curve

INTERIOR CHANGES IN THE DIKE WITHEMBANKMENT COMPLETION
Analysis Conditions
Figure 2 shows the analytical model. The
foundation ground was 3m long and 45m long, andthe dam body was 5m long, 5m at the top, and 25m at
the bottom, with a slope of 1: 2 [8]. The mesh of themodel is 1m each for the foundation ground, 10 cm
for the levee body, and it was divided into 25 parts
horizontally. The right side of the analytical model
was assumed to be the river side, and the drainage
layer was 0.6m in length and 6m in width from theleft end of the embankment.

Table 1.1 Foundation ground material parameters
λ
0.18
e0
1.2

λ
0.18
e0
1

5m
3m

Drainage layer

Fig. 2

10m

5m

10m

M
m
1.33
0.8
kx(m/day) ky(m/day)
0.1
0.1

n
1
Sr0
0.15

nE
1.3
Gs
2.7

k
M
m
n
1
0.013
1.33
0.8
1
Dryingv 0.8 kx(m/day)ky(m/day)
Sr0
D
0.33
8.7
8.7
0.15
AD=-34.7,B
=5.9
0.6

nE
1.3
Gs
2.7

Table 1.2 Levee material parameters

Drainage conditions：Exhaust conditions ：

10m

k
0.037
v
0.33

10m

Degree of saturation Sr (-)

Fig. 1

consolidation yield stress in unsaturated state, nE:
Fitting parameter of EC model, e0: Initial void ratio,
ν: Poisson's ratio, kx: Horizontal hydraulicconductivity, ky:vertical hydraulic conductivity, Sr0:
critical saturation, and Gs: soil particle specificgravity. The initial saturation of the material used forembankment was set to 60%, and the initial suction
was determined from Fig.3.2. The drainage layer hasa permeability coefficient and an air permeability
coefficient set to 500 times that of the embankment.
The material constants other than the permeability
coefficient and the permeability coefficient of thedrainage layer are the same as those of the bank body.
The displacement boundary was fixed vertically at thebottom of the foundation ground and horizontally at
the top of the foundation ground. For the hydraulicboundary, the lower and upper ends of the foundation
ground and the upper part of the dam body weredrained. As for the air boundary, the upper end of thefoundation ground and the upper end of the dam were
exhausted.

Finite element mesh diagram

The material used in the analysis was silt mixed
with sand for the foundation ground and sand mixed
with silt for the dam body. Table 1 shows the material
constants, and Fig. 3 shows the moisturecharacteristic curve. Table 1.1 is the material constantused for the foundation ground, and Table 1.2 is thematerial constant used for the dam body. Figure 3.1
shows the moisture characteristic curve used for thefoundation ground, and Fig. 3.2 shows the moisturecharacteristic curve used for the dam body. The
material constants shown in Table 1 are λ:swelling
index, k: compression index, M: critical stress ratio,
m:shape parameter of unsaturated hydraulicconductivity, n: magnification parameter of

0.4
Wetting0.2
AW=-24.0,BW=4.6
00
200 400 600
Suction s (kPa)

800

Fig. 3.1 Soil-water characteristic curve(Foundation ground)
Degree of saturation Sr (-)

1

Fig. 3.2132

0.8

Drying
AD=-24.6,BD=5.2

0.6
0.4
0.2
Wetting
AW=-14.6,BW=4.0
0
0
100
200
Suction s (kPa)

Soil-water characteristic curve (Levee)GEOMATE – Melbourne, Australia, 11-13 November 2020

3.2.1 Analysis method

0.3m

In the embankment analysis, layers were spreadone-by-one. An evenly distributed load was applied,and compaction was repeated until the height of theembankment reached 5m. Layers of 0.3m and 0.6m
were prepared. The rolling strength was 300kPa. In
the analysis, elements were generated to create a heap,
and loading and unloading was repeated with an
evenly distributed load to simulate compaction. This
schematic diagram is shown in Fig. 4.

②Loading

0.6m

9.8

(ⅰ)

45.1

80.4

0.3m

151.1

185.4 (kPa)

0.6m

0.98

0.96

(ⅱ)

0.99

1.00

1.03

1.02

Void ratio e

0.3m

First embankment
completed

115.8

Effective mean stress p’

0.6m③Unloading
0.0

14.3

(ⅲ)

28.5

42.9

71.3

57.1

(kPa)

Deviatoric stress q

Second embankment
completed

0.3m

・
・
・

0.6m

0.000

0.003

(ⅳ)
Fig.4

0.006

0.008

0.011

0.014

Shear Strain εs

Embankment analysis schematic
0.3m

0.6m

3.2.2 Analysis result
116.7

Fig. 5 shows a visualization of each stress at thecompletion of the embankment (layer thickness: left
0.3 m, right 0.6m). From the top, the effective meanstress p’, void ratio e, deviatoric stress q, shear strainεs, suction s, and degree of saturation Sr are shown.
First, the effective mean stress p’ exhibits a higher
value in the foundation ground than in theembankment due to the effect of compaction during
embankment construction. In addition, the gap ratio e
is higher toward the upper part of the levee body, andthe layer thickness of 0.6m is particularly remarkablein this respect. Next, the shear strain εs shows a higher
value near the boundary between the foundation andthe levee than in the levee. Therefore, it is possiblethat there was already weakening when theembankment was completed, although not enough tocause failure. In addition, as the position where thedrainage layer is located contains more water than thesurrounding area, the void ratio e is low. Comparing
layer thickness, the value of 0.6m shows smaller
mean effective principal stress, and the gap ratio e islarger in the upper part of the dam body. At 0.6m,
layer boundaries occur at the mean effective stress p’,
the void ratio e, and the deviatoric stress q. Therefore,
it is considered that the levee body compacted with alayer thickness of 0.6m has a discontinuity at the layerboundary, in contrast to the lack of this discontinuity
with 0.3m layer thickness, resulting from insufficientcompaction.

134.4

(ⅴ)

151.1

0.3m

186.4

204.0 (kPa)

0.6m

0.15

(ⅵ)
Fig. 5

168.7

Suction s

0.25

0.35

0.45

0.54

0.64

Degree of saturation Sr

Summarizes analysis results

CHANGES IN THE LEVEE DUE TO RISINGWATER LEVELS
Water level fluctuation analysis was carried out
using the analytical model used for embankment
analysis. The right side of the embankment was used
to raise the water level. This is expressed by applying
water pressure to the levee body and the foundation
ground after raising the head of water on the rightslope of the embankment where water infiltrates,
along with the foundation ground. The water head andwater pressure applications were repeated until thecrown reached 3m. In addition, the rate of water level
rise was set to 1.4cm /min and 14cm/min [9]. (Riserate: (A)1.4cm /min, (B)14cm/min)
Next, we paid attention to the difference in the
speed of water level rise. Figure 6 shows the analysis
results for each stress. Each figure shows the average
effective principal stress p’, axial differential stress q,

133

GEOMATE – Melbourne, Australia, 11-13 November 2020

shear strain εs , void ratio e, suction s, an d degree ofsaturation Sr from the top. From the left of the figure,the water level of each stress is extracted at 1.5m and3m.
First, from results of the effective mean stress p’,
strength is considered to decrease as the water level
rises, and the value inside the levee decreases fromthe infiltration surface. In addition, the void ratio ealso decreases inside the levee from the infiltration
surface as the water level rises, and volume
compression is considered to occur. Second, from thedegree of saturation Sr, it can be seen that values forthe foundation ground and the lower part of the levee
become lower as the water level rises. At a slower
velocity of (A)1.4cm/min this value is higher than ata faster velocity of (B)14cm/min. Finally, the shear
strain εs indicates that the value at the tail end of theembankment is higher, suggesting that the tail end
becomes a weak part of the embankment as the waterlevel rises. For each stress, the change in velocity in(A) was more remarkable than that in (B). This isprobably because at the slower the rising speed, theinfiltration time of the river water was longer. In
particular, the degree of saturation Sr was
significantly different with different rising speeds,
and a large difference in the formation of theinfiltration surface was confirmed. The velocity (A)formed a smooth infiltration surface, and the velocity(B) formed a vertical infiltration surface. It isconsidered that (A) first infiltrated from thefoundation ground and then infiltrated upward fromthe ground to the bank body.
At this time, the levee body can be considered as
subject to buoyancy due to leftward infiltration fromthe levee exterior and upward infiltration from thefoundation ground, suggesting the possibility of
piping. In contrast to (A), (B) is considered to havecaused infiltration only from the lower left side of theembankment to form a vertical infiltration surface,suggesting that water overflow may occur.
1.5m

1.5m
(a)
(b)
(kPa)
0.0

87.3

(ⅰ)

124.6

0.000

(kPa)
198.2

14.7

54.0

92.2

130.5

169.7

(kPa)
207.9

87.3
0.99

124.6
1.00

0.0

161.9
1.01

198.2
1.03

14.7
0.96

54.0
0.97

92.2
0.99

1.00
130.5

169.7
1.01

207.9
1.02

46.8

31.2

(ⅲ)

Deviatoric stress q

1.5m

3m

0.003

0.006

0.009

0.012

62.3

77.9

0.015

0.000

0.006

0.011

0.017

0.022

0.028

243.3

(kPa)
214.8

Shear Strain εs
3m

44.1

132.4

88.3

177.6

(ⅴ)

(kPa)
221.7

0.0

43.2

128.5

86.3

Suction s

1.5m

3m

(a)

(b)
13.7
0.20

50.0
0.30

87.3
0.50

(ⅵ)
Fig. 6

0.70
124.6

0.80
161.9

198.2
1.00

14.7
0.20

54.0
0.30

92.2
0.50

130.5
0.70

0.80

207.9
1.00

Degree of saturation Sr

Summarizes analysis results

Furthermore, in order to study the detailed
behavior of saturation Sr in the levee, changes in thewater level rise of the elements in the levee arevisualized graphically. The position of each element
is shown in Fig. 7, and the behavior in the ascending
process is shown in Fig. 8 (Fig. 8.1: 1.4cm/min, Fig.
8.2: 14cm/min). The graph shows the time (unit:day)horizontally and the saturation Sr vertically. From Fig.
8.1, it can be confirmed that not only the elements 126,
160, 885 and 1385, but also the element 113 and theelements 101 and 187, are changing from theunsaturated state to the saturated state. From this,water is considered to have penetrated to the innerside of the bank, forming water channels, and thisresult also suggests that piping may occur. Because
all the elements located on the foundation ground aresaturated and element 101 is saturated earlier thanelement 187 on the ground side of the embankment,
it can be confirmed that water permeates from thefoundation ground earlier than from the embankment
exterior. Figure 8.2, shows that the time required forsaturation of each element remains almost unchanged,
and that the levee body is saturated at a stretch in a

3m

(ⅱ)

15.7

(b)

(b)
50.0
0.97

(kPa)
0.0

(a)

(a)

13.
0.967

82.4

1.5m

Effective mean stress p’
1.5m

65.7

(ⅳ)

3m

161.9

49.1

33.3

(b)

(b)
50.0

16.7

(a)

(a)

13.7

3m

Void ratio e134

GEOMATE – Melbourne, Australia, 11-13 November 2020

short time compared to the speed of 1.4cm/min. From
this, it is possible to confirm the vertical infiltrationin the levee body, and it is unlikely that water
channels form. Therefore, it is considered that theslower the rising speed is, the higher the possibility ofwater channel formation.

Fig.7

is considered to occur within the levee. In
addition, collapse progresses into the levee bodyas the water level rises.
(4) When the rate of water level rise is slow there isa high possibility of collapse due to the long
infiltration time of river water. If the ascending
speed is high, it is likely that the river will
overtop the levee before collapse occurs.
(5) A gentle infiltration surface formed when the
speed of water level rise was slow, and a verticalinfiltration surface was formed when the watervelocity was high. In particular, when the speed
is slow, the dam body may be subjected to
upward penetration from the foundation groundin addition to leftward penetration from theoutside of the embankment. This results inbuoyancy and the formation of water channels in
the foundation ground to cause piping.

Position of each element

Degree of saturation Sr

1
0.9
0.8
0.7
0.6
350

Fig.8.1

REFERENCES

Element101
Element113
Element126
Element160
Element187
Element873
Element885
Element1385

360

370
Time(day)380[1] Japan Institute of Country-ology andEngineering:River embankment guide(Revised
edition), 2012, pp.1.
[2] Kanazawa, S., Toyoshima, K., Kawai, K.,
Tachibana, S. and Iizuka, A.: Analysis ofmechanical behavior of compacted soil with F.E.
method, journal of JSCE, No.68 (2), 2012,
pp.291-298.
[3] Ohno, S., Kawai, K. and Tachibana, S.:
Elastoplastic constitutive model for unsaturated
soil applied effective degree of saturation as aparameter expressing stiffness, Journal of JSCE,
Vol.63/No.4,2007,pp.1132-1141.
[4] Mualem, Y.: A new model for predicting thehydraulic conductivity of unsaturated porous
media, Water Re-sources Research, Vol.12, No.3,
1976 ,pp.514-522.
[5] Van Genuchten: A closed-form equation forpredicting hydraulic of unsaturated soils, Soil
Science Society American Journal, Vol.44,
1980,pp.892-898.
[6] Kawai, K., Wang, W. and Iizuka, A.: The
expression of hysteresis appearing on water
characteristic curves and the change of stresses
in unsaturated soils, Journal of appliedmechanics, Vol.5, pp.777-784, 2002. Kimura S.,
Journal Paper Title, J. of Computer Science, Vol.
1, Issue 2, 1987, pp. 23-49.
[7] Sugii T. and Uno T.: Modeling the New Moisture
Characteristic Curve, Journal of JSCE,
1995 ,pp.130-131.
[8] Japan Road Association,:Roadway Workers Fill Construction Guidelines, 2010,pp.163.
[9] Ministry of Land,Infrastructure,Tranceport andTourism Hokuriku Regional Development
Bureau:Chikuma River Embankment Survey
Committee materials, 2019, pp.8.

390

Transition of saturation of each element

Degree of saturation Sr

1
0.9
0.8
0.7
0.6
350

Fig.8.2

Element101
Element113
Element126
Element160
Element187
Element873
Element885
Element1385

352

356
354
Time(day)

358

36

Transition of saturation of each element

CONCLUSION
Based on the results obtained from theembankment analysis and the water level rise analysis,
the following conclusions are developed.
(1) When the layer thickness is large, the average
effective principal stress p’ and the void ratio e
are small at the completion of the embankment,
resulting in a discontinuity at the layer boundary.
(2) When the water level rises, the osmotic pressureof the outer water level is dominant, andtherefore, the dominance of collapse due to thechange in layer thickness was not observed.
Therefore, it was confirmed that the difference inheight of padding did not significantly affect the
infiltration, and destruction was caused mainly
by infiltration due to the rise of the water level.
(3) As the water level rises, the strength inside thelevee decreases, the volume is compressed, andthe inside of the levee is saturated, thus collapse
13510th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ANALYTICAL STUDY ON QUALITY EVALUATION OF
EMBANKMENT STRUCTURE WITH A VIEW TO LONGER LIFE
Emika Nakamura1, Shin-ichi Kanazawa2
1

Social Environmental Systems Engineering Course，National Institute of Technology, Fukushima
College, Advanced Course Program, Japan
2
Civil and Environmental Engineering, National Institute of Technology, Fukushima College, Japan
ABSTRACTIn recent years, the demand for quality evaluation of earth structures has been increasing, coupled with a strong
demand for cost reduction in construction projects. In order to respond to such social trends, it is urgently necessary
to carefully examine the design conditions and construction processes for earth structures. At present, mechanicalconstruction capability and the number of experienced site technicians are improving, and it is necessary to study
the construction conditions that will lead to the quality of the embankment from a long-term perspective.
Additionally, in the future, simplification of work such as high-speed construction using AI or unmanned operation
will be required. Therefore, in this study, we evaluate the change in stress during long-term service by performing
the analysis after changing the rolling strength and the spreading thickness construction conditions of theembankment. Understanding the effects of these techniques will contribute to shortening the construction periodwith a view to extending the life of the embankment.
Keywords: Embankment, Longer life, Cost reduction, Finite element method

rainfall during construction weakens the inside of theembankment, but timing of construction cannot be
fixed, and the amount of rainfall received during
construction varies. In addition, construction is
generally not carried out in the rainy season, but insome cases this is unavoidable. To counter theseadverse weather conditions, drainage measures aretaken while paying attention to the weather, but theeffects of rainfall during construction and the effectof the initial stress state at the completion ofembankment have not been clarified. At normal sites,
the embankment is exposed to climatic conditions,such as repeated dry and wet conditions, and theeffects of the behavior of unsaturated soil cannot beignored. Therefore, in order to obtain a precise initialstress state, it is necessary to consider the influence ofthe dry and wet mechanical conditions. It is also
necessary to propose a method for estimating theinitial stress state in consideration of the dry and wetmechanical conditions, such as rainfall andevaporation.
In addition, mechanical construction capability onsite and the experience of engineers are improving,and it is necessary to study the construction
conditions that will lead to high quality embankment
from a long-term perspective. Additionally, in the
future, simplification of work, such as high-speed
construction using AI and unmanned operation will
be required.
In this study, embankment analysis considering
compaction and rainfall / evaporation history is
performed using soil / water / air coupled finite
analysis code. Differences in compaction strength of

INTRODUCTION
Soil structures are compacted to improve stability
and deformation characteristics. However, manycases of dikes collapse due to local torrential rains andtyphoon torrential rains have been reported, and thecollapse mechanism has not been completely clarified.
The stability of the embankment against heavy
rainfall is considered to depend strongly on four
factors: the treatment of the foundation ground, thequality of the embankment material, the degree ofcompaction, and the treatment of water. Among these,
the cause of embankment collapse in many cases is
the treatment of water.
Although embankment structures are initiallyproperly drained according to land and environmental
conditions, embankments frequently collapse. One
causal factor is that the construction and maintenanceof the embankment depends on engineer experience.
Soil consists of three phases: a solid phase, liquid
phase and gas phase, and the embankment andunderlying ground below the water table are often in
an unsaturated state containing air or dissolved air. It
is considered that gas is dissolved and released into
the liquid phase as the pore air pressure changes due
to rainfall infiltration, resulting in volume
compression. As a result, it is possible that current
construction guidelines do not have sufficientdrainage measures and embankment maintenancerequirements to withstand recent heavy rainfall. In
addition, drainage measures for the embankment andthe time at which it is affected by rainfall are
important factors. It is thought that the influence of136

GEOMATE – Melbourne, Australia, 11-13 November 2020

the embankment, layer thickness of construction, andthe amount of rainfall are analyzed, and stress change
during the long-term service process are evaluated.
This analysis contributes to shortening theconstruction period with a view to extending the lifeof the embankment. In addition, by grasping themechanical behavior inside the embankment throughthis analysis, this study also aims to enable
understanding the design needed for required
performance of the embankment in the future.

'

the dilatancy coefficient; psat is the yield stress at
saturation; a and ns are the parameters representing

the increase in yield stress due to unsaturation; λ isthe compression index; and κ is the expansion index.
Equation (8) shows pore water velocity. Equation (9)shows air velocity. Pore water and air flow follow
Darcy’s law.

vw =−k w ⋅ gradh

RESEARCH METHODp−k a ⋅ gradha , ha =ava =Here, vw is the pore water velocity; va is the air

The finite element analysis code (DACSAR-MP)[1] used in this study formulates the unsaturated soil
constitutive model proposed by Ohno et al [2]. Thismodel is framed as the soil/water/air coupled problem
using the three-phase mixture theory. Equation (1)shows the effective stress. Equation (2) shows thebase stress tensor and suction stress. Equation (3)shows suction.
σ' = σ net + ps 1

(1)

σ net =
σ − pa 1, ps =
Se s

(2)

S r − S rcs =−pa pw , Se =1 − S rc(3)

velocity; k w is the hydraulic conductivity; k a is thecoefficient of air permeability; h is the total head;γ w is the unit weight of water; and ha is thepneumatic head. Equations (10)-(11) show hydraulicconductivity and the coefficient of air permeability by
way of Mualem's [3] formula and the Van Genuchten[4] formula.

Se is the effective degree of saturation; and S rc is thedegree of saturation at s→∞. Equations (4), (5), (6)and (7) provide the yield function.

nSr − S r εv + divvw =0(4)(1 − Sr ) εv + nSr − n (1 − Sr )(5)

1 + e01
3p ' = σ' : 1, q =
s:s
3
2

(6)

1
s =σ' − p ' 1 =A : σ', A =I − 1 ⊗ 1
3

(7)

2m

ares

(11)

air permeability in dry conditions. Equations (12)(13) show the continuous formula of pore water and
air using three-phase mixture theory.

pore water pressure; S r is the degree of saturation;s) k

kra is the ratio of coefficient of air permeability; mis the Mualem constant; k wsat is the hydraulicconductivity at saturation; k ares is the coefficient ofsuction stress; pa is the pore air pressure; pw is the



1

(10)

Here, krw is the ratio of hydraulic conductivity;

net

λ −κ
n
ζ =
exp (1 − Se ) ln a  , MD =(kra k ares =ka =(1 − Se ) 2 1 − Se m

Here, σ' is the effective stress tensor; σ is thebase stress tensor; 1 is the second order unit tensor;
σ is the total stress tensor; s is the suction; ps is the

nE

)

(

2

m
1
1 

k=
= Se 2 1 − 1 − Se m  k wsat
krwk wsat
w



1

p'
MD  q +− ενp 0='nE  Mp ' ζ psat(9)γw

Soil/Water/Air Coupled Finite Analysis Code=f ( σ',
ζ , ενp ) MD ln

(8)

(12)

p a
− divva =0 (13)
pa + p0

Here, n is porosity; εv is volumetric strain; andp0 is atmospheric pressure. The elasto-plastic
constitutive model obtained from Equation (4) andthe equilibrium equation [Equations (12) - (13)] areformulated as the soil/water/air coupled problem.

Here, nE is the shape parameter; εν is the plastic
pvolume strain; M is the q/p’ in the limit state; D is137

GEOMATE – Melbourne, Australia, 11-13 November 2020

STRESS ANALYSIS DURING THE SERVICEPROCESSStress analysis conditions in the service process due
to differences in construction conditions

Analysis Condition

In this study, layer thickness and compaction
strength are considered as construction conditions.
Compaction was expressed by loading andunloading each layer under the load conditions of 300,
600, and 900 kPa, after construction at 0.3 and 0.6 m
layer thicknesses during a construction period of one
month. Figure 2 shows the analysis area at a layer
thickness of 0.3 m, and Figure 3 shows the analysis
area at 0.6 m.

In this study, embankment analysis ofembankment structure considering rainfall andevaporation was performed using unsaturated soil /water / air coupled finite element method analysis
program (DACSAR-MP). Table 1 shows the material
parameters used in the analysis, and Fig. 1 shows the
moisture characteristic curve, where λ :the swelling
index, κ :the compression index, M = (p'/q) :the stressratio, nE : the fitting parameter of the EC model,a :parameter for determining the magnification of
consolidation yield stress due to desaturation, m:
unsaturated hydraulic conductivity parameters, Sr0:
saturation of adsorbed water, k a : air permeability
coefficient, ν: Poisson's ratio, Gs: soil particle
specific gravity, and k w : saturated hydraulicconductivity.
The analysis area is assumed to be foundation
ground measuring 15 m in length x 45 m in width,with embankment 3 m wide at the top, 15 m wide at
the bottom and 6 m high. The foundation ground has
a saturation level of 50% up to 3 m from the groundsurface, and a 90% saturation level deeper than 3 mfrom the ground surface. The displacement boundary
was fixed vertically and horizontally at the lower endof the foundation ground, and the hydraulic boundary
was drained at the top and bottom and undrained at
the left and right.
Table 1Analysis area with a thickness of 0.3m

Fig.3

Analysis area with a thickness of 0.6m

material parameters

λ

κ

M

m

Sr0

0.18

0.037

1.33

0.8

0.15

1.0

n

nE

a

v

Gs

kw(m/day)

1.0

1.3

10.0

0.33

2.7

0.01

Fig.1

Fig.2

ka(m/day)Stress analysis conditions in the service process due
to differences in climatic condition
In this study, rainfall during construction isconsidered a climatic condition.
Because the amount of precipitation changes frommonth to month, the amount of precipitation theembankment receives during the civil engineering
work will differ depending on the month in which theconstruction is implemented. Therefore, by changing
the construction time, the stress change due todifferences in rainfall during construction will be
investigated and compared. The rainfall history usedin this study is shown in Fig. 4. For the rainfall
applied during construction, the annual rainfall
history of Fukuoka City in Fukuoka Prefecture in2010 was used to simulate average rainfall in Japan.
For stress analysis during embankment service due toSoil water characteristic curve138

GEOMATE – Melbourne, Australia, 11-13 November 2020

the differences in construction conditions, total
rainfall of 1729 mm was corrected to one month, andrainfall of 144.1 mm was applied. For stress analysis
in service due to different climatic conditions, weprepared 3 cases with characteristic rainfall, as shownin Fig. 4.
These three cases provide the lowest rainfall in
January, the highest rainfall July, and an average
rainfall in April; i.e. 50.5mm in January, 199.5mm in
April, and 453.5mm in July. These rainfall conditions
were applied throughout construction, and then theembankment analysis was performed.

be seen that there is a tendency for expansion due tothe large void ratio value in the surface layer of theembankment, which indicates loose compaction ofthis region. It also seems that there is a change in thesurface layer of the embankment at layer thicknessesof 0.3m and 0.6m. However, as the difference in value
is about 0.001, it can be said that there is no
significant difference due to layer thickness. Also, no
significant difference due to compressive strength canbe confirmed.
Finally, from the shear strain distribution shown in
Fig. 8, it can be confirmed that the morphology ofsurface slip has already appeared; although, thefracture risk is low because of the low shear
distribution value. This is because this value at thesurface layer is small, even when looking at the axial
stress distribution in Fig. 9, suggesting that theembankment surface layer is likely to become weakduring embankment. Therefore, caution is advised intheses cases, as the surface layer may be damaged if
it is affected by heavy rain after construction.
From the above, it can be said that there was almost
no change in the initial stress due to the difference inrolling strength (300, 600, 900 kPa), and there was no
significant change due to the layer thickness (eachinitial stress value) (0.3, 0.6 m). However, at a layer
thickness of 0.6 m, large discontinuities occur at thelayer boundaries, suggesting that different layer
thicknesses affect compaction performance. Insummarizing the results of the initial stress analysis
due to the difference in the construction conditions, it
can be said that the differences in the layer thickness
and load conditions, which are the construction
conditions, have little effect on each initial stress.

Fig.4 Fukuoka Prefecture monthly rainfall andevaporation history (2010)[5]Analysis Result
Stress analysis results in the service process due todifferences in construction conditions
Figures 5 to 10 show the initial stress distributions
under load conditions of 300, 600, and 900 kPa,
divided into layer thicknesses of 0.3 and 0.6 m,
respectively.
From the average effective principal stress
distribution in Fig. 5, no significant difference due tothe difference in rolling compaction strength (300,600, 900 kPa) can be confirmed. The same is true
when looking at the comparison based on layer
thickness (0.3m, 0.6m). One notable difference is thatthe layer boundary at the layer thickness of 0.6 m isclearer than the distribution of layer boundaries at thelayer thickness of 0.3 m.
In all stress distributions, the average effective
principal stress in the foundation ground is large, andthe void ratio distribution is small, as shown in Fig. 7.
This suggests that the embankment fully reflects thecompaction effect from the e-logP' relationship. In the
degree of saturation distribution shown in Fig.6, adiscontinuity at the layer boundary of theembankment is apparent. From these visualizations,embankment with a layer thickness of 0.6 m has alarger discontinuous surface than with a layer
thickness of 0.3 m, which tends to affect thecompaction performance. Thus, although a change inthe stress distribution due to the difference in layer
thickness could be confirmed, there was no
significant difference due to the compressive strength.
Next, from the void ratio distribution in Fig. 7, it can
139

Fig.5

Effective mean stress p’

Fig.6

Degree of saturation Sr

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig.7

Void ratio e

Fig.8

Shear Strain εs

Fig.9

Deviatoric stress q

compressive strength of 300 kPa. Precipitation
increases in the order of January, April, and July.
From Fig. 11, it can be confirmed that the effective
stress in the embankment decreases as the amount of
rainfall applied during construction increases. In
addition, the distribution of the void ratio, as shownin Fig. 13, increases at the embankment surface layer
in the order of January, April, and July. From thesetendencies, it can be said that the embankment surface
layer is loosely compacted. Furthermore, the value ofthe void ratio in the lower part of the embankment
becomes smaller as the amount of rainfall increases,
which suggests that the self-weighting effect ofrainfall causes the interior of the embankment tobecome dense. A similar tendency can be confirmed
from the shear strain distribution in Fig.14. Also,
although the shear strain in the surface layer of theembankment is large and the surface slip takes form,
the risk of fracture is low at this point because thevalue is small. From the axial stress distribution
shown in Fig. 15, it can be confirmed that theembankment surface layer during construction is
weak. No significant difference in stress distribution
due to layer thickness could be confirmed. As
described above, changes in initial stress distribution
due to differences in construction time, that is,differences due to rainfall during construction, wereconfirmed. In other words, it was found that levees
constructed during periods of low rainfall werestronger than levees constructed during periods ofhigh rainfall. The higher the rainfall, the higher therisk of damaging the weaker embankment surface.
a a
1月

7月a

4月a

0.3ｍa

86

223

314
224.4

406

543 (kPa)

77

216

310
224.4

403

543 (kPa)

0.6ｍa

Fig.10

Suction s
Fig.11Stress analysis results in the service process due todifferences in climatic conditions
This section describes the results of embankment
analysis at applied rainfall of 50.5 mm in January,
199.5 mm in April, and 453.5 mm in July. However,when comparing the initial stress distributions withdifferent rolling strengths for each construction timeby layer thickness, no stress change due to different
loading conditions could be confirmed at any
construction time. In examining this, it wasdetermined that carrying out the stress analysis only
for the embankment with a compaction strength of
300 kPa would yield valid results. Therefore, Figures
11 to 16 show the outline of each stress distributionof each layer thickness when the construction time
was changed, using the analysis results at the

Effective mean stress p’
a a
1月

7月a

4月a

0.3ｍa

15.4

37.8

52.7
224.4

67.7

90.0 (%)

15.5

37.9

52.8
224.4

67.7

90.0 (%)

0.6ｍa

Fig.12140

Degree of saturation Sr

GEOMATE – Melbourne, Australia, 11-13 November 2020a a
1月

formed. As a result, there is almost no difference incompressive strength (300, 600, 900 kPa) and layer
thickness (0.3m, 0.6m) due to differences inconstruction conditions over a long period of time.
Therefore, at the present time, no problems are
anticipated with carrying out the embankment
construction a compressive strength of 300 kPa,
which can reduce costs, and a layer thickness of 0.6m, which can shorten the construction period.
The stress analysis results during embankment
service due to the difference in climatic conditions
confirmed that dikes constructed during the low
rainfall season are stronger than dikes constructedduring the high rainfall season. In other words, it isadvisable to build levees during periods of low
rainfall. However, it is necessary to continue studying
heavy rainfall after construction and changes in stress
distribution after several years. In summary, climaticconditions are more likely to change embankmentstress conditions than construction conditions.
However, these results are only trends at this stage. In
the future, we would like to study additional
construction conditions and climatic conditions andconsider various process scenarios. The changes instresses during long-term use of levees can also beassessed by analytically examining how thesedifferences change the internal stresses of levees after
decades. From the viewpoint of extending the life ofthese structures, it is also possible to conduct
feedback analysis after the embankment isconstructed and to monitor vulnerable points.

7月a

4月a

0.3ｍa

0.989

0.997
224.4

1.004

0.992

0.998
224.4

1.003

0.6ｍa

Fig.13

Void ratio e
a a
1月

7月a

4月a

0.3ｍa

0.0

0.2
224.4

0.4

(%)

0.0

0.15
224.4

0.3

(%)

0.6ｍa

Fig.14

Shear Strain εs
a a
1月

7月a

4月a

0.3ｍa

0.07

19.7

32.8
224.4

45.8

65.4 (kPa)

0.02

19.0

31.6
224.4

44.2

63.2 (kPa)

0.6ｍa

Fig.15

REFERENCES

Deviatoric stress q
a a
1月[1] Shinichi Kanazawa, Shinya Tachibana, Atsushi
Izuka: Analytical study on drainage capacity ofembankment structure, JSCE Proceedings A2（ Applied mechanics ） ,Vol.71, pp.429-436,
2015.
[2] Ohno, S., Kawai, K. and Tachibana, S.:
Elastoplastic constitutive model for unsaturated
soil applied effective degree of saturation as aparameter expressing stiffness, Journal of JSCE,
Vol.63/No.4, pp.1132-1141,2007.
[3] Mualem, Y.: A new model for predicting thehydraulic conductivity of unsaturated porous
media, Water Re-sources Research, Vol.12, No.3,
pp.514-522, 1976.
[4] Van Genuchten: A closed-form equation forpredicting hydraulic of unsaturated soils, Soil
Science Society American Journal, Vol.44,
pp.892-898, 1980.
[5] Japan
Meteorological
Agency:Japan
Meteorological
Agency:http://www.jma.go.jp/jma/index.html.

7月a

4月a

0.3ｍa

40.1

92.6

128
224.4

163

215 (kPa)

40.1

93.4

129
224.4

164

218 (kPa)

0.6ｍa

Fig.16

Suction s

CONCLUSION AND FUTURE OUTLOOKFrom the results of this study, it was possible toconfirm the effects of differences on the stress inside
the embankment in construction conditions, which
depended on the compaction strength and the layer
thickness of the embankment, as well as climaticconditions, which depended on season of
construction,. The stress analysis results during
embankment service due to the difference inconstruction condition confirmed no change in theload conditions compared in this study but adifference in the size of the discontinuous surface that141

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

NUMERICAL INVESTIGATION ON PILE BEHAVIOR
DUE TO THE RISING GROUNDWATER EFFECT
Harutus Phoban1, Uma Seeboonruang1 and Prateep Lueprasert1
1

Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
ABSTRACT

Since the late 1990s, the changing of groundwater level in the aquifer beneath Bangkok has been realized
because the enforcement of groundwater laws and improvement of water supply have been adopted. Then, thegroundwater with piezometric drawdown has rising to almost hydrostatic equilibrium. In the principle of soil
mechanics, when the porewater pressure in soil mass increases, the effective stress decreases. This can affect tounderground structures. Under the soft ground condition such as Bangkok, Thailand, pile foundations arecommonly constructed to support structures. With decreased effective stresses, the loss of ultimate load carrying
capacity of existing piles can be occurred and the structures can be damaged. This research investigates the effectsof the groundwater rising on pile foundation in Bangkok subsoil. Advanced numerical model with the fully coupledflow deformation analysis, both groundwater flow and soil deformation behaviors with time dependent conditions
are modelled, is conducted by using PLAXIS 2D code. The results found that the calculation type of fully coupledflow deformation can express the pile-soil movement behavior. The pile-soil heave and the pile load capacity besignificantly reduced during the groundwater rising event. Despite the reduction of pile load capacity withgroundwater changed not enough to failure (Factor of safety <1.0), the pile stability should be concerned and the
optimum groundwater level must be controlled.
Keywords: Groundwater rising, Pile capacity, Numerical modeling, Bangkok subsoil, Fully coupled flow
deformation analysis
INTRODUCTION

ultimate load carrying capacity of existing piles are
occurred. Recently research investigated the impactof the groundwater rising in Bangkok, for example,[2], [3] to study effects of groundwater drawdown andrebound from deep well pumping in Bangkok on
piled foundation of buildings. They reported that fora short pile of length in the order of 30 m, the bearing
capacity may be reduced by 40% in the future when
the groundwater level is allowed to rebound close to
the ground surface for the pile installed during theperiod of maximum ground drawdown in 1997.
However, they considered only analytical method on
pile capacity. In this research, the numerical model
was carried out to investigate the pile behavior due tothe rising groundwater event. A Fully coupled flowdeformation analysis is conducted to analyse thesimultaneously development of deformations andpore pressures in saturated and partially saturated
soils as a result of time-dependent changes of thehydraulic boundary conditions [4], [5]. The pile-soil
movement, load distribution along the pile, and the
reduction of pile capacity were observed.

In the past, Bangkok has been mainly taken thewater resource from deep well pumping from aquifers
underneath the city. Specifically, the groundwaterpressure was declined to 31 m below ground surface(m bgs) in 1997 in central city area (RommaninatPark) [1]. After that, the groundwater level was
slightly increased, the Thailand Department of
Groundwater Resources prompted the policy.
Groundwater levels are rising in many urban areas
because of reduced pumping from the underlying
aquifers for industrial processes. That is a problem
common to many cities founded and effected onunderground structures (i.e., metro tunnels, deep
foundations, etc.). Recent many researches, anincreasing trend in groundwater level due to thedeindustrialization process was observed in urban
aquifers all around the world: Aswan (Egypt),
London (England), Paris (France), Naples (Italy),
Milan (Italy), Tokyo (Japan) and Bangkok (Thailand).
The groundwater pressure rising in the urban areas
was the result of a decreasing in groundwater
pumping, which was not appropriately managed. Thatthus caused damages to underground structures andinfrastructures.
Pile foundations are commonly installed to carryloads from building into the soil by skin friction andend bearing. The increasing of porewater pressure
leads to decreasing of the effective stresses, a loss in

GEOLOGY AND HYDROGEOLOGY OF THE
STUDY AREA
The data obtained from central plain area ofThailand are used for this study. During the past 50
years, the study area, has been rapidly developed with142

GEOMATE – Melbourne, Australia, 11-13 November 2020

NUMERICAL MODELING OF PILE
FOUNDATION ANALYSIS

urbanization, especially Bangkok, the capital of
Thailand. The geology and hydrogeology beneath thestudy area are significantly changed. Thus, thegeological and hydrological change of this area havebeen studied by many researchers. However, there are
well-documented reports in few literatures [1], [6].
The geology conditions of study area werecovered by the marine and alluvial deposits. At theshallow depth of the entire area, the subsoils were
almost uniform with consisting of soft clay, stiff clay
and sand. At the greater depth, the stiff to hard clay
and dense sand layers were found. The first dense
sand layer with thickness ranging about 5-10 m isgenerally located below stiff clay layer (the depth ofabout 20-25 m from the ground surface). The hardclay layer was found underneath first dense sand layer.
Second dense sand layer locating at depth about 4560 m from ground surface is below the hard clay layer.
Bangkok aquifer system is identified as 8 aquifers
by JICA [6] with continuous relatively impermeable
aquitards (clay layers). Each layer with a thickness of
30-70 m, from ground surface consisting of the
Bangkok (BK), Phra Pradeang (PD), Nakorn Luang(NL), Nonthaburi (NB), Sam Khok (SK), Phayathai(PT), Thonburi (TB) and Pak Nam (PN) aquifers,
respectively. Groundwater is mainly pumped fromthe PD, NL and NB aquifers at the depths about 100
m to 200 m.
In the past decade, the groundwater demand for
utility and industrial systems was very high as shownin Fig. 1. [1], [7]. With serious controlling the amountof water pumping and utilizing the tap water supply,
the groundwater pumping decreased while the trendof piezometer rise up to the hydrostatic after 1997.
This current research focuses on PD aquifer (about
100 m bgs) due to the rising trend of piezometer may
impact to the pile.

Reference case
The geological condition and static pile load test
pile data of Silom-Sathon located in the inner-cityzone of Bangkok are used to model in this research.
Construction of this building was completed in 1999,
having 20 floors. The pile foundation of this building
was installed during the period of maximum
groundwater drawdown (1997-1999). The bored pile
diameter of 1m with pile tip of -55 m bgs wasconstructed by using wet process.
The change of groundwater levels with time at
Phrapadaeng aquifer for modelling was acquired
from observation data at the monitoring groundwater
station PD0023(Fig. 1), where was located at
Metropolitan Electricity Authority Substation Thung
Maha Mek(about 1 kilometers form the buildingreference). The rate of groundwater rising wasapproximated of +0.75 m/year from years 1997(GWL-30 m bgs) to 2037(GWL -0 m bgs)
Modelling and Boundary Condition
Soil model and Parameters
Finite element (FE) method using software
PLAXIS 2D was used to investigate the mechanicalbehavior of single piles foundation under axial loadand groundwater level rising load on Bangkok soft
clay. The Linear-Elastic Perfectly-Plastic MohrCoulomb model and hardening soil with small strain
stiffness (HSS) model which can simulate stiffness
dependent on stress, strain and paths was applied.
Parameters were adopted and modified from [1], [8][10], who studied and calibrated the model
parameters for Bangkok soils. All parameters are
summarized in Table 1.
The numerical result of pile load test wascompared with monitoring data in field test as shownin Fig. 2. The failure load of 1600 ton was evaluatedby Mazurkiewicz(1972)’s method. The working loadof 640 ton are applied on the pile head for this study
that was considered with factor of safety (F.S) of 2.5.

Settlement, mm

Load at pile head, ton

Fig. 1 Piezometric levels changed with time in
Bangkok [1]

0
-5
-10
-15
-20
-25
-30

0

200 400 600 800 1000 1200 1400 1600

Refference Case
Plaxis 2D

Fig. 2 Measured load settlement curve compared withfield test and numerical analysis

143

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1. Model parameters used in analyses
Parameters

Unit

Model
Material type𝜸𝜸𝒔𝒔𝒔𝒔𝒔𝒔
E50ref
Eoedref
Eurrefpower (m)
C'ref
𝝋𝝋′
𝝍𝝍
𝜸𝜸𝟎𝟎.𝟕𝟕
G0ref
v'ur
Pref
K0nc
Rf
Rinter
OCR
kx
ky

kN/m3
kN/m2
kN/m2
kN/m2
kN/m2
degreedegree
kN/m2
kN/m2
m/day
m/day

Weatheredclay
MC
Undrained
17
E’=6000
Su=40
0
0
0.30
8.640E-7
8.640E-7

Soft clay
HS SmallDrained
16.5
800
850
8000
1.00
1.00
23
0
0.05000
10000
0.20
100
0.74
0.90
0.75
1.2
8.640E-5
8.640E-5

Medium stiff Stiff to veryclay
stiff clay
HS SmallDrained
18
1650
1650
5400
1.00
10.00
25
0
0.00100
16000
0.20
100
0.65
0.90
0.75
2
8.640E-4
8.640E-4

HS SmallDrained
20
8500
9000
30000
1.00
25.00
26
0
0.00100
50000
0.20
100
0.50
0.90
0.75
2
8.640E-4
8.640E-4

Width, mPile Load

2nd sand

Hard clay

Pile

MC
Drained20
E’=85800
0.1
36
5
0.30
0.5
0.75
1.600
1.600

MC
Drained20
E’=85800
0.1
37
5
0.30
0.5
0.75
0.800
0.800

HS SmallDrained
20
30000
30000
120000
1.00
40.00
24
0
0.00150
124000
0.20
100
0.50
0.90
0.75
1.1
8.640E-5
8.640E-5

Linear elastic
Non-porous
24(Unsat)
E =2.70E7v(nu) = 0.15
1-mesh is set to be no flow condition (no vertical flow)
while the horizontal flow is set in all vertical side ofmesh. The groundwater head was controlled by flow
functions, the groundwater level change with timedependent was defined in this step. The size of modelis 80 m for width and 100 m depth. The size of modelis greatly larger than the required distance of six times
the pile diameter at the side and the pile base.

Pile Top. at -0.0 m bgl
Weathered clay
Soft clay
Medium stiff clay
Stiff to very stiff clay
1st SandDepth, m bgs

1st SandThe analysis steps
This analysis focus on the pile-soil movement,load distribution along the pile, and the reduction ofpile capacity during groundwater level rising event.
The soil without piled in initial stage, the groundwater
starts from the ground surface (Hydrostatic pressure)
was controlled by flow function to decrease of -0.75
m/year in 40 years. Then, the pile foundation of thisbuilding was installed during the period of maximum
ground drawdown (1997). The load at top of pile wasmaintained to constant by working load for a periodof 40 years that had been rising of groundwater rateof +0.75 m/year.
In the first step, stress in soils was initializedaccording to K0,NC. After that, bored pile wasconstructed, Rinter between pile and soils wereactive. This model was assumed that the soil
displacement has not occur during the bored pile wasinstalling. For pile loading, Surface load is applied oncross section area of pile by 8120 kN/m2 or 640 ton(working load). In groundwater rising step, thegroundwater level changing with time dependent was
defined. The calculation type to Fully coupled flow
deformation analysis is condition in this step. The
time interval are 14,600 days (Approximate 40 years
from 1997 to 2037). The groundwater rising was
defined rate +0.75 m/year. The groundwater headwith time of this model was presented in Fig. 4, that

Stiff to very stifflPile Tip. at -55.0 m bgl
2nd sand

Hard clay

Fig. 3 Geometry of the problem and 2D Finite
element mesh used in this analysis
Geometry Model and Boundary Condition
The 2D FE mesh indicating the single bored pile,soil profile and element discretization is depicted inin Fig. 3. The bored pile with diameter of 1.0 m anddepth of 55.0 m bgs is considered. For the
displacement boundary conditions, all vertical sides
were restrained against lateral movement but
permitted for vertical movements. The bottom of themesh was fixed (no movement all directions). For the
ground water condition, groundwater flow on bottom
144

GEOMATE – Melbourne, Australia, 11-13 November 2020

related to piezometric levels changed with time in
Bangkok at PD0023 in Fig. 1. The summaries of stage
modeling and pile-soil movements during the
groundwater changing were demonstrated in Fig. 4,
the ground surface movements depend on
groundwater head changing, the similarly behavior
was presented in full field test by Armishaw and Cox[11], and the pile foundation show less heave than thesurrounding soil surface during the groundwaterrising event as a results of centrifuge tested byMorrison [12], [13].

uplift, cause the pile was heave. The load distribution
was transferred form compression zone to tension
zone. For after rising groundwater event, the end
bearing was expressed at the end of pile.

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040
0
0
-5
-50
-10
-100
-15
-150
-20
-200
-25
-250
-30
-300
-35
-350
-40
-400
-45
-450
Years
Stages1957

GH drop down

1997

GH rise up

2037

Groundwater head (GH), m bgl

Movement, m

Years

Displacement, mm

Working Load

100
50
0
100
50
0
100
50
0

-0.00 m bgs
Pile top.

0

2

4

6

8

10

0

2

4

6

8

10

0

2

4

6

8

10

0

2

4

6

8

10

-20.00 m bgs-40.00 m bgs
Neutral point-55.00 m bgs
Pile tip.

Distance from center of the pile, m

Vector of soil movement.

Before groundwater rising (1997).

Vector od Pile movement.

After groundwater rising (2037).

Fig. 5 Soil displacement at varies depth aftergroundwater rising event

Soil settlement,
Pile head (working load remained)
and soil surface (x=2 m) movementno piled
were observed
Pile installed,
Working load at pile head

Pile head
Ground surface

150
100
50
0

-2500
0

Groundwater head

Vertical total Stress (𝜎𝜎1 ), kN/m²
0

2500

5000

7500

10000

-5

Fig. 4 Pile-soil movements with groundwaterchanging in the model.

-10
-15

Depth, m

RESULTS AND DISCUSSIONS
Pile-soil movement
The pile-soil movements before and after the
groundwater rising event was showed in Fig.5, thepile foundation and the soil heave. The large soil
heave was found near the soil surface. However, thepile foundation show less heave than the surrounding
soil above neutral point (level of zero pile soil
displacement), that represents decreasing of friction
force between soil and pile because the effective
stress was reduced by the rising groundwater. The
behaviors were seen to effect pile settlement relative
to the ground surface during the rising ofgroundwater.

-20
-25
Years

-30

1997

-35

1998
2007

-40

2017

-45

2027
2037

-50
-55

TensionCompression

Fig. 6 Stress distribution along the pile during
groundwater level rising event
Pile capacity

Stress distribution in pile

The reduction of pile capacity was represented in
Fig.7. The magnitude of pile settlement was decided
at -20 mm, comparable to that of the pile capacity
reduce by 20%, that relative to the loss of the ultimate
load for the increased pore water pressure. Despite to
the pile will not reach up to fail, but the some of pileFig. 6 shows vertical stress distribution along thedepth under the friction pile. Which represent stressprofile at each year. The neutral point demonstrating
the depth of soil causing pile settlement relative to thesurface of pile. According to groundwater levelraising, the soil surround pile surface was forced by
145

GEOMATE – Melbourne, Australia, 11-13 November 2020

that have less of factor of safety (F.S.) mustreconsider to the stability of the pile.

Well Pumping in Bangkok, Master thesis, AIT,
2016.
[3] Department of Groundwater Resources and Sevenassociated consultants Co., Ltd., The effect tounderground structure cause by the groundwaterrising in Bangkok aquifer around Bangkok
Metropolitan Region, Final report, 2011.
[4] Galavi, Vahid & Brinkgreve, R. & Bonnier, Paul& Gonzalez, Nubia. Fully coupled hydromechanical analysis of unsaturated soils,
Computational Geomechanics I (ComGeo I), At
Juan-les-Pins, France, 2009, pp.486-495[5] Galavi, Vahid, Groundwater flow, fully coupledflow deformation and undrained analyses in
PLAXIS 2D and 3D, Internal report, Plaxis BV
2010, research department, 2009.
[6] JICA, DMR, and PWD, The study on
management of groundwater and land subsidence
in the Bangkok Metropolitan area and its vicinity,
Interim report, 3, Kokusai Kogyo Co. Ltd, Tokyo,
Japan, 1995.
[7] N. Tussanee, Situation of groundwater in
Bangkok metropolitan area, Available at:
http://www.gwcr.org, Accessed June 10, 2015.
[8] Thayanan B. et al., Settlement prediction of large
diameter bored pile in Bangkok soils. Sixth
International Conference on Geotechnique,
Construction Materials and Environment, 2016.
[9] Surarak, C., Likitlersuang, S., Wanatowski, D.,
Balasubramaniam, A., Oh, E., Guan, H., Stiffness
and strength parameters for hardening soil modelof soft and stiff Bangkok Clays, Soils andFoundations 52 (4), 2012, pp.682–697.
[10] Likitlersuang, S., Teachavorasinskun, S.,
Surarak, C., Oh, E. and Balasubramaniam, A.,
Small Strain Stiffness and Stiffness Degradation
Curve of Bangkok Clays. Soils and Foundations,
53(4), 2013, pp.498–509.
[11] Armishaw J. W. and Cox D. W., The effects ofchanges in pore water pressures on the carrying
capacities and settlements of driven piles end
bearing in sand and gravel stratum, Proc. Conf.
on Recent developments in the design andconstruction of piles. I.C.E., London, 1979,
pp.227-236.
[12] Morrison P. R. J. and Taylor R. N. Foundations
in a rising groundwater environment, Proc. Int.
Conf. on Groundwater Problems in Urban Areas,
ICE, London, 1994a, pp.342-354.
[13] Morrison P. R. J. and Taylor R. N. Modelling offoundations in a rising groundwater environment,
Proc. Int. Conf. Soil Mechanics and Foundation
Engineering. New Delhi, 1994b, Vol. 2 pp.655658.

Settlement, mm

Load, ton
0
-5
-10
-15
-20
-25

0

200 400 600 800 1000 1200 1400 1600

reduce by 20%

After groundwater rising event
Maximum groundwater drop down

Fig. 7 Reduction of pile capacity.
CONCLUSIONS
The fully coupled flow deformation analysis, both
groundwater flow and soil deformation behaviors
with time-dependent conditions in Plaxis program
can express the pile-soil movement behavior, that
correspond the previous study in a full field test andcentrifuged test by Armishaw and Cox [11] andMorrison [12], [13] respectively. The results of thenumerical modelling found that:- The pile foundation show less heave than thesurrounding soil surface during the groundwaterrising event.
- The load distribution along the pile wastransferred form compression zone to tension
zone at lower part near neutral point of the pile.
- The pile load capacity be significantly reducedby the increase in groundwater head.
Despite the reduction of pile load capacity withgroundwater changed not enough to failure (Factor ofsafety <1.0), the pile stability should be concerned
and the optimum groundwater level must be
controlled.
ACKNOWLEDGMENTS
The authors would like to extend deep gratitude to
the Research and Researchers for Industry (Grant No.
PHD58I0003) and King Mongkut's Institute ofTechnology Ladkrabang Research Fund (Grant No.
KREF046012) for the financial sponsorship(PLAXIS Software).
REFERENCES[1] DGR., Groundwater and Land Subsidence
investigation project, Department of Groundwater
Resources, 2013.
[2] K. Intachai, Analytical Assessment on Pile
Capacity in View of Effects of Groundwater
Drawdown and Rebound Associated with Deep146

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

COUPLED ANALYSIS CONSIDERING DENSITY CHANGE
OF UNSATURATED SOIL

1

Daiki Yamashita1 and Shin-ichi Kanazawa2
Social Environmental Systems Engineering, National Institute of Technology Fukushima College, Japan;
2
Civil and Environmental Engineering, National Institute of Technology Fukushima College, Japan

ABSTRACTIn recent years, with the increase in sudden heavy rainfall, such as typhoons and guerrilla heavy rain, there
are many reports of the collapse of soil structures on a global scale. People are suffering from the danger of soil
structure disasters almost every year. This is partly due to the analysis of the factors leading to the collapse of soil
structures and the fact that the mechanisms are not fully understood. In this study, the ground behavior ofembankment and river embankment considering rainfall and rising water level were examined using the airdissolved unsaturated soil / water / air coupled finite analysis code. In the current analysis code for unsaturated
soil, “ ρ g = 0 ” is used in the incremental balancing equation. Therefore, it is not possible to consider the influenceof a state change due to external factors, that is, the change of the unsaturated density. The purpose of this study
is to propose an incremental balancing equation applied to unsaturated soil and reflect it in the conventional
analysis code. In this way, this study attempts to construct an analysis code that considers density change due toexternal factors in unsaturated soil.
Keywords：Numerical analysis, Density change, Unsaturated soil, Finite element method

current analysis cannot consider state change due toexternal factors, that is, the influence of the densitychange of the unsaturated state. This is because thetime change of the density “ ρ ” is not formulated.
Therefore, the purpose of this research is to propose
an analysis code considering the time change of thedensity by newly proposing the time change of thedensity and reflecting it in the DACSAR-MP program.
In addition, we compare the analysis results with theconventional analysis code (without density change)
and the newly constructed analysis code (with densitychange) to verify the usefulness of the change.

INTRODUCTION
In recent years, due to the effects of global
warming, sudden heavy rains such as typhoons andguerrilla heavy rains are increasing. Along with this,
there are many reports of collapse of and damage tosoil structures on a global scale. Even in Japan, due tothe effects of Typhoon No. 19 in 2019, about 140
river dikes broke and suffered enormous damage
nationwide [1]. At that time, many river dikes under
the jurisdiction of the country, such as the YOSHIDARiver (Miyagi), the ABUKUMA River (Fukushima),
and the CHIKUMA River (Nagano), broke. This waspartly due to the analysis of the factors leading to thecollapse of soil structures and the fact that themechanisms are not fully understood. Therefore, it isurgently necessary to understand the behavior of thefoundation ground of soil structures over a long
period of time. In geotechnical engineering, there aremany studies looking at the optimal design of anembankment structure that focus on predicting thestructure’s long-term performance by using
numerical analysis. In this laboratory, we haveclarified the ground behavior of embankment andriver embankment considering rainfall and risingwater level using the air-dissolved unsaturated soil /water / air coupled finite analysis code "DACSARMP"[2]. However, in the current analysis code forunsaturated soil, the term of the density change overtime in the incremental balancing equation is" σ + ρ g =0 " is " ρ g = 0 ". For this reason, the

DENSITY CHANGE OVER TIME
Necessity
In conventional finite element analysis, it is notpossible to consider the effect of state change due toexternal factors, that is, the effect of unsaturated
density change. Taking the collapse of a riverembankment as an example, overflow, erosion,
seepage, and earthquakes are all causes of riverembankment failure due to fluctuations in external
water level. Of these, three, excluding earthquakes,
are due to rainfall. Therefore, using the existing
analysis code, we have tried to understand the
behavior of soil structures by performing analysis
with rainfall and water level rise for the riverembankment. However, the existing analysis code
cannot consider the effect of unsaturated density

147

GEOMATE – Melbourne, Australia, 11-13 November 2020change. In other words, it is impossible to obtain moreaccurate analysis results because the density does notchange with time, and it is not possible to consider the
inflow and outflow of water into the ground.
Therefore, it is necessary to propose the equilibrium
formula of unsaturated soil for more precise analysis.

・ρ=((1 − n) ρ s ) = −n ρ ssProposal of a Term for Density Change withTime

Finally, the time variation of the overall density
is expressed by Equation (12), so ρ is proposed asin Equation (13).

 r ρ w + nSr ρ w
ρ w = ( nS r=
ρ w )・ nS・ρ =( n(1=− S ) ρ ) n (1 − S ) ρ − nS ρaFirst, Equation (1) shows the relationship ofrelative densities in each phase.
M
V

α
ρ=
α =

Vα
ρα (α s, f , a )
=
V

Sr =

Vw
Vw V
V
V
V − Vs=⇒ w = Sr v = Sr
= nSr
Vv Vv
V
V
V
V

Va V − Vs − Vw=
= n(1 − S r )
V
V

r

a

r

a

(10)
(11)

r a

ρ = ρ s + ρ w + ρ a

(12)

(1)
 r ρ w + nSr ρ w + n (1 − S r ) ρ a − nSr ρ a=ρ −n ρ s + nS

Here, the relative volume of each layer isexpressed as follows from the relationship between
the void ratio, porosity, and saturation and the threephase diagram of soil. Equations (2), (3), and (4) are
the relative volumes of the soil, liquid, and gas phasesin this order.
Vsn n(1 − n)1===
= 1− nV 1+ e e
n

(9)

mass

(13)

volume

Ari
Water
Solid

(2)

Fig. 1 Three-phase diagram of soil

(3)

Density Change Reflected in the Analysis CodeFor the effective stress equation considering theterm of density change with time, which was
proposed in the previous section, weak formulation
was performed. After that, spatial discretization was
performed using the finite element method.
Furthermore, time discretization was performed by
the Euler method. This formula, reflected in the
program of the analysis code "DACSAR-MP" (seethe next chapter), is used in this laboratory, and a new
analysis code was created in consideration of theeffect of temporal density changes.

(4)

From this, the relative density of each phase is
as follows. Equations (5), (6), and (7) are the relative
densities of the soil, liquid, and vapor phases in thisorder.

ρ s= (1 − n) ρ s(5)

ρ w = nSr ρ w(6)

SOIL / WATER / AIR COUPLED FINITEANALYSIS CODEρ=a n (1 − S r ) ρ a(7)

The finite element analysis code “DACSAR-MP”[2] used in this study formulates the unsaturated soil
constitutive model proposed by Ohno et al [3]. Thismodel is framed as the soil/water/air coupled problem
using the three-phase mixture theory. Equation (14)shows the effective stress. Equation (15) shows thebase stress tensor and suction stress. Equation (16)shows suction.

Therefore, Equation (8) shows the overall
density relationship.

ρ = ρs + ρw + ρa

(8)

Next, let us consider the time variation of thedensity of each phase. Equations (9), (10), and (11)are the changes over time in the density of the soil,
liquid, and gas phases.

148

σ' = σ net + ps 1

(14)

σ net =
σ − pa 1, ps =
Se s

(15)

GEOMATE – Melbourne, Australia, 11-13 November 2020

S r − S rcs =−pa pw , Se =1 − S rc

Here, σ' is the effective stress tensor; σ is thebase stress tensor; 1 is the second order unit tensor;
σ is the total stress tensor; s is the suction; ps is the
net

1

1
3
p ' = σ' : 1, q =
s:s
3
2

(19)

1
s =σ' − p ' 1 =A : σ', A =I − 1 ⊗ 1
3

(20)



1 + e0(1 − Sr ) εv + nSr − n (1 − Sr )'sat is the yield stress

In this study, we examined the validity of the
newly proposed analysis code that includes the effectof temporal density changes. Analyses were
performed using the conventional analysis code that
does not consider the influence of temporal densitychange (hereinafter, old analysis code) and theanalysis code proposed here that considers theinfluence of temporal density change (hereinafter,
new analysis code). The two analysis codes were used
under the same analytical conditions, and acomparative study was conducted.

at saturation; a and ns are the parameters
representing the increase in yield stress due tounsaturation; λ is the compression index; and κ isthe expansion index. Equation (21) shows pore watervelocity. Equation (22) shows air velocity. Pore waterand air flow follow Darcy’s law.
(21)

γw

(26)

COMPARATIVE ANALYSIS

plastic volume strain; M is the q/p’ in the limit state;p−k a ⋅ gradha , ha =a
va =

p a
− divva =0
pa + p0(25)

and p0 is atmospheric pressure. The elasto-plastic
constitutive model obtained from Equation (17) andthe equilibrium equation [Equations (25) - (26)] areformulated as the soil/water/air coupled problem.

p

vw =
−k w ⋅ gradh(24)Here, n is porosity; εv is volumetric strain;

Here, nE is the shape parameter; εν is the

D is the dilatancy coefficient; pares

nSr − S r εv + divvw =
0

λ −κ

ζ =
exp (1 − Se ) ln a  , MD =
2m

air permeability in dry conditions. Equations (25)(26) show the continuous formula of pore water and
air using three-phase mixture theory.

(17)

(18)

ns

) k

kra is the ratio of coefficient of air permeability; mis the Mualem constant; k wsat is the hydraulicconductivity at saturation; k ares is the coefficient of

Se is the effective degree of saturation; and S rc is thedegree of saturation at s→∞. Equations (17), (18),(19) and (20) provide the yield function.
nE

1

(23)

Here, krw is the ratio of hydraulic conductivity;

pore water pressure; S r is the degree of saturation;p'
MD  q +− ενp 0='nE  Mp ' ζ psat(kra k ares =ka =(1 − Se ) 2 1 − Se msuction stress; pa is the pore air pressure; pw is the=f ( σ',
ζ , ενp ) MD ln

)

(

2

m
1
1 

k=
krwk wsat= Se 2 1 − 1 − Se m  k wsat
w



(16)

(22)

Analysis Condition
The new and old analysis codes for the soil / water/ air coupled finite code "DACSAR-MP" [2] wereused under the same conditions.

Here, vw is the pore water velocity; va is the air
velocity; k w is the hydraulic conductivity; k a is the

Moisture characteristic curve used by analysis

coefficient of air permeability; h is the total head;γ w is the unit weight of water; and ha is the

For a soil-water characteristic curve model, amodel capable of hysteresis expression, as proposedby Kawai et al. [6], is used. In addition, to determine
the logistic curve in the case of DRY and WET,
derived from arbitrary suction and the degree ofsaturation, the logistic curve equation of Sugii andUno [7] was used. This makes it possible to grasp the

pneumatic head. Equations (23)-(24) show hydraulicconductivity and the coefficient of air permeability by
way of Mualem's [4] formula and the Van Genuchten[5] formula.

149

GEOMATE – Melbourne, Australia, 11-13 November 2020

moisture conditions of sloped ground where acomplex water balance occurs. Figure 2 shows thesoil-water characteristic curve used in this study.

Left side：x-axis fixed
Here, the water head boundary is given an
undrained condition. The air boundary is a nonexhaust condition.

Fig.2

Drying

0.8

9

10

0.6

7

8

0.4

5

6

3

4

1

2

AD=-34.7,BD=5.9

Wetting0.2
AW=-24.0,BW=4.6
00
200 400 600
Suction s (kPa)

800

Soil water characteristic curve

1m×2 = 2m

Material parameters

Fig. 4

Table 1 shows the material parameters used inthis study. Material parameters were determinedconsidering the results of previous studies by Honda
et al [8]. Here, λ is the compression index; κ is theswelling index; M is the marginal state parameters; mis the unsaturated permeability coefficient ofMualem; n is the 𝑬𝑬𝒄𝒄 model parameters; 𝒏𝒏𝑬𝑬 is themagnification ratio of yield surface; 𝒆𝒆𝟎𝟎 is the initial
gap ratio; ν is the Poisson’s ratio; and 𝑺𝑺𝒓𝒓𝒓𝒓 is thecritical degree of saturation.

Flow condition

Table 1Analysis Result

0.180

0.037

1.33

0.80

1.0

1.3

1.2

0.33

0.15

2.7

0.10

In this study, we compare the analysis results of
the new and old analysis codes regarding saturation,
volume strain and total stress. In addition, for the new
analysis code, we verified the reaction force at thebottom of the model.

0.0012In this analysis, the sand-mixed silt used forgeneral soil structures is assumed.

Saturation (𝑺𝑺𝒓𝒓 )Figure 5 shows the degree of saturation values
and the differences in the analysis using the new andold analysis codes at typical STEP numbers. (Thedifference is the result of the new analysis code minus
the result of the old analysis code.)
From Figure. 5, we can confirm the difference inthe degree of saturation in the analysis using the new
and old analysis codes. Here, both results show asimilar degree of saturation increase as the number of
STEPs increase. Furthermore, the evaluation is
performed by focusing on the number of steps. First,
from the time immediately after the analysis to theearly stage of analysis, the results from the oldanalysis code show a slightly higher degree ofsaturation, but the difference is only 0.0095% at thetime of STEP10000. In other words, from the time
immediately after the analysis to the beginning, there
is almost no effect due to the consideration of ρ inthe incremental balance equation. However, as thePermeability coefficient [ k (m/s)]
𝟏𝟏𝟎𝟎−𝟏𝟏𝟏𝟏

𝟏𝟏𝟎𝟎−𝟗Practicallyimpermeable

Cohesive
soil

Fig. 3

Very low

𝟏𝟏𝟎𝟎−𝟕Low

sand-mixed silt

𝟏𝟏𝟎𝟎−𝟓

Medium𝟏𝟏𝟎𝟎−𝟑

Sand and gravel

High

Analysis model

As the flow rate condition in this analysis,
1.0×10-3 m/day rainfall was applied to the uppersurface of the model for 900 days (90000 steps). At
this time, the daily average rainfall of 5.0×10-3 m/day
was calculated from the annual average rainfall of
1529mm/year in Tokyo [9], and the rainfall was set
so that it did not differ much from the actual rainfall.
The initial saturation was 𝑺𝑺𝒓𝒓 = 𝟔𝟔𝟔𝟔%.

Material parameters

0.10

1m×5 = 5m

Degree of saturation Sr (-)

1

𝟏𝟏𝟎𝟎𝟎𝟎

GravelPermeability coefficient

Analysis model
Figure 4 shows the analytical model used for thecomparative analysis. The numbers in the figure
indicate element numbers. The displacementboundaries are as follows.
Upper side：Free
Bottom：x, y axis fixed
Right side：x-axis fixed150

GEOMATE – Melbourne, Australia, 11-13 November 2020

number of STEPs increases, the difference in degreeof saturation increases rapidly. Figure 6 shows thedifference in degree of saturation between the new
and old analysis codes. As shown here, the differencein saturation exhibits an exponential relationship. For
this reason, the difference in degree of saturation inthis analysis becomes noticeable at around STEP
40000. Furthermore, it is clear that at STEP80000, thedifference becomes a remarkable discrepancy of
about 2.1%.

New
analysis
code

STEP
10000

old

400

Total Stress [kPa]

Ex.
Step
No.

Old
analysiscode

incremental balance formula. It is considered that the
newly proposed 𝝆𝝆 term affects the distribution of
total stress immediately after the analysis. Thissuggests that the density term has a large effect on the
total stress.

65.2395 > 65.2352

Difference (New-Old)-0.0043

newold

84.0083 < 84.1170

0.01470.1087

old

STEP
80000

new

90.9500 < 91.3758

old

new

2.3954＊The unit is [%]Change in degree of saturation

Fig.8

3.49

The
difference[%][%]
The difference

ー：New analysis code
ー：Old analysis code

300
200
100
0
0

4.00
3.99

2.99
3.00

200

400
TIME[Day]

600

800

Total stress at the bottom of the model

Reaction force verification

2.49
1.99
2.00

In this study, we also verified the reaction force
to confirm the consistency of the new analysis code
considering the time variation of density. Figure 9 isa model that measures the reaction force. The model
is a 1m x 1m element with nine nodes. The
displacement boundaries are as follows.

1.49
0.99
1.00
0.49
-0.01
0

800

100

60

Fig. 5600

400

91.9942 < 94.3896

0.4258400
TIME[Day]

new75.3350 < 75.3497

STEP
70000

200

Total stress at the top of the model

Total Stress [kPa]

old

100
0
0

Fig.7
STEP
50000

200

new

Saturation> Saturation

STEP
30000

ー：New analysis code
ー：Old analysis code

300

0

20000
20000

40000
40000

60000
60000

80000
80000

100000

STEP
No.
STEP
No.

Upper side：Free
Bottom：x, y axis fixed
Right side：x-axis fixed
Left side：x-axis fixed

Fig. 6 Difference in saturation between new andold analysis codes
Total stressIn addition, the water head boundary is an
undrained condition and the air boundary is a nonexhaust condition. As the flow rate condition, 10 days
(1000 steps) of 𝟏𝟏. 𝟎𝟎 × 𝟏𝟏𝟏𝟏−𝟐𝟐 𝐦𝐦/𝐝𝐝𝐝𝐝𝐝𝐝 rainfall wasapplied to the top of the model. Table 2 summarizes
the results. Table 2 shows the reaction force values at
the main STEPs at the contact points at the bottom of
the model, "No. 1, No. 2, No. 3," and their totals. As
a result, the reaction force at the bottom of the modelincreases as the number of steps increases. ThisThe changes over time of total stress in theanalysis using the new and old analysis codes areshown below. Figure 7 shows the upper model, andFig. 8 shows the lower model. As seen in Figs. 7 and8, the total stress value in the new analysis code
exceeds the total stress value in the old analysis codein both the upper and lower parts of the model. Thisis the effect of the consideration of ρ on the151GEOMATE – Melbourne, Australia, 11-13 November 2020

this strongly suggests that the effect of the change indensity over time cannot be ignored. Moreover, manyof the numerical analyses in geotechnical engineering
aim at long-term prediction. Therefore, in long-term
analysis, such as embankment analysis, a large
difference between the results of the old analysis codeand the new analysis code are expected. In the future,
we will perform more detailed analysis, in addition tochanging external factors, to confirm embankment
behavior more precisely.

confirms that the new analysis code is properly
processing the flow rate condition, and the analyticalconsistency is therefore recognized.
Flow Rate

1m

1m

REFERENCES
12

3[1] Cabinet Office: About the damage situation toaffect typhoon 19th in the first year of Reiwa,
2020, pp24-25.
[2] Kanazawa, S., Toyoshima, K., Kawai, K.,
Tachibana, S. and Iizuka, A.: Analysis ofmechanical behavior of compacted soil with F.E.
method, journal of JSCE, No.68 (2), 2012,
pp.291-298.
[3] Ohno, S., Kawai, K. and Tachibana, S.:
Elastoplastic constitutive model for unsaturated
soil applied effective degree of saturation as aparameter expressing stiffness, Journal of JSCE,
Vol.63/No.4, 2007, pp.1132-1141.
[4] Mualem, Y.: A new model for predicting thehydraulic conductivity of unsaturated porous
media, Water Re-sources Research, Vol.12, No.3,
1976, pp.514-522.
[5] VanGenuchten: A closed-form equation forpredicting hydraulic of unsaturated soils, Soil
Science Society American Journal, Vol.44, 1980,
pp.892-898.
[6] Kawai, K., Wang, W. and Iizuka, A.: The
expression of hysteresis appearing on water
characteristic curves and the change of stresses inunsaturated soils, Journal of applied mechanics,
Vol.5, 2002, pp.777-784. Kimura S., Journal
Paper Title, J. of Computer Science, Vol. 1, Issue
2, 1987, pp. 23-49.
[7] Sugii T. and Uno T.: Modeling the New Moisture
Characteristic Curve, Journal of JSCE, 1995,
pp.130-131.
[8] Honda M., Iizuka A., Ohno S. Kawai K. andWang W.: A study of the evaluation method for
deformation characteristic of compacted soil,
Journal of JSCE, Vol.40, No.2, 2000, pp.99-110.
[9] Ministry of Internal Affairs and Communications
Statistics Bureau: Japanese statistics 2019, 2019,
p6.

Reaction Force Judgment

Fig.9

Reaction force verification model

Table 2

Total stress value
No.1

No.2

No.3

Total

250

1.128

3.963

0.657

5.749

500

1.364

4.719

1.020

7.102

750

1.638

5.562

1.452

8.652

1000

1.933

6.377

1.923

10.232＊Positive Upward

× 𝟏𝟏𝟎𝟎−𝟐𝟐 [𝐤𝐍]

CONCLUSIONSFirst, the reaction force verification confirmed
the consistency of the new analysis code. The analysis
results using the new analysis code also confirmedthat saturation and total stress tended to increase
compared to the old analysis code. This demonstrated
that the influence of the temporal change of density
in the incremental balance equation cannot be ignoredin the numerical analysis of unsaturated soil. It
became particularly clear that the difference in the
degree of saturation increases with the increase in thenumber of STEPs, suggesting that the difference
becomes large in long-term analysis. Furthermore, inthis study, analysis was performed considering the
average flow rate in Japan. However, the change inthe degree of saturation becomes more remarkable
when the analysis considers a flow rate that
corresponds to the sudden heavy rain which has
frequently occurred in recent years. In other words,152

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

MUTUAL RELATIONSHIP BETWEEN SUFFUSION AND SHEAR
BEHAVIOR IN TRIAXIAL COMPRESSION APPARATUS
Taichi Ishimaru1, Motoyuki Suzuki1 and Tomoki Wakamatsu1
1

Graduate School of Science and Technology for Innovation, Yamaguchi University, Japan
ABSTRACTAged embankment structures, affected by long-term seepage, are safety hazards. Internal erosion occurs whensoil particles move inside open spaces and eventually flow out of embankment structures together with water.
Among the types of internal erosion, the phenomenon whereby only the fine particles flow out, while the skeletonof the coarse soil particles remains, is called “suffusion”. It has been pointed out that changes in the skeletal
structure cause changes in the strength of embankments. However, the behavior of suffusion and the factors that
it affects are still unclear. The authors previously investigated the influences of suffusion on soil strength. In thepresent study, a modified triaxial compression testing system was established in order to simulate the suffusion
inside specimens. A series of tests was conducted under conditions of different void ratios and filter pore opening
sizes. Before and after the water was supplied to the specimens, drained triaxial compression was imposed on them.
The amount of discharged soil and the turbidity of the drained water were examined after the tests. As a result, inmany cases, both the peak and the residual strength decreased regardless of changes in the void ratio by suffusion.
Moreover, no difference in the rate of internal erosion due to shearing was observed.
Keywords: Suffusion, Internal erosion, Embankment structure, Seepage

INTRODUCTION
Aged embankment structures affected by seepage
are safety hazards. The phenomenon by which soil
particles move inside open spaces of the ground andeventually flow out of embankment structures
together with water is called internal erosion. It is oneof the main causes of embankment structure failures.
There are four types of internal erosion. This paper
focuses on one of them, “suffusion”. Suffusion is thephenomenon whereby only the fine particles flow out,
while the skeleton of the coarse soil particles remains,
as shown in Fig. 1. It has been pointed out thatchanges in the skeletal structure cause changes in thestrength of the embankment [1], [2], [3]. However,
suffusion behavior and the many factors that it affects
are still unclear. In this study, a modified triaxial
compression testing system was established in order
to simulate the suffusion inside a specimen. In theapparatus, only fine particles can be discharged fromthe specimen through the sieve by seepage. A seriesof tests was conducted under conditions of different
void ratios and filter pore opening sizes. Two
approaches were taken in this study. First, toinvestigate the changes in the drained shear behaviorof the soils affected by suffusion, drained triaxial
compression was imposed on the specimen after
water was supplied to it. Second, to investigate theinfluence of shearing on the suffusion behavior, theamount of discharged soil and the turbidity of thedrained water were examined before and after
shearing. Measuring the turbidity helped to capture
the small suffusion behavior.

Fig. 1

Image of suffusion

TEST MATERIAL
The soil samples used in this study contained
decomposed granite soil collected in Ube, Yamaguchi,
Japan and called “Ube Masado”. There are many
reservoir embankments made of decomposed granite
soil in the chugoku region of Japan. The fines content
𝐹𝑐 of Ube Masado is 5.2%. Figure 2 shows thephysical properties and the particle size distributionof Ube Masado. The stability of the internal erosionof these soil samples was evaluated using the index
proposed by [5], and it was found that internal erosion
is likely to occur.
153

GEOMATE – Melbourne, Australia, 11-13 November 2020

100

Percentage finer by weight (%)

TEST METHOD
Test Apparatus
Figure 3 shows a schematic diagram of themodified triaxial compression apparatus used in thisstudy with photos of the modified parts. It was
produced by referring to the experimental apparatus
designed by [6]. The soil specimens wereapproximately 50 mm in diameter and 100 mm inheight. At the bottom, where each specimen was
located, an acrylic plate, with 21 holes each having a
diameter of 5 mm, and mesh with a diameter of either
250 µm or 420 µm, were installed. One or the otherof the meshes was selected for each experiment.
These parts allowed only fine particles, smaller than
the size of the sieve, to flow out of the specimen under
the application of water flow. In addition, soil
particles were easily discharged by providing afunnel-shaped depression in the bottom of thespecimen and changing the size of the tube locatedbetween the pedestal and the lower tank from one
with an inner diameter of 2 mm to one with an innerdiameter of 4.5 mm. The upper and lower tanks wereconnected to the upper and lower parts of thespecimen, respectively, and the difference between
the water level of the upper and lower tanks gave thespecimen a downward seepage flow. The drainage,
consisting of discharged water and soil particles,
collected in the lower tank.

Ube
80

MasadoFines content 𝐹𝑐 (%)

Uniformity coefficient
60

5.2
𝑐

19.4

Specific gravity

2.625

Plasticity index

NP

40

20

0
0.001

0.01

0.1

1

10

Particle size (mm)Fig. 2

Physical properties
distribution curves

andparticleMesh
425µm 250µm

Acrylic
plate

size

Hydraulicgradient 𝑖
∆ℎ
𝑖=
∆𝐿
LVDT

Upper tank

Load cellBackpressure∆𝐿

Pedestal with funnel∆ℎshaped depression

Lower tank

Test Procedure

Drainage in thelower tank

Preparation of Specimens

Fig. 3All the soil specimens were prepared by the moist
tamping method and had the same initial watercontent (𝑤 = 10%). The specimens here were madewith various void ratios, 𝑒, in the range of 0.45 to 0.65.
After placing each specimen on the pedestal, thesaturation process was conducted in the same way asthat in general triaxial compression tests. After the B
value was 0.95 or greater, all the specimens were
isotopically consolidated to the mean effective stressof 30 kPa. Up to this point, the procedure is the samefor all the specimens. Three tests, called “NE: No
Erosion”, “EBS: Erosion Before Shearing”, and“EAS: Erosion After Shearing”, respectively, were
performed after this process. The procedure for thethree tests will be described in the following.

Loading system

Backpressure

Schematic diagram of apparatus used in thisstudy

Pattern 2: EBS (Erosion Before Shearing)
These tests were performed to investigate thechanges in the drained shear behavior of the soil
affected by erosion in comparison to the results of NE.
After the consolidation process, the difference
between the water levels in the upper and lower tanks
was adjusted to approximately 500 mm, and initial
hydraulic gradient 𝑖 was 5. The hydraulic gradient
was defined as
𝑖=

∆ℎ
∆𝐿

(1)

where ∆ℎ is the water head difference (difference
between the water levels in the upper and lower tanks)and ∆𝐿 is the height of the specimen.
It should be noted that the hydraulic gradient inthis study (a value of 5) was too high compared to thecritical hydraulic gradient. However, suffusion did
not occur in the case of the lower value, and the tube

Pattern 1: NE (No Erosion)
These tests were performed to investigate thedrained shear behavior of the soil affected by no
erosion. After the consolidation process, a drained
shear test was performed with an axial strain rate of
0.5 %/min until the axial strain reached 15%.

154

GEOMATE – Melbourne, Australia, 11-13 November 2020

was clogged with discharged soil particles in the caseof the higher value. Therefore, this value of hydraulicgradient was adopted in the present study.
After the consolidation process, the permeation
process was performed until the lower tank was full(about 785 ml). In this process, the seepage direction
was downward and suffusion occurred. The change inaxial displacement was measured by an LVDT
displacement sensor. Next, a drained shear test was
performed at an axial strain rate of 0.5 %/min until
the axial strain reached 15%.

Table 2 Test conditions in this study

Pattern 3: EAS (Erosion After Shearing)
These tests were performed to investigate theinfluence of shearing on the suffusion behavior. Afterthe consolidation process, a drained shear test was
performed at an axial strain rate of 0.5%/min until theaxial strain reached 15% by the same method as NE.
The permeation process was then performed with thesame hydraulic gradient as EBS, while maintaining
the axial strain of 15%.
After EBS and EAS Tests
After the EBS and EAS tests, the amount ofdrainage and the turbidity of the drainage were
measured. Then, the mass of the discharged soil
particles was measured after they had been oven dried.

𝑒0

𝑒𝑎𝑐

NE-1
NE-2
NE-3

0.487
0.467
0.571

0.469
0.453
0.550

-

-

-

NE-4

0.607

0.587

-

-

-

NE-5

0.581

0.564

-

-

-

EBS-250-1

0.488

0.476

0.476

-

EBS-250-2

0.528

0.506

0.483

-

0.37

EBS-250-3

0.596

0.575

0.557

-

0.11

EBS-420-1

0.478

0.465

0.463

-

0.20

EBS-420-2

0.480

0.461

0.438

-

0.33

EBS-420-3

0.565

0.549

0.560

-

0.68

EBS-420-4

0.614

0.594

0.600

-

0.44

EAS-250-1

0.466

0.450

-

0.512

0.33

EAS-250-2

0.545

0.526

-

0.565

0.31

EAS-420-1

0.588

0.564

-

0.446

0.07

EAS-420-2

0.549

0.519

-

0.511

0.24

Note:
Test-ID: {Experimental code - (Sieve size of mesh) - Number}𝒆𝟎 : Initial void ratio, 𝒆𝒂𝒄 : Void ratio after consolidation, 𝒆𝒂𝒔 : Voidratio after shearing, 𝒆𝒃𝒔: Void ratio before shearing, 𝑹𝒆: Erosion rate.
0.8
0.7

𝑚𝑑
𝑅𝑒 =
× 100 (%)
𝑚0

Erosion rate Re (%)

Table 2 shows the test conditions in this study.
Each parameter is explained in a note located below
the table. Erosion rate 𝑅𝑒 was defined by [1] as(2)

where 𝑚𝑑 is the dry mass of the discharged soil
particles and 𝑚 0 is the initial dry mass of eachspecimen.
Figure 4 shows the relationship between 𝑒𝑏 and𝑅𝑒 . 𝑅𝑒 is generally low, namely, 0.7% or less. Whenusing the mesh 250 µm in diameter, 𝑅𝑒 is higher as
the specimen becomes denser. On the other hand,when using the mesh 420 µm in diameter, 𝑅𝑒 isrelatively higher as the specimen becomes looser. It
was found in this study that suffusion is likely tooccur with the combination of the 420-µm diameter
mesh and a loose specimen. There was no difference
in 𝑅𝑒 between EBS and EAS.
Figure 5 shows the relationship between theconcentration and the turbidity of the drainage.
Concentration 𝑆𝑆 is defined as

𝑒𝑏

0.39

TEST RESULTS
Status of discharged soil particles

𝑒𝑎

𝑅𝑒
(%)

Test-ID

0.6
0.5

EBS-250-1
EBS-250-2
EBS-250-3
EBS-420-1
EBS-420-2
EBS-420-3
EBS-420-4

EAS-250-1
EAS-250-2
EAS-420-1
EAS-420-2

0.45

0.50

0.4
0.3
0.2
0.1
0.0
0.55

0.60

Void ratio before shearing ebsh

Relationship between 𝒆𝒃𝒔 and 𝑹𝒆
𝑚𝑑
𝑆𝑆 =
𝑉𝑑𝑤

Fig. 4(3)

where 𝑚𝑑 is the dry mass of the discharged soil
particles and 𝑉𝑑𝑤 is the volume of discharged water.
The relationship between the concentration andthe turbidity of the calibration suspensions is also
shown in Fig. 5. Four calibration samples were takenfrom the Ube Masado using four sieves, with meshsizes of 75 µm, 106 µm, 250 µm, and 420 µm, andnamed C75, C106, C250, and C420, respectively. 80
types of suspensions were made, and their turbidity
was measured. The 80 types of suspensions differedin calibration samples (4 types: C75, C106, C250, and
155

800
Turbidity

700

Turbidity (degree)

600

Turbidity ratioat the same
concentration

Suspension
A
Rt

Reference line
1500

Concentration

400

75
-C
e
lin 6
0
ce
en C1
r
e
f
e
R

300

0
C25

200

C420

Turbidity ratio at the same concentration Rt

GEOMATE – Melbourne, Australia, 11-13 November 2020

EBS-250-1
EBS-250-2
EBS-250-3
EBS-425-1
EBS-425-2
EBS-425-3
EBS-425-4
EAS-250-1
EAS-250-2
EAS-425-1
EAS-425-2

100
0

0

1

2

3

4

EBS-250-1
EBS-250-2
EBS-250-3

5

3
2
1
0

0.1

0.2

Deviator stressq (kPa)NE-1(ebsh=0.469)
NE-2(ebsh=0.453)
NE-3(ebsh=0.550)
NE-4(ebsh=0.587)
NE-5(ebsh=0.564

0.6

0.7

0.8

EBS-250-1(ebsh=0.476)
EBS-250-2(ebsh=0.506)
EBS-250-3(ebsh=0.575)

EBS-420-1(ebsh=0.465)
EBS-420-2(ebsh=0.461)
EBS-420-3(ebsh=0.549)
EBS-420-4(ebsh=0.600)

200
150
100
50

Volume strain
εv (%)

0
-8
-4
0
4
0

3

6

9

12

15

Axial strain εa (%)
Fig. 7 Relationship between 𝜺𝒂 ~ 𝒒 ~ 𝜺𝒗 in cases of NE
and EBSEAS-250-1(ebsh=0.450)
EAS-250-2(ebsh=0.526)

Deviator stressq (kPa)C420), 𝑆𝑆 (5 types: 0.2, 0.6, 1.0, 2.0, and 3.0 g/L),
and volume of suspensions (4 types: 0.05, 0.25, 0.50,
and 2.5 L). According to Fig. 5, the concentration andturbidity of the calibration samples showed a linear
relationship when the type of calibration samples was
the same. Further, the slope of the best fitting line
became smaller as the size of the soil particles
contained in the suspensions was larger. These results
are thought to reflect the turbidimeter’s
characteristics whereby the surface area of the soil
particles in the unit volume increases as the particle
size of the suspended substance decreases and theamount of turbidity increases [7]. There was no effecton the turbidity measurement due to the differencesin the volumes of the suspensions. Here, each best
fitting line is called the “reference line”. The
turbidities of the experimental drainage were plottedabove reference lines of C420 and C250. In addition,a lot of data were plotted above the reference line of
C75. It is supposed that many soil particles with aparticle size of 75μm or less were discharged from thespecimen, although meshes with a sieve size of 250µm or 420 µm were used in the test. Furthermore, as
shown schematically in Fig. 5, when the relationshipbetween the concentration and the turbidity ofsuspension A is plotted, the ratio of the turbidity ofsuspension A and the turbidity on the reference linein the same concentration of suspension A is called "Turbidity ratio at the same concentration 𝑅𝑡 ". Itmeans that the larger the value of 𝑅𝑡 , the larger the
distribution of soil particles with a smaller particle
size than the calibration samples used in the referenceline.
Figure 6 shows the relationship between 𝑅𝑒 and𝑅𝑡 . In calculating 𝑅𝑡 , the reference line of C250 wasused with the 250-μm diameter mesh, and thereference line of C420 was used with the 420-μm
diameter mesh. The lower the 𝑅𝑒 , the larger the 𝑅𝑡 .
The fact that only the small soil particles flowed out
is thought to have contributed to the low erosion rate.
The 𝑅𝑡 of EBS-420-3 and EBS-420-4 for 𝑅𝑒 were

0.3 0.4 0.5
Erosion rate Re

Fig. 6 The relationship between 𝑹𝒆 and 𝑹𝒕

andEAS-420-1(ebsh=0.564)
EAS-420-2(ebsh=0.519)

200
150
100
50
0

Volume strain
εv (%)

Relationship between concentration
turbidity of suspensions

EAS-250-1
EAS-250-2
EAS-425-1
EAS-425-2

4

Concentration (g/L)

Fig. 5

EBS-425-1
EBS-425-2
EBS-425-3
EBS-425-4

-8
-4
0
4
0

Fig. 8
EAS
156

3

6

9

Axial strain εa (%)

12

15

Relationship between 𝜺𝒂 ~ 𝒒 ~ 𝜺𝒗 in case of

GEOMATE – Melbourne, Australia, 11-13 November 2020

180

200

Residual strength qres (kPa)Peak strength qmax (kPa)

180

SRL
160
140
120
100
80
0.40

Fig. 9

NE，EAS
EBS-250-1
EBS-250-2
EBS-250-3
EBS-420-1
EBS-420-2
EBS-420-3
EBS-420-4

0.44

0.48

0.52

0.56

NE，EAS
EBS-250-1
EBS-250-2
EBS-250-3
EBS-420-1
EBS-420-2
EBS-420-3
EBS-420-4

160

140

SRL

120

100

80
0.40

0.60

0.44

0.48

0.52

0.56

Void ratio before shrearing ebsh

Void ratio before shearing ebsh

Relationship between 𝒆𝒃𝒔 and 𝑹𝒆

Fig. 10 Relationship between 𝒆𝒃𝒔 and 𝑹𝒆

0.60

30

Rate of change in qmax (%)

𝑞𝑚𝑎𝑥 or 𝑞𝑟𝑒

40

Strength estimated in thecase of no suffusion 𝑞𝑒 𝑡

Actual strength 𝑞𝑎𝑐𝑡
Void ratio before shearing

Fig. 11 Relationship between concentration andturbidity of suspensions

20
10
0

EBS-250-1
EBS-250-2
EBS-250-3
EBS-420-1
EBS-420-2
EBS-420-4
EBS-420-3

-10
-20
-30
-40
0.0

higher than the other data. As shown in Fig. 4, thesecases for the combination of the 420-µm mesh and theloose specimen, 𝑅𝑒 , were also high. It is thought thatsudden clogging is less likely to occur here than in theother test cases and that the soil particles with a small
particle size flowed out smoothly from many parts ofthe specimen.

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.7

0.8

Erosion rate Re (%)

Fig. 12 Relationship between 𝑹𝒆 and 𝑹𝒕
40

Rate of change in qres (%)

30

Relationship between 𝐞𝐛𝐬𝐡 and 𝐪𝐦𝐚𝐱 , 𝐪𝐫𝐞𝐬Figure 7 shows the relationship among axial strain
𝜀𝑎 , deviator stress 𝑞, and volume strain 𝜀𝑣 in the casesof NE and EBS. Figure 8 shows the relationshipamong 𝜀𝑎 ~ 𝑞 ~ 𝜀𝑣 in the case of EAS. In these tests,
it is difficult to perfectly match the void ratio before
shearing 𝑒𝑏 ℎ (𝑒𝑏 ℎ =𝑒𝑎𝑐 : in the cases of NE and EAS
and 𝑒𝑏 ℎ = 𝑒𝑏 : in the case of EBS), and it is notpossible to simply compare the difference in shear
behavior due to suffusion. This is because there arethree factors involved in the change in the gap ratioof the specimen: (1) the change in volume due toconsolidation, (2) the change in volume due toseepage, and (3) the change in the drying density ofthe specimen due to the loss of soil particles.
Therefore, direct focus was placed on the relationshipbetween 𝑒𝑏 ℎ and peak strength 𝑞𝑚𝑎𝑥 or residual
strength 𝑞𝑟𝑒 , and the effect of suffusion on strength

20
10
0

EBS-250-1
EBS-250-2
EBS-250-3
EBS-420-1
EBS-420-2
EBS-420-3
EBS-420-4

-10
-20
-30
-40
0.0

0.1

0.2

0.3

0.4

0.5

0.6

Erosion rate Re (%)

Fig. 13 Relationship between 𝑹𝒆 and 𝑹𝒕
was examined. Here, 𝑞𝑟𝑒 was the deviator stress at
the time the axial strain was 15%.
Figure 9 shows the relationship between 𝑒𝑏 ℎ and𝑞𝑚𝑎𝑥 , and Fig. 10 shows the relationship between
𝑒𝑏 ℎ and 𝑞𝑟𝑒 . The solid lines shown in Figs. 9 and
10 are the best fitting lines to the data of the cases ofNE and EAS. These data were not affected by157

GEOMATE – Melbourne, Australia, 11-13 November 2020

seepage. Through these solid lines, the reasonable
results whereby 𝑞𝑚𝑎𝑥 and 𝑞𝑟𝑒 decrease withincreasing 𝑒𝑏 ℎ can be understood. These solid lines
are called “Strength Reference Lines (SRLs)”. The
data for EBS are plotted at positions outside the SRL.
It was found that 𝑞𝑚𝑎𝑥 and 𝑞𝑟𝑒 are different among
NE, EAS, and EBS, even if 𝑒𝑏 ℎ is the same. [1] and[2] have pointed out that there is a factor that changes
the strength besides the change in the void ratio. The
results of these tests support the point.
Here, the ratio of the change in 𝑞𝑚𝑎𝑥 , influenced
by suffusion, was calculated as
𝑞𝑎𝑐𝑡 − 𝑞𝑒 𝑡
× 100
𝑞𝑒 𝑡

1.

2.

3.

4.

(3)

where 𝑞𝑎𝑐𝑡 is the strength measured in the EBS and𝑞𝑒 𝑡 is the strength estimated in the case of no
suffusion. A schematic diagram is shown in Fig. 11
to explain these parameters. The ratio of the change
in 𝑞𝑟𝑒 was also calculated in a similar way. Figure 12
shows the relationship between 𝑅𝑒 and the ratio of thechange in 𝑞𝑚𝑎𝑥 , and Fig. 13 shows the relationshipbetween 𝑅𝑒 and the ratio of the change in 𝑞𝑟𝑒 . The
ratio of the change for each strength was a negativevalue for many cases, so it is thought that eachstrength decreased regardless of the change in void
ratio. On the other hand, each strength increased by
suffusion in some cases. In the case of EBS-420-2,
the peak strength increased and the residual strength
decreased. Noticeable strain softening was observed.
For both peak strength and residual strength, there
was no relationship between 𝑅𝑒 and the ratio of thechange in each strength. Since 𝑅𝑒 is less than 0.7%, it
is considered that not only the discharged soil
particles, but also the moving soil particles in thespecimen, affected the strength greatly.

5.

It was found in this study that the combinationof a 420-µm diameter mesh and a loose
specimen is likely to cause suffusion.
The higher the erosion rate, the smaller theturbidity ratio at the same concentration. Whenthe situation makes erosion difficult, only thesoil particles with a small particle size can passthrough the gaps, and the soil particles with alarge particle size stay in the specimen.
No difference was observed between before andafter shearing for either the erosion rate or theturbidity ratio at the same concentration.
Even though the void ratio before shearing was
the same, the peak strength and the residual
strength differed depending on the condition of
suffusion or no suffusion.
No relationship was observed between theerosion rate and the rate of change for eachstrength. Since the erosion rate was less than
0.7%, it is thought that not only the discharged
soil particles, but also the moving soil particles
in the specimen, affected the strength greatly.

REFERENCES[1] Sato, M. and Kuwano, R., Laboratory testing forevaluation of the influence of a small degree ofinternal erosion on deformation and stiffness,
Soils and Foundations, Vol. 58, Issue 3, 2018,
pp.547-562.
[2] Ke, L. and Takahashi, A., Experimental
investigations on suffusion characteristics and its
mechanicalconsequencesonsaturatedcohesionless soil, Soils and Foundations, Vol. 54,
Issue 4, 2014, pp.713-730.
[3] Ouyang, M., Takahashi, A., Influence of initial
fine content on fabric of soils subjected to internal
erosion, Canadian Geotechnical Journal, Vol. 53,
2016, pp.299-313.
[4] Kenney, T. C., and Lau, D., Internal stability of
granular filters, Canadian Geotechnical Journal,
Vol. 22, 1985, pp. 215–225.
[5] Ke, L. and Takahashi, A., Triaxial erosion test forevaluation of mechanical consequences ofinternal erosion, Geotechnical Testing Journal,
Vol. 37, Issue 2, 2014, pp.347-364.
[6] Yokoyama, K., The influence of particle size on
turbidity output and the instructions for using
turbidity sensor in the field, Journal of JSCE, Vol.
698, 2002, pp.93-98 (in Japanese).

CONCLUSIONSIn this study, two approaches were taken for thetriaxial compression apparatus. First, to investigate
the changes in the drained shear behavior of the soil
affected by suffusion, drained triaxial compression
was imposed on specimens after water had been
supplied to them. Second, to investigate the influenceof shearing on the suffusion behavior, the amount ofdischarged soil and the turbidity of the drained water
were examined before and after shearing. The main
results of this paper are summarized as follows:158

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INVESTIGATION OF TIME-SERIES EXTENSOMETER DATA IN A
CONCRETE LINED MINE SHAFT
Jordan Tyler Kalyvas1, John Victor Smith1 and Gang Ren1
1

RMIT University, Australia
ABSTRACT

Data from extensometers installed in a concrete lining of a mine shaft is analysed to interpret the pattern of
deformation. The shaft is an important mine infrastructure which is at risk of damage. Five years of monitoringdata are reviewed for this study. Analytical and numerical methods are applied to resolving the geometriccomponents of the deformation. The deformation pattern is interpreted in terms of the in situ stress conditions andmining activities at the location. The lining is shown to undergo elliptical and translational deformation
components. The deformation is interpreted in terms of stress fields. The local mining history and the presenceof two significant faults influence the deformation pattern observed in the mine shaft. The deformation recordedby the extensometers is inferred to represent a sequence of slow stress waves passing through the shaft pillar during
mine excavation.
Keywords: Mine shaft, extensometers, concrete lining, deformation

event 6, however, these events commenced a numberof hours before the blasting time at the mine.
Therefore there is no indication that the events are
directly related to blasting at the mine.

INTRODUCTION
A copper mine shaft is outfitted with six
extensometers embedded in the shaft lining rock massinterface. The mine shaft is located in Mt Isa in thenorth of Queensland, Australia [1]. The shaft has
shown signs of degradation and cracking dating backto 1980 [2]. Selected data from six extensometers,
which have been collecting data wirelessly since May,
2015. A period from July to September 2017 isanalysed in this study.
Mining in the location will be briefly reviewed toprovide context for the instrument observations. The
mining occurring closest to the shaft is toward thenorthwest from the shaft and approximately 200 m
below the level of the extensometers use to collectdata for this study (Fig. 1).
Mining via this shaft during 2017 included
approximately 340,000 t of stoping in the first half of2017 concluding on 25th July 2017. During that time
120,000 t was excavated from a stope approximately
60 m from the shaft. Mining of areas accessed fromthis shaft did not recommence until 21st November
2017. During that time mining occurred in locations
accessed by other shafts. Therefore events 1-5
occurred near the conclusion of a phase on mining
close to the shaft. Whereas events 6 to 8 occurred
during a time of no local mining activity.
The time of day at which mine blasting occurs islimited for safety reasons. Excavation blasting at themine is tightly restricted to a 45 minute window
centred on 8am and 8pm such that blasting does notoccur at other times. Only event 6 occurred at a timethat corresponds with the blasting time. The
following two events (7 and 8) appear to haveoccurred at approximately 24 hour intervals afterFig. 1
Layout of the mine shaft, faults andmined stopes, after [3].
This paper investigates the detailed deformationof the recorded displacements to determine if theevents also include other systematic components suchas elliptical or translational deformation. The
direction of the deformation components is studied to
identify internal consistency and to compare with
regional geological structure and stress conditions.
The variation of deformation magnitude over time isinvestigated to infer time-dependent rheologicalbehaviour of the rock mass in the mine.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Summary of events recorded inextensometers in shaft ling

DATA COLLECTION
Displacement data from the extensometers has
been wirelessly collected in real time since
installation. The six extensometers lie in thehorizontal plane around the shaft and are anchored in
the rock mass (Fig. 2)..

Event

Max
displ.
mm

1

2.33

2

1.57

3

2.40

4

4.20

5

1.10

6

5.50

7

6.40

8

9.97

Time of max.

Duration

D/M/Y Hr:Min
23/07/2017
17:36
26/07/2017
18:23
28/07/2017
18:39
1/08/201714:26
3/08/2017
23:52
20/09/2017
8:10
21/09/2017
5:26
22/09/2017
6:36

Hours
N/A
2.55
0.50
0.92
N/A
8.42
8.09
4.82

DATA ANALYSIS
Fig. 2
Horizontal layout of the mine shaft andextensometers (Ground movement monitors, GMMs).

Episodes of contraction are shown as incremental
changes in GMM readings over time. The
deformation observed by the extensometers is
calculated as an incremental radial contraction over agiven period of time. An example of this data isshown in Fig. 3.
Within each event, the sequence of readings withthe highest magnitude have been selected for detailed
analysis in order to maintain a higher signal to noise
ratio. The raw data has been processed by comparing
each data point during an event to a baseline valuethat the extensometer showed before and after theobserved event. The difference between the values
during each event and a baseline was obtained. These
values are the radial displacements recorded by eachextensometer.
Radial displacements were fitted to least squares
models which are derived from three superimposedcomponents. Circular (uniform radial), elliptical(deviatoric) and translational. The sum of residuals
squared in reference to the recorded incremental
radial displacement was calculated. This is a familiar
approach when using monitoring data to find changesof shape within tunnels [4].
The displacement episodes show a variety of peak
shapes (Fig. 3). Radial displacements are fitted toleast squares models which are derived from three
superimposed components. Circular (uniform radial),
elliptical (deviatoric) and translational. Parameters in
one example are calculated for each component ineach model. Results show a significant reduction in
the sum of the residuals squared as components are

An example of readings from extensometer ring 2
at a depth of 577 m is shown in Fig. 3. Ring 2 is in
the approximate location where two steeply dipping
faults intersect the shaft (Fig. 1). The time interval
between records (spacing on x-axis) varies as thesystem does not take a reading if all extensometers
show displacement below a threshold.
The data shown in Fig. 3 were collected from Julyto September of 2017. Within this data set, a numberof ‘events’ appear to be recorded by similar
movements of each extensometer (GMM1-6). Eightof the apparent events have been selected for detailed
analysis (labelled 1-8 in Fig. 3). The events in July
are lower in magnitude, less distinct in shape and lessregular in timing compared to the events in
September. The three events in September are of thehighest magnitude and show distinct rises and falls of
the displacement during the events. of the peak
displacements and durations are shown in Table 1.
During this time period, and in the preceding 12months, other events of sufficient magnitude to beanalysed were not observed.

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GEOMATE – Melbourne, Australia, 11-13 November 2020

systematic pattern of rising and falling of thedisplacements and the consistent orientation of theelliptical component of the displacements supports
the interpretation of these records as transient stresspulses or waves moving through the rock mass.
The mechanical interpretation would be that of aspring and dashpot in series [9]. Where shaft lining
and surrounding rock-mass act as a spring and thefaults act as a viscoelastic system of spring anddashpot in series. Initially strain is accommodated
entirely in the spring. The stress in the spring
subsequently decays as time-dependent displacement
occurs on the dashpot. In the situation being
investigated, elastic strain is caused by the initial
partial closure of stopes excavated by mining. The
stress associated with the strain is dissipated by creep
deformation in the weak rock of the fault zones and/or
the backfilled stopes (Fig. 1).
The inferred relationships are as follows. The
mine shaft is fitted with a rosette of extensometers.
Excavation of stopes causes deformation of the rockmass. The deformation can be considered in terms ofelastic and creep behaviours. Faults and excavatedstopes can dissipate the elastic strain by creep. Thisall occurs within the remote/in situ stress conditions.
The shape of the highest magnitude events comprises
a steep increase in magnitude followed by a less steep(slower) reduction in magnitude. This shape isinferred to be consistent with a more rapid loading of
the rock mass followed by a slower dissipation of thestress.
The translational component of deformation isinferred to represent the effects of deformation out of
the horizontal plane. It is possible that the translation
represents a bending or buckling deformation of the
shaft. At present no attempt at explaining therelationship between the N-S compression and the NE
translation is made. However, it is anticipated that
this will provide an interesting relationship to look forin numerical simulations.

added to the least squares model. Indicating eachcomponent added increases the quality of fit for themodel. Further residual analysis shows that theaddition of each component improves the residual
plot distribution. The observation that eachextensometer records a displacement of similar
magnitude suggests that a significant component of
the deformation is a uniform radial contraction of theliningDISCUSSION
The eight events analysed in this study wereselected as they represented a period with clear
signals with all six extensometers functioning. Oneof the key aspects of the observations to explain is theduration of the events. The duration of the events
ranges from a few minutes (single reading) to over 8
hours. Vibration from blasting or pressure waves
from the initial changes of stress in the rock massaround excavated slopes would be expected topropagate at a far higher velocity than the events
observed. Previous research has identified the mainsituations where creep should be taken into accountare the deformation of pillars and progressive closureof caverns in soft rock mines and the slow ‘diffusion’of stress between faults that may trigger earthquakes[5]. The time-dependence of deformations associatedwith faults has been investigated in the temporal andspatial relationships of Californian earthquakes andthe associated stress changes [6]; limitations on
spatial interaction of faults globally [7] and numericalsimulation of viscous contributions to the relationshipbetween displacement and stress distribution in fault
system [8].
The rock mass at Mount Isa is unlikely to be as
low in strength as the coal and salt deposits referenced
by [5]. Mining activities in the vicinity of the shaft
culminated and concluded around the time of the first
five events analysed in this study. Mıning activities
did not occur in the vicinty of the shaft for theremaining two months during which the remaining
three events occurred. It is possible that adjustment ofstress following mining in the vicinity of the shaft is
responsible for the events observed in the
instrumental data. The presence of two significant
fault zones near the location of the instruments could
also provide the local low rock mass strength toexplain the inferred creep movement. Deformation
observed in the monitoring data is seen to rise to apeak deformation which returns over time to its
original position. This deformation pattern is inferred
to represent a stress wave which has moved throughthe rock mass and been recorded by theextensometers in the mine shaft. The time-scale of thestress waves appears to vary from rapid events
leaving only a single record in the continuously
monitored instruments to slower waves leaving
multiple instrument records over many hours. The

CONCLUSIONS
Extensometers installed in the mine shaft lining
record compression of the lining occurring
sporadically. Such compression has been investigatedin eight separate events over a five-month period.
The duration of the events is variable but most lasted
between 1 record to over 8 hours. The events areinferred to represent time-dependent (creep)
deformation in the rock mass. The creep rate may becontrolled by the rate at which nearby fault zones orbackfilled stopes dissipate stress by ductile creep.
The initiation of the sequence of events in this study
is inferred to have commenced due to priorexcavation of stopes in the mine and the consequent
readjustment of stress. The displacement of theextensometers is approximated by a uniform circular
contraction. Model accuracy was increased by
161

GEOMATE – Melbourne, Australia, 11-13 November 2020

applying an elliptical deformation (ovalisation) andfurther increased by also applying a translational
component.
The elliptical component of thedeformation indicates north-south compression. The

Fig. 3translational component is inferred to represent
longitudual elastic bending of the shaft.

Displacements of extensometers recorded in a mine shaft. Vertical axis in millimetres and horizontal
axis labelled by days.

162

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 3(continued) Displacements of extensometers recorded in a mine shaft. Vertical axis in millimetres andhorizontal axis labelled by days
2003b. pp.113-125.
[4] Xie, X., and Lu, X., Development Of A 3D
Modeling Algorithm For Tunnel Deformation
Monitoring Based On Terrestrial Laser Scanning.
Underground Space, Vol. 2, Issue 1, 2017, pp.1629.
[5] Jaeger, J.C., Cook, N.G., and Zimmerman, R.,
2009. Fundamentals of rock mechanics. John
Wiley & Sons.

ACKNOWLEDGMENTS
Mine Design Technologies Australia are
acknowledged for provided the extensometer data
used in this study.
REFERENCES[1] Bruneau G., Tyler D.B., Hadjigeorgiou J., andPotvin Y., Influence Of Faulting On A Mine
Shaft—A Case Study: Part I—Background And
Instrumentation. International Journal of RockMechanics and Mining Sciences. Vol. 40, Issue 1,
2003a, pp. 95-111.
[2] Potgieter, G.S., November. Work Conducted In
Preparation For Partial Extraction Of X41 Shaft
Pillar At Mount Isa Mines. In Proceedings of theInternational Seminar on Design Methods inUnderground Mining. Australian Centre forGeomechanics. 2015., pp.273-290.
[3] Bruneau, G., Hudyma, M.R., Hadjigeorgiou, J.,
and Potvin, Y., Influence of faulting on a mine
shaft—a case study: part II—Numericalmodelling. International Journal of RockMechanics and Mining Sciences, Vol. 40, Issue 1,[6] Ziv, A. and Rubin, A.M., Static Stress Transfer
And Earthquake Triggering: No Lower Threshold
In Sight?. Journal of Geophysical Research: Solid
Earth, Vol. 105, Issue B6, 2000, pp.13631-13642.
[7] Huc, M. and Main, I.G., Anomalous Stress
Diffusion In Earthquake Triggering: Correlation
Length, Time Dependence, And Directionality.
Journal of Geophysical Research: Solid Earth,Vol. 108, Issue B7, 2003, pp2324-2336.
[8] Sone, H. and Uchide, T., Spatiotemporal
Evolution Of A Fault Shear Stress Patch Due To
Viscoelastic Interseismic Fault Zone Rheology.
Tectonophysics, Vol. 684, 2016, pp.63-75.
[9] Bland, D.R., 1960. The theory of linear
viscoelasticity. Pergamon, Oxford and New York.

163

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

LIMIT ANALYSIS OF THE FUNDAO TAILINGS DAM SLOPEFAILURE IN BRAZIL 2015
Juan Hernandez Espinoza1, John V. Smith2 and Majid Nazem2
1

GHD Perth, Australia; 2RMIT University, Australia
ABSTRACTThe investigation intends to ascertain the failure mechanisms that lead to the collapse of one of the biggest
mining structures in South America, the Fundao Tailing dam. The results obtained during the before mentioned
investigation suggested that the dam was stable under drain and undrained conditions. The most recent
investigation published in 2016 an valuation of the Fundao Tailing Dam Failure using limit analysis approach,
implemented the Limit Equilibrium approach to study the dam’s stability. The software Optum G2 was used togenerate a virtual representation of the studied sections and to evaluate the stability conditions. Detailed event
reports were used to construct the layering of weak slimes and stronger sand tailings. The analysis carried out
during this investigation using the Limit Analysis method and introducing the notion of continues layers of slimes
with specific strength parameters, indicated that the dam would have failed under these two conditions.
Keywords: Tailings, Limit analysis, Slope failure, Dam stability

design method. The disaster that occurred on
November 2015 at the Fundao dam resulted in the
discharge of a torrent of mud that buried buildings
and people. It has been reported that around 43
millions of cubic meters of iron were released killing
19 people [4].
The report released by the Fundao tailing dam
review panel “Immediate Causes of the Failure of theFundao Dam” concluded that a chain of events andconditions contribute the failure of the dam. It has
been argued that the main failure mechanism that led
to the flow slide of the dam was the liquefaction of
the saturated tailings. The panel indicated that seismic
activities were recorded in the area the same day of
the incident and therefore assumed the phenomena
was accountable for initiating the dam’s flow slide [4].
The Fundao tailing dam review panel havereported that eyewitness descriptions and videos
ascertain several things. The failure begun in theFundao dam left abutment, the dam flow slide
occurred due to the liquefaction of the tailings andthat the transformation from solid too little occurredsuddenly.
This study investigates the conditions and failure
mechanisms that could have led to the structural
instability of the Fundao tailing dams. A study of the
failure mechanism through the implementation oflimit analysis method is implemented to demonstrate
how saturated sands and interbedded slimes
contributed to the failure. Results obtained by limit
analysis methods will be discussed and compared to
the results obtained from the Fundao review panel.

INTRODUCTION
The storage of mining waste is a problem in which
geotechnical engineering has a wide field of
application. Current mining processes of extraction
generates a massive volume of waste as fine-grainedrock silt called tailings. Tailings consist of a mixtureof a fine ground rock with water in variable
proportions, additives are also added during themineral extraction process.
In the traditional mining industries, the tails leave
the extraction plant with a high liquid ratio: solid,
with a consistency of a viscous sludge. In the form of
paste the tailing can be pumped, so that its
transportation can be done through spigots andpipelines and eventually poured in along the dam
embankment to generate the uniform growth of thedam [1]. Once the slurry materials are poured insidethe dam the settlement and consolidation of thetailings begin. The thickest fraction is deposited nearof the discharge points while the fine fraction flows
by gravity towards the centre of the tank and finallysettle there [2].
In upstream raising method, dikes are raised stepby step mainly on the tailing beach. Due to thesecontinue increases of heights, water pressure
develops within the tailing when low hydraulicconductivity is present. The increase of waterpressure within tailing may result in the decrease ofshear strengths which may result in the failure of thedam. Dams constructed in upstream are several times
more likely to fail as less material to retain the tailingis used [3].
The latest disasters occurred in Brazil drawn theattention of specialist on the field due to the fact thatmost of tailings have been associated to the upstream

BACKGROUNDIn November of 2015, the iron ore mine in the164

GEOMATE – Melbourne, Australia, 11-13 November 2020

South East of Germano, close to the city of Mariana,
Brazil, suffered a fissure at the Fundao tailings dam.
The failure released 43 millions of cubic meters ofwaste into the environment. Nineteen people died,including five residents of the village and 14
employees of the Samarco mining company that
operated the mine. The mud travelled 650 kilometresand ended up in the Atlantic Ocean 17 days later [4].
According to Garcia et al. [5] there is evidence
that the 7000 km2 of toxic plume reached importantbiodiversity conservation areas in the Atlantic Ocean,
including Abrolhos National Park, one of the mostemblematic protected areas in Brazil, and three othermarine protected areas, Costa das Algas, Santa Cruz,and Comboios in Espirito Santo state, threatening
endemic and rare species of marine fauna. Models ofriver discharge dispersion predict long- term
consequences near the city of Rio de Janeiro and theconsequences of the dam burst in the Atlantic Ocean
are still not fully assessed [6].
The limit equilibrium method (LE) has beencommonly used during the last decades to study thegeotechnical stability of tailing dams, this numericalanalysis method was implemented by the Fundao
tailing dam review panel. The principal advantages
this method offers is that complex soil profiles can bemodelled in a variety of loading conditions and canalso provide quick results [7].
Despite the fact the limit equilibrium analysis has
been refined during the lasts years using verticalslices and global analysis methods it has been agreed
that it has some disadvantages when compared to theLimit analysis (LA) approach [8, 9]. According to[10], Limit equilibrium frequently assumes thatcollapse will follow a particular assumed geometry,
which are effective for simple geotechnical problems,
yet may encounter difficulties when considering
complex problems. Conversely, the limit analysis
approach has demonstrated that it can provide
agreeable results without assuming a determine slip
surface or the statics of slopes, is suitable to analysevarious geotechnical conditions with complex
geometries, loadings and boundary conditions and it
accounts for nonlinear materials [7].

Although a direct comparison between the various
methods is not always possible, the factors of safetyobtained with the Bishop's method differ
approximately by 5% with respect to more precise
solutions, while the simplified method of Janbu
generally underestimates the factor of safety up to
30%, although in some cases overestimate them tovalues of 5% [11]. All the before mentioned methods
are based on static equilibrium forces of interslice
forces, moments or overall moment. Figure 1
presented below exemplifies how the sliding masses
are divided into vertical segments.

LIMIT EQUILIBRIUM

TAILING DAM CONSTRUCTION METHODSThe number of methods used, which give different
results and sometimes contradictory are a sample ofthe uncertainty that characterizes the analysis ofstability. The most popular limit equilibrium methods
used by geotechnical engineers are the proposed by
Bishop and Janbu which in their theoreticalconception the equilibrium of the forces andmomentum are not balanced. The factor of safetyobtained through these methods generally differs
significantly from the results obtained when using
procedures that satisfy balance, such as the Spencer’sand Morgenstern-Price method [11].

The construction of dams and dikes to collectmining-metallurgical waste is an innovation of the20th century. Before the arrival of treated minerals,
the extraction processes were carried out with muchthicker particles and the waste was not systematicallycollected. Waste from ore recovery operations was
generally discharged on a surface near the mine orbodies of water. There are still some farms with
discharges to rivers or the sea, but in most cases,
barren dikes have become an essential part of mining
operations. Currently, the most common way to store

LIMIT ANALYSISThis method is based on the concepts of lowerlimit and upper-limit, the same concepts have beenused in the calculation of the safety factor for the
analysis of slopes by some researchers [8]. LA does
not employ assumptions for statics; however, theselection of failure mechanism is required. In thesemethods, the failure mechanism must be imposed
under the conception of a rigid body. Some of the
failure mechanisms that have been employed arerotational plane, translational plane and logarithmic
spiral [12].
Upper bound theorem
According to [13], if a set of external loads acts on
a failure mechanism and the work done by the work
done by the external loads in an increment of
displacement equals the work done by the internal
stresses, the external loads obtained are not lower
than the true collapse loads.
Lower bound theorem
The lower limit theorem is based on a staticallypermissible field of stresses in which the work doneby the surface loads in the contour, where the speed
is prescribed, must be greater than or equal to thework done by surface loads in any field of staticallypermissible efforts [13].

165GEOMATE – Melbourne, Australia, 11-13 November 2020

waste from the mining process is throughconstruction of a dam in which the tails are depositedby the hydraulic filling procedure. Solids minerals are
mixed with a certain amount of water and/or
chemicals so that they acquire the consistency of a
mud that is pumped by pipes to the points ofdischarge [14]. It is usual that for economic reasonsthe dam is built in stages, by successive rises. There
are three fundamental methods of construction ofwaste dams according to the method of regrowth of
the retention dam which we will describe below:

appendix describes all the outputs that resulted fromthe analysis using the geographic information system(GIS). The GSI is a computer program that allowed
to store, capture check and display all the data related
to the progressive elevations of the Fundao’s dam
surface.
Reconstruction of the tailings stratigraphy
The section B6 of the appendix B of the Fundao
investigation describes the procedures carried out toestablish the raising history of slimes. Moreover, thissection discloses the spatial distribution of slimes aswell as the slimes production history. This
information is considered to be essential for the
analysis of the model since the stability analysis isstrongly affected by this aspect. In appendix A of thisreport the slimes depositional history is presented.

Lateral Extrusion
The lateral extrusion can be defined as the lateral
deformation of the materials when is being squeezed
due to the continuous loads by which materials are
subjected as the dam increases in height. The expert
panel report [4] contended that the materials that are
immediately above are forced to conform to thismovement, experiencing a reduction in the horizontal
stress that confined them. Experts have also indicatedthat lateral extrusion as static liquefaction trigger
mechanism is not new or without precedent [15].

ANALYSIS
The purpose of the following analysis is todetermine whether the interbedded slimes layering
had an impact on the stability of the Fundao Dam ornot. During the validation of the model, the slimes
were considered to be continuous and a homogeneousmass as per the assumptions made by the review panel.
However, investigations on the historical tailing’s
discharges show that the deposition of the slimes was
not consistent and was constantly interrupted by therelease of loose sands. The slimes distribution tableshows that 47 layers of slimes were deposited in thissection, the thickness of these layers varied from0.5m to 13.5 (Fig. 1). Studies developed by [16],point out that the stability of the slopes can bestrongly influenced by the presence of interbedded
layers of clays. The authors concluded that thestability of the slopes containing interbedded slimes
using the Limit analysis approach depicted a deeper
slip surface and an unfavourable factor of safety thanwhen they ran the same analysis using the Limit
Equilibrium method.
The flow-slide that occurred at the Fundao dam
could not be demonstrated trough the analysis Limit
Equilibrium approach. The intention of the following
analysis is to incorporate the existence of the slimes
deposited in discontinued layers and to prove that thiscondition could have influenced the structural
integrity of the dam. In addition, the properties of theslimes obtained during the laboratory test wereconsidered for this analysis. It is expected that due tothe nature of these two materials slimes and sandwould have affected the stability of the Fundao Dam
differently from a material with combine strengths
parameters. The specific strength parameters of theslimes were collected from the Appendix I of the

VALIDATION
The following section presents a detailed
description of the procedures carried out to determine
the stability conditions of the Fundao tailing Dam.
The main objective of each validation process is tomake sure all the necessary steps are taken towards
fulfilling deliverables and proposed milestones. The
step-by-step plan implemented to develop a feasiblemodel that represents the conditions of the dam at thetime of failure are presented following:
Formulation of the problem: The problem
formulation consisted on clearly establish what are
the problems that are intended to be addressed, theglobal objectives to be achieved and what resources
are required to achieve them in the expected time.
These aspects have been stated in the chapter 1 of thisinvestigation.
Collection of the relevant data: All the datarequired to build the model have been previously
collected by the Fundao Review Panel and isprovided in the “Report on the Immediate Causes of
the Failure of the Fundao Dam” [4]. The elements of
the investigation used for the development of themodel are described below.
Reconstruction of the dam geometry
The dam’s geometry was re-constructed byfollowing the data provided in appendix B of the
before mentioned report and the monthly topography
surveys presented in the same appendix. This
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fundao Investigation. The adjusted strengthparameters used during the analysis are shown in
Table 1. The configuration of the model followed thesame guidelines used for the validation of the models.

SlimesWeathered
PhylliteCompacted
Sand
Tailings
Loose Sand
Tailings(kN/m3)

Effective
frictionangle(degrees)

22

35

5

22

33

-

Unit
weight

12.4

-

22

32

40

The analysis of the section 01 with thecontinuous layers of slimes shows a significant
dropped in the factor of safety, however, themechanism of failure is similar to the obtained by thereview panel. A factor of safety of 1.03 under drained
conditions indicates that the stresses are at theallowable limit, yet the factor obtained is dangerously
close to be less than 1 which suggest the failure could
have occurred even at this state (Table 2, Fig. 2).

Table 1 Slimes strength parameters

Material

22

Effectivecohesion(kPa)

Table 2 Comparison table Limit analyses vs Limit equilibrium.

Analysis Type

SlimesDistribution

Analysis mode

SlimesStrength
Assumption

Factor of
Safety(degrees)
Limit Equilibrium
Limit Analysis

Fig. 1

Continuous
SlimesInterbedded
Slimes

Undrained

28

1.87

Undrained

12

0.79

Section 01: number and thickness of slimes layers detected in tailings during post-failure investigation
[4].

167

GEOMATE – Melbourne, Australia, 11-13 November 2020(a)Fig. 2(b)Section 01 (a) Long term analysis – Displacement. (b) Long term analysis – Shear dissipation

CONCLUSIONS

the Total Environment. Vol. 242, Issue 1, 1999
pp.3-11.
[4] Morgenstern NR, Vick SG, Viotti CB, Watts BD.
Fundão tailings dam review panel report on theimmediate causes of the failure of the Fundão
dam. Cleary Gottlieb Steen & Hamilton LLP,New York. 2016.
[5] Garcia L.C., Ribeiro D.B., de Oliveira Roque F.,
Ochoa‐Quintero J.M. and Laurance, W.F.,
Brazil's Worst Mining Disaster: Corporations
Must Be Compelled To Pay The Actual
Environmental Costs. Ecological Applications,Vol. 27, Issue 1, 2017, pp.5-9.
[6] Palu MC, and Julien PY. Modeling the Sediment
Load of the Doce River after the Fundão Tailings
Dam Collapse, Brazil. Journal of HydraulicEngineering. 2019 May 1; Vol. 145, Issue 5,2019, pp.0501900201-0501900214.
[7] Duncan J.M., State of The Art: Limit
Equilibrium and Finite-Element Analysis of
Slopes. Journal of Geotechnical Engineering,Vol. 122, Issue 7, 1996, pp.577-596.
[8] Cheng Y.M. and Zhu L.J., Unified Formulation
for Two Dimensional Slope Stability Analysis
and Limitations In Factor Of Safety
Determination. Soils and Foundations, Vol. 44,
Issue 6, 2004, pp.121-127.
[9] Khabbaz H., Fatahi B., and Nucifora C., Finite
Element Methods Against Limit Equilibrium
Approaches for Slope Stability Analysis. In

The results obtained using the Limit analysis
approach using the lower and upper bound theorems
are presented in terms of safety factors. The values
obtained during the validation of the sections A and
B agrees well with the values obtained by the review
panel. The formulation of the problem is rather simple
and it can be concluded that the upper and lower
bound approach provides more accurate and effective
results.
ACKNOWLEDGMENTS
This study was conducted as part of a Master ofEngineering qualification.
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Bjelkevik A. and Knutsson S., Numerical
Analysis of Strengthening By Rockfill
Embankments On An Upstream Tailings Dam.
Canadian Geotechnical Journal, Vol. 50, Issue 4,2013, pp.391-399.
[2] Blight G.E., Formation of Tailings Dam Beaches.
In International Conference On Soil Mechanics
andGeotechnicalEngineering.
XVICSMGE,Istanbul, 2001, pp.1893-1897.
[3] Grimalt J.O., Ferrer M., Macpherson E., The
Mine Tailing Accident in Aznalcollar. Science of168

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Australia New Zealand Conference onGeomechanics 2012. Geomechanical Society
and New Zealand Geotechnical Society.
[10] Leshchinsky B., Comparison of Limit
Equilibrium and Limit Analysis for Complex
Slopes. In Geo-Congress 2013: Stability and
Performance of Slopes and Embankments III
San Diego, California, United States, pp.12801289.
[11] Fredlund D. G. and Krahn J., Comparison of
Slope Stability Methods of Analysis. Canadian
Geotechnical Journal, Vol. 14, Issue 3, 1977,
pp.429-39.
[12] Michalowski R.L., Slope Stability Analysis: A
Kinematical Approach. Geotechnique, Vol. 45,
Issue 2, 1995, pp.283-293.
[13] Yu H.S., Salgado R., Sloan S.W. and Kim J.M.,
Limit Analysis Versus Limit Equilibrium for

Slope Stability. Journal of Geotechnical andGeoenvironmental Engineering, Vol. 124, Issue
1, 1998, pp.1-11.
[14] ANCOLD, Guidelines On Tailings Dams –Planning, Design, Construction, Operation and
Closure – Revision 1. Australian National
Committee On Large Dams Incorporated, 2012.
[15] Martin T.E. and McRoberts, E.C., Some
Considerations in The Stability Analysis of
Upstream Tailings Dams. In Proceedings of the
Sixth International Conference on Tailings andMine Waste, Vol. 99, pp. 287-302. Rotterdam,
Netherlands: AA Balkema, 1999.
[16] Sazzad M.M., Mazumder S. and Moni M.M.,
Seismic Stability Analysis of Homogeneous andLayered Soil Slopes by LEM. International
Journal of Computer Applications, Vol. 975,
2015, p.8887.

169

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EXPERIMENT REGARDING MONTMORILLONITE CONTENT
AND STRENGTH PROPERTIES IN BENTONITE UNDER THE
INFLUENCE OF HEAT

1

Rie Suzuki1, Shin-ichi Kanazawa2, Masaki Yanai1 and Yoshino Osamu3
Social Environmental Systems Engineering Course, National Institute of Technology, Fukushima College,
Advanced Course Program, Japan
2
Civil and Environmental Engineering, National Institute of Technology, Fukushima College, Japan
3
Nishimatsu Construction CO., LTD

ABSTRACTAt present, underground geological disposal at depths greater than 300 m is considered a viable disposal
option for high-level radioactive waste generated from the reprocessing of spent fuel used in nuclear power
plants. In geological disposal, bentonite is employed as the primary component of buffer material to fill gaps
between waste and geologic rock. However, specification details have yet to be completely determined. It is
anticipated that the buffer material will be exposed to high temperature due to the heat generated by the vitrified
solid. This study aims to understand the mechanical properties of bentonite buffer material by employing
uniaxial compression tests with temperatures between 30°C to 90°C applied to bentonite specimens having
received a temperature history treatment of 200°C applied over six months. Results show that the maximum
compressive strength of the sample given the temperature history is lower than that of the sample without thetemperature history. Furthermore, the montmorillonite content of the sample given a temperature history of
200°C also decreased. The reason for the decrease in strength seems to be that the compaction energy during
fabrication decreased due to the decrease in the montmorillonite content. This is because the specimens were
fabricated so that the dry density was constant. It seems that the maximum compressive stress decreased withincreasing temperature regardless of temperature history. The reason for this seems to be microcracks, whichappear to decrease rigidity and, therefore, compressive strength.
Keywords: Bentonite，Silica sand，Uniaxial compressive strength，Montmorillonite content

characteristics.
(1)
Ability to control water movement due
to low water permeability.
(2)
A mechanism for expanding with theinfiltration of water to fill voids in thecompressed bentonite and cracks in theadjacent rock mass.
(3)
Cation exchange capacity to adsorb
cation nuclides.
Therefore, bentonite is considered to be the mosteffective cushioning material. Bentonite is a clay
whose main component is the smectite. Smectite is a
generic name for montmorillonite, beidellite,
nontronite, saponite, hectorite, sauconite, andstevensite
clay
minerals.
Among
them,
montmorillonite is a representative clay mineral. The
smectite contained in bentonite, which has beenverified as a cushioning material in Japan, is also
montmorillonite. The crystals of clay minerals arecomposed of overlapping silicic acid tetrahedral
sheets and alumina octahedral sheets. Bentonite hasremarkable swelling and water-absorbing properties,
very low water permeability, and high cationexchange capacity. However, details about themechanical properties of bentonite cushioning

INTRODUCTION
The spent nuclear fuel generated by nuclear power
generation produces high-level radioactive waste
when it is reprocessed. As a disposal method,geological disposal to a depth of 300m or below isoften employed1). Geological disposal is a disposal
method that isolates and confines radioactive
materials from the human environment for an
extremely long period of time. It is a multi-barriersystem that combines an artificial barrier made ofvitrified solid, carbon steel over pack, and bentonite
cushioning material with a natural barrier made ofstable rock, deep underground. The cushioning
material filled between the waste and the bedrockhas water-impermeable properties, self-sealing
properties, various sorption delay properties, thermal
conductivity, chemical buffering properties, overpack supporting properties, and stress buffering
properties, among others. These properties are
expected to be maintained for long periods of time.
Naturally occurring clay is drawing attention as amaterial that can relatively satisfactorily satisfy
these functions. Among these natural clay materials,
compressed
bentonitehasthefollowing170

GEOMATE – Melbourne, Australia, 11-13 November 2020

materials considering the temperature history due tonuclide decay heat have not been fully grasped.
Therefore, in this study, uniaxial compression tests
were performed on specimens containing bentonite
and silica sand (either with an applied temperature
history or without an applied temperature history),
and the results were compared. From this, thestrength characteristics of bentonite cushioning
material with temperature are better understood.

(4)

(5)

EXPERIMENTAL OUTLINE(6)

In this study, changes in the strengthcharacteristics are clarified by applying change intemperature to bentonite-silica sand mixed samples
that have been given a temperature history. In theexperiment, a cylindrical specimen with a diameterof 35 mm and a height of 80 mm was prepared. The
temperature history was applied to powderedbentonite. In an actual disposal facility, bentonite
compacted at high pressure is considered the bestmaterial from the viewpoint of improving theperformance and workability of the cushioning
material. Therefore, a method of applying
temperature history by placing the compacted
bentonite specimen into the drying furnace isconsidered valid. However, in the above method, atemperature gradient is likely generated inside thetest piece, creating a nonuniform temperature history
condition. Therefore, in this study, we adopted themethod of applying temperature history to powderedbentonite. Heat was applied by placing the specimen
in a water tank whose temperature was kept constant.
After a certain period of time, a uniaxial
compression test was performed with the specimen
kept in the water tank. The montmorillonite content
was measured by performing a methylene blue
adsorption measurement test on the sample after
destruction.

(7)

sprayer. The water content was measured by
the microwave oven method (500w, 15
minutes).
The mass at which the dry density was
1.6 Mg/m3 was determined and weighed with
an electronic balance.
The measured sample was divided into 5 parts,
put into a mold, and compacted with a tampingrod.
The top of the mold was covered andcompressed with a hydraulic jack for 10
minutes. The molding pressure was set to 40
MPa.
After 10 minutes, the height of the specimen
and the diameter of the upper and lower
specimen were measured with a caliper. The
mass was weighed with an electronic balance.

Temperature Measurement Test Inside The
Specimen
In order to determine the time needed to apply thetemperature to the test piece, the internal
temperature was measured while applying thetemperature. A sample without temperature history
was compacted to make a specimen, and paulownia
was used to make a hole. After that, a thermocouple
was inserted inside the specimen, and the rate of
temperature increase was measured while thetemperature was applied.

Temperature(℃)

90.0

EXPERIMENT METHOD
Specimen Preparation(1) Bentonite and silica sand No. 8 were mixed at aratio of 7: 3.
(2) The sample was placed in a stainless steel vat
and placed in a temperature-controlled oven for
6 months to provide a temperature history. The
temperature history was applied to thebentonite sample while the vat was opened, andthe drying furnace was kept at atmosphericpressure. In this study, the temperature
measured in the drying furnace was the heating
temperature of bentonite.
(3) The sample was taken out and the watercontent was adjusted using a hand mixer and a

90°C①

60.0

90°C②
30°C
30.0

0.0
0

10

20

30

time(min)Fig. 1 Relationship between temperature inside thespecimen and time
Figure 1 shows the relationship between internal
temperature and time when the specimen was placedin a water tank maintained at various temperature.
When the specimen was placed in a water tankmaintained at 30 °C, it can be seen that 10 minutes
after the temperature was applied, the internal
temperature reached 30°C and achieved anequilibrium state. When the specimen was placed in
171

GEOMATE – Melbourne, Australia, 11-13 November 2020a water tank maintained at 90 °C, it can be seen thatthe internal temperature reached about 90°C 20
minutes after the temperature was applied andachieved an equilibrium state. Therefore,
temperature is considered to be adequately applied
inside the specimen by these set heating times.
Based on these results, the heating time was set to 15
minutes at 30°C, 20 minutes at 60°C, and 25
minutes at 90°C.

with warm water.
(2) The heating time was set to 30°C for 15 minutes,
60°C for 20 minutes, and 90°C for 25 minutes.
(3) After applying heat, the uniaxial compression
test was performed with the specimen kept in
the water tank. At this time, the loading speed
was set to 0.4 mm/min. The reason for this isthat the bentonite-silica sand mixed specimen
used in this test exhibits a very brittle fracture
morphology, and this speed allowed for minutedeformation up to the fracture to be observed indetail.
(4) The water content of the destroyed specimen
was measured.
(5) The maximum compressive stress, the strain at
the maximum compressive stress, and theelastic modulus were obtained based on the
results.

Uniaxial Compression Test
Figure 2 shows a schematic diagram of the
Uniaxial compression tester. Table 1 shows the test
condition, and table 2 shows the sample condition.

Methylene Blue Adsorption Test [2](1) 1.000 g of methylene blue powder was weighedin a crucible and dried in a drying oven for 10 to
26 hours.
(2) The material was removed from the drying oven,let stand to cool in a desiccator for about 1
minute, and then weighed with an electronicbalance.
(3) The amount of methylene blue needed was
calculated according to the following formula,and the original reagent bottle was used toweigh the medicine packing paper. The dried
methylene blue was discarded.

Fig. 2 Uniaxial compression tester
Table 1

Test condition
silica sand No.

No.8

silica sand ratios (%)

30

saturation (%)

30

dry density (Mg/m3)

1.6

Table 2 Sample condition
Silica sand No. 8 particle density(Mg/m3)

Methylene blue amount (g)
＝

2.606

Silica sand No. 8 particle size (mm)0.08～0.2

Kunigel V1 particle size (mm)

～0.05

(1)

(4) Methylene blue was transferred to a beaker, ionexchange water was added, and the mixture wasstirred with a glass rod to dissolve methylene
blue.
(5) The solution was transferred to a 500 ml brownvolumetric flask, and ion-exchanged water wasadded to a volume of 500 ml.
(6) The solution was allowed to stand overnightwith occasional shaking to ensure complete
dissolution.
(7) 17 g of sodium diphosphate + hydrate wasweighted and dissolved it in 500 ml of ionexchanged water.
(8) 50 ml of 2% sodium pyrophosphate solution
was placed in a 200 ml conical beaker, and thesample (bentonite) was placed in the solution.
(9) The beaker was covered, stirred gently with a

2.702

Kunigel V1 particle density (Mg/m3))×3.74×(1) A compression auxiliary device and a rubber
sleeve were attached to the specimen. Heat
resistant rubber was attached to the top andbottom to prevent water from entering. The
specimen was placed in a water tank and heated172

GEOMATE – Melbourne, Australia, 11-13 November 2020

magnetic stirrer, and then boiled gently with therotor in place.
(10) After allowing to cool, the solution was stirred
and gently boiled again.
(11) After allowing the solution to cool to room
temperature, 60% of the saturated adsorption
amount of methylene blue solution was added
and stirred for 30 seconds.
(12) The solution was sampled with continuous
stirring and by dropping it onto the filter paper.
(13) Titration was performed by adding 1 ml ofmethylene blue solution.
(14) Steps (12) and (13) were repeated until a pale
blue halo was confirmed.
(15) Titration was performed, and when a pale blue
halo was confirmed, the mixture was stirred for
2 minutes and then dropped onto the filter paper.
Negative ion-bearing montmorillonite adsorbs
positive ion-bearing methylene blue. The halo
appears as a blue smear around spots ofunabsorbed methylene blue.
(16) Step (15) was repeated until the halo width
became 1.5 to 2.0 mm. The halo width wasmeasured three times with a caliper and theaverage value was used.
(17) The montmorillonite content (methylene blue
adsorption amount) was calculated from theamount of methylene blue added when the halo
width was about 1.5 to 2.0 mm. The calculation
formula is shown below.

TEST RESULTS AND DISCUSSION

Uniaxial compressive strength (KPa)

1400
1200
1000
800
600
400

○：No temperature history▲：Temperature history(200°C, 3month)

200
0

1
0.5
compressive strain ε (%)

0

1.5

Fig. 3 Maximum compressive stress-compressive
strain result
Table 3 compressive strain and elastic modulus
compressive strain
1.05(temperature history) εf (%)
Elastic coefficient
149.3(temperature history) E50 (MN/m2)
compressive strain
0.97(temperature history) εf (%)
Elastic coefficient
138.9(temperature history) E50 (MN/m2)
compressive strain
1.02(no temperature history) εf (%)
Elastic coefficient
94.9(no temperature history) E50 (MN/m2)

Adsorption amount of methylene blue
(mmol / 100g)= Methylene blue solution consumption (ml)×2 (2)(18) The amount of methylene blue adsorbed on
100% montmorillonite Kunigel V1 is 140 mmol
/ 100g. The montmorillonite content wasdetermined by this ratio. After this, thebentonite-silica sand mixed sample wasconverted to the value of 100% bentonite. The
calculation formula is shown below.

Uniaxial compressive strength (KPa)

1400

Adsorption amount of methylene blue＝
(3)
0.50：Mass of sample used for test (g)0.35：Mass of bentonite during the test (g)○：No temperature history▲：Temperature history(200°C, 6month)

1200
1000
800
600
400
200
0
0

20

40
60
Temperature(℃)

80

100

Fig. 4 Relationship between maximum compressive
173

GEOMATE – Melbourne, Australia, 11-13 November 2020

strength and temperature

Montmorillonite content (%)

51.0

Table 6 Results of methylene blue adsorption test
(Temperature history 6 months, Temperature change
30 degrees)2
1.8
compressive strain εf (%)

1.6
1.4

MB solution consumption (ml)

25

1.2

MB adsorption amount (mmol/100g)

501Montmorillonite content (%)

51.0

0.8
0.6

○：No temperature history▲：Temperature history(200°C, 6month)

0.4
0.2

Table 7 Results of methylene blue adsorption test
(Temperature history 6 months, Temperature change
90 degrees)

0
0

20

40
60
Temperature（℃）

80

100

Fig. 5 Relationship between compressive strain andtemperature

Elastic coefficient E50 (MN/m2)

140
120

○：No temperature history▲：Temperature history(200°C, 6month)

80
60
40
20
0
0

20

40
60
Temperature（℃）

80

100

Fig. 6 Relationship between elastic modulus andtemperature
Table 4 Results of methylene blue adsorption test
(No temperature history, no temperature change)
MB solution consumption (ml)

28

29.5

MB adsorption amount (mmol/100g)

56

59

Montmorillonite content (%)

57.1

60.2

Table 5 Results of methylene blue adsorption test
(Temperature history 3 months, No temperaturechange)MB solution consumption (ml)25

MB adsorption amount (mmol/100g)

50

25

MB adsorption amount (mmol/100g)

50Montmorillonite content (%)

51.0

Figure 3 shows the changes in the stress-strain
curve of the specimen with and without temperature
history. The maximum compressive stresses of thesamples without temperature history were
1180kN/m2 and 1300kN/m2. For the samples withtemperature history maximum compressive stresses
were 980kN/m2. It can be seen that the maximum
compressive strength of the samples withtemperature history is reduced by 200-320kN/m2
compared to the samples without temperature
history. Table 4-7 shows that the montmorillonite
content of the sample given a temperature of 200°C
decreased by 6.1 to 9.2%. In general, it is said thatthe swelling property decreases as themontmorillonite content decreases [1]. Takegahara
et al. [3] stated that the higher the content ofmontmorillonite, the higher the swelling property, so
the energy required to fabricate specimens with thesame density increases and the strength increases.
On the other hand, in this study, as the specimens
were manufactured so that the dry density wasconstant, it is considered that the compaction energy
at the time of preparation decreased and the strength
decreased due to the decrease in the montmorillonite
content.
Figure 4 shows the results of a uniaxial
compression test with varying temperatures. It canbe seen that the maximum compressive stressdecreases with increasing temperature regardless ofthe presence or absence of temperature history. A
possible cause of the decrease in compressive
strength due to temperature rise is the effect of
cracks caused by thermal expansion of the specimen.
Bentonite is generally considered to have very low
air permeability. Microcracks may be generated on
the surface or inside the material due to the thermal

160

100

MB solution consumption (ml)174

GEOMATE – Melbourne, Australia, 11-13 November 2020

200 °C, the characteristics of the cushioning material
in the actual disposal facility were not reproduced.
Therefore, in order to reproduce the cushioning
material in an actual disposal facility, it is necessary
to apply heat at high temperature and perform auniaxial compression test after manufacturing thespecimen. This time, the temperatures applied after
the specimens were set to 30 ℃, 60 ℃, and 90 ℃.
In the future, we would like to improve the watertank heater and perform a uniaxial compression tests
at higher temperatures.
A swelling test is also planned in parallel with theuniaxial compression test. Future work would
investigate the swelling characteristics of bentonite
cushioning materials under various conditions, withand without temperature history, and clarify the
relationship between strength characteristics andswelling characteristics. We would also like toinvestigate the mechanical properties of bentonite.
Furthermore, we would like to build a databaseusing a methylene blue adsorption amountmeasurement test results and improve the accuracyof the data.

expansion of internal interstitial air and theevaporation of interstitial water caused by the
influence of heat. It is considered that thecompressive strength decreased due to the decreasein rigidity caused by the inclusion of microcracks.
As this effect increases with rising temperature,compressive strength is considered to decrease withincreasing temperature.
CONCLUSION
In this paper, the mechanical behavior of
bentonite cushioning material was investigated inconsideration of the existence of temperature history
and the method of applying temperature in thegeological disposal facility. From this, the following
conclusions were obtained.
(1)

When a temperature history of 200°C
for 6 months was provided, the maximum
compressive stress and elastic modulus of thespecimen decreased, and the montmorillonite
content also decreased. It is considered that thedecrease in montmorillonite content decreased
the compaction energy specimen production.
With this, the strength also decreased.
(2)
The maximum compressive stressdecrease as the temperature of the specimen
rises, regardless of the presence or absence oftemperature history. The reason for this isconsidered to be the decrease in rigidity and thedecrease in compressive strength due to theeffect of cracks caused by thermal expansion of
the specimen. As this effect increases withrising temperature, the compressive strength isconsidered to decrease with increasing
temperature.

REFERENCES[1] Japan
Nuclear
Cycle
Development
Institute(JAEA):
Technical reliability ofgeological disposal of high-level radioactive
waste in Japan-The second report of geologicaldisposal research and development-Generalreport，JNC TN1400 99-020，IV-3[2] Yu H., Tetsuichi T., (National Institute ofAdvanced Industrial Science and Technology,
Research Institute for Geo-Resources andEnvironment, Mineral Resources ResearchGroup) ： Method of the methylene blue
adsorption test for bentonite at AIST ，Geological Survey of Japan Research Materials，no.555(2012)[3] Tatsuhiro T., Ryouichi M., (Radioactive Waste
Management Funding and Research Center) ：Mechanical test and mechanical stability studyof bentonite / sand mixture ， 58th Annual
Scientific Lecture Meeting of JSCE (September
2003)

In actual disposal facilities, the use of bentonite
compacted at high pressures is considered from theviewpoint of improving the performance andworkability of the cushioning material. The
compacted bentonite has a high dry density and isconsidered to be affected by the decay heat of highlevel radioactive waste [1]. Furthermore, it is said
that the temperature inside the cushioning material isin the range of 65 to 165 °C due to the decay heat.
However, in this test, using powdered bentonite
compacted after given a temperature history of175

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A VALIDATION OF SHALLOW SLOPE FAILURE MONITORINGMETHOD BASED ON THE FIELD SOIL MOISTURE OBSERVATIONS
Mitsuru Komatsu1 and Keigo Koizumi2
1
Graduate School of Environmental and Life Science, Okayama University, Japan;
2
Graduate School of Engineering, Osaka University, Japan
ABSTRACTIn recent years, attempts have been made to utilize the soil moisture data observed in the field to monitor theslope stability and issue an alert if necessary. The method examined in this study is based on the initial quasisaturated volumetric water content at which the soil water content temporarily equilibrates with rainfall
infiltration, and the field saturated water content reached when the groundwater table rises. In order to issue analert, it is necessary to determine thresholds for these parameters. In previous studies, the method has been
applied only to relatively homogeneous embankment slopes and natural slopes of decomposed granite soil. The
applicability to other soil types is unknown. In this study, focusing on the natural slopes composed of cohesive
soils originating from sandstone and mudstone, the thresholds were obtained from laboratory and field
experiments. Evaluation of the field data using the obtained thresholds indicated that when using theexperimental values, accuracy of unsaturated hydraulic conductivity is essential. It was also suggested that, soil
moisture needs to be measured at various depths based on the heterogeneity of the site to capture the profile witha reasonable spatial resolution.
Keywords: Shallow landslide, Volumetric moisture content, Unsaturated hydraulic conductivity, Quasi saturated,
Field saturated

Time precipitation (mm/h)

Rainfall-induced shallow landslides have become
more frequent owing to recent occurrences ofextreme weather in Japan. In July 2018, heavy
rainfall events led to serious damages to the geostructures and natural slopes in Okayama, Japan [1].
Fig.1 shows the cumulative precipitation measured
at the Okayama weather station in July 2018. The
two peaks in the rainfall intensity exceed 20 mm/h
during the precipitation (total 385.5mm) over 48 h
were the main cause of the damages to natural slopes
and geo-structures.
In recent years, attempts have been made toutilize the soil moisture data observed in the field tomonitor the slope stability and issue an alert if
necessary [2]–[3]. The method examined in thisstudy is based on the initial quasi-saturated
volumetric water content (θIQS) at which the soil
water content temporarily equilibrates with therainfall infiltration, and the field saturated watercontent (θfS) reached when the groundwater table isformed. In order to issue an alert, it is necessary to
determine thresholds for these parameters. In
previous studies, the method has been applied only
to relatively homogeneous embankment slopes andnatural slopes of decomposed granite soil. The
applicability of the method to other soil types is
unknown. In this paper, focusing on the naturalslopes composed of cohesive soils originating fromsandstone and mudstone, the thresholds were

40

400Time precipitation
Cumulative precipitation

35
30

350
300

25

250

20

200

15

150

10

100

5

50

0
7/3

Fig. 1

7/4

7/5

7/6

7/7

7/8

Cumulative precipitation (mm)obtained from laboratory and field experiments.

INTRODUCTION

0
7/9
Date (2018)

Precipitation in July 2018 Okayama.

METHOD
Field Locations
Field locations are shown in Fig. 2. Within thenatural slopes on the east side of “Handayama” site
located in Okayama-city, two locations where ashallow landslide took place by a heavy rain in July,
2018 were studied [4]. These natural slopes
composed of cohesive soils originating fromsandstone and mudstone. The scale of the slope
failure and monitoring point are shown in Fig. 3.
The angle of the slope is less than 30 degrees and is176

GEOMATE
E – Melbournee, Australia, 11-13 November 2020

ntent is show
wn in Fig. 4. The depth off the baseconlayyer (approxim
mately 60cm) was confirmeed from asimmple dynamicc cone peneetration test result as
sho
own in Fig.5
5. It was ddecided to in
nstall soilmooisture sensorrs at three depths (20cm
m, 40cm,
60ccm). Then, the model of thhe unsaturated
d hydraulicconnductivity waas identified after carrying out thebassic property characterizatiocons, saturated hydraulictesst, pressurizedd hydraulic teest and waterr retention
tesst for the soil sample.
s
The soil moiisture sensor w
was calibrated
d using thesoiil sample withh varied wateer content bassed on thesoiil water retenttion test resullts, and this calibrationcwaas used for thee field injectiion test and too interpretthee field-monito
ored data. Furtthermore, the field datawaas evaluated afteracarrying out field injeection test,
andd the IQS curvec(relatioonships betweeen water
con
ntent and rain fall intensity)) was developped.

relativelyy small, but presentspa beench shape. T
Theinvestigattion and obseervation were carried out inn an
unstable llump.

Field investigaation,
surveying
Handy dynammic
cone penetrattion
test
Confirmaation depthof the baseb layer

Field soil samppling

Settingg of a soil
water sensor,stherain gauge

Basic physicalproperties test

Fig. 2

Saaturated hydraulicteest, pressurization
type test

Field locatioon of monitoriing point.

Water retention
tests

Calibration ofo soilwater senssor

Organizaation of test
results

Unsaturated
hydraulicconductivity

Field wateer
injection teest

Volumeetric watercontentt – rainfall
inteensity

IQS curvve

Monitoring ofslope failurres

Fig
g. 4

Flow ofo an estim
mate of IQS and theevaluation method.

Depth （GL- cm)

0m
poiint.
Fig. 3 Scale of slope failure and monitoring

10

20

30

400

50

60

70

Nd
80

0
10
20
30
40
50
60

o early warn
ning informattion
Evaluatioon method ofof rainfaall-induced sloope failure

70
80
90

A floow chart to determinedwhhether or not theslope moonitoring is required usingusoil waater

g. 5
Fig
177

Test resultsrof ssimple dynammic cone
penetraation.

100
90
80
70
60
50
40
30
20
10
0
0.001

Fig. 6

Hydraulic conductivity, k(cm/s)

Passing mass percentage (%)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Depth0-20cm
Depth 20-40cm
Depth 40-50cm
0.01

0.1

1

10
100
Gran size (mm)Fig. 8Fig. 6 illustrates the cumulative grain size curveof the samples. The disturbed sample from each
depth (0-20cm, 20-40cm, 40-50cm) was collected
although the molding was difficult with the presenceof gravels when collection of the block sampling
was tried in the test field. It was assumed that eachsample would represent the depth of the soil water
sensor (20cm, 40cm, 60cm), despite the differencein the collection depths of the samples.

Estimation of the IQS curve

0-20cm0-20cm_VG (α=0.015，n=1.297)
20-40cm
20-40cm_VG (α=0.082，n=1.237)
40-50cm
40-50cm_VG (α=0.030，n=1.434)

1E+03
1E+02
1E+01
1E+00
0.0

Fig. 7

0.1

0.2
0.3
0.4
0.5
0.6
Volumetric moisture content (cm3/cm3)

Unsaturated hydraulic conductivity fromsaturated hydraulic test and pressurized test,
and the fitted VG-model.

Fig. 9Hydraulic conductivity, k(cm/s)

Negative pressure head (cm)The van Genuchten–Mualem (VG) model [7]fitted to the water characteristic curve data obtainedby the water retention test (the soil column method;
diameter 50mm, height 100cm, the cyclometer
method; WP4C, METER Environment Inc.) is
shown in Fig. 7. Then, the unsaturated hydraulicconductivity curve estimated based on the saturated
hydraulic test and water retention test are comparedwith the pressurized hydraulic test results. These
data has difference to become the tendency tosuddenly decrease near saturation, a fitting data of
estimated unsaturated hydraulic conductivity curve
again is shown in Fig. 8.

1E+04

0.1

Furthermore, the field injection test [6] shown in
Fig. 9 was carried out to measure a volumetric watercontent and hydraulic conductivity under the fieldsaturated condition. The soil moisture sensor was
emplaced at the bottom of the borehole and water
was injected under a constant head. The result thatwith correction of saturated hydraulic conductivity isshown in Fig. 10, because a difference of up to twoorders of magnitude was seen when comparing thefield test result with the estimated unsaturated
hydraulic conductivity curve.

RESULTS AND DISCUSSION

1E+05

0-20cm_VG (n=2.5)
20-40cm_VG (n=2.5)
40-50cm_VG (n=2.5)
0-20cm_ks
0-20cm_k
20-40cm_ks
20-40cm_k
40-50cm_k
40-50cm_ks
0.0

Grain size distribution of the field soils.

1E+06

1E-02
1E-03
1E-04
1E-05
1E-06
1E-07
1E-08
1E-09
1E-10
1E-11
1E-12
1E-13

Field water injection test.

1E-01
1E-02
1E-03
1E-04

20cm_VG (n=2.5)40cm_VG (n=2.5)60cm_VG (n=2.5)
20cm_kfs
40cm_kfs
60cm_kfs

1E-05
1E-06
1E-07

0.2
0.3
0.4
0.5
0.6
Volumetric moisture content (cm3/cm3)

0.1

Results of water retention tests and fitted
VG-model curves [7]

0.2

0.3
0.4
0.5
0.6
Volumetric moisture content (cm3/cm3)

Fig. 10 Adjustment by the spot injection test result

178

GEOMATE – Melbourne, Australia, 11-13 November 2020

precipitation events with more than 10mm/h wereobserved during this time period, there were a few
changes observed in the inclination angle.

Verification of the IQS curve

Volumetric moisture content (cm3/cm3)

Field water spray test shown in Fig.11 was
conducted to inspect the validity of the estimated
IQS curve. A stainless steel ring with a fixture with adiameter of 10cm is inserted to a depth of 5cm fromthe ground surface, and water is sprayed with thesame intervals in the ring.
Spray
ADR sensor
FixtureCasing

50mm

0.6
0.5
0.4
0.3
0.2
0.1

20cm_IQS
60cm_IQS
40cm

40cm_IQS
20cm
60cm

30

40
50
Rainfall, R (mm/h)

0.0
0

10

20

Fig. 12 Comparison between IQS curves and fieldwater spray test results

φ100mmFig. 11 Field water spray test
Table 1 Comparison of the volumetric moisture
content between IQS curves and field water
spray test results

The resulted IQS curve arranged for the rainfall
intensity and the relations of the volume watercontent based on Fig.10 is shown in Fig. 12 andTable 1. The results show that the IQS curves
generally agree well (errors: 7-12%) with the fieldwater spray test results within an exception of one ofthe volumetric water content values for 20cm (error:
28%).

Depth
(cm)

R
(mm/h)IQS

θ(θ-IQS)/IQS(%)

6.3

0.256

0.328

28

3.6

0.327

0.287

-12

40

8.0

0.183

0.195

7

50

8.2

0.310

0.348

12

20

Evaluation of field monitoring dataθ（cm3/cm3）

0.4

20cm

40cm

60cm

Rain

30

0.3

20

0.2

10

Rainfall（mm/h）

The measurement result of approximately fivemonths is shown in Fig.13. Although a couple of

0

0.1
7/13

7/28

8/12

8/27

9/11

9/26

10/11

10/26

11/10 11/25
Date (2019)x_to the downward ('+)

y_to the right (+)

Rain

30

1.5

20

1.0

10

0.5

0
7/13

7/28

8/12

8/27

9/11

9/26

10/11

10/26

(b) Transient changes in the inclination angle
Fig. 11 Results of field monitoring
179

11/10 11/25
Date (2019)

Rainfall（mm/h）

Imclination angle（°）(a) Transient changes in the volumetric water content
2.0θ（cm3/cm3）

0.4

20cm

40cm

60cm

Rain

30

0.3

20

0.2

10

0.1
7/13

7/15

7/17

7/19

7/21

7/23

7/25

7/27

40cm

60cm

Rainfall（mm/h）

GEOMATE – Melbourne, Australia, 11-13 November 2020

0
7/29
7/31
Date (2019)θ（cm3/cm3）

0.35

20cm

Rain

30

0.30

20

0.25

10

0.20

Rainfall（mm/h）(a) July, 20190
8/5 8/7 8/9 8/11 8/13 8/15 8/17 8/19 8/21 8/23 8/25 8/27 8/29 8/31 9/2 9/4 9/6
Date (2019)(b) From August to September, 2019

Volumetric moisture content (cm3/cm3)

Fig. 14 Extraction of the value of IQS
0.6

is lower 60cm than 40cm, and 20cm is lower than
60cm as shown in Fig. 10.
Three values turned out higher than the IQS
curves (◆ and ■ in a figure) at depth 40cm and
60cm, and the rest is lower than the IQS curves.
The IQS is a dimensionless parameter defined as
the volumetric water content divided by the IQS
corresponding to the rainfall intensity:θfs (20cm)=0.513

0.5θfs (60cm)=0.416

0.4θfs (40cm)=0.388

0.3
0.2
0.1

20cm_IQS
20cm_measured

0.0
0

2

4

6

40cm_IQS
40cm_measured

8

10

12

60cm_IQS
60cm_measured

IQS =

14
16
18
Rainfall, R (mm/h)θθ, FS =
θIQS
θfs

(1)

where IQS and FS is the index of θiqs and θfs, ,respectively. θ is the measured value of volumetricwater content. When IQS = 1.0, the volumetric watercontent reaches the quasi-saturated state. In addition,
when IQS > 1, a water layer forms at the bottom of
the surface layer and the volumetric water content
starts increasing and reaches the saturated
volumetric water content from deep to shallow. FS= 1 show in a ground water , FS > 1 shows thatground water table increase.
The measured volumetric water content are
divided by IQS and FS and plotted as a function of
the rainfall intensity in Fig.14 and Fig.15,
respectively.
From these results, the IQS index is less than 1
except for those at 40cm IQS index; in this period
was judged to be at low risk of the slope failure.

Fig. 15 Evaluation by the figure IQS curve
For four times of large precipitation events ofthis period observed from July to September, it wasevaluated using the greatest rain intensity and thepeak value after the rise of the volumetric watercontent as shown in Fig.14.
The cause for the extremely slow responses
observed at the depth of 20cm is unknown. It coulde.g., be; setting condition of the sensor at 20cm,
existence of a low permeable layer around 20cm, thewater bypass influence that increases water content
at deeper locations.
The results are plotted together with the IQS
curves in Fig.15. This graph shows that permeability
180

IQS index, θ/IQS (cm3/cm3)

GEOMATE – Melbourne, Australia, 11-13 November 2020

1.8

REFERENCES

20cm
40cm
60cm

1.6
1.4

[1] Nishimura S., Takeshita Y., Nishiyama S.,
Suzuki S., Shibata T., Shuku T., Komatsu M.,
Kim B. : Disaster report of 2018 July Heavy
Rain for geo-structures and slopes in Okayama,
Soils and Foundations, Vol.60, Issue 1, 2020,
pp.300-314.
[2] Tohari, A., Nishigaki M., Komatsu M.,
Laboratory Rainfall-Induced Slope Failure with
Moisture Content Measurement, Journal of
GeotechnicalandGeoenvironmental
Engineering, ASCE, Vol.133, No.5, 2007,
pp.575-587.
[3] Koizumi k, Sakuradani K, Oda K, Ito S, Fukuda
Y, Feng Q M and Takemoto M Fundamental
research on advancement of regulation standard
for expressway management against rainfallinduced surface failure J. Jpn. Soc. Civil Eng.,
Ser.C (Geosphere Engineering) Vol.73, No.1,
2017, pp.93–105[4] Koizumi K, Sakuradani K, Oda K, Komatsu M
and Ito S., Relationship between initialquasisaturated volumetric water content andrainfall-induced slope deformation based on amodel slope experiment J. GeoEng. Vol.13,
No.4, 2018 , pp.179–87.
[5] Koizumi K., Oda K., Komatsu M., Ito S. andTsutsumi H., Slope structural health monitoring
method against rainfall-induced shallow
landslide, 7th International Conference on Euro
Asia Civil Engineering Forum, IOP Conf.
Series: Materials Science and Engineering 615,
2019.
[6] Komatsu M., Kita, H., Koizumi K., A Basic
Study on Measuring Field Saturated andEffective Porosity Using an Injection to
Borehole, Ground Engineering, Vol.35, No.1,
2017, pp.225-232 (in Japanese)[7] Van Genuchten, M.Th.: A closed-form equation
for predicting the hydraulic conductivity ofunsaturated soils, Soil Science Society ofAmerica Journal, vol. 44, No.5, 1980, pp.892–
898.

1.2
1.0
0.8
0.6
0.4
0.2
0.0
0

2

4

6

8

10

12
14
16
Rainfall, R (mm/h)

θfs index, θ/θfs (cm3/cm3)

Fig. 14 Evaluation by the IQS index.
1.8

20cm
40cm
60cm

1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
0

2

4

6

8

10

12
14
16
Rainfall, R (mm/h)

Fig. 15 Evaluation by the FS index.
CONCLUSIONSIn this paper, focusing on the natural slopes
composed of cohesive soils originating fromsandstone and mudstone, the thresholds were
determined from laboratory and field experiments.
Evaluation of the field monitoring data using theobtained the soil moisture-based indice, IQS and FS
indicated that when using the experimental values,
accuracy of unsaturated hydraulic conductivity isessential. It was also suggested that, soil moistureprofile needs to be measured at various depths basedon the heterogeneity of the site.
ACKNOWLEDGMENTS
The authors acknowledge R.Yamamoto fortechnical assistance with the field work andexperiments. This study was partially supported by
Grant-in-Aid for Scientific Research (17K00615 and
19K04599).

181

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A STUDY ON MIGRATION OF KAOLIN CLAY SUSPENSION IN THE
PORES USED FOR REDUCING SAND LAYER PERMEABILITY
Keisuke Takahashi1, Mitsuru Komatsu1, Toru Iwata1, and Kouji Takimoto2
1

Graduate School of Environmental and Life Science, Okayama University, Japan;
2
Sanyou Clay Industry Co., Ltd., Okayama, Japan
ABSTRACT

When constructing tunnels in the urban areas, NATM method is adopted in some cases due to restrictions on
the construction conditions. A highly permeable sand layer is often encountered, which hinders the safe
operation in the construction work. In such a case, an additional cost is needed for a proper engineeringcountermeasure, which could also lead to an excessive burden on the environment. The countermeasures against
groundwater inflows are roughly based either on draining the water to lower the groundwater pressure or
grouting to reduce the permeability of the ground. Cement, water glass and urethane are often used as grout
materials, which raise issues of possible impact to the environment as well as their cost. Kaolinite is a naturalclay material that has a small impact on the environment. In this study, for specimens of glass beads with various
particle sizes, we investigated the behavior of the injected clay suspension, as well as the migration/clogging
behavior in the pores via a set one-dimensional horizontal column experiments with varied hydraulic gradient.
The results showed that the particle size ratio of the clay to the glass beads can be well explained by theconventional clogging index, and that the migration speed in the pores is dominated by the viscosity of the clay
suspension. The findings are helpful in selecting the clay suspension with an appropriate particle size for an
efficient reduction of the permeability of the water conducting layer.
Keywords: Kaolin clay suspension, Viscosity, Hydraulic conductivity reduction, Hydraulic gradient, Criticalvelocity

pouring diluted clayey water into the rock. It thus
took a long time for the permeability of theextensive rock mass to get reduced.
Kaolinite is a natural clay material (Kaolin clay,
hereinafter referred to as clay) that has a small
impact on the environment. There are some studies
to reduce permeability by injecting kaolin clay
suspension into the sand layer. It has been suggested
that there are certain clay types and mixing ratios for
effectively reducing the permeability [5], andinvestigated the behavior of the injected clay
suspension, as well as the migration behavior in the
pores via a set one-dimensional horizontal column
experiments with varied hydraulic gradient [6]. In
this study, same column tests was conducted in thelaboratory experiment for specimens of glass beads
with various particle sizes.

INTRODUCTION
When constructing tunnels in urban areas,
NATM (New Austrian Tunneling Method) is
adopted in some cases due to restrictions on theconstruction conditions [1]. However, like the
Hakata station square cave-in accident which
occurred on November 8, 2016, there is great
concern to influence the surface of the ground withthe progress of the construction in the shallow
subsurface [2]. A highly permeable sand layer isoften encountered, which hinders the construction
work [3]. In such a case, additional cost arises for aproper engineering countermeasure, which could
also lead to an excessive burden on the environment.
The countermeasures against groundwater inflows
are generally based either on draining the water tolower the groundwater pressure or grouting to
reduce the permeability of the ground. Cement,water glass and urethane are often used as the grout
materials. Because these are not of natural origin,however, their impact to the environment is
unknown, and their cost is high. A non–pressurized
grouting of clay with non–hardening and non–swelling characteristics was applied to theconstruction of underground oil storage caverns [4].
Because of clay weakness and instability (nonsolidification) as a grouting material, the injection
was conducted without a grouting pump but by

MATERIAL AND METHODS
Test setup and procedures
The schematic diagram and photograph of theexperimental apparatus are shown in Fig. 1. This
experiment consisted of two steps, (a) injection of
the clay suspension, (b) one-dimensional flow testwith a stepwise controlled hydraulic head. The soil
specimen was wet-packed into the acrylic column

182

GEOMATE – Melbourne, Australia, 11-13 November 2020

applied on the upstream side was increased stepwise(25cm/10min), and the water flow rate wasmeasured by a using a flow meter installed at the
inflow side of the test specimen. The hydraulic headon the upstream side was measured by a manometerand pressure transducer at the head in the cap of the
inflow.

with a diameter of 5 cm and a length 50 cm. A
stainless steel mesh and a porous plate were placedat both ends. The bulk density of specimen wasincreased by manually tapping the column wall. A
perforated injection pipe with opening rate 45%, 1
cm in diameter and 4 cm in length, covered bystainless steel mesh was installed at the central
location of the test specimen.

Materials
Compressor
Back Pressure
buretteKaolin clay
suspension

Constant headinflow tank
Stainless mesh
Porous plate
Injection
Pile
φ5cm

Kaolin clay and its suspension
Fig. 2 illustrates the grain size distributions curveof the clays used in this experiment. Three types ofclays (c3, c5, and c7) with various mean particle
diameters (M.P.D.s) were prepared. The soil grain
density (ρs), grain size, uniformity coefficient, andcurvature coefficient of each clay are listed in Table
1.
Fig. 3 illustrates the viscosity of the clay
suspensions for various clay–water weight (c/w)
ratios. Then rotational viscometer was carried out
for each suspension. The grain size of c5-type clay isapproximately the same as that of c3, and that of c7is the largest. In addition, c3 and c5 resemble theviscosity coefficient, but c7 shows that the viscosity
does not increase even if the c/w ratio increases.

Constant headoutflow tank

Stainless mesh
Porous plate

Injection

Specimen
4cm

Drainage
L=50cmφ1cm(a) Injection of clay suspension.
Constant headinflow tank

Water
supplyΔhCumulating passing weight（%）

Constant headoutflow tank

25cm / 10min
Flow

Drainage

Flow
meter

Data Logger

Specimen(Kaolin clay suspension
injected)

Manometer
Pore water pressure transducer(b) One-dimensional flow test with stepwise
controlled hydraulic head.
Fig. 1

100
90
80
70
60
50
40
30
20
10
0

c3
c5
c7

10

1

100
Grain size（μm）

Fig. 2

Schematic diagram of the experimental
apparatus.

Grain size distribution curve of three typesof clay used in this experiment.

Table 1 Selected physical properties of the Kaolin
clays.
Physical properties

Photo. 1 Photograph of the specimens with injectedclay suspension.
Photo. 1 shows the specimens with injected clay
suspension. After the injection, the water head

183

Kaolin clay
c5
c7
2.736 2.713

Clay grain density, ρ s （g/cm3）

c3
2.748

85% grain size, D85 (μm)
50% grain size, D50 (μm)
15% grain size, D15 (μm)
Uniformity coefficient, Uc
Curvature coefficient Uc’

7.49
6.14
3.16
1.02
1.94

7.48
6.33
3.27
1.02
1.90

12.36
10.95
4.53
1.02
2.12

M.P.D.s (μm)

4.93

5.03

7.48

Coefficient of viscosity, η (dPa･s)

GEOMATE – Melbourne, Australia, 11-13 November 2020

25

calculated by using the numerical seepage analysis.
As a result, the water level of the inflow/outflow
difference condition before the injection of clay
suspension was set so that the hydraulic gradient is i
= 3.0.

20
15
10

Experiment condition setting
c3 (c/w=0.25)
c5 (c/w=0.21)
c7 (c/w=0.52)

5

Table 3 and Table 4 list the experimental cases
and the physical properties of clay suspensions.
From the previous research [5], the relationships
between the clay–water weight (c/w) ratio and the
maximum to reduce for hydraulic conductivity ofriver sand showed that the most suitablecombination (optimal) c/w ratio of c3, c5 and c7 are
0.25, 0.21, and 0.52, respectively. In addition, a caseof c7 which c/w of 0.30 is added for nearly samelevel of the density as c3 and c5.

0
0.0

0.4

0.6

0.8

1.0
1.2
c/w ratio (g/g)

Viscosity of the clay suspensions.

1.2
c3 (c/w=0.25)
c5 (c/w=0.21)
c7 (c/w=0.52)

1
0.8
0.6

Caruculating Passing Weight (%)

Clay mass ratio (upper / lower)

Fig. 3

0.2

0.4
0.2
0
1

Fig. 4

4

7

10

13

16

19
22
25
Elapsed time (h)Time efficiency of sedimentation of clay
suspension. (clay mass ratio of upper part/lower part)Fig. 4 illustrates the time efficiency of
sedimentation of clay suspension. The upper
suspension of the test specimen and suspension of
the lower part were pulled out using a cylindershaped container every time, and the clay was
weighed after drying in an oven, respectively. It was
assumed a ratio of clay mass that floated in the eachpart (upper clay mass / lower clay mass). It showedthat sedimentation rate of c5 was the slowest, andthe sedimentation speed of each kind of clay slowed
down roughly after eight hours.

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

Fig.5

GB#20
GB#40
GB#50
GB#60
GB#80

0.1

1

10
Grain Size(mm)Grain size distribution curve of the riversand and grass beads.

Table 2 Selected physical properties of the grassbeads.
Physical propertiesρs （g/cm3）
D50 (μm)
D15 (μm)
Uc

Specimen (Grass beads)
Fig. 5 illustrates the grain size distribution curveof the grass beads used as specimen in thisexperiment. Five types (#20, #40, #50, #60, #80)
with various mean grain diameters were used. The
glass beads grain density (ρs), grain size, uniformity
coefficient, and curvature coefficient of each grassbeads are listed in Table 2.

Grass beads#20 #40 #50 #60
2.561 ←←←
550 160 150 140
500
98
98
95
1.27 2.17 1.17 1.20

#80
←
76
66
1.21

Table 3 Experimental cases and physical propertiesof clay suspensions.

Case

Flow condition before the clay injection

c3 (0.25)
c5 (0.21)
c7 (0.52)
c7 (0.30)

Based on the observation in the actual tunnel
construction [3], the largest hydraulic gradient andvelocity acting beyond the excavation face were184

Cay–water
weight ratio
c/w ratio (g/g)
0.21
0.25
0.52
0.30

Clay suspension
Density
Coefficient ofviscosityρc （g/cm3）
η (dPa･s)
1.148
1.46
1.188
2.51
1.385
1.10
1.149
0.10

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 4 Experimental cases and physical propertiesof grass beads.

#20
#40
#50
#60
#80

Grass beads
Dry density
Porosityn (%)ρd （g/cm3）
1.604
37.4
1.583
35.7
1.569
38.7
1.576
38.5
1.558
39.1

Injected volume of suspension (ml)

RESULTS AND DISCUSSIONSClay injection tests
According to the filter law, the filters need tosatisfy the condition without entry of the soil thatwhen D15/d85 < 6, where D15 means 15% by mass ofthe filter particles are finer than that size and 85% of
the particles are finer than d85 particle size of thebase material [7]. Therefore, when D15/d85 > 6, finerparticles pass through the filter materials. Here in,
d85 is the particle size of the clay, D15 is the particle
size of the grass beads. D15/d85 < 6 implies that theclay suspension cannot be injected into the pores of
the grass beads if there is no effect of viscosity ofsuspension.
Table 5 lists the experimental results of theinjection tests. Clay c7 was not able to inject into
GB#80. The value of filter law was calculated 5.33,
less than 6 of the index. It was suggested that the
migration of this clay suspension could be well
predicted by the filter law.

GB#20
Y
[66.67]

GB#40
─[12.67]

GB#50
─
[12.67)

GB#60
─[12.33]

GB#80
Y[8.53]

c5(0.21)

Y[66.67)Y[12.67]─[12.67)─[12.33]Y[8.53]

c7(0.52)

Y[41.67]─[7.92]

─[7.92]

─[7.71]

N[5.33]

c7(0.30)

Y[41.67]Y[7.92]

Y[7.92]

Y[7.71]

N[5.33]

400

40

Void volume of specimen

350

35

300

30

250

25

200

20

150

15

100

6

50
0

Fig.6

10
5

GB#20

GB#40

GB#50

Injected volumec7(0.30).

GB#60

of

clay

GB#80

0

suspension,

Permeability tests
Hydraulic behavior under the changing hydraulicgradient
Fig. 7 and Fig. 8 illustrate the total head and theflow rate in the permeability test cases for GB#20
and c3(0.25), c5(0.21),c7(0.52), respectively. The
total head for c3 shows the tendency that is slightly
lower than that of the constant head tank in
h=50m~75cm and became more significant after
h=100cm. The flow rate increases immediately
following the change in the head and quickly
stabilizes afterwards, it showed large change at thesetting head h=100cm. Case c7 showed a tendency
similar to c3 up to h=50cm, but the total headdecreased much below from at the setting head fromh=75cm, and the flow rate greatly increased
progressively from h=25cm of the initial stage. c5
followed that a difference occurred at the setting
head was h=125cm, and it followed that the flowrate greatly increased setting at the setting head fromh=100cm.
Fig. 9 and Fig. 10 illustrate hydraulicconductivity and hydraulic gradient calculated basedon Fig. 7 and Fig. 8, respectively. For c3, it isestimated that a large amount of clay flowed out by
migration of the clay through the void because thehydraulic conductivity rose at a hydraulic gradient

Table 5 Results of the injection tests.
casec3(0.25)

45

450

D15 / d 85

Casetest specimen. Therefore, the clay suspension may
not fully fill the pores near the both ends. In GB#20
and GB#40, the same amount was injected whileslightly less for GB#50. D15/d85 of GB#40 andGB#50 are same, 7.92, but 85% particle size ofGB#50 is larger than GB#40. There is a less volume
of injection for GB#60, it resulted likely from theclogging during the injection. Therefore, thefeasibility of the injection well followed the filter
law, but the quantitative evaluation regarding thevolume of injection requires an additional
procedure/index.

Note: “Y” means the possible to inject, “N” means the notpossible to inject. [ ] is the calculated D15/d85 .

Fig. 6 illustrates the injected volume for thec7(0.30) suspension cases. The viscosity of this casewas 0.10, close to water. The injection was
terminated when the clay suspension has beenvisually confirmed at the both end boundaries of the185

GEOMATE – Melbourne, Australia, 11-13 November 2020

Total head, h (cm)Hydraulic conductivity, k15 (cm/s)

i=1.0. Case c5 showed the smallest hydraulicconductivity after the injection, the variation of
hydraulic conductivity became large in hydraulicgradient i=1.5, hydraulic conductivity finally sameas other two kinds level. In case c7, it was assumed
that the migration of the clay in the void began andflowed out when the hydraulic gradient reachedi=0.25, and c7, and as for the subsequent hydraulicgradient, it was found that a residual clay effect of
this phenomenon.

1.E-01

1.E-02c3 (0.25)
c5 (0.21)
c7 (0.52)
1.E-03150

0Inflow tankc3 (0.25)
c5 (0.21)
c7 (0.52)

125

10

20

30

40
50
60
Elapsed time, t (min)Fig.10 Hydraulic conductivity with time of test
case GB#20.

100
75

In these results, the hydraulic conductivity after
the clay injection varied according to the type of theclay, and it also became clear that the permeabilitychange with the increase of the hydraulic gradient
was different.

50
25
0
0

Fig.7
Flow rate, Q (g/min)

1.E+00

10

20

30

40
50
60
Elapsed time, t (min)Residual and runoff clay

Total head with time of test case GB#20.

The injected, residual and runoff masses of eachclay are shown in Fig. 11. Clay c7 was injected mostin terms of the density of clay suspension. The
fraction of the residual clay (residual mass /injection mass) was 12.5, 17 and 15.5% for c3, c5
and c7, respectively, showing the highest value for
c5.

1000
c3 (0.25)
c5 (0.21)
c7 (0.52)

800
600

160

Mass of clay (g)

400
200
0
0

10

20

30

40
50
60
Elapsed time, t (min)

140
120

Injection
Residual
Runnoff

100
80
60
40

Hydraulic gradient, i

Fig.8

20

Flow with time of test case GB#20.

0clay
c/w（g/g)μ（dPa/s)

2.5
2.0

c3
0.25
1.46

c5
0.21
2.93

c7
0.52
1.00

Fig.11 Mass of clay of test case GB#20.

1.5

Multi Particulate Critical Velocity
1.0

Multi particulate critical velocity under the onedimensional horizontal flow is shown in Eq. (1) [8].

c3 (0.25)
c5 (0.21)
c7 (0.52)

0.5
0.0
0

Fig.9

10

20

30

40

50
60
Elapsed time, t (min)Vcm = n1⁄𝑚𝑚

Hydraulic gradient with time of test caseGB#20.

ρ �ρ − ρw � g d 3
6μ�� w s+1 − 1�
ρw d
54 μ2

(1)

where, Vcm : multi particulate critical velocity (cm/s),
n : porosity, m : constant decided with the Reynolds
186

GEOMATE – Melbourne, Australia, 11-13 November 2020

Velocity (cm/s)

number, μ : coefficient of viscosity of the water(g/cm ･ s), ρw : density of the water (g/cm3), d:particle diameter (cm), ρs : soil particle density(g/cm3), g : gravitational acceleration (cm/s3).
The multi particulate critical velocity (Vcm)
calculated for each of the permeability tests undervaried hydraulic gradients are shown in Fig. 12.
It is understood that Vcm is drastically smaller
than the pore velocity observed in the experiments.
In other words, it shows that the particles start tomigrate, and the clay runoff occurs under the large
velocity rather than the calculated Vcm. Furthermore,
it was assumed that a factor that affects the Vcm
existed because the clay particles remained in the
pores of the glass beads in the experiments.
Therefore, the coefficient of viscosity of Eq.(1) is
not considered the viscosity of the suspension.

was partially supported by Grant-in-Aid for FUSO
Innovative Technology Fund (2019).
REFERENCES[1] Matsushita Y., Shibata T., Kinoshita K.,
Hashimoto T. and Imanishi H., Deformation
Behavior and Stability of the Tunnel Crown
Rock Shallow NATM In Urban Site,
Proceedings of Tunnel Engineering, JSCE,
Vol.1, 1991, pp.29-34. (in Japanese withEnglish abstract)[2] Mitani Y., Cave-in Accident and its Restoration
on New Nanakuma-line of Fukuoka City
Subway, Geotechnical Engineering Magazine,
Vol.67, No.6, 2019, pp.4-7. (in Japanese)[3] Takahashi K., Mitsumasu T., Yoshimoto H.,
Oogane T. and Komatsu M., Adoption Exampleof the Tunnel Auxiliary Construction Method
for Soft Unconsolidated Ground in Urban Area
Using the Mountain Tunneling Method, NATM,
Ground Engineering, Chugoku Branch ofJapanese Geotechnical Society, Vol.35, No 1,
2018, pp.53-160. (in Japanese)[4] Miyanaga Y., Makita T., Ebara M. and Hatano
T., A Non-Pressurized Grouting Method using
Clay for Controlling Groundwater - A Theoryof Clay Grouting and the Construction Recordof Kuji Underground Oil Storage Plant Journalof the Japan Society of Engineering Geology,
Vol.35 ， No.4 ， 1994, pp.23-35 (in Japanesewith English abstract)[5] Komatsu M., Takahashi K. and Takimoto K.,
Pressurized clay injection method using
kaolinite for controlling groundwater of asaturated sand layer, 7th International
Conference on Euro Asia Civil Engineering
Forum, IOP Conf. Series: Materials Science andEngineering, 615, 012047, 2019, pp.1-8.
[6] Takahashi K., Komatsu M. and Takimoto K.,
Evaluation of Kaolin Clay-injected Ground
Permeability Based on Changes in Hydraulic
Gradient, Ground Engineering, Chugoku
Branch of Japanese Geotechnical Society,Vol.37 ， No.1 ， 2019, pp.145-150. (inJapanese)[7] Bertram G. E., An Experimental Investigationof Protective Filters. Harvard Soil Mechanics
Series, No.7, 1940, 26p.
[8] Sugii T., Teranishi K. and Zju H., Evaluation of
Erosion Resistance Performance of River Levee
Using Multi Particulate Critical Velocity,
Advances in river engineering, Vol.24, 2018,
pp.619-624. (in Japanese with English abstract)

1.E+1
1.E+0
V-c3 (0.25)
V-c5 (0.21)
V-c7 (0.52)
Vcm-c3
Vcm-c5
Vcm-c7

1.E-1
1.E-2
1.E-3
1.E-4
1.E-5
0

10

20

30

40
50
60
Elapsed time, t (min)Fig.12 Measured pore velocity (V) and the multi
particulate critical velocity (Vcm).

CONCLUSIONSIn this study, for specimens of glass beads withvarious particle sizes, we investigated the behaviorof the injected clay suspension, as well as the
migration behavior in the pores via a set onedimensional horizontal column experiments with adiameter of 5 cm and a length of 50 cm under thevaried hydraulic gradient.
The results showed that the ratio of the particle
sizes of the clay to the glass beads can be well
explained by the conventional clogging index, andthat the migration speed in the pores is dominated by
the viscosity of the clay suspension.
ACKNOWLEDGMENTS
The authors acknowledge T. Kojima fortechnical assistance with the experiments. This study187

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A REVIEW ON THE UNDERSTANDING OF THE PROPERTIES OFGRANULATED BENTONITE MIXTURES IN THE RADIOACTIVE
WASTE DISPOSAL CONCEPTS
Mazhar Nazir 1, Ken Kawamoto 2 and Toshihiro Sakaki 3
1 Ph. D Student, Department of Civil and Environmental Engineering, Saitama University, Saitama, Japan.
2 Professor, Department of Civil and Environmental Engineering, Saitama University, Saitama, Japan.
3 Former Professor, Department of Civil and Earth Resources Engineering, Kyoto University, Kyoto, Japan.

ABSTRACT
Since long, bentonite has been examined as a potential sealing material in the Engineered Barrier Systems(EBS) for the geological disposal of radioactive waste due to its favorable physical and chemical properties. The
research evolution has led to the adaption of Granulated Bentonite Mixtures (GBM) as a candidate buffer/backfill
material owing to the high compaction properties, operational advantage and closure of gaps between the seal andhost rock. A thorough understanding of the behavior of GBM is essential to design and construct an efficientrepository. This paper summarizes a review of former experimental studies conducted on the GBM over the past
two decades. The contents include Thermal, hydraulic and mechanical (THM) characteristics, gas transport andmicrostructure. The work comprises preparation of a summary table listing material properties, sample scale,methodology, test conditions and graphical representation of results. Based on the review, prospects for further
investigations/studies on the GBM’s THM and gas transport properties from the microstructural perspective havealso been recommended for better understanding of the behavior of GBM.
Keywords: Engineered barrier systems (EBS), radioactive waste disposal, granulated bentonite mixtures (GBM),
experimental studies
waste canister and other complex hydro-mechanicalprocesses resulted from the infiltration ofgroundwater or hydration from the surrounding rockas well as the generation of various gases as a resultof metal corrosion or by the degradation of organic
wastes. Therefore, an assessment of complex Thermo
Hydro-Mechanical (THM) and thermo-hydrogeochemical processes is essential to achieve a safe
repository design and ensuring the long-run safety.
The emplaced buffer GBM should be capable; tomaintain its low water permeability and develop a
sufficiently high swelling pressure to maintain a good
contact between the host rock and waste, and to ensure
rapid dissipation of radiogenic heat as well as holdenough permeability to allow the transport of gases
without cracking and rupturing of the buffer/barrier[3]. Various studies have been conducted tounderstand the behavior of GBM. This paper
summarizes a review of the former experimental
studies performed in the last couple of decades tounderstand the thermal, hydraulic, mechanical andgeochemical behavior of the GBM.

INTRODUCTION
Various repository design concepts have been
evolved and studied over the last four decades [1].
These include encapsulation of nuclear waste in a
metallic canister surrounded by a backfilling/buffermaterial emplaced in deep geological formations invertical holes or horizontal tunnels [1,2]. Owing to its
favorable physical and chemical properties, bentonite
has been studied as a backfilling/buffer material in the
nuclear waste disposal concepts [3]. These properties
include low permeability and high retention capacities[4,5], high swelling capacity [3,5], thermal
characteristics [3] and micro-porous structure [5].
Many design concepts use pre-compacted blocks ofbentonite as barrier and backfilling materials [4].
Alternatively, Granulated Bentonite Mixtures (GBM)
consisting of granules (highly compressed pellets)and powders of bentonite have also been considered[6]; and adopted by National Cooperative for the
Disposal of Radioactive Waste (NAGRA) in the
prototype Full-scale Emplacement (FE) experiment[7] and Engineered Barrier (EB) experiment [8,9] at
the Mont Terri rock laboratory in Switzerland. The
objective of using GBM is to achieve goodcompaction properties and operational advantages
including easy transportation and in-situ placement/
backfilling. Additionally, the GBM is considered toprovide a high capability to adapt to the irregular rockwall conditions [10].
In a typical repository environment, the GBM
barrier will be subjected to radiogenic heat of the

THERMAL PROPERTIESThe thermal properties of various GBMs havebeen obtained in the laboratory by thermal probe
analyzers and the results are presented in Fig.1. Table
1 provides a summary of the relevant details on thematerials, and experimental methodology. The results
indicate that, for a fixed water content, thermal

188

GEOMATE – Melbourne, Australia, 11-13 November 2020

conductivity increases with increasing dry density[11,12]. Tang [11] noted high thermal conductivity
values at higher water contents. In the FE experiment,
Müller [7] observed a slight increase in the thermal
conductivity values measured for a period of almost
one year after the start of heating indicating slow
wetting of the GBM due to water inflow at a very low
rate (Fig. 2).

constant volume conditions. The results indicated thatthe hydraulic conductivity decreased with increasing
temperature (Fig. 4). Masuda [13] reported the workon the effect of salinity content of groundwater on
hydraulic conductivity of granulated bentonite.
Salinity content had a strong influence on thehydraulic conductivity. The saline water resulted inhigher hydraulic conductivity values as compared to
the fresh water.

Fig. 1 Effect of dry density and moisture content on
thermal conductivity [11,12]

Fig. 3 Effect of dry density on hydraulic conductivity[8,9,13]

Fig. 4 Effect of temperature on hydraulic conductivity[14]
Fig. 2 Transient changes in in-situ GBM thermal
conductivity [7].

MECHANICAL PROPERTIESHYDRAULIC PROPERTIESThe swelling characteristic of the GBMs has beentested in the laboratory using oedometers/rigid cells
under constant volume conditions. Table 1
summarizes the experimental conditions adopted forthe swell pressure testing. The results show that lower
density samples developed low swell pressure values
and the swelling pressure increases with increasing
dry density (Fig. 5) [2,8,9,14-20]. The temperature
controlled swelling characteristics have been
observed in a thermostatically controlled oedometer

Hydraulic conductivity of the GBM in saturated
conditions was determined in laboratory permeability
tests. The materials, methodology and theexperimental conditions adopted are summarized inTable 1. The results show that hydraulic conductivity
decreases with increase in dry density (Fig. 3) [8,9,13].
Villar [14] investigated the effect of temperature on
the saturated hydraulic conductivity by athermostatically controlled oedometer cell under189

GEOMATE – Melbourne, Australia, 11-13 November 2020

cell and results depicted that the swelling pressure
decreases with increase in temperature (Fig. 6) [14].

Fig. 7 Gas permeability as function of porosity in
specimens compacted at different dry densities withwater contents [21]Fig. 5 Variation of swelling pressure as a function ofdry density [2,8,9,14-20], y-axis swelling pressure

Fig. 8 Average gas permeability as function of dry
density for different water contents [22]MICROSTRUCTURE
Microstructural changes occur in the bentonite asa result of compression at the time of emplacement
and swelling during the hydration process afterwards.
Hence, investigations at microstructural level are
crucial for better understanding of the behavior of thebentonite at higher structural levels [24].
Microstructural features have been investigated byseveral methods including Mercury Intrusion
Porosimetry (MIP), Scanning Electron Microscopy(SEM) and Microfocus X-ray Computed Tomography(MFXCT). Lloret [24] studied the effect ofcompaction (dry density 1.4 g/cm3 to 1.68 g/cm3) on
Pore Size Distribution (PSD) characteristic of FEBEX
granular bentonite by MIP. A bi-modal PSD was
observed, with a dominant value of 20 nm
representing pores inside the clay aggregates andlarger pores ranges from 20 µm (at 1.68 g/cm3) to 30µm (at 1.4 g/cm3) corresponding to the inter-granularFig. 6 Effect of temperature on swelling pressure insamples prepared at different dry densities [14], y-axis
swelling pressure
GAS TRANSPORT
Table 1 summarizes the methodology adopted tomeasure the gas permeability. The results indicate thatgas permeability; increases with increasing porosity(Fig. 7) [21] and decreases with increase in dry
density (Fig. 8) [22]. Liu [23] conducted gas
permeability experiments on the in-situ GBM samples
retrieved from the EB experiment. The results
indicated extremely low permeability (in the order of
10-21 m2) and they suggested the bentonite barrier
hardly a medium for gas transport.

190

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Summary of the materials and methodology adopted for the determination of thermal, hydraulic, mechanical and gas transport properties
Properties

ThermalHydraulicMechanical

Gas
Transport

Bentonite/Mater
ial Type
MX-80
Wyoming USA(similar to
MX80)
GBM in FE
experiment
Spanish GBM

Gradation(mm)<2
0.1-6.3

Samplesize (mm)50 x 70
60 x 150

Apparatus/Methodology

Controlling Parameter(s)Ref.

Heating wire probe thermal analyzer
KD2 Pro thermal analyzer TR-1 probe

Dry density, water content
Dry density

[11]
[12]

0.1-9.5

full-scale

Field measurements by thermal sensors

Time

[7]

0.01-10n/aConstant gradient permeability tests, steady state
Dry density
conditions
MX-80
< 10
35 x 10
Small sample holders, 20°C, 17 days hydration time
Dry density
Pellets mixture
0.075 – 19 n/aLaboratory experiments
Dry density
FEBEX GBM
<550 x 12
Oedometer cell equipped with a silicone oil
Temperaturethermostatic bath, constant volume conditions
MX-80 Pellet /Powder (80/20)
Samples prepared at DD of 1.49 g/cm3 in constant volume cell of 70 mm dia.
Pellet (7x7mm), Powder (<2mm)
oedometer, 440 days hydration time
MX-80, Pellet /Powder
60 x 120
Constant volume conditions cell, monitored for 160 days, sample prepared at dry(80/20), Pellet 7x7mm,
density of 1.49 g/cm3
MX-80
0.16-1070 x 483.9 Heated Column experiment, sample prepared at dry density of 1.47 g/cm3,
monitored for 3 years
FEBEX GBM
<550 x 12
Oedometer cell, constant volume conditions, 30°C
Dry density, temperature
MX-80
< 10
35 x 10
Small samples holders, 20°C, 17 days hydration time
Dry density
FEBEX GBM,
0.075-9.5
full-scale
In-situ swelling pressure measured by sensors, in-situ
Dry density
EB experimenttemperature and relative humidity (17 °C, 48%)
Spanish GBM
0.01-10n/a
Wetting at constant volume
Dry density
FoCA, pellets and powder
Oedometer tests, samples of 50, 100 and 120 height samples prepared at dry density of 1.6
materialg/cm3, Monitored for 100 days to more than 1-year evolution of swelling pressure with time
MX-80
0.065-475x15
Constant volume swelling tests in rigid cells
Dry density
MX-80 shot-clay, sand (60%) 75.3 x18.8 In-situ core samples recovered from the shot-clay experiment at the Grimsel URL,
and gravel (30%) fractions
constant volume swelling tests in laboratory, maximum swell pressure observed 10days after the flooding
FEBEX grains<5n/aNitrogen gas used as fluid, injected at low pressure,
Effect of porosity
FEBEX<538 x 78
A triaxial cell, Nitrogen gas as fluid.
Dry density
GBM in EB
In-situ specimens retrieved from EB experiment at Mont Terri Rock Laboratory Switzerland, argon gas used
experimentas fluid, steady state method, triaxial cell, Confining pressure 1MPa, gas pressure 0.6 MPa

191

[8]
[9]
[13]
[14]
[15]
[16]
[2]
[14]
[9]
[17]
[8]
[18]
[19]
[20]

[21]
[22]
[23]

GEOMATE – Melbourne, Australia, 11-13 November 2020

REFERENCES

pores. The results indicated that the pore structure of
the inter-granular pores was mainly controlled by thecompaction density. The SEM observations also
complimented the PSD data. SEM photomicrographs
were used to study the size of intergranular pores asfunction of compaction intensity in the samples
compacted at dry density 1.40 g/cm3 and 1.65 g/cm3 ;larger intergranular pore sizes identified in the lowdensity sample [24]. Structural changes including
homogenization processes and fractures were
investigated by MFXCT during the hydration ofpellet and powder mixtures [16,25].

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Backfilling of deposition tunnels: Use of
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R. J., and Gutiérrez-Rodrigo V., Saturation of
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CONCLUSIONS: FUTURE AREAS OF STUDY
Many research programs are in progress since
1970s to evolve a safe and sustainable repository
concept for the disposal of high-level radioactive
waste, yet, no final repository has been constructed[26]. Despite the large amount of studies on the
bentonite materials used as a buffer/backfill in therepository, some knowledge gaps remain. In-situ
experiments with a large focus on GBM are also inprogress to understand the coupled effects of complex
thermo-hydro-mechanical and chemical processes,
e.g. FE and HEE experiments and EB experiment(dismantled) at the Mont Terri rock laboratory and in
Switzerland.
Several factors influence the heat and masstransport processes through the porous medium. The
thermal characteristics of the porous medium aregoverned by the various factors including
composition, grain and pore size structure, dry
density [27], porosity [27,28] and moisture content[28]. The gas diffusion and advection are theprevailing mechanisms for transport of gases throughthe soil [26,29]. Selin [36] pointed out the diffusion
to be a dominant transport mechanism for the gastransport through bentonite barriers unless the appliedpressure becomes greater than the swelling pressure
and back pressure (total stress). The gas transport
through the porous media is controlled by various
factors including dry density, particle size, andmacro-porosity [29]. The review has shown that theabove listed factors coupled with the microstructuralfeatures need further study in future for better
understanding of the behavior of GBM as the buffer
and the mass transport processes through the GBM
and bentonite-based granular materials.
ACKNOWLEDGMENTS
This work was supported by JSPS KAKENHI
Grant Number 17H03301.

192

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Shot-clay MX-80 bentonite: an assessment of thehydro-mechanicalbehavior,
Engineering
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Romero F. J., and Barcala, J. M., Gas Transport
in Bentonite, Technical Report, Informes
Técnicos Ciemat, December, 2013.
[23] Liu J-f., Wu Y., Cai C-Z., Ni H-y, Cao X-l, Pu
H., Song S-b., Pu S-y., and Skoczylas F.,
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Experimentation and numerical modeling,193

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE NEW ADVANCED PROTOTYPE OF AIRBORNE VISUALCONTROL OF A GROUND ROBOT
Oleg Kupervasser1,2*, Hennadii Kutomanov1, Vitalii Sarychev2, and Roman Yavich1
1

Department of Mathematics, Ariel University, Israel;2Transist Video Llc, Skolkovo, Russia*Corresponding Author
ABSTRACT

Unmanned autonomous robots will be widely used very soon for land use, treatment, and monitoring. The
paper addresses the problem of visual navigation of ground robots using a camera positioned at a certain
elevation above the confined area. The main idea is that robot’s “eyes” is not located on the robot but are
independent autonomous system. As a result, the “eyes” can go up and observe the robot from above. Thispatented technology of airborne vision-based control of ground robots is already described previously in"International Journal of GEOMATE" and Geomate2019 conference paper (Ehrenfeld et al., 2020). There wasdescribed in detail the small toy prototype with very simple algorithms and very shortly the next more complex
prototype. In the current paper we present detailed algorithms for navigation and control of this complex
prototype. The system consists of the USB camera on the tower (4 meters) connected to the computer which
controls the big ground robot by sending Wi-Fi commands. For navigation (recognition of the ground robot,
finding its position and orientation) we use deep learning (artificial neural network). It was demonstrated that
this robot can pass two main tests: travelling along some predefined path and random walk inside of somepredefined boundary.
Keywords: Vision-based navigation, Visual Navigation, Ground Robots, Tethered Platform, Airborne Control,
Prototype, Vision-Based Navigation, Artificial neural network, Deep learning convolution network, Autopilot,
Time delay, Stability of differential equations
developments is to take into account delays in thecontrol system.
The described above system may be used for
coordination, navigation and control of groundrobots (automated transport, automated agricultural
machines, municipal and aerodrome vehicles, garden
lawnmowers and so on).
The key innovation of the study is the system ofnavigation and intercoordination. The system
includes one or more robots, located on thecontrolled area; one or more robot tracing devices on
the suspended tethered platforms; natural or artificial
markings; charger; and central module for robot
coordination and orientation detection. The
information is transferred to the central module fromall the tracing devices. The system central module isequipped with the calculation module located or on
the suspended platform, or on the ground, or on the
charger or on the robot. The calculation module ispossible to calculate coordinates and orientation ofrobots and to form the control commands, based on
the information received from all the above
described devices. This visual system can also be
used for preventing collision of ground robot withchildren or animals. Also, we can use this visualsystem for security purposes.
The technical result is the development of therobot efficient coordination using the devices
located on the towers, the aerial apparatuses tracing

INTRODUCTION
Unmanned autonomous robots will be widelyused very soon for land use, treatment, andmonitoring [1-3]. The most popular outdoor robots
are currently robot-lawnmowers used for bounded
operation area. However, the different very
interesting applications for such robots can be found.
Let us give yet several examples:1) It is a remote-controlled slope mower [4].
Currently, remote control of a human operator isused for such mowers. However completely
autonomous robots can be made using
technology proposed in this paper.
2) Robotic Fruit Harvester - the FFRobot [5]3) Robots for fire protection [6]4) Autonomous wheelchair for disabled people [7]The paper addresses the problem of visualnavigation (vision-based navigation) of groundrobots using a camera positioned at a certain
elevation above the confined area. Vision-basednavigation of robots is like human navigation by thehelp of eyes vision. However, it is not necessary tobuild eyes into the robot. It is possible to put eyes on
a top position and from the top position the robot
can see itself and its motion. It means airborne
terrestrial robot control [3,8-12]. One of the194GEOMATE–Melbourne, Australia, 11-13 November 2020

the robots on the controlled area, supervising theirenvironment including natural and artificial
markings. One of the developments is to take into
account delays in the control system.
The system (the proof of concept) was defined asfollowing (Fig. 1):
1) The tower (4 meters).
2) The USB camera on the tower connected tocomputer.
3) The computer which controls the big groundrobot by sending Wi-Fi commands, supplied bysoftware for finding its position and orientation
using deep learning (artificial neural network)from USB camera images.
4) The ground robot.
It was demonstrated that this robot can pass two
main tests: travelling along some predefined pathand random walk inside of some predefinedboundary. A reader can find these tests here [13,14].

Fig. 1. Airborne terrestrial robot control.

Fig. 2. Transformed AlexNet in Matlab.
195

GEOMATE–Melbourne, Australia, 11-13 November 2020

For this, we used the part of the sample in which
the robot is fully open and clearly visible. Numberof such images in the sample was 9293.
We built a network containing 4 outputs - thetwo coordinates of the robot in the image, as well as
the sine and cosine of its rotation course angle. We
did not use the direct value of the angle whentraining the network, since the angle values have a
gap at -180: 180, which leads to big errors at angles
close to these values.
The sine and cosine SinNN and CosNN calculatedby the network were used to calculate the angle ofrotation of the robot through a weighted average,
where the weights of each of the calculated angles is
directly proportional to the rate of function change.
For example, for SinNN>0 and CosNN>0 we get:

CONTROL AND NAVIGATION SYSTEM
Most visual recognition methods use set ofcorrespondent characteristic points [15] to locate theground robot with respect to an observation camera.
In this case, various SIFT functions are usually used
to highlight the correspondent characteristic points[15]. These methods require a large database offunctions, efficient search methods and a lot of time
for calculations.
Using a deep learning convolution neuralnetwork can significantly reduce data processing
time and increase accuracy.
Despite the massive enthusiasm for neuralnetworks and their active implementation incomputer applications, their use to determine thecoordinates of objects relative to cameras is notinvestigated practically.
However, there are a series of interesting works
by A. Kendall, M. Grimes and R. Cipolla(University of Cambridge) [16, 17], who used a
convolutional neural network to determine camera
coordinates, and are pioneers of the use of deeplearning methods in navigation. In their works, they
determined the coordinates and angles of rotation of
the camera using images of Cambridge Landmarks
building, made by pedestrian smartphone’s camera.
Unlike the authors of [16,17], who used imagesof buildings from ground level and using cameras
mounted on ground robots, we need to find theposition of a ground robot relative to a camera in an
upper position. The neural network was based on the
popular AlexNet network with minor changes in the
structure and training mode. AlexNet [18] is thename of a convolutional neural network (CNN),
designed by Alex Krizhevsky and published withIlya Sutskever and Krizhevsky's doctoral advisor
Geoffrey Hinton. AlexNet competed in the
ImageNet Large Scale Visual Recognition Challenge
on September 30, 2012, significantly outperforming
other networks. In the original AlexNet, there are
only 8 levels (5 convolutional and 3 fully connected).
For our tasks, we removed the last two fully
connected layers, and replaced them with one fully
connected layer with 4 outputs (x, y coordinates andtrigonometric functions sin() and cos() for therotation angle α) or 7 outputs (x, y, sin(α) , cos(α),
coordinate, angle and total errors) and one
regression layer. That is, we used the pre-trained
AlexNet network to solve the regression problem:
determining two coordinates and course angle of therobot (Fig. 2).
We used two-step network training method.
At the first stage, we set the task of teaching the
network to determine the coordinates and course
angle of the robot in ideal visibility conditions.

α≈𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂(𝑺𝑺𝑺𝑺𝒏𝒏𝑵𝑵𝑵𝑵 ) ∗ |𝑪𝑪𝑪𝑪𝒔𝒔𝑵𝑵𝑵𝑵 | + 𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂𝒂(𝑪𝑪𝑪𝑪𝒔𝒔𝑵𝑵𝑵𝑵 ) ∗ |𝑺𝑺𝑺𝑺𝒏𝒏𝑵𝑵𝑵𝑵||𝑪𝑪𝑪𝑪𝒔𝒔𝑵𝑵𝑵𝑵 | + |𝑺𝑺𝑺𝑺𝒏𝒏𝑵𝑵𝑵𝑵|(𝟏𝟏)

Training was carried out using the solver “adam”,
training took place in 200 epochs with 500 batch
size. This network made it possible to determine thecoordinates of the robot with an accuracy of 2.4
pixels in an image of 227 X 227 pixels size and anangle with an accuracy of 4 degrees.
Further, this network was tested on a full sample
of 12,000 images, and for each image three values
were determined:- coordinate error
𝑒𝑒𝑒𝑒𝑟𝑟𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = �(𝑥𝑥𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑥𝑥𝑛𝑛𝑛𝑛𝑛𝑛 )2 + (𝑦𝑦𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 − 𝑦𝑦𝑛𝑛𝑛𝑛𝑛𝑛 )2 (2)- angle error

𝑒𝑒𝑒𝑒𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 = 1000 ∙∙ �(𝐬𝐬𝐬𝐬𝐬𝐬 𝜶𝜶𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓 − 𝐬𝐬𝐬𝐬𝐬𝐬 𝜶𝜶𝒏𝒏𝒏𝒏𝒏𝒏 )𝟐𝟐 + (𝐜𝐜𝐜𝐜𝐜𝐜 𝜶𝜶𝒓𝒓𝒓𝒓𝒓𝒓𝒓𝒓 − 𝐜𝐜𝐜𝐜𝐜𝐜 𝜶𝜶𝒏𝒏𝒏𝒏𝒏𝒏 )𝟐𝟐(𝟑𝟑)- total error

errfull=errcoor +errangle(4)

Finally, the errfull is total error value for images,
in which the robot is fully visible, is ranged from 0
to 150, for images where the robot is partially visible- from 150 to 1500, and for images where the robot
was not visible - from 500 to 4500 (Fig. 3).
At the second stage a network was built, where,
in addition to the coordinates and trigonometricfunctions of the angle, the network was trained to196

GEOMATE–Melbourne, Australia, 11-13 November 2020

a set of images reduced to 227 X 227 pixels was
prepared.
AUTOMATIC CONTROLLet us define the following variables andparameters used in equations of motion for the
ground robot (see Fig. 5):
1) For variables describing motion:
𝒙𝒙 и 𝒚𝒚– coordinates of ground robot; α - angle ofrotation of the robot on the plane; 𝒗𝒗 – translation
velocity of the robot; ω - angle velocity of the robot2) Ground robot parameters:
R – wheel radius; 𝒍𝒍 – distance between wheels
3) Controlling signals:
𝝎𝝎𝑹𝑹 и 𝝎𝝎𝑳𝑳 – angle velocities of rotation of the rightand left wheels
It is shown in [19] that forward movement androtation are described by the system of equations:𝒙𝒙̇ = 𝒗𝒗 𝒄𝒄𝒄𝒄𝒄𝒄 𝜶𝜶 ; 𝒚𝒚̇ = 𝒗𝒗 𝒔𝒔𝒔𝒔𝒔𝒔 𝜶𝜶 ; 𝜶𝜶̇ = 𝝎𝝎;(𝟓𝟓)
𝑹𝑹(𝝎𝝎𝑹𝑹 + 𝝎𝝎𝑳𝑳 )𝟐𝟐𝑹𝑹(𝝎𝝎𝑹𝑹 − 𝝎𝝎𝑳𝑳 )𝒗𝒗 =; 𝝎𝝎 =(𝟔𝟔)
𝟐𝟐
𝒍𝒍
Since the system is nonlinear, it’s too hard to use
those equations to analyze stability. We need tolinearize those equations on the premise that theparameters x(t), y(t), α(t), v(t), ω(t) corresponding
with steady flight get small increments δx(t),δy(t),δα(t), δv(t-τ), δω(t-τ) caused by perturbations
action on a path.
We estimate robot trajectory by polygonal chain
path. This path consists of linear motion along line
segments with constant translational velocity andzero angular velocity (Rotation), and rotations invertices with constant angular velocity and zerotranslational velocity (Linear motion).
For the case when stationary parameters cannot
provide stability of the desirable stationary trajectorythemselves, we need to use autopilots (Fig. 6). An
autopilot states the controlling parametersδv(t-τ), δω(t-τ) to be functions of the output
controlled parameters (), which are deviations fromthe desirable stationary trajectory δx(t); δy(t); δα(t).
The values of the output parameters can be obtainedby autopilot from navigation measurements, forexample, from vision-based navigation, inertial
navigation, satellite navigation and so on. On thebasis these navigation measurements, the autopilot
forms controlling signals to decrease undesirable
deviation. Unfortunately, there always exists somein getting information about the output-controlledparameters to autopilot for any navigation
measurements. So, we have a problem, because of
the lack of some necessary information forcontrolling. In the paper [20] we demonstrate that
we are possible even for such conditions with thetime delay to get controlling signal providing astable path.
Final solution from [20] are following:
For rotation, the stationary solution isFig. 3 The total error distribution for different
visibilities of the robot.

Fig. 4. The different images of the robot from thedatasetpredict errfull, errcoor, errangle. Thus, by teaching the
network to predict the possible error value, we got atool that allows us not only to determine thecoordinates, but also to verify the accuracy of theirdetermination. If the robot turned out to becompletely or partially covered by environmental
objects, we saw this from the increase in thepredicted errors.
This approach allowed us to avoid working with
two networks, the first of which would beclassifying, and determining the presence of therobot in the image, and the second would be finding
its coordinates and angle. The classification problem
has been replaced by the regression task of error
prediction.
Training was carried out using full sample of
10,000 images and the solver “adam”, training took
place in 200 epochs with 500 batch size. Thanks tomodeling in Unity, the dataset was generated
programmatically. At each step, the position andangle of rotation of the robot, as well as the locationof the elements of the working platform, wererandomly selected. Lighting simulated sunlightfalling from various angles and directions. As abackground, 50 different textures were used toachieve independence of determining thecoordinates of the robot from background elements(Fig. 4). 12,000 rgb images of size 3036 X 3036
containing robots were generated. Among theimages were also shots where the robot was partiallyor completely covered by environmental objects or
the tower of the camera. A file was also preparedwith information about the coordinates of the robot
and the rotation angle. Then, based on these images,
197

GEOMATE–Melbourne, Australia, 11-13 November 2020

α(t)=ωt+φ; v(t)=0; φ=0(7)where control parameters are following:𝛿𝛿𝛿𝛿 (t − τ) = −2 𝑎𝑎𝑟𝑟 𝑐𝑐𝑐𝑐𝑐𝑐�𝜔𝜔 (t − τ)�𝛿𝛿𝛿𝛿(t − τ)
−2 𝑎𝑎𝑟𝑟 𝑠𝑠𝑠𝑠𝑠𝑠�𝜔𝜔 (t − τ)�𝛿𝛿𝛿𝛿(t − τ)
where 𝜔𝜔 ≠ 0, 𝑎𝑎𝑟𝑟 > |𝜔𝜔|;(8)𝛿𝛿𝛿𝛿(t − τ) = 𝑏𝑏𝛼𝛼 𝛿𝛿𝛿𝛿 (t − τ) where 𝑏𝑏𝛼𝛼 < 0.
(9)
For linear motion, the stationary solution isα(t)=α; v(t)=v(10)
where control parameters are following:𝛿𝛿𝛿𝛿 (t − τ) = −𝑎𝑎𝑙𝑙 𝑐𝑐𝑐𝑐𝑐𝑐(𝛼𝛼 )𝛿𝛿𝛿𝛿(t − τ)−𝑎𝑎𝑙𝑙 𝑠𝑠𝑠𝑠𝑠𝑠(𝛼𝛼 )𝛿𝛿𝛿𝛿(t − τ)
(11)where 𝑎𝑎𝑙𝑙 > 0;𝜹𝜹𝜹𝜹(𝐭𝐭 − 𝛕𝛕) = 𝒂𝒂 𝐬𝐬𝐬𝐬𝐬𝐬(𝜶𝜶) 𝜹𝜹𝜹𝜹(𝒕𝒕 − 𝛕𝛕)−𝒂𝒂 𝐜𝐜𝐜𝐜𝐜𝐜(𝜶𝜶) 𝜹𝜹𝜹𝜹(𝒕𝒕 − 𝛕𝛕) − 𝟐𝟐𝒃𝒃 𝜹𝜹𝜹𝜹(𝐭𝐭 − 𝛕𝛕)
where 𝒂𝒂𝒂𝒂 > 𝟎𝟎; 𝒃𝒃 > √𝒂𝒂𝒂𝒂.
(12)
The delay time is following:1
1
1
1
τ ≤ min �
,
,
,
�
(13)
2𝑒𝑒|𝑏𝑏| 𝑒𝑒|𝑎𝑎𝑙𝑙 | 𝑒𝑒|𝑏𝑏𝛼𝛼 | 2𝑒𝑒|𝑎𝑎𝑟𝑟 |out two options to drive the ground robot:
a. A random movementboundaries
b. A user predefined route

in

user

predefined

As a result, we proved that it is possible tomaintain stable movement of a ground robot evenwhen time delay exists in transfer information about
output control parameters from navigation
measurement devices to autopilot.
We can find control parameters for the particularcase of visual airborne navigation of ground robot
and estimated max possible delay of the system.
We also demonstrate that the neural network is
an effective tool for determining the coordinates ofan object: fast, high-precision, insensitive tointerference, changes in the environment andlighting.
The quality of determining coordinates allows
you to build a high-quality control system for aground robot.
Obviously, the proposed methods can also be
used to solve other problems where it is necessary to
determine the coordinates of the observed object.
The future work includes the following:

Fig. 5 Ground robot

1) Creation a system for developing possible
trajectories
2) Creation a system for inter-coordination ofseveral robots.
3) Recognition of the environment of robots:
ground markers, obstacle, moving objects
4) Recognition of robot operation results: howmuch grass is mowed, which part of theoperation area is treated.

Fig. 6 Automatic control
EXPERIMENTAL RESULTS
We developed software using designed
algorithms. It was demonstrated in real experimentsthat it is possible to carry out two options to drive
the ground robot: random movement in user
predefined boundaries and user predefined route([13,14] and Fig. 7).

However, the developed robot is only prototype. The
main future work is creation of working robot for
mass-production. The working robot can be:1) It is a remote-controlled slope mower [4].
2) Robotic Fruit Harvester - the FFRobot [5]3) Robots for fire protection [6]4) Autonomous wheelchair for disabled people [7]CONCLUSIONS
It was demonstrated that it is possible to carryFig 7. Two experimental tests - random movement in user predefined boundaries and user predefined route.
198

GEOMATE–Melbourne, Australia, 11-13 November 2020

REFERENCES[10] Kupervasser
O.Yu., Kupervasser
Yu.I.,
Rubinstein A.A., German Utility model:
Vorrichtng fur Koordinierung automatisierter
Vorrichtungen. Patent Nr. 21 2013 000 225,
Germany, Jul 10, 2015[11] Kupervasser O. Yu., Franch Utility model
application: Coordination System for Ground
Movable Automated Devices. Utility model,
publication number 3037157, France, Jun 4,2016,
http://bases-brevets.inpi.fr/en/documenten/FR3037157.html?s=1482862654519&p=5&
cHash=1f6f99c78cfb2124b5accb92be338388[12] Kupervasser O. Yu., Russian Invention: Method
for coordination of ground moving automateddevices with the help of single central control
system, Patent № 2691788, Russia, July 05,
2015[13] https://www.youtube.com/watch?v=ekxAM9Uytzc[14] https://www.youtube.com/watch?v=KYuXCAz
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Convolutional Network for Real-Time 6-DOF
CameraRelocalization,
arXiv:1505.07427[cs.CV][17] Kendall A., Cipolla R., Geometric Loss
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H., Yavich R.,A method for stabilization ofground robot path controlled by airborne
autopilot with time delay, accepted to Springer
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GEOMATE, Vol.14, Issue 45, 2018, pp. 10-15[2] Djaja K., Putera R., Rohman R A. F., SondangI., Nanditho G. and Suyanti E., The Integrationof Geography Information System (GIS) andGlobal Navigation Satelite System-Real Time
Kinematic (GNSS-RTK) for Land use
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GEOMATE, Vol.13, Issue 36, 2017, pp. 31-34[3] Ehrenfeld I., Kupervasser O., Kutomanov H.,
Sarychev V. and Yavich R., Algorithms
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Almeida A.T., Autonomous wheelchair fordisabled people, Proceedings of the IEEE
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INVESTIGATION OF OPEN-PIT MINE DRAINAGE FEATURES
Cristhiana Perdigão1, Ashley Dyson 2, Mohammadjavad Yaghoubi3 and Thomas Baumgartl4
School of Engineering, Information Technology and Physical Sciences, Federation University,
Australia

1,2,3,4

ABSTRACTAs control measures of ground and surface water, horizontal bores have played a very long term and essential
role in maintaining the stability of mine batters. In coal open-cut mines, joints or geological discontinuities incoal seams can control the groundwater flows. The water pressure, caused by the flow and hydrostatic pressuresinside the joints can contribute to batter failure due a greater active force than the shear strength of the slip
surface. In this research, the drainage performance of horizontal bores is assessed to investigate and compare theprofiles of various bores. A recently developed camera for bore investigation is employed in this project tovisually inspect the horizontal drains. This study has taken place in an operational brown coal mine located in
Gippsland, region of Victoria, Australia. Joints and water flow variation along the bores, observed through thecamera inspection, are discussed. Results to date showed that the camera is an effective tool to inspect theboreholes and allow the interpretation of the visual observations.
Keywords: Horizontal bore, Bore outflow, Visual inspection, Drainage

joints can potentially impact the slope stability byincreasing the likelihood of block sliding and/or
increasing the potential for adverse pore pressures
within the coal units [3].

INTRODUCTION
Horizontal bores are essential infrastructures formaintaining the stability of open-pit mines. The
installation of horizontal drains helps to decrease thewater table elevation and reduce pore pressures
within soil or rock units. Reduction of the waterpressures results in the increase of the shear strengthof soil, and accordingly, improvement of slope
stability [1]. Using horizontal bores for lowering the
groundwater levels are generally recognized as themost economical method available [2]. They areusually used where the groundwater cannot bereached by other conventional drainage methods suchas trench drains. Although the implementation ofhorizontal drains is a common practice for slope
stability, there seems to be limited information
available on the assessment of their performance overtime and requirements for maintenance.
The primary objective of this paper is to describe
a methodology that has been conducted to assess thedrainage performance of horizontal bores and toinvestigate their features. Profiles of various bores arecompared with the aim to correlate their features (e.g.
intersecting joints and joint density) with theirperformance. This study has been conducted at
Yallourn Mine, an operational brown coal mine in the
Latrobe Valley, located in Victoria, Australia. The
Latrobe Valley coal seams are broken up by sets ofwidely spaced continuous joints, which provide
planes for movement to occur and allow direct accessfor water to depth. The dominant joint set within thecoal seams is generally sub-vertical and continuous
through the full thickness of the coal. These joints
can connect to any surface water body or readily
accessible water source. Water flow through the

METHODOLOGY
The bores for this research were selected according totheir characteristics presented in Table 1 (age, depth
and location). Results presented in this paper are fromthe bores presented in Figure 1.
Table 1 Selected bore’s characteristics
Bore Code
N6853
N5224
N6852

Total Depth (m)
300
200
400

Installation Year
2013
1999
2013

Figure 1: Locations of the studied bores at Yallourn200

GEOMATE – Melbourne, Australia, 11-13 November 2020

camera unit has been developed by researchers at theGeotechnical and Hydrological Engineering
Research Group (GHERG) for this project, presented
in Figure 3. The unit includes a submergible camera
which can be inserted into the drain bores using adrilling rig to collect visual internal information of theboreholes including presence and characteristics ofjoints such as location, infill material, flow condition,evidence of shear and blockage. This unit can capture
and record real-time images along the length of thedrain up to 400 m.

Water Flow
This part of the study addresses the linkage of
rainfall and response time of the magnitude of waterflow on the performance of individual boreholes.
Water flow and temperature analysis provide a better
understanding about whether or not a borehole isdraining groundwater or simply withdrawing shortterm recharge water from surface water. Each waterflow meter (Figure 2) contains a data logger thatrecords the number of tips (events) and watertemperature. The temperature profile may be usefulas it indicates whether the water being drained by theborehole is originating from the groundwater or fromthe surface as the groundwater tends to have higher
temperatures than the surface recharge water due tocoal low thermal conductivity.

Figure 3: Horizontal Borehole Camera.
RESULTS AND DISCUSSION
Water Flow
Figure 2: Flow meter connected to a selected bore
The effects of rainfall on the moisture conditions
within residual soil slopes through pore-waterpressure measurements using tensiometers andpiezometers were discussed in [4], [5]. These studies
observed a 1-2 day delay response in water pressure,with respect to the start of the rainfall event. Figure 4
presents data collected in regards to the outflow for
three different bores during the same period of time
and therefore, under the same precipitation rate
conditions.

Horizontal Borehole Camera
There are a variety of options to investigate theconditions of drilled horizontal boreholes. Due to thelength of bores, which can vary from 150m to 500m,
and the high-moisture environment they are installed
in, conventional instrumentation such as boreholes
televiews or fibre optic cables would be impracticalor ineffective for the exploration of bores. As such, a201

GEOMATE – Melbourne, Australia, 11-13 November 2020

50

The data collected for this research revealed thatdifferent sets of boreholes behave differently in termsof flow and response to rain events. The response isgoverned by mine activity and local geology,
particularly, joint networks.
Observations in the field have shown there is no
relationship between the age and discharge rate ofbores, as some of the recently drilled drains are dry,but some drilled in 1984 are discharging well. The
reason behind this is unknown. One assumption isthat internal stress releases within the coal may causesome disconnections due to transverse coal block
movements along horizontal drains, thereby reducing
drainage capacity of the drain bores. A second
assumption is that sediment accumulation in the bores
restricts flows or even causes blockages. A further
cause may be the changes in the catchment for thisbore and groundwater replenishment from the surface.
Physical parameters of water are included in theinvestigation to reveal potential relationships of theduration of flow through the coal matrix andtemperature changes and can be used to detect thetravel time of water. Figure 5 shows the outflow
temperature for the selected bores and how for N5224
and N6853 it varies with the air temperature. While
N6852 outflow water temperature is mostly constantregardless of the variation of air temperature over thedays and rain events during a certain period. Bore
N6852 is an example of one of the selected bores thatare draining groundwater (Figure 6).

1200

45
1000

35

800

Flow (L/day)

Precipitation (mm)

40
30
25

600

20

400

15
10

200

5
0

0

Precipitation

N6863 flow

N5224 flow

N6852 flow

Fig 4: Precipitation rate and the outflow rate of
selected bores
Different flow responses to rain might be related
to local joints characteristics, for instance how tightly
the joints are, how they are connected and theirorientation.

Temperature °C

25

50
45
40
35
30
25
20
15
10
5
0

20

15
10
5
11/05/2020

10/05/2020

8/05/2020

6/05/2020

4/05/2020

2/05/2020

1/05/2020

29/04/2020

27/04/2020

25/04/2020

24/04/2020

22/04/2020

20/04/2020

18/04/2020

Date

0

Daily Cumulative Rainfall

Day Temperature
6853 Water Temp, °C (LGR S/N: 10265557, SEN S/N: 10265557)
6852 Water Temp, °C (LGR S/N: 20723916, SEN S/N: 20723916)
5224 Water Temp, °C (LGR S/N: 20782021, SEN S/N: 20782021)

Figure 5: Bore's outflow temperature variation along with precipitation events and day temperature
202

Daily Cumulative Rainfall (L/m2)

Bore's outflow and day temperature combined with rainfall events

GEOMATE – Melbourne, Australia, 11-13 November 2020

25.0

facilitate drainage of rainfall-runoff that has entered
the joints in the coal and to drain groundwater in coal.
It has been evidenced through camera inspection, thatin some occurrences, a bore intersecting a joint may
function as an alternative pathway for waterflow anddivert flow using the bore as a connecting medium
through a different joint and associated bore (Figure
8).

Average
Averagedaily
dailywater
watertemperaturetemperature
N5224
N6852
Averagedaily
air air
temperature
Averagedaily
temperature

Temperature (°C)

20.0
15.0
10.0
5.0

Figure 6: Outflow average temperature of N6852
Camera Inspection
Various features inside deep horizontal bores suchas joints as well as variations in water flow, material
properties and geometry were observed during thevisual inspection. These features are discussed below.

Figure 8: Variation of water flow along the bore

Joints and Variations in Water Flow

CONCLUSIONSAs expected, joints could be observed inside thebores (Figure 7) as they are the most numerous andobvious structural features in the brown coals in thisspecific region [6]. Flow of water through a joint into
the borehole can be observed in the top left photo.

Flow data collected for this study has revealed thatdifferent sets of boreholes behave differently in termsof flow and response to rain events and groundwater
recharge.
Outflow temperature has shown to be a good
indicator of whether the water being drained by theborehole is coming from the groundwater or thesurface as collected data has shown that for bores
draining surface water, the outflow temperature
varies according to the air temperature. In contrast,
for bores draining groundwater, the outflow
temperature is mostly constant as observed on bore
N5224.
Features inside the deep horizontal bores such as
joints as well as variations in water flow wereobserved with a camera specifically designed for thispurpose. Results to date showed that the camera is aneffective tool to inspect the boreholes.
Future camera inspections, flow and temperaturemonitoring will lead to further knowledge about thepossible causes of the reduction in the efficiency ofhorizontal boreholes.

Figure 7: Joint observed through the camera
inspection inside the bore pointed by white arrows.

ACKNOWLEDGMENTS

Joints in the brown coal batters are typically subvertical, comprise two sub-orthogonal sets andpenetrate the full depth of the coal seam [7]. The
horizontal bores are drilled in a way to ensureintersections within vertical joints are created to

Financial support for this research has been
provided by Energy Australia and the Geotechnicaland Hydrological Engineering Research Group fromFederation University, Australia.

203

GEOMATE – Melbourne, Australia, 11-13 November 2020

[5] Ng, C., Springman, S. M., & Alonso, E. (2008).
Monitoring the Performance of Unsaturated Soil
Slopes. (pp. 26(6):799-816). Geotechnical andGeological Engineering Kimura S., Journal Paper
Title, J. of Computer Science, Vol. 1, Issue 2,
1987, pp. 23-49.
[6] Barton, C. (1981). Regional stress and structure
in relation to brown coal open cuts of The Latrobe
Valley, Victoria. Journal of the GeologicalSociety of Australia, 28, 333-339[7] Shaghaghi, T., Tolooiyan, A., & Mackay, R.
(2018, April 10 to 13th). Numerical investigationof potential pore water pressure changes in jointed
brown coal due to waste material placement
against a brown coal open-cut batter. Sevilla,
Spain: XIV Congreso Internacional de energia y
Recursos Minerales.

REFERENCES[1] Pohll, G., Carrol, R., Reeves, D., Parashar, R.,
Muhunthan, B., Thiyagarjah, S., Willoughby, K.
(2013). Design Guidelines for Horizontal Drainsused for Slope Stanilization. Washington State
Department of Transportation.
[2] Rahardjo, H., Satyanaga, A., & Leong, E. (2011).
Unsaturated soil mechanics for slope stabilization.
Unsaturated Soils: Theory and Practice, 103-117.
[3] Department of Economic Development, Jobs,
Transport and Resources. (2015). Guideline forManaging Ground Control Risks in Victorian
Brown Coal Mines.
[4] Ng, C., Zhan, L., Bao, C., Fredlund, D., & Gong,
B. W. (2003). Performance of an Unsaturated
Expansive Soil. (pp. 53(2): 143-157).
Géotechnique.

204

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A COMPREHENSIVE LEACHATE STUDY OF FIRED-CLAY BRICKSINCORPORATING CIGARETTE BUTTS
Halenur Kurmus and Abbas Mohajerani
School of Engineering, RMIT University, Australia
ABSTRACTThe use of cigarette butts (CBs) in the construction industry has received considerable interest in recent years
to develop environmentally friendly materials. This present paper reports the leaching behavior of Ni, Cu, As, Hg,
Cd, Pb, Se, Cr, Ag, Zn, and Ba for both green and fired clay bricks incorporating CBs according to the Australianbottle leaching procedure (ABLP). The leaching of heavy metals was compared for both green (before firing) andfired clay bricks (after firing) to study the influence of firing on the leaching ability of bricks. To assess thesuitability of fired clay bricks incorporating CBs, the leachate concentrations were compared to the concentration
limits for heavy metals set by the EPA solid industrial waste hazard categorization thresholds. To investigate how
the pH affects the leaching behavior of the metals studied for various environmental situations, three pH values,
2.9, 5.0, and 9.2, were used. The findings revealed that the leaching of heavy metals from the fired clay bricks was
substantially lower than that of the concentration limits and green bricks. Therefore, bricks modified with CBs canbe categorized as non-hazardous waste. The paper also presents the mechanical and physical properties of fired
clay bricks incorporated with CBs.
Keywords: Cigarette Butts; Leachate Analysis; Recycling; Fired Clay Bricks; Waste Management
include recycling CBs in the production of cellulose
pulp [20], as a corrosion inhibitor [21, 22], as soundabsorbing material [23, 24] and as a biofilm carrier[25, 26]. However, placing CBs in fired clay bricks
pose an environmental risk of toxic leaching during
the use and disposal of the construction material.
Therefore, it is crucial to perform a leachate analysisof heavy metals to classify and quantify those harmful
constituents.
Therefore the objective of this study was toperform a leachate analysis on green (before firing)and fired bricks (after firing) incorporating 0%, 0.5%,
1%, 1.5%, and 2% CBs by wt based on the Australianleaching procedure (ABLP) [27]. The Inductively
Coupled Plasma Mass Spectroscopy (ICP-MS) was
utilized to analyze the samples for heavy metals. To
examine the effect of different environmental
conditions on the leaching capability of bricks
incorporated with CBs and the metals studied, pH
values 2.9, 5.0, and 9.2 were selected. The leachate
concentrations of metals Se, As, Cd, Cu, Ba, Pb, Zn,
Hg, Cr, Ag, and Ni were examined for both green(before firing) and fired bricks (after firing) andcompared to analyze the effect of firing on theleaching capability of heavy metals. Moreover, theleachate results were compared to the solid industrial
waste hazard categorization published by the EPA
Victoria Industrial Waste Resource Guidelines [1] todetermine the suitability of the fired clay bricks
incorporating CBs.

INTRODUCTION
Cigarette butts (CBs) are one of the most common
litter items found on beaches and represent 25-50%of the litter collected from streets and roads [2]. It isestimated, worldwide every year, 6 trillion cigarettes
are smoked, and roughly 4.5 trillion are discarded in
the environment [3]. CBs contain carcinogenic andtoxic substances, therefore, when littered canpotentially pollute groundwater, soil, and waterways
and harm the biota and aquatic life [4].
Littered CBs can remain in the environment for up
to 18 months due to the poor biodegradability ofcellulose acetate filters while leaching heavy metals
and toxic chemicals [5]. Cellulose acetate filters aredesigned to partially preserve particulate smoke
components. The major toxic agents include nicotine,polycyclicaromatichydrocarbons(PAHs),
compounds specific to Solanaceae, nicotiana
alkaloids, and catechols [6, 7].
Various studies have been conducted toinvestigate the transfer of toxic metals in tobacco to
the filter of a cigarette [8-10]. The common andharmful heavy metals found in CBs include Se, As,
Cd, Cu, Ba, Pb, Zn, Hg, Cr, Ag, and Ni.
Landfilling and incineration are the common
methods applied for the disposal of collected CBs.
However, neither method is environmentally friendly
or economical. Therefore, the method of recycling
has gained growing interest. Mohajerani et al. [11]carried out comprehensive studies on recycling CBs
in fired clay bricks [12-16]. In addition Mohajerani etal. [17] investigated extensively the use of CBs inasphalt concrete [18, 19]. Other recycling methods

MATERIALS AND METHODS
Characterization of Raw Materials
205

GEOMATE – Melbourne, Australia, 11-13 November 2020

have been determined in accordance with theAustralian Standards [29]: water absorption [30],initial rate of absorption (IRA) [31], shrinkage,compressive strength [32], efflorescence [33] anddensity [34].

Clay material was supplied by PHG Bricks in
Victoria. The particle size distribution (PSD) of thesoil was determined using sieve analysis. According
to the Unified soil classifications, the soil can beclassified as sandy, silty clay (MC) [28]. The CBs
were supplied by Buttout Australia Pty Ltd. The asreceived CBs were firstly subjected to thepretreatment of drying in the oven at 105° for 24 h.
The chemical composition of the raw materials
was determined by X-ray fluorescence (XRF). It canbe seen in Fig. 1 that the main constituents of the bricksoil were silicon dioxide and aluminum oxide. X-ray
diffraction (XRD) was used to evaluate the crystalline
phase. As can be seen in Fig. 2, quartz was the main
phase of the raw material.
2.46% 1.62%
4.87%

1.19%

Leachate Analysis
The ABLP was used to evaluate the leaching ofheavy metals from the prepared brick samples. Heavy
metals Chromium (Cr), Copper (Cu), Silver (Ag),
Barium (Ba), Mercury (Hg), Selenium (Se), Nickel(Ni), Cadmium (Cd), Arsenic (As), Lead (Pb), andZinc (Zn) were analyzed for green and fired clay
bricks incorporating CBs. In following the ABLP, thebrick samples were initially crushed and sieved using
a 2.4 mm sieve. To simulate various environmental
conditions and determine the correlation between theleachate and the pH solution, pH values 2.9, 5.0, and9.2 were selected and prepared. The leachate samples
were prepared based on a 1:20 ratio in screw-cappedbottles. The samples were then placed in an agitator
for 18 hr to be mixed and then filtered through a 0.45
microfilter. The filtered samples were analyzed using
an ICP-MS for heavy metals.

0.27%
SiO2

6.15%

Al2O3
Fe2O3
K2O

19.95%
62.84%Na2O
MgO

RESULTS AND DISCUSSION

TiO2

Properties of Brick Samples

CaO

Fig. 1Clay material chemical composition

Fig. 2

XRD patterns of clay material

Shrinkage is a critical concern. Samples with ahigher content of CB waste, 2%, resulted in asignificant decrease in both height and diametricshrinkage of 1.96% and 3.1%, respectively. This
compared to controlled bricks with a height anddiametric shrinkage rate of 3.07% and 4.64%,
respectively.
The addition of CBs reduced the bulk density of
the clay brick, and the values varied depending on thequantity of waste incorporated. Bulk density
decreased from 2114.33 kg/m3 to 1971.8 kg/m3 withthe addition of 1.5% CBs by wt. The addition of CBs
has an effect on water absorption. Bricks
incorporating 1% and 2% CBs by wt showed a water
absorption rate of 12.1% and 13.1% compared to 9%
for controlled clay bricks.
The results demonstrate that the controlled bricks
have a higher strength capacity compared to thebricks incorporating CBs. The highest compressive
strength was 48.64 MPa for bricks with 0% CBs anddecreased to 28.34 MPa with the inclusion of 1.5%
CBs by wt. The decrease in strength is associated withthe loss of organic content in the CBs, which resulted
in the formation of macropores and lowered thestrength of the CB amended bricks.

Preparation of Brick Samples
Selected mixtures containing 0.5%, 1%, 1.5% and
2% CBs by wt were prepared. The raw mixtures weremixed and homogenized by using a Hobart
mechanical mixer for 25 min with a 15.5% watercontent. Cylindrical specimens were prepared using acompactor with a compaction pressure of 240 kPa anddried at 105º for 24 h. The green bricks were fired at
1050º for 3h. The firing step was carried out in anelectrical furnace. The following technologicalproperties of the fired clay bricks incorporating CBs

Leachate Analysis of Fired Clay Bricks
Table 1 summarizes the results for heavy metals
206

GEOMATE – Melbourne, Australia, 11-13 November 2020

Generally, the increase in CB content in the brickresulted in a decrease in metal contamination for Zn,
Ba, Cr, and Ni. Bricks incorporating CBs were more
prone to leaching heavy metals in areas with highly
acidic rain compared to the natural range ofprecipitation.
As shown in Fig. 3, a direct relationship between the
pH and the metal concentration leachate was foundfor Zn, Vu, Ba and Ni. A decrease in pH from 9.2 to
2.9 resulted in an increase in the metal concentrationleached for bricks incorporating 1% and 2% CBs by
wt. Therefore we can confirm metals Zn, Cu, Ba, andNi are more likely to leachate in areas with highly
acidic rain compared to the natural scope ofprecipitation.

for fired clay bricks incorporating 0%, 0.5%, 1%,
1.5% and 2% CBs using the ABLP method for pH 2.9,
5.0 and 9.2. As shown in Table 11, the metal
concentrations of the leachate are trivial whencompared to the regulatory threshold limits set by theIndustrial Waste Resource Guidelines [1]. The results
confirm that fired clay bricks incorporating 0%, 0.5%,
1%, 1.5%, and 2% CBs by wt. are categorized as nonhazardous waste for distinct pH values as they are lessthan the regulatory limits for heavy metals.
The results show that differences in pH over awide range of precipitation affect the leaching
behavior of metals in the fired clay bricks.

Table 1

Leachate results for heavy metals in crushed brick samples using the ICP-MS.
Concentration Level (mg/L)
Percentages of CBs by mass

EPA
Heavy Victoria
Metals solid waste pH
thresholdsa
2.9

0%

0.5%

1%

1.5%

2%

pH

pH

pH

pH

pH

pH

pH

pH

pH

pH

pH

pH

pH

pH

5.0

9.2

2.9

5.0

9.2

2.9

5.0

9.2

2.9

5.0

9.2

2.9

5.0

9.2

Cr

2.5

0.004 0.014 0.009 0.005 0.015 0.007 0.028 0.013 0.056 0.007 0.012 0.009 0.011 0.016 0.016

Ni

1.0

0.007 0.044 0.004 0.010 0.043 0.003 0.056 0.041 0.025 0.008 0.039 0.003 0.203 0.043 0.010

Cu

100

1.559 0.523 0.035 1.536 0.557 0.043 1.134 0.543 0.053 1.496 0.480 0.042 0.860 0.499 0.035

Zn

150

1.792 0.628 0.104 1.909 0.645 0.140 2.291 0.406 0.267 1.867 0.335 0.045 2.251 0.543 0.222

As

0.35

0.005 0.010 0.003 0.011 0.011 0.005 0.004 0.014 0.006 0.044 0.038 0.015 0.005 0.015 0.001

Se

0.5

-

0.002

-

Ag

5.0

-

0.001

-

-

0.009

-

-

-

-

-

-

-

-

0.001

-

Cd

0.1

0.001 0.001

-

-

0.001

-

-

0.001

-

-

-

-

-

0.001

-

Ba

35.0

1.450 1.648 1.447 1.473 1.770 1.100 3.708 1.481 2.279 1.530 1.044 1.312 2.747 2.098 1.032

Hg

0.05

Pb

0.5
a

-

-

0.002 0.002

-

-

-

-

0.013 0.002 0.003 0.003 0.003 0.002 0.015 0.003

-

-

-

-

-

-

-

-

-

-

-

0.012 0.010 0.001 0.005 0.008 0.001 0.050 0.006 0.006 0.003 0.007 0.001 0.011 0.010 0.002

Industrial Waste Resource Guidelines [1]207GEOMATE – Melbourne, Australia, 11-13 November 2020

pH 5.0content bricks before and after firing.

pH 9.22.0

Before Firing

1.2
0.8
0.4

Zn
Cu
Ni
Cr

0.0
Ni

Cu
Zn
MetalsBa

0.00

pH 2.9pH 5.0

0.50

1.00

1.50

2.00

Concentration (mg/L)
Fig. 6 Heavy metal concentration of 1% CB
content bricks before and after firing.

Fig. 3 Relationship observed between pH value
and metal concentrations leached from 1% CB bricks.
pH 9.2

Before Firing

2.8

After Firing

Ba

2.4

Heavy Metals

Concentration (mg/L)

After FiringBa

1.6
Heavy Metals

Concentration (mg/L)

pH 2.9

2.0
1.6
1.2
0.8

Zn
Cu
Ni
Cr

0.4
0.00

0.0
Ni

Cu
Zn
Metals

0.50

1.00

1.50

Concentration (mg/L)

Ba

Fig. 7 Heavy metal concentration of 1.5% CB
content bricks before and after firing.

Fig. 4 Relationship observed between pH value
and metal concentrations leached from 2% CB bricks.
Green Bricks and Fired Bricks Comparative
Analysis

Before Firing

After Firing

0.50

1.00

Heavy Metals

Ba

To investigate the disparities between the metal
concentration leachate before and after firing of 0%,
0.5%, 1%, 1.5%, and 2% CB brick samples, pH 9.2
was selected (Figs. 7 – 8). It was found after the firing
process of bricks, the leachate concentrations were
notably lower. This is generally due to the sintering
process which confined the metals within the brickstructure during the high temperatures.

Zn
Cu
Ni
Cr
0.00

1.50

Concentration (mg/L)
Before Firing

After FiringFig. 8 Heavy metal concentration of 2% CB
content bricks before and after firing.

Heavy Metals

Ba
Zn

In addition the leaching percentage drop wasdetermined for 0.5%, 1%, 1.5%, and 2% CB bricksamples. It was found Ni and Cr achieved a 95% and
93% drop in leachate concentration for bricks
incorporating 1.5% CB content. While bricks
incorproating 0.5% and 1% CBs by wt. achieved aleaching drop of 56% and 61% for Zn. In general,over 50% of all tested heavy metals were
immobilized after firing for 0.5%, 1%, 1.5%, and 2%
CB bricks compared to green bricks. Incorporating

Cu
Ni
Cr
0.00

0.50

1.00

1.50

Concentration (mg/L)
Fig. 5

Heavy metal concentration of 0.5% CB
208

GEOMATE – Melbourne, Australia, 11-13 November 2020

environment?, Environmental pollution. 2019,249, 932-939.
[5] Novotny, T.E., Lum, K., Smith, E., Wang, V.,
and Barnes, R. Cigarettes butts and the case foran environmental policy on hazardous cigarette
waste, International journal of environmental
research and public health. 2009, 6(5), 16911705.
[6] Lee, W. and Lee, C.C. Developmental toxicity
of cigarette butts–An underdeveloped issue,
Ecotoxicology and environmental safety. 2015,
113, 362-368.
[7] Micevska, T., Warne, M.S.J., Pablo, F., andPatra, R. Variation in, and causes of, toxicity ofcigarette butts to a cladoceran and microtox,
Archives of Environmental Contamination and
Toxicology. 2006, 50(2), 205-212.
[8] Kazi, T., Jalbani, N., Arain, M., Jamali, M.,
Afridi, H., Sarfraz, R., and Shah, A. Toxic
metals distribution in different components ofPakistani and imported cigarettes byelectrothermal atomic absorption spectrometer,
Journal of hazardous materials. 2009, 163(1),302-307.
[9] Ifelebuegu, A., Lale, E., Mbanaso, F., andTheophilus, S. Facile Fabrication of Recyclable,
Superhydrophobic, and Oleophilic Sorbent from
Waste Cigarette Filters for the Sequestration of
Oil Pollutants from an Aqueous Environment,
Processes. 2018, 6(9), 140.
[10] Behera, S.N., Xian, H., and Balasubramanian, R.
Human health risk associated with exposure to
toxic elements in mainstream and sidestream
cigarette smoke, Science of the total
Environment. 2014, 472, 947-956.
[11] Mohajerani, A., Kadir, A.A., and Larobina, L. A
practical proposal for solving the world’s
cigarette butt problem: Recycling in fired clay
bricks, Waste management. 2016, 52, 228-244.
[12] Kadir, A.A. and Mohajerani, A. Recycling
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clay bricks, International Journal of Civil andEnvironmental Engineering. 2008, 2(9), 137141.
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study on recycling cigarette butts in fired clay
bricks, paper presented to WEC2019: WorldEngineers Convention 2019, 2019,[15] Kurmus, H. and Mohajerani, A. Recycling ofCigarette Butts in Fired Clay Bricks: A New
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CBs in fired clay bricks resulted in a decrease in themetal contamination of the toxic waste, which can be
an environmental advantage compared to the litteringof CBs.
CONCLUSION
In conclusion, the incorporation of CBs in fired
clay bricks offers a sustainable and environmentallyfriendly solution to a growing pollution issue.
Concerns regarding the possible leachate of heavy
metals from bricks recycled with CBs during the use
and disposal of the construction material are clarified
with encouraging results.
The compressive strength of CB brick samples
was greatly dependent on the percentage of organic
content in the brick mixtures. However, they wereconsiderably above the minimum compressive
strength requirement of 17.2 MPa. The addition of
CBs delivered less dense and porous bricks, withimproved thermal insulation properties. The water
absorption and IRA results were within the acceptable
range, and the CB brick manufactured samples
indicated they were capable of withstanding adequate
weather conditions.
The leachate results for fired clay bricks
incorporating 0%, 0.5%, 1%, 1.5%, and 2% CBs bymass were found to be below the regulatory limits set
by the EPA Victoria solid industrial waste guidelines.
Therefore, bricks modified with CBs can be
categorized as non-hazardous waste. The leaching of
Cr and Ni was almost completely hindered after thefiring process of clay bricks incorporating 0.5% and1.5% CBs by mass. In addition, more than 50% of all
the tested heavy metals were immobilized after firingfor 0.5%, 1%, 1.5%, and 2% CBs by mass bricks.
Hence the addition of CBs in fired clay bricks may be
a feasible option to hinder the heavy metals present in
CBs.
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Industrial Waste Hazard Categorization andManagement, E.P.A. Victoria, 2009. Viewed
April
12,
2018,
<file:///C:/Users/s3432918/Downloads/IWRG6
31.pdf>.
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Performance evaluation of cigarette filter rods
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[26] Sabzali, A., Nikaeen, M., and Bina, B.
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[27] Standards Australia 2019. Wastes, sediments
and contaminated soils. Part 3: Preparation of
Leachates-Bottle
Leaching
Procedure(AS4439.3). SAI Global Limited.
[28] Standards Australia 2009. Methods for Testing
Soils for Engineering Purposes. Soil
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method of analysis by sieving (AS1289.3.6.1).
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[29] Standards Australia 2003. Masonary Units andSegmental Pavers and Flags - Methods of Test Sampling for Test (AS1289.5.5.1). SAI Global
Limited.
[30] Standards Australia 2003. Masonry units andsegmental pavers and flags - Methods of test Determining water absorption properties(AS/NZS 4456.14), SAI Global Limited.
[31] Standards Australia 2003. Masonry units andsegmental pavers and flags - Methods of test Determining initial rate of absorption (AS/NZS
4456.17), SAI Global Limited.
[32] Standards Australia 2003. Masonry units andsegmental pavers and flags - Methods of test Determining compressive strength of masonryunits (AS/NZS 4456.4), SAI Global Limited.
[33] Standards Australia 2003. Masonry units andsegmental pavers and flags. Methods of test Determining potential to effloresce (AS4456.6).
SAI Global Limited.
[34] Standards Australia 2003. Masonry units andsegmental pavers and flags - Methods of test Determining moisture content, dry density andambient density (AS/NZS 4456.4), SAI Global
Limited.

clay fired bricks incorporated with cigarettebutts, 2008.
[17] Mohajerani, A., Tanriverdi, Y., Nguyen, B.T.,
Wong, K.K., Dissanayake, H.N., Johnson, L.,
Whitfield, D., Thomson, G., Alqattan, E., andRezaei, A. Physico-mechanical properties ofasphalt concrete incorporated with encapsulated
cigarette butts, Construction and Building
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[18] Rahman, M.T., Mohajerani, A., and Giustozzi,
F. Possible Recycling of Cigarette Butts as Fiber
Modifier in Bitumen for Asphalt Concrete,
Materials. 2020, 13(3), 734.
[19] Rahman, M.T., Mohajerani, A., and Giustozzi,
F. Published. Recycling cigarette butts inbitumen for flexible pavements, in WEC2019:
World Engineers Convention 2019, 2019,
Engineers Australia. p. 701.
[20] Teixeira, M.B.d.H., Duarte, M.A.B., Garcez,
L.R., Rubim, J.C., Gatti, T.H., and Suarez,
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[21] Zhao, J., Zhang, N., Qu, C., Wu, X., Zhang, J.,
and Zhang, X. Cigarette butts and theirapplication in corrosion inhibition for N80 steel
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[22] Zhao, J., Zhang, N., Qu, C., Zhang, J., andZhang, X. Comparison of the Corrosion
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210

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EFFECT OF FINES CONTENT ON LIQUEFACTION
CHARACTERISTICS
Wen LIU1, Shoji KAMAO2 and Kei YOKOYAMA1
1

2

Graduate Student, Department of Civil Engineering, Nihon University, JAPAN
Associate Professor, Department of Civil Engineering, Nihon University, JAPANABSTRACTThe 1995 Hanshin Earthquake confirmed that soil containing fine fraction would be liquefied. In manycases, the effect of fine fraction on liquefaction strength has not been clarified yet. In particularly, it is not
so many researches that have used samples containing a large amount of fine fraction, although the fine
fraction presents the predomination in its field. Therefore, the purpose is to clarify the effect of fine fraction
on the liquefaction strength of sand clay within the parameters of the skeleton void ratio. The fine fraction
content of the specimens has a distinct span from small to large. The coarse-grained soil used Toyoura sandwhich was mixed with 0~40% Kaolin clay in a dry state. The specimens (D=50mm H=100mm) are madeby using the dry tamping method. These specimens were used to do the undrained cyclic triaxial test. The
results of the tests show that when the skeleton void ratio does not exceed the maximum void ratio of sand,the fine fraction does not affect liquefaction strength regardless of its type. And the liquefaction strength of
the specimen has a certain relationship with the skeleton void ratio. When the skeleton void ratio exceeds
the maximum void ratio of sand, the fine fraction is in the remarkable position. And the liquefaction strength
has a good correlation with the fine fraction void ratio.
Key words: liquefaction strength, fines content, undrained cyclic test, sand clay

The purpose of this study is to clarify the effectof fines content on liquefaction characteristics byperforming undrained cyclic triaxial tests on
specimens with the same method from small tolarge fines content. In this study, the skeleton void
ratio es [5] is defined as the void ratio of all the finegrains in the sand as pore space. The skeleton void
ratio ef is defined as the porosity of clay only,
assuming that the silt content is part of the skeletal
structure. The void ratio eff is the void ratio that
ignores the volume of the coarse grains and focuses
only on the fine grains. It is an indicator of thedegree of grain blockage.

INTRODUCTION
The main grounds where seismic liquefaction
events occur are generally recognized as loosely
deposited, saturated sand grounds. However,liquefaction of fine fraction has been confirmed by
the 1995 Hanshin Earthquake [1]and the 2011 Great
East Japan Earthquake ， which caused severeliquefaction of reclaimed land in the Tokyo Bay
area from Shinkiba to Urayasu. And the researches
have been carried out to clarify the liquefaction
mechanism of fine fraction and its effects onliquefaction strength. As a result of these studies,
it was found that (1) The liquefaction strength
decreases with the increase of fine fraction content,
and there is a fine fraction content with the lowest
liquefaction strength [2]. (2) There is a goodcorrelation between the skeleton void ratio and theliquefaction strength, where a part of the fine grain
content is considered as a void [3],[4]. However,there is no unified view on the effect of fine
fraction of liquefaction strength, due to different
methods of density control and specimen
preparation among researchers. In particular, few
studies have been conducted on samples that
contain a large amount of fine fraction up to theregion where fine fraction are dominant. And theeffect of fine fraction on liquefaction strength in
the region where fine fraction are dominant has notbeen clarified.

EXPERIMENT OVERVIEW
In this study, undrained cyclic triaxial tests
were carried out to determine the liquefaction
strength of each sample. An overview of theexperiment is given below
SampleFor the all samples, Toyoura sand was used as
the coarse-grained material and Kaolin clay wasused as the fine fraction material. And the mixing
ratio (percentage of dry mass of fine fraction
material to total dry mass) was varied by 5% in therange of 0~40%. In order to examine the effect ofthe difference in the properties of the fine fraction,211

GEOMATE – Melbourne, Australia, 11-13 November 2020
Fujinomori clay was also used. And the sample of
Fujinomori clay with a mixing ratio of 5~15% wasused. Each soil mixture shall be referred to as K5
or F10, depending on the initials of the names ofthe fine fractions and the proportion of the mixture.
Each mixed soil was prepared by mixing eachsample in an air-dried state until it became
homogeneous in a container. The main physicalproperties and particle size distributions of eachsample are shown in Table 1 and Fig. 1.
As shown in Table 1, Kaolin clay andFujinomori clay are both low plasticity clays, but
as shown in the clay content, Fujinomori clay
contains more silt than Kaolin clay.

the B-value more than 0.95. After confirming thatthe B-value was more than 0.95, the material was
repeatedly loaded with a sinusoidal wave of 0.1 Hz
frequency at the prescribed cyclic stress ratio
R(=σd/2σc'). The test conditions for each sample
are shown in Table 2.
Table 2

Test conditions for cyclic triaxial tests

Table 1 Physical properties of the sample*

The Confining pressure of Consolidation is 100 kPa
The load repeats with a sinusoidal wave of 0.1 Hz frequency
at the specified cyclic stress ratio*

Density Management Method
In liquefaction studies, the relative density Dr isoften used as a parameter to express the density of
specimens, and the density is also controlled by the
relative density Dr in studies with samples
containing fine fraction. However, the applicable
scope of the "Minimum and Maximum Density
Test of Sand (JIS A 1224)" for calculating therelative density is defined as the sand that remains
more than 95% in a 75 μm sieve. And it is difficultand inappropriate to control the density by using
the relative density for a sample containing manyfine fraction such as the one used in this study.
Therefore, the minimum and maximum
densities of Toyoura sand (ρdmin=1.341 g/cm3 andρdmax=1.633 g/cm3) were used for liquefaction tests
by using only Toyoura sand in this study. In order
to consider the effect of density change, the relative
density Dr was adjusted to be 40%(Loose),
60%(Medium), and 80%(Density). And the void
ratio e0 was adjusted to 0.828(Loose),
0.757(Medium), and 0.684(Density) to be the samevalue as the void ratio e0 when the density was
adjusted by using Toyoura sand only, then theeffect of density change was considered.

Fig. 1 Particle size distribution curves the sample
Experimental Method
The specimen is a cylindrical sample of 5 cm indiameter and 10 cm in height. The specimens weredivided into ten equal parts and then divided into
ten layers, each layer of which was solidified witha rammer. After the specimens were prepared at apredetermined density, the specimens were firstallowed to stand on their own at a constraintpressure of 20 kPa, and then saturated by applying
200 kPa of back pressure with de-aired water,which is about three times the volume of thespecimens. Subsequently, consolidation was
performed with effective constraint pressure
σc'=100 kPa. The consolidation time was set asshown in Table 2 for each sample, taking into
account that the consolidation could be completed
sufficiently. After the consolidation was completed,the B-value was measured to confirm the saturation,and the back pressure was further applied to make212

GEOMATE – Melbourne, Australia, 11-13 November 2020

EXPERIMENT RESULTS
Liquefaction Strength Curve
Figure 2(a)~(c) shows the liquefaction test
results for each sample. It shows the relationshipbetween the cyclic stress ratio R and the number ofcycles N required to reach both amplitude axial
strains DA=5%. Fig. 2(a) shows the cyclic strength
curves for the liquefaction test by using Toyoura
sand only and for the cyclic triaxial test using
Fujinomori mixed soil with a constant density andvarying only the fines content. As the density of
Toyoura sand increases, the cyclic stress ratio at a
certain number of loading cycles increases, and thecurve moves upward, indicating that the resistanceto liquefaction becomes stronger. In addition,
Dense shows a tendency for the curves that arecommonly found in dense sands to rise.
Besides, the cyclic strength curve of
Fujinomori mixed soil moved to the bottom as thefines content increased, indicating that the soil's
resistance to liquefaction became weaker. Fig. 2(b)shows the cyclic strength curves for K5 to K15
with varying densities; as for K5, the curve moves
upward with increasing density, as in the case of
Toyoura sand, but it becomes weaker as the content
of K10, K15 and Kaolin clay increases. For thesame densities, the cyclic strength curve is locatedat the bottom of the curve as the fines content
increases. Fig. 2(c) shows the cyclic strength
curves for the cyclic triaxial tests using from K20
to K40.
It was found that the curve was located upward
for samples with higher fines content, and theresistance to liquefaction was higher.
Relationship Between Fine Particle Fraction
Content And Liquefaction Strength
In this study, the cyclic shear stress R was used
as "liquefaction strength R20" to indicate theresistance to liquefaction when both amplitudes ofstrain DA=5% were reached at 20 times of cyclicloading (shown as dashed line in Fig.2). Fig. 3
shows the relationship between the liquefaction
strength R20 and the fines content FC calculated
from the cyclic strength curve for each sample. The
fines content FC in the figure is equal to the ratioof fines content in the Kaolin mixed soil, but isslightly smaller than the ratio of fines content in the
Fujinomori mixed soil. The reason is that a small
amount of sand is contained in the Fujinomori clay.
The figure shows that the liquefaction strength of
the Kaolin mixed soil, Medium, decreases with theincrease in the fines content, and then increases
after the lowest liquefaction strength R20, is foundat FC=20%. In the range of FC=0~15%, theliquefaction strength R20 increased with rising

Fig. 2 Cyclic strength for each sample
density in all the samples, but the growth inliquefaction strength R20 became smaller withincreasing fines content, and the liquefaction
strength R20 in K15 was almost the same for Looseand Medium. The liquefaction strength of R20
decreased due to an increase in the fraction of fine213

GEOMATE – Melbourne, Australia, 11-13 November 2020
fraction in the soil, although it was slightly
different from that of Kaolin mixed soil.

Fig. 4

Fig. 3 Effect of fines content on liquefaction
strength R20
Relationship Between Various Void Ratios andFine Particle Content
When investigating the strength of a soil with agrain size intermediate between sand and clay,such as the sample used in this study, it is very
important to pay attention to the skeletal structureof the soil.
Therefore, in this study, with the void ratio e,
the effect of fine fraction on liquefaction properties
was considered using "Skeleton void ratio es, ef [4]"and "Fine fraction void ratio eff [6]". The skeletal
void ratio es is the void ratio when all fine fractions
are considered as pores, while the skeletal void
ratio ef is the void ratio When only clay isconsidered as a void, it was assumed that the silt
content contributes to the skeletal structure. The
fine fraction void ratio eff is a void ratio that ignoresthe volume of coarse-grains and focuses only on
the fine fraction, which is an indicator of how well
the fine fraction is plugged. The void ratios can beobtained by the following Eq. (1)~(3) using thesymbols shown in Fig. 4.
𝑉𝑉𝑣𝑣(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)(1)

𝑉𝑉𝑣𝑣(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) −𝑉𝑉𝑠𝑠(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)

(2)

𝑉𝑉𝑣𝑣

(3)

Skeleton void ratio 𝑒𝑒𝑠𝑠 = 𝑉𝑉𝑠𝑠(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)

Skeleton void ratio 𝑒𝑒𝑓𝑓 = 𝑉𝑉𝑠𝑠(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) +𝑉𝑉𝑠𝑠(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)

Fine fraction void ratio 𝑒𝑒𝑓𝑓𝑓𝑓 = 𝑉𝑉𝑠𝑠(𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐) +𝑉𝑉𝑠𝑠(𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)

214

Schematic of mixed samples

The changes in void ratio e, skeleton void ratio
es and ef, and fine fraction void ratio eff withincreasing fine fraction content in Medium ofKaolin mixed soils are shown in Fig. 5. The void
ratios in the figure are values immediately before
the cyclic loading process, and are calculated fromthe initial void ratio e0 of the specimen, the axial
displacement until the consolidation process, andthe consolidated drainage amount. From the figure,the void ratio e is smaller than the initial void ratioe0 (dashed line in the figure) in the range of
FC=0~25%. It decreased with the increase in fine
fraction content. This may be due to the fact thatthe fine fraction content in the specimens made in
the dry state shrinks due to the pore water and thevolume compression increases with the increase of
the fine fraction content.
Like the void ratio e, the void ratio of fine
fraction, eff, also tends to decrease with theincrease in the content of fine fraction same as thevoid ratio e. The void ratio decreases sharply in theregion with lower content of fine fraction, and thengradually decreases, ascending to the void ratio ofKaolin clay when it is compacted at 100kPa (single
dotted line in the figure).
On the other hand, the void ratio of bothskeletons is increasing due to the growth of fine
fraction content. The void ratio of both skeletonstends to increase rapidly from around FC=25%,
suggesting a structural change. The skeletal void
ratio es, exceeds the maximum void ratio of
Toyoura sand only (the two-dotted line in the
figure) at around FC=15%. From this, it isconsidered that before FC=15%, sand particles
form a skeleton, and fine fraction have a skeleton
structure that enters the gap formed by sandparticles. while after FC=15%, it is thought thatfine fraction form a skeleton between sandparticles or that sand particles are floating in thefine fraction.

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 5 Relationship between fines content andFig. 6 Relationship between void ratio e andliquefaction strength R20

various void ratios
Relationship Between Various Void Ratios And
Liquefaction Strength
The relationship between the void ratio e andthe liquefaction strength R20 is shown in Fig. 6. In
the figure, it can be seen that the volume
compression tends to increase with a looser initial
void ratio. And that liquefaction strength, R20,
decreases and then increases with an augmentationin the fine fraction content. However, there is no
direct relationship between the void ratio e and theliquefaction strength R20, since the liquefaction
strength varies greatly depending on the sample
even at the same void ratio.
Figures 3 and 5 show that the relationshipbetween the void ratio and the liquefaction strength
R20 decreases in the range of FC=0~20%, eventhough the density rises with increasing void ratio
e. This suggests that the decrease in liquefaction
strength R20 in the range of FC=0~20% is due tothe increase in the skeleton void ratio, and theskeleton void ratio may have a strong influence on
the liquefaction strength. The relationship between
the skeleton void ratio es and ef and the liquefaction
strength R20 of each sample with FC = 0~20% isshown in Fig. 7. The liquefaction strength of bothskeleton void ratio decreased with the increase in
the skeleton void ratio. However, the liquefaction
strength R20 varies in the small void ratio ef,
whereas the liquefaction strength es lies on thesame curve in all the void ratios, regardless ofwhether the soil is mixed with Kaolin or Fujinomiri,and it can be said that the void ratio es and theliquefaction strength R20 are categorically related.
It is found that in the range of FC=0~15%, theskeletal structure of the soil is formed by sand, andthe fine fraction content has no effect on theliquefaction strength, the liquefaction strength R20
is determined by the skeleton void ratio es.

Fig. 7 Relationship between Skeleton void ratio es,
ef and Liquefaction Strength R20

215

GEOMATE – Melbourne, Australia, 11-13 November 2020
Furthermore, from Figs 3 and 5, the skeletal
gap ratio es increases rapidly at FC=25% and since
the liquefaction strength R20 has also begun toincrease, in the skeletal structure of soil, fine
fraction cannot fit in the gaps of sand, it isconsidered that the sand is in a state of floating in
the fine fraction and has a skeletal structure withproperties similar to those of cohesive soil.
Therefore, it is presumed that the liquefaction
strength depends on the state of the fine fraction.
It is considered to be closely related to the fine
fraction void ratio eff. The relationship between thefine fraction void ratio eff and the liquefaction
strength R20 is shown in Fig. 8. The liquefaction
strength R20 of K100 (Kaolin clay only) in the
figure is taken from the study by Kuwano [2]. It is
the same as the one in the previous article. In Fig.
8, the liquefaction strength R20 is almost the same
regardless of the fine fraction content and density,
at FC=30% the liquefaction strength R20 increases
rapidly. From this, it can be said that FC=30-40%
sand particles floats in the fine fraction, and theliquefaction strength R20 has a good correlation
with the fine fraction void ratio eff. In the case of
FC=15~30%, the skeleton void ratio es exceeds the
maximum void ratio emax of Toyoura sand, butthere was no increase in the liquefaction strength
observed after FC=30%. In the range, it is possible
to judge it as an intermediate region between theregion where sand particles forms the skeleton andthe region where fine fraction is predominant.

(1) In the region with a skeletal void ratio es that
does not exceed the maximum void ratio of sandonly (es<emax), the fine fraction makes nodifference to the liquefaction strength. And theliquefaction strength R20 is categorically related tothe "Skeleton void ratio es", notwithstanding thetype of fine fraction.
(2) With a skeleton void ratio es above themaximum void ratio for sand only (es>emax), theliquefaction strength R20 has a good correlation
with the "Fine fraction void ratio eff" in the region
where the fine fraction is predominant.
(3) An intermediate region exists between these
two regions, and the liquefaction strength R20 isapproximately equal to the liquefaction strength
R20 when the skeleton void ratio es is total up to the
maximum void ratio of sand only.
REFERENCES[1] Kazama M., Kagatani T., Yanagisawa E.,
Peculiarities of liquefaction resistance ofmasado, Journal of JSCE No.645/Ⅲ-50
pp153-160, 2000. (In Japanese only)[2] Kuwano J., Iimura H., Nakazawa H.,
Sugihara K., Liquefaction strength of sandcontaining kaolin clay，50th JSCE Annual
meeting, pp.506-507, 1995. (In Japanese only)[3] Ishikawa S., Masayuki H., Ikuko S., RolandoO., Nakata Y., Norimasa Y., Cyclic shear
properties of non-plastic low plastic fine
fraction mixed soil, 42nd JGS Annual
meeting, pp.158-159, 2007. (In Japanese only)[4] Nabeshima Y., Nagasaw T., Tamotsu M.,
Evaluation of liquefaction strength of sands
containing fines based on clay fraction, 38th
JGS Annual meeting, pp.239-240, 2003. (InJapanese only)[5] Thevanayagam S., Shenthan T., Mohan S.,
Liang J., Undrained fragility of clean sands,
silty sands, and sandy silts, Journal of
geotechnicalandgeoenvironmental
engineering 128 (10), pp.849-859, 2002.
[6] Sato M., Oda M., Kazama H., Ozeki K.,
Fundamental study on the effect of fines onliquefaction strength of reclaimed ground,
Journal of JSCE No.561/Ⅲ-38, pp.271-282,
1997. (In Japanese only）

Fig. 8 Relationship between Fine fraction void
ratio eff and liquefaction strength R20
CONCLUSION
In this study, the effect of fine fraction content
on liquefaction characteristics was investigated byundrained cyclic triaxial test by using a mixture ofsamples with a constant void ratio e and varying
FC in the range of 0~40%. As a result, thefollowing findings were obtained.
216

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STUDY ON ROTATION ANGLE ESTIMATION METHOD OF UPLIFTFOUNDATION CONSIDERING PLASTICITY REGION
Yasuhide Mochida 1 and Makoto Minemoto2
Prof.Ritsumeikan University, Japan
2
Graduate School of Science and Engineering, Ritsumeikan University, Japan
1

ABSTRACT
Seismic stability calculation on the direct foundation of continuation is designed always and by the seismic
intensity method (level 1 ground motion), and it is not necessary to check horizontal load bearing capacity
method (level 2 ground motion). On the other hand, it is thought that a reasonable design method for a direct
foundation during a large-scale earthquake can be established by appropriately evaluating the non-linear
behavior caused by the floating of the end of the direct foundation. Therefore, a method for estimating therotation angle of a floating foundation considering nonlinear behavior during a large-scale earthquake wasproposed. At this time, as the evaluation at the time of plasticization, a uniform contact pressure was generated
by a linear function. However, finite element method (FEM) analysis and previous experiments have confirmedthat a high contact pressure is generated directly at the compression end of the foundation. In this study, we
improve the idea that the slope of the contact pressure is uniform, and propose an evaluation formula thatconsiders that a high contact pressure occurs at the compression end side. By improving the evaluation formula,
the state of the contact pressure can be made closer to the actual condition of foundation, and it is possible torealize an evaluation closer to an experiment.
Keywords: Four or five keywords (First characters of each keyword are in uppercase letters), Italic

INTRODUCTION
The moment footing base subgrade reaction
shows by the past nonlinear rotation angle
estimation method is expressed with that and a
resisting moment. Distribution of subgrade reaction
with a change in a resisting moment is considered asfollows. The moment which acts on a basic basefirst, the subgrade reaction state will be the trapezoid
distribution at the small stage. Because the tension
doesn't act on it between the basis and thefoundation when the moment which acts after thatexceeds uplift foundation limit moment 𝐌𝐌𝟎𝟎 theground reaction state will be the triangle
distribution, and leap RI occurs to a basis. A relation
between a resisting moment and the footing rotation
angle is expressed in the form of the formula (1)here. All over the formula (1) in the formula (2)reaches and is the formula (3), and basic uplift limit
moment 𝐌𝐌𝟎𝟎 and uplift limit rotation angle 𝐚𝐚𝟎𝟎 are
shown from an indicated relation.
𝐌𝐌
𝐁𝐁𝐁𝐁
(𝟏𝟏)
(𝟐𝟐)
𝐚𝐚 =
𝐌𝐌𝟎𝟎 =
𝟔𝟔
𝐈𝐈･𝐊𝐊 𝐯𝐯
𝟐𝟐𝟐𝟐
𝐚𝐚𝟎𝟎 = 𝟐𝟐(𝟑𝟑)
𝐁𝐁 𝐃𝐃𝐊𝐊 𝐯𝐯
𝐌𝐌：Resisting moment
𝐚𝐚 ：Footing rotation
angle
𝐈𝐈：Moment of inertia of area 𝐌𝐌𝟎𝟎 ：Uplift limit
moment𝐚𝐚𝟎𝟎 ：Uplift limit rotation N：Vertical force whichacts on a basic base
𝐊𝐊 𝐯𝐯 ：Vertical coefficient of subgrade reaction B：

It's said to be the one which doesn't need always
to come about stable calculation at the time of anearthquake in present spread foundation, design byseismic intensity method (level 1 earthquake
motion) and verify a ultimate lateral strength way(level 2 earthquake motion) at the time of anearthquake. This is because an energy absorption
could expect a basic edge by going up because there
was a margin to the load bearing capacity of thefoundation because spread foundation was generally
supported in the good ground, and I could think
excessive damage didn't occur to foundation. On theother hand, it's able to establish a rational design
way in the time of a large-scale earthquake of the
spread foundation to estimate nonlinear behavior byan edge's of such spread foundation going up uplift
appropriately. A rotation angle estimation method of
the uplift foundation which considered nonlinear
behavior in the time of a large-scale earthquake is
proposed by the past study.
But the situation that the high ground pressure
which isn't uniform in a compression edge by an
FEM and the past experiment occurs isn't considered.
The valuation plan which considered expensive
contact pressure distribution in the compressionedge using FEM and past formula are proposed bythis research.
PAST NONLINEAR BEHAVIOR VALUE IN
LARGE-SCALE EARTHQUAKE217

GEOMATE – Melbourne, Australia, 11-13 November 2020NONLINEAR BEHAVIOR EVALUATION
WHICH CONSIDERED THE PLASTIC LEVELOF THE FOUNDATION
When 𝑎𝑎/𝑎𝑎0 approaches ∞ by an estimation
method of the past nonlinear rotation angle, 𝑀𝑀/𝑀𝑀0
will be the curved line which approaches 3. Thismeans that the action location of the resultant
approaches an edge of a footing, but plasticity
occurs to the ground in a basic edge at the real
ground, and I approach the load of the ultimate
bearing power state finally. The state that groundreaction in a basic edge reached limit bearing unit
capacity of foundation for the first time is set as thesurrender critical state(Fig.3 𝑀𝑀 = 𝑀𝑀1 ) so also to
consider plasticity of this foundation by this research,
and we assume that the ground reaction distribution
after that will be the trapezoid distribution again, andafter that, it is closer in the ultimate bearing capacity
state(Fig.3 𝑀𝑀 = 𝑀𝑀2 ).

Fig.3 Change in the vertical subgrade reaction
distribution which considered a plastic area
The formula (9) reaches and it's the formula (10)respectively from vertical balance, and the surrendercritical state and the subgrade reaction action width
which can be put in the ultimate bearing power state
can show here.
𝟐𝟐𝟐𝟐
(𝟗𝟗)
𝒙𝒙𝟏𝟏 =
𝒙𝒙𝟐𝟐 =
𝑵𝑵

𝑫𝑫𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎

𝑫𝑫𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎(𝟏𝟏𝟏𝟏)

The formula (11) reaches and is the formula (12),and the surrender critical state and the resisting
moment which can be put in the ultimate bearing
power state are shown.
𝑩𝑩
𝟐𝟐𝟐𝟐(𝟏𝟏𝟏𝟏)
𝑴𝑴 𝟏𝟏 = 𝑵𝑵 � −�
𝟐𝟐 𝟑𝟑𝟑𝟑𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎
𝑩𝑩𝑵𝑵(𝟏𝟏𝟏𝟏)
𝑴𝑴𝟐𝟐 = 𝑵𝑵 � −�
𝟐𝟐 𝟐𝟐𝟐𝟐𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎
When we assume that the subgrade reaction
distribution will be the trapezoid distribution aftersurrender critical state arrival again, the relation
which is indicated by the formula (13) in groundreaction action width 𝑿𝑿𝟏𝟏 , 𝑿𝑿𝟐𝟐 at this time andsubgrade reaction action width x_2 which can be putin the ultimate bearing capacity state stands up.
(13)
2𝑥𝑥2 = 2𝑋𝑋1 + 𝑋𝑋2
When an indicated relation is used for the formula(13), the resisting moment which can be put behindfoundation plasticity is expressed in the formula𝛔𝛔𝐦𝐦𝐦𝐦𝐦𝐦

𝛔𝛔𝐦𝐦𝐦𝐦𝐦𝐦

Basic width of the horizontal load direction of
application
D：Basic width which crosses at right angles inhorizontal load direction of application
A basic rotation is considered only at the part I do
subgrade reaction work of in uplift starting later. The
formula (4) reaches and is the formula (5), androtation angle which answers to a resisting moment
and that at this time is shown.
𝑩𝑩 𝒙𝒙𝒂𝒂
𝑴𝑴 = 𝑵𝑵 � − � (𝟒𝟒)
𝟐𝟐 𝟑𝟑
𝟐𝟐𝟐𝟐
(𝟓𝟓)
= 𝟐𝟐
𝒙𝒙 𝑫𝑫𝑲𝑲𝒗𝒗
The subgrade reaction action width which is x:
horizontal load direction of application here.
The one which normalized by uplift limit
moment 𝑀𝑀0 and uplift limit rotation angle 𝑎𝑎0 is
indicated in the formula (6) to show a relation
between a resisting moment and the rotation angle
irrespective of the basic size to an action of the load
beyond uplift ascent limit moment 𝑀𝑀0 .
𝑴𝑴
𝒂𝒂𝟎𝟎
(𝟔𝟔)
= 𝟑𝟑 − 𝟐𝟐�𝑴𝑴𝟎𝟎𝒂𝒂
Further, 𝑀𝑀/𝑀𝑀0 shows the weight location of thesubgrade reaction distribution, and a relation withthe weight location of the triangle shows that it's the
uplift critical state in 𝑀𝑀/𝑀𝑀0 = 1.
At this time, contact pressure distribution is
asked as the distribution of the primary windingshaped in conformity with the trapezoid distribution
and the triangle distribution, etc. Calculation
formula of the maximum ground contact pressure isindicated by trapezoid distribution formula (7) andtriangle distribution formula (8).
𝝈𝝈𝒎𝒎𝒎𝒎𝒎𝒎
𝟐𝟐𝟐𝟐(∑𝒏𝒏𝒊𝒊=𝟏𝟏 𝒂𝒂𝒊𝒊 + 𝒚𝒚)(𝟕𝟕)= 𝒏𝒏−𝟏𝟏∑𝒊𝒊=𝟏𝟏 𝒂𝒂𝒊𝒊 𝒃𝒃𝒊𝒊 (𝟐𝟐 ∑𝒏𝒏𝒌𝒌=𝟏𝟏 𝒂𝒂𝒌𝒌 − 𝒂𝒂𝒊𝒊 + 𝟐𝟐𝟐𝟐)
𝝈𝝈𝒎𝒎𝒎𝒎𝒎𝒎
𝟐𝟐𝟐𝟐(∑𝒏𝒏𝒊𝒊=𝟏𝟏 𝒂𝒂𝒊𝒊 − 𝒚𝒚)= 𝒏𝒏−𝟏𝟏𝒏𝒏∑𝒊𝒊=𝟏𝟏 𝒂𝒂𝒊𝒊 𝒃𝒃𝒊𝒊 (𝟐𝟐 ∑𝒌𝒌=𝟏𝟏 𝒂𝒂𝒌𝒌 − 𝒂𝒂𝒊𝒊 − 𝟐𝟐𝟐𝟐) + (𝒂𝒂𝒏𝒏 − 𝒚𝒚)𝟐𝟐 𝒃𝒃𝒏𝒏
(𝟖𝟖)
𝒂𝒂𝒊𝒊 ：Basic width of the eccentricity direction where
B is divided into the optional width𝒃𝒃𝒊𝒊 ：Optional basic width of the crossing at rightangles which corresponds to 𝒂𝒂𝒊𝒊 direction
𝐲𝐲 ：The distance of the point that the groundpressure will be 0 from a compression opposite edge

Fig.1 Trapezoid ground Fig.2 Triangle groundpressure distributionpressure distribution

218

GEOMATE – Melbourne, Australia, 11-13 November 2020(14).

𝐁𝐁 𝟏𝟏𝟏𝟏𝒙𝒙𝟐𝟐 𝟐𝟐 + 𝑿𝑿𝟐𝟐 𝟐𝟐
𝑴𝑴 = 𝑵𝑵 � −�𝟐𝟐𝟐𝟐𝟐𝟐𝒙𝒙𝟐𝟐

foundation which can be put behind foundation
plasticity by solving the ceremony derived from theformula (20) using the formula of the solution (21).

(𝟏𝟏𝟏𝟏)

The formula (15) reaches, and the moment which
thinks a basic revolution occurs only by subgrade
reaction in 𝑿𝑿𝟐𝟐 part and exerts a geometrical momentof inertia and a revolution at this time is the formula(16), and the rotation angle of the basis is expressedin the formula (17).
𝑿𝑿𝟐𝟐 𝟑𝟑 𝑫𝑫(𝟏𝟏𝟏𝟏)𝑰𝑰𝑿𝑿𝟐𝟐 =𝟏𝟏𝟏𝟏
𝟐𝟐
𝑿𝑿𝟐𝟐 𝑫𝑫𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎(𝟏𝟏𝟔𝟔)𝑴𝑴𝑿𝑿𝟐𝟐 =𝟏𝟏𝟏𝟏
𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎(𝟏𝟏𝟕𝟕)𝒂𝒂 =𝑲𝑲𝒗𝒗𝒗𝒗 𝑿𝑿𝟐𝟐

𝒂𝒂𝑵𝑵
𝒂𝒂𝟎𝟎

�

𝟏𝟏

𝒂𝒂𝟏𝟏
𝒂𝒂𝟎𝟎

�

𝒂𝒂
𝒂𝒂
� 𝒅𝒅𝒅𝒅
𝒂𝒂𝟎𝟎
𝒂𝒂𝟎𝟎

𝒂𝒂𝟎𝟎
𝒂𝒂
= � �𝟑𝟑 − 𝟐𝟐�� 𝒅𝒅𝒅𝒅
𝒂𝒂
𝒂𝒂
𝟎𝟎
𝟏𝟏
+�

𝒂𝒂𝑵𝑵
𝒂𝒂𝟎𝟎

𝒂𝒂𝟏𝟏
𝒂𝒂𝟎𝟎

�

𝛂𝛂 �

Fig.4 Ground reaction force from M₁ to M₂

𝑴𝑴𝟐𝟐
𝑩𝑩𝟑𝟑 𝑫𝑫𝟑𝟑 𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎 𝟑𝟑𝒂𝒂−� 𝒅𝒅𝒅𝒅𝟑𝟑
𝟐𝟐𝑴𝑴𝟎𝟎 𝟏𝟏𝟔𝟔𝑵𝑵 (𝒂𝒂/𝒂𝒂𝟎𝟎 )
𝒂𝒂𝟎𝟎

𝒂𝒂 𝟐𝟐
𝒂𝒂
� + 𝜷𝜷 � � + 𝜸𝜸 = 𝟎𝟎
𝒂𝒂𝟎𝟎
𝒂𝒂𝟎𝟎
−𝛽𝛽 + �𝛽𝛽 2 − 4α𝛾𝛾
𝑎𝑎 =
𝑎𝑎0
2α

FEM ANALYSIS OUTLINE

Relation of the 𝑴𝑴/𝑴𝑴𝟎𝟎 ~𝒂𝒂/𝒂𝒂𝟎𝟎 which can be
put behind foundation plasticity is expressed in theformula (18) finally here.
𝑀𝑀
𝑀𝑀2 𝐵𝐵3 𝐷𝐷3 𝑞𝑞𝑚𝑚𝑚𝑚𝑚𝑚 3
=
−
(18)
𝑎𝑎 2
𝑀𝑀0 𝑀𝑀0
16𝑁𝑁 3 �𝑎𝑎 �
0(𝟏𝟏𝟗𝟗)(𝟐𝟐𝟐𝟐)(21)

To find the ground pressure you can't estimate bya valuation plan of existence, to make the FEM
analysis model which can express basic rising
according to real ground, reproduction of level load
testing is tried. Experiment was the one which added
the horizontal force until considerable rising
installed model foundation on the artificial ground,and formed, and subgrade reaction and the rotation
angle were recorded. A flow of experimental
reproduction by an FEM is indicated on figure 6.
The plate bearing test put into effect targeted for theexperimental artificial ground first is reproduced bya vertical loading analysis.

ESTIMATIONOFTHENONLINEARROTATION ANGLE WHICH CONSIDERED A
PLASTIC AREA

We assumed thatthe rotation angle
ofthespreadfoundation which
can be put behindfoundation
plasticity applies aregulationofenergy to a relationof 𝑴𝑴/𝑴𝑴𝟎𝟎 ~𝒂𝒂/𝒂𝒂𝟎𝟎
and presumes. This
Fig.5 Estimation of theway of thinking is nonlinear rotation angle usinga regulation of energy
the way of thinking
with which transformation of elastoplastic bodyproceeds until the occasion which thought a spring
was linear and the transformable energy when aspring has the nature of the elastoplasticity, become
equivalent（Vertical line area = Slanted line area）.
It's to apply a regulation of energy to a relation of
the uplift curved line which prescribed the 𝑴𝑴𝑳𝑳 whichshows a basic linear reply in alignment_large andconsidered the upper limit value of these linear reply
and degree of subgrade reaction through the starting
point and the point which is the uplift limit by thisresearch, and I find the rotation angle. When it's
possible to express this relation in the formula (19),and this is put in order, it's possible to get a quadratic
equation of the form as shown in the formula (20).
It's possible to get the rotation angle of the spread

Fig.6 FEM Experimental Reproduction flow
VERTICAL LOADING ANALYSIS BY FEM
FEM analysis model is built so that a load
sinking relation like flat plate loading test result may
be indicated.
(1) Analysis model outline
An analysis model of a vertical load analysis isindicated on figure 7. The loading plate is madestrength. The physical properties value of the groundmodel in an early stage is indicated in table 1. The
physical properties value of the foundation isconsulting an experiment and uses a correction R-Omodel for nonlinear characteristics. The system of
the correction R-O model is shown as follows.
𝐆𝐆
𝐆𝐆=
𝛃𝛃 (22)𝐆𝐆𝟎𝟎

219

𝟏𝟏+𝛂𝛂|𝛄𝛄𝛄𝛄|

GEOMATE – Melbourne, Australia, 11-13 November 2020

between the test results and the analytical results.
Since it was not possible to obtain the same
subsidence curve with the properties shown in Table
1 as in the flat-plate loading test, it was repeated byadjusting the values of 𝛄𝛄𝟎𝟎.𝟓𝟓 and 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 were
adjusted. Finally, the highest value of 𝛄𝛄𝟎𝟎.𝟓𝟓 =𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 and 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐 was obtained for thetest results. Close load-settlement relationships areshown.

P
2500
7000m
Fig.7 Vertical loading analysis
𝛂𝛂 = �

𝟐𝟐

𝛄𝛄𝟎𝟎.𝟓𝟓 𝐆𝐆𝟎𝟎

𝛃𝛃𝟐𝟐𝟐𝟐𝐡𝐡𝛃𝛃 = 𝟐𝟐−𝛑𝛑𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦

� (23)(24)

𝐦𝐦𝐦𝐦𝐦𝐦HORIZONTAL LOADING ANALYSIS BY FEM
The foundation weight of 2.7t with a clear
rotation angle is analyzed as Case 1, and thefoundation weight of 3.4t with a recorded groundreaction force distribution is analyzed as Case 2. t as
Case 1 and 3.4t as Case 2, where the ground reaction
force distribution is recorded. Repeatability ischecked by comparing the rotation angle and neutral
axis position.
(1) Outline of the analysis model
The analytical model for horizontal loading

At this point, 𝐆𝐆 : modulus of rigidity, 𝐆𝐆𝟎𝟎 early
item

symbolphysicalproperty
16.17

unit

unit weightγ
kN/㎥
modulus of
20.05
𝑬𝑬𝟎𝟎
MN/㎡
elasticity
modulus of
7.71
𝑮𝑮𝟎𝟎
MN/㎡
elasticity of shear
Poisson's ratio
ν
0.3
Table.1power
Soil model characteristics
adhesiveC
kN/㎡(initial)20
Internal friction
Φ
deg
28.8
angle
Reference strain0.001
𝜸𝜸𝟎𝟎.𝟓𝟓
Maximum
0.21
𝒉𝒉𝒎𝒎𝒎𝒎𝒎𝒎
attenuation constant

900mm
1000m

stage modulus of rigidity, 𝛄𝛄 : The shearing strain,
𝛄𝛄𝟎𝟎.𝟓𝟓 : standard distortion（The shearing strain when
𝐆𝐆⁄𝐆𝐆𝟎𝟎 will be 0.5）, 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 : biggest damping
constant. Among these it's𝐆𝐆𝟎𝟎 , 𝛄𝛄𝟎𝟎.𝟓𝟓 , 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 to have toestablish it, and 𝐆𝐆𝟎𝟎 presumes from the early stage
gradient of the flat plate loading test. 𝛄𝛄𝟎𝟎.𝟓𝟓 , 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 uses
mean in a sandy soil. The boundary condition fixes abase of ground model and makes the side a verticalroller. A loading plate and a boundary in the groundinclude the nonlinear connection element even alateral loading analysis uses. The input load of thevertical loading analysis is indicated in inside front.
Incremental analysis is performed using 7 loads
recorded by a test.
(2) Analysis result
The settlement of the ground just below the load

2500

Fig.9

1
1.98
1.98

2
2.75
4.73

3
4
5.23
6.21
9.96 16.17

4

5
6
7
4.43
4.11
4.92
20.6 24.71 29.63
10

3.5

2
試験結果

1.5
1

荷重（kgf/㎠）

荷重（kgf/㎠）

3
2.5

0.1

1

Detachment
1.0

10.0

初期解析結果

試験結果

最終解析結果初期解析結果100.0

最終解析結果

0.5
0

7000m
Horizontal loading model

analysis is shown in Figure 4. The model foundation
is assumed to be rigid, and the physical properties of
the ground are 𝛄𝛄𝟎𝟎.𝟓𝟓 , 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 is obtained from thevertical loading analysis, and The boundary
conditions are the same as in the vertical loading
analysis, and the values in Table 1 are used in othercases. The boundary conditions are the same as in
the vertical loading analysis, and Figure 5 shows thecharacteristics of nonlinear connecting elements atthe boundary between the model foundation and theground. The loadings in Case 1 and Case 2 areassumed to be horizontal loads. Table 3 shows the
input loads for the horizontal loading analysis,
where the loads in Case 1 and Case 2 are assumed tobe the horizontal loads for the incremental analysis.
do.
Detachment

Table.2 Lead straight load analysis of input
Step No.
⊿P(kN)
P(kN)

P

0.0

5.0

10.0
沈下量（mm）

15.0

20.0

0.1

closure

沈下量（mm）(a) arithmetic scale (b) logarithmic scale
Fig.8 Flat Plate Load Test Results and(a) axial direction

Fig.10plate at each loading was determined and comparedwith the test results. Figure 3 shows a comparison
220(b) shear direction

nonlinear connection element
characteristics

GEOMATE – Melbourne, Australia, 11-13 November 2020

𝟑𝟑

(26)
𝛂𝛂ｙ + 𝛃𝛃𝐲𝐲 𝟐𝟐 + 𝛄𝛄ｙ + 𝐜𝐜 = 𝟎𝟎
where y: the distance from the opposite edge of thecompressed edge to the neutral axis, 𝛂𝛂, 𝛃𝛃, 𝛄𝛄, 𝐜𝐜: from𝐚𝐚𝐧𝐧 , 𝐛𝐛𝐧𝐧 and d coefficients (shown in Table 4), n: thenumber of foundation divisions, 𝐚𝐚𝐧𝐧 , 𝐛𝐛𝐧𝐧 : the width of
the nth division of the foundation and Depth, d: thedistance from the compression edge to the locationof eccentric load action. The solution to equation(25) is a trapezoidal distribution with the neutral axis
outside the base, and the solution to equation (26) isa triangle with the neutral axis in the baseRepresents the ground pressure distribution. Groundpressure is obtained using formulas (7) and (8).
The calculation was carried out by dividing thebottom of the model foundation into 6 compartmentsof 150×900mm (n=6). Table 5 shows the calculation
results of the neutral axis and maximum groundpressure at each load, the trapezoidal distribution at
3.7kN, and the maximum ground pressure at 3.7kN.
The distribution of ground pressure was triangular
except for the following two points. Overall, theneutral axis of the ground pressure distribution wascloser to the experimental data than the FEM results,
but the ground pressure tended to be smaller than theexperimental value, and the ground pressure waslower than the experimental value. The simple
method was used to calculate the ground pressure
distribution. In the experiments and FEM, theground pressure tended to increase at the foundation
end, and the simplified calculation method showedthat the ground pressure distribution was higher than
the ground pressure distribution at the foundationend. The neutral axis position is assumed to be(2) Analysis results
Table.3 Comparison of experimental andanalytical results

Table 3 shows a comparison of the experimental
and analytical results. The distribution of groundreaction forces in the experiment is rough, recorded
for each of the six pressure receiving plates, so a
linear approximation was used to determine the The
approximate neutral axis position was determined.
Neutral axis and rotation angle were not close to theexperimental values in the ground properties
obtained by vertical loading analysis .
The adjusted values in Table 4 are the nonlinear
properties of the subgrade model modified so thatthe rotation angle matches the experimental values,
and 𝐆𝐆𝟎𝟎 = 𝟏𝟏𝟏𝟏 𝐌𝐌𝐌𝐌⁄𝐦𝐦𝟐𝟐 , 𝛄𝛄𝟎𝟎.𝟓𝟓 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎, 𝐡𝐡𝐦𝐦𝐦𝐦𝐦𝐦 =𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐. As a result of the investigation of the neutral
axis by changing the physical properties of theground, the neutral axis position of the FEM waslower than that of the experimental value. A
tendency to decrease in size was observed. Based on
the above, we give priority to reproduction bycomparison of the rotation angle. Figure 7 shows therelationship between the experimental and FEM
resistive moments and the rotation angle.

Table.4 General formula for eachcoefficient in the basic division number nαFig.11
Case1.M-θβγｃlinear, so the neutral axis position is considered to besmall.
CONSIDERATION OF GROUND PRESSURE
CORRECTION VALUE

We were able to create an analytical model thatshows a rotation angle close to the experimental
values, and we can say that the FEM was able toreplicate the lifting of the foundation.

Table.5 Simplified calculation results

COMPARISON OF EXPERIMENTAL AND
FEM
ANALYSISANDSIMPLECALCULATION METHODS
The ground pressure calculation method is amethod to calculate the neutral axis by dividing thebasement into arbitrary number of n compartments
and solving the linear and cubic equations shown
below.
𝛄𝛄𝛄𝛄 + 𝐜𝐜 = 𝟎𝟎
(25)

Calculate the correction value of the maximum
ground pressure to express the increase in the
ground pressure at the edge using the simple
calculation method. The correction factor isdetermined by comparing the ground contact
221

GEOMATE – Melbourne, Australia, 11-13 November 2020𝐩𝐩 ≒ 𝟏𝟏. 𝟑𝟑𝟑𝟑 and , 𝐪𝐪 ≒ 𝟎𝟎. 𝟗𝟗𝟗𝟗, regardless of the physicalproperties of the ground It is possible to obtain the
ground pressure distribution that takes into accountpressure distribution and the experimental groundreaction force distribution. Figure 12 shows therelationship between the ground pressure
distribution and the corrected ground pressure
distribution based on the results of the FEM
analysis. Since the ground pressure distribution
tended to increase from the position of 𝒂𝒂𝒄𝒄𝒄𝒄 =𝟏𝟏⁄𝟔𝟔 𝑩𝑩, Figure 12 shows that the Assume that thechange in ground pressure increases at the positionof 𝒂𝒂𝒄𝒄𝒄𝒄 , as shown in the figure. Assuming that thecorrection factor for the maximum ground contactpressure 𝝈𝝈𝒎𝒎𝒎𝒎𝒎𝒎 at the compression side is p and thecorrection factor for the ground contact pressure 𝝈𝝈𝒄𝒄𝒄𝒄
at the change point 𝒂𝒂𝒄𝒄𝒄𝒄 is q, and the corrected
ground contact pressure distribution is balanced withthe vertical load N, the following equation is
obtained.
𝟐𝟐𝟐𝟐
𝒂𝒂𝒄𝒄𝒄𝒄 𝝈𝝈𝒎𝒎𝒎𝒎𝒎𝒎 𝐩𝐩 + (𝑩𝑩 − 𝒚𝒚𝒆𝒆 )𝝈𝝈𝒄𝒄 𝐪𝐪 =(27)
𝑎𝑎1 [𝑏1 ]𝑎𝑎𝑐𝑝

𝜎𝑚𝑚𝑚𝑚𝑚𝑚

𝑉𝑒1

EXPERIMENTALEVALUATION

𝑦𝑒

𝜎𝑐𝑝
𝜎1
𝑞𝑞𝜎𝑐𝑝

𝑦

𝑎𝑎𝑐𝑝 :圧縮側端部から接地圧変化点までの距離
𝜎𝑐𝑝 : 𝑎𝑎𝑐𝑝 における接地圧
𝑝:𝜎𝑚𝑚𝑚𝑚𝑚𝑚 の補正係数
𝑞𝑞: 𝜎𝑐𝑝 の補正係数
𝑉𝑒1 :実験地盤反力
𝑦𝑒 :実験中立軸位置

CONCLUSIONSIn the present study, the critical bearing capacity of
the ground is taken into account to ensure the highaccuracy of raising foundations even after groundplasticity. The nonlinear rotation angle andmaximum ground pressure can be estimated.
However, in this study, the dynamic inertia forces
are calculated by replacing them with static loads, so
the behavior of the earthquake Therefore, we believe
there is room for improvement in the proposed
estimation method. Therefore, we believe that there
is room for improvement in the proposed estimation
method.

𝑝𝜎𝑚𝑚𝑚𝑚𝑚𝑚
Fig.12Concept of corrected groundpressure distribution

𝑩𝑩−𝒚𝒚 −𝒂𝒂𝒂𝒂𝒄𝒄𝒄𝒄 𝝈𝝈𝒎𝒎𝒎𝒎𝒎𝒎 𝐩𝐩 + �𝒂𝒂𝟏𝟏 + 𝑩𝑩−𝒚𝒚 𝒆𝒆−𝒂𝒂 𝟏𝟏 �𝒂𝒂𝟏𝟏 − 𝒂𝒂𝒄𝒄𝒄𝒄 �� 𝝈𝝈𝒄𝒄𝒄𝒄 𝐪𝐪 =
𝒆𝒆

𝒄𝒄𝒄𝒄

AND

With regard to the rotation angle, the calculated
value based on the estimation method obtained in (19)(21) and the rotation angle of the foundation obtainedby the experiment We compared the calculated values
by the inferred method with the experimental values.
Before geotechnical soil stabilization, there is no
significant difference between the experimental andcalculated values of the existing estimation methods.
After geotechnical plasticization, the difference
between the experimental and the predicted values islarge as the resistance moment increases. However, byapplying the estimation method that takes into accountthe critical bearing capacity of the ground, it was foundthat after the ground was plastised, there was atendency to become In addition, relatively highaccuracy was recorded in (27) and (28).
The equations (27) and (28) for the maximum
ground pressure distribution are more accurate than theexisting equations.

𝒃𝒃𝟏𝟏

𝑁𝑁

DISCUSSION

𝟐𝟐𝑽𝑽𝒆𝒆𝒆𝒆
𝒃𝒃𝟏𝟏

(28)

Here, except for p and q, we use the simplified
calculation method and experimental results. Using
equations (27) and (28), the correction factor for
Case2P=3.72kN and P=5.00kN is calculated and
The p and q values of the two were close, 𝐩𝐩 ≒ 𝟏𝟏. 𝟑𝟑𝟑𝟑
and 𝐪𝐪 ≒ 𝟎𝟎. 𝟗𝟗𝟗𝟗 . Verification of p and q values
Therefore, the maximum corrected ground pressure
at P=9.34 kN and the corrected neutral axis were
found to be The ground pressure was 189 kN⁄m^2
and the corrected neutral axis was 440 mm, and theneutral axis agreed with the experimental values.
The neutral axis is consistent with the experimental
values. Since the maximum ground contact pressure
exceeded the limit bearing capacity at 70 kN, themaximum ground contact pressure after correction
was also calculated in accordance with the actual
condition. In addition, it can be said that the effect ofground stiffness and nonlinearity on the maximum
ground contact pressure is small, as shown in Figure
11. The ground pressure increase at the edge of theground due to the ground pressure correction value

REFERENCES[1] Nishimura,A and Haya,H : Study on Design Methodof Direct Foundation Considering Plasticity Range,
Report of Railway Research Institute, 1992
[2] Ueda,K: Simple Calculation Method for Ground
Pressure Distribution Based on the Interaction between
a Rigid Foundation Base and the Ground , 2014.9[3] Committee on Design Examples of Highway Bridges:
Design Calculation Examples of Road Bridges,
2000.11[4] Furuyamada,K and Miyamoto,Y and Miura,K: Surface
soils evaluated from in-situ sampling data at multiple
sites. Nonlinear Characteristics of the GeotechnicalEngineering, The 38th Annual Conference of the
Geotechnical Engineering Society of Japan, 2003.7.

222

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE EFFECT OF CEMENT ADDITIVE TO IMPROVE STRENGTH
OF GRATI SOFT SOIL
Yulvi Zaika1, Harimurti2, Adista Fahara3, Nisa Safira4, Wahid Darmawan5
1,2,3,4,5
Engineering Faculty, University of Brawijaya, Indonesia
ABSTRACTSoft soils identified as soil with high compression and low strength in high water content. The various methods
are carried out to decrease the compressibility and increase the carrying capacity. The use of Portland Cement(PC) as a soft soil stabilization material has been known for a long time. Although in terms of cost is higher thanlime or waste material such as fly ash, but the content of the compound in cement is complete and the quality ismore assured, thus cement is still very reliable. This research aims to investigate the improvement in the strengthof soft soils after mix additives cement and changes in physical properties. Laboratory experiments will be carriedout such as physical properties test, compaction, and CBR tests to determine semen content of 5%, 8%, 12%, and15%. Furthermore, triaxial and consolidation tests will also be conducted to investigate several aspects related tostrength such as curing time and the percentage of improved soil on site. Based on the results of the triaxial test,
there was an improvement in the shear strength of the soil with an increase in the friction angle in the soil but adecrease in cohesion. The shear strength and stiffness is directly proportional to the percentage of PC in the soil.
Keyword: cement, remedial, soft soil, strength
using PVD, remediation of soil to increase thestrength. The PVD which is very much discussed byseveral experts [1-4]. PVD usage is discussed in thecase of road structures with high embankment.
Numerical analysis using finite element program todetermine the behaviour of soft soils after PVD isinstalled by assume as a channel that havepermeability coefficients.
The method which is widely used to improve thebearing capacity of soils and reduce compressibility
by using additive material. The additives that havebeen highly recommended are PC and lime. Some
researchers conducted experiments to mix the lime
for different soft soils such as in Malaysia [5] andEgipt [6-7] . From this study it is known that curing
time [8] is can significantly increase soil strength.
The use PC to improve the stability of soft soils has
also been widely investigated by researcher [7-10]
which can be done on the improvement of shallow
or deep soils by using the deep soil mixing (DSM)
method[11].
The purpose of this study is to investigate
physical and especially mechanical properties of
cement improved soft soil

IINTRODUCTION
Kedaung Wetan Village is a village that is partof the Grati sub-district, Pasuruan, East Java,
Indonesia. Grati is one of the districts that is passedby the Gempol Pasuruan Toll Road (Fig.1) which ispart of the Trans Java Toll Road on the toll road thatconnects Grati and Tongas which has a length of
13.5 km.

SampleFig. 1. Location of soil sample
SPT data shows that soils up to 12 m deep are very
soft soils and 12 m to 16 m are categorized as soft
soils, where previously they were paddy fields. The
soil require to be improved to sustain the pavement
structure and vehicles.
Soft soil problems are found in various places in
the world, and the improvement technology is also
developing very fast. There are some alternatives
methodology such as accelerate consolidation by

EXPERIMENTAL INVESTIGATION
Soil Sample
Soil sample is hight-plasticity silt soils with low
shear strength, which classified as soft soils. The
properties of soil can be shown in Table 1[12]223GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1. Physical characteristic of soil sample
Parameter
Liquid Limit
Plastic Limit
Shrinkage Limit
Saturated Density
Coefficient of Permeability
Maximum Dry Density
Optimum Water Content
Coefficient Consolidation
Coefficient Compression

Unit
%
%
%
kN/m3
cm/dt
kN/m3
%
cm2/dtwhere silica and alumina in the soil will react withcalcium silicate hydrate. This formation occurs
continuously in a long time and causes the soil tobecome hard.
Pozzolan reaction is a chemical reaction that
occurs in PC after the addition of pozzolan.
Pozzolan reaction changes the conversion of silicarich precursors without cementing properties, tocalcium silicate, with good cementing properties.In
chemical terms, a pozzolanic reaction occurs
between calcium hydroxide, also known as
portlandite (Ca (OH)2), and silicate acid (written as
H4SiO4 or as Si (OH)4) as Eq.2 :

Value
56,12
43,36
11,86
17,21
0.00037
12,46
31
0,001
0.445

Based on [12], the compressive strength of naturalsoil are depended on moisture content of soil in suchas Eq. (1)qu = -6.6  + 382.9(kPa)

Ca (OH) 2 + H4SiO4 → CaH2SiO4 · 2 H2O

(2)

or

(1)

CH + SH → C-S-H

where  is the water content in percent. The soil will
be in soft condition (25 <qu <10 kPa) at 54.2 <<56.5% water content and in very soft condition (qu<10 kPa) at water content more than 56,5.2%.

Product CaH2SiO4 · 2 H2O is calcium silicate
hydrate, also abbreviated C-S-H in the chemical
notation of cement,
Pozzolan reaction is a long-term reaction, which
involves dissolved silicic acid, water and CaO or Ca(OH)2 or other pozzolan to form a strong
cementation matrix. This process is often
irreversible. A high amount of free calcium ions andhigh pH 12 and above is required to start andmaintain a pozzolanic reaction. This is because at a
pH of around 12, the solubility of silicon andallumina ions is high enough to support the
pozzolanic reaction.

Methodology Testing
Laboratory experiments conducted to investigatechanges in the natural soil mixed with PC ofphysical and mechanical properties. The key tosuccess in getting the maximum strength is todetermine the percentage of additives that are mostrepresentative by considering various aspects that
influence them. The compaction test was carried out
to find out the optimum water content of eachpercentage of additive replacement that is 5%, 8%,10%, 12% and 15%. Every dosage of remedial soil
will be investigated by plasticity, unconfine
compressive, CBR and triaxial test. CBRexperiments will be carried out under submerged
and unsubmerged conditions to investigate whether
the remedial soil is retained to the water. The triaxial
and unconfined compression tests were run toobtain the shear strength parameter. The
experiments were also carried out after the samples
were treated for 4, 7, 14 and 28 days.

Table 2. Soil Plasticity
Soil
Composition

LiquidLimit(%)

Plastic
Limit(%)

Plasticity
Index
(%)

Natural Soil
5%
8%
10%
12%
15 %

56,12
53,58
51,56
51,05
50,05
46,18

43,36
41,69
41,55
41,41
41,24
41

12,75
11,89
10,05
9,63
8,8
5,18

The presence of water in soil pores has an effecton the technical properties of fine-grained soils
which are defined within the boundary that
distinguishes the properties of the soil, namely theliquid limit as the lower limit of viscous flow, theplastic limit as the lower plastic limit and theshrinkage limit as the lower limit of changes in me.
The addition of PC in soft soils reduce the liquid
limit, plastic limit and also the plasticity index asshown in Table 2. as a result of the decline in soil
plastic properties causes changes in soil type fromRESULT AND DISCUSSION
Pozzolanic effect on soil plasticity
Three stages occur when the soil is mixed withcement, namely: absorption of water by calcium incement which occurs quickly and gives the effect ofheat and reduction of soil water content. The second
stage is the replacement of ions where negatively
charged soil will attract Ca in the cement quickly
causing an increase in the cohesiveness of the soil
grains. The third stage is the pozzolanic reaction
224

GEOMATE – Melbourne, Australia, 11-13 November 2020

dominant clay( A-7-5) to soil dominated by silt (A5)based on AASHTO classification.

40
35

Pozzolanic effect for strength of soil

30

Remedial of clay soil with (PC) is a chemical
process that can change the structure of the soil byforming larger aggregate grains that will have a very
beneficial effect. Chemical events occur between
soil and (PC), when both are mixed by adding anamount of water.
CBR test results in Fig. 2 show the increase in
the un-soaked and soaked CBR with increasing PC
content with the difference of curves insignificant.

f (0)

25
20
15
10
5
0

80

0

5

15

20

PC (%)

70
60

Fig. 3. Influence PC to friction angle

50

140,0

40
un-soaked CBR

30
20

120,0
cohesion (kN/m2)

CBR (%)

10

soaked CBR

10
0
0,0

5,0

10,0
PC (%)

15,020,0Fig.2. Percentage of PC versus CBR

100,0
80,0
60,0
40,0
20,0

The value of the friction angle in moving up with thepresence of a PC like Fig. 3, but the cohesivenessreduce after reach some value insignificant as show
in Fig.4.

0,0
0

5

10

15

20

PC (%)
Fig. 4. Influence PC to cohesion
Besides the strength, changes in soil stiffness due tothe addition of PC need to be known by presenting
the axial stress and strain relationships fromunconfined experiments like the Fig. 5.

225

GEOMATE – Melbourne, Australia, 11-13 November 2020

312,5

(5)

𝑞𝑢 = (𝑤 ⁄𝐶 )0,285 ; 𝑅2 = 0,949

300

𝑐

250
200
250
150

5% PC

200

qu(kPa)Axial Stress (KPa)

300

8% PC

100

10% PC50

12% PC100

15% PC

50

0
0

1

150

2

3

0

Axial Strain (%)

0,0

2,0

Fig. 5. Axial stress and strain of variation PC
improved soil

Fig. 6 Fitted curve of strength development PC
stabilized soil.
10,0
9,0

Table 3. Mechanical Properties of stabilized soft soil

%
5
8
10
12
15

Compressive
Strength
kPa
197,5
219,7
240,7
271,6
278,5

8,0

SecantModulus
Mpa
5,64
7,32
7,52
7,76
9,28

7,0

Es (MPa)

FailureStrain
%
1,75
1,5
1,6
1,75
1,5

6,0

w/c

It was noted that the failure strain changed
irregularly but the compressive stress and secant
modulus increased with the addition of OPC content
in the soil mixture as shown Table 3.

PC

4,0

6,0
5,0
4,0
3,0
2,0
1,0
0,0

Based on [13-14] there is a relationship between theratio of water and PC content with compressive
strength (qu) and secant modulus (Es), which is thehigher the PC content, the optimum water content of
the mixture will be decreased. This relationship ispresented in the power function in the form of Eq.(3)and (4):𝐴
𝑞𝑢 = (𝑤⁄𝐶 )𝐵𝑀

𝐸𝑠 = (𝑤⁄𝑐)𝑁

0,0

2,0

4,0

6,0

w/c
Fig. 7. Fitted curve of secant modulus development
PC stabilized soil
10,231

𝐸𝑠 = (𝑤 ⁄𝐶 )0,343 ; 𝑅2 = 0,911
𝑐

(3)

(7)

Shear strength parameter prediction(4)

The hypotheses [15] of w/c as prime parameterto achieve phenomenological model for predicting
the mechanical properties of specimen stabilized
PC. From this research it is proposed formulation topredict the friction angle and cohesion based on w/cparameter.

Where A, B, C and D are empirical constants.
Result of the unconfined compressive test therelation between qu and w / c presented in the Fig.6
and Es and w/c in Fig. 7 , which formulation in Eq.
5 and 6 as follows:

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GEOMATE – Melbourne, Australia, 11-13 November 2020

40

Influence of curing time for improved strength35

The results of data analysis determined that the
optimum of cement content in the soil mixture is10%. The strength will be developed in 4, 7, 14 and28 days.

30

20
15

400

10

350

5

300

0

250

0

2

4

qu (kN/m2)

f (o)

25

6

w/c
Fig. 8 Fitted curve of friction development PC
improved soil

45,33

0

cohesion (kPa)

100
80

60
40
20
0
6

w/c
Fig. 9 Fitted curve of cohesion development in PC
stabilized soil
Formulations that can be used to predict cohesion of
the w / c parameter are expressed in the equation.
𝒘 𝟐

20

30

It is took the time for pozolanic processes toreach very significant high strength. An increase in
the strength of soft soil after mixed with a PC at apercentage of 10% and immediately compacted
optimally, an increase of up to 387%. If treatment iscarried out 4, 7 and 14 days, then the increase inaround 400% more and if the curing time takes 28
days, the compressive strength reaches 646%.
To find out whether the effect of water on the
improved soil the soaked CBR test will be
conducted. Fig11 shows the deviation of value of
unsoaked and soaked CBR without treatment around
6%. If curing is carried out more than 24 days, theimproved soil becomes more stable.

120

4

10

Fig. 10 Effect curing time to the compressive
strength

140

2

0

Curing time (days)

If the cohesion is related to the w / c parameter,
the regression analysis that is different from thefriction angle must be performed. The formulation
represented the best curve fit is in the Fig.9 and
Eq.9.

0

150

50

(8)

𝑐

200

100

Figure 8 and Eq. 8 estimated friction of w/c.
𝜙 = (𝑤 ⁄𝐶 )0,654 ; 𝑅2 = 0,909

y = 238,51e0,0152x
R² = 0,9773

𝒘

𝒄 = −𝟓, 𝟑 ( 𝒄 ) + 𝟑𝟗, 𝟒 𝒄 + 𝟓𝟓. 𝟑; 𝑹𝟐 = 𝟎, 𝟗𝟕 (9)227GEOMATE – Melbourne, Australia, 11-13 November 2020

4. Chai, J.C; Shen, S.L.; Miura, N.; and Bergado,D.T.(2001). Simple method of modeling PVDimproved Subsoil, Journal of geotechnical andgeoenvironmental engineering, 127(11), 965972.
5. Zukri A.: Pekan Soft Clay Treated with Hydrated
Lime as a Method of Soil Stabilizer, MalaysianTechnical
Universities
ConferenceonEngineering&
Technology,
ProsediaEngineering 53, 37-41, Elsevier, (2013).
6. Sakr, M., A, Shahin, M., A, and Metwally, Y.,
M.: Utilization of Lime for Stabilizing Soft Clay
Soil of High Organic Content, Geotechnical,
andGeologicalEngineering,
Springer
Science+Business Media B.V. (2009).
7. Mansour, M., A., Samieh, A., M., and Matter, H.,
E.: Engineering Properties of Cement/limestabilized Egyptian Soft Clay, International
Symposium on Geohazards and Geomechanics,
IOP Conf. Series: Earth and Environmental
Science 26 (2015) 012041.
8. Harichane K, Ghrici M, Kenai S, Effect of
curing time on shear strength of cohesive soils
stabilized with combination of lime and natural
pozzolana,
GeotecnicalEngineering,International Journal of Civil Engineering, Vol.
9, No. 2, June 2011
9. Cong M, Longzho C and Bing C, Analysis ofstrength development of soft clay stabilized withcement-based stabilizer, Construction and
Building Materials, 71(2014) 354-362, Elsivier.
10. Pandey A, Soil Stabilization using cement,International Journal Civil Engineering
Technology (IJECIET), Volume 8, Issue 6, June2017.
11. Frikha, W, Zargayoun, H., Boussetta, S., andBouassida, M.: Experimental Study of Tunis
Soft Soil Improved by Deep Mixing Column,
Geotechnical, and Geological Engineering,
Springer International Publishing Switzerland,(2017)
12. Zaika Y and Rachmansyah A, Geotechnicalbehaviour of soft soil in East Java, Indonesia,
IOP Conference Series: Materials Science andEngineering, 615 (2019) 012043, IOP
Publishingdoi:10.1088/1757899X/615/1/012043.
13. Horpibulsk S, Rachan R Sluddeepong A andChinkulkijniwat A, Streng development incement admixed Bangkok Clay; Laboratory and
Field Investigation
14. Miura M, Horpibulsuk S and Nagaraj T S,
Engineering behavior of cement stabilized clay
at high water content, Soils and Foundations
Vol.41, No.5, 33- 45, 2001
15. Cong M, Longzhu C and Bing C, Analysis ofstrength development in soft clay stabilized withcement based stabilizer, Construction and
Building Materials, 2014, Elsevier, 71, 354-362.

80,0

70,0

CBR (%)

60,0
50,0
40,0

un-soaked CBR

30,0soaked CBR

20,0
10,0
0,0

0

10

20

30

Curing time (days)
Fig 11. Curing time to develop the strength
CONCLUSION
The water content of soft soils will affect thestrength of soils in this case is a type of clayey silt
soil. The use of additives such as PCs improved thephysical and mechanical properties. Changes inphysical properties can be found by the reduction inplastic.
Based on the unconfined compressive test results
the parameters qu and Es can be formulated by theratio of water and PC content (w / c).The additionof PC dosage will reduce w / c but increase qu andEs. The same phenomenon also applied to thefriction angle
that it can be defined in amathematical equation.
The cohesion define as polynomial second ordeequation to define in w/c factor.
When the curing time applied the improved soil
will rise the strength l and also more stable because
the water intrusion from the environment.
References
1. Sakleshpur, V.; Pezzi, M.: and Salgado, R.
(2018). Ground Engineering Using PrefabricatedVertical Drain: A review, GeotechnicalEngineering Journal of the SEAGS &AGSSEA ,49(1), 45-64.
2. Tedjakusuma, B.(2012). Application ofPrefabricatedVertical
Drain
in
Soil
Improvement, Civil Engineering Dimension,
14(1), 51-56
3. Rajesh, B.G’; Chukka, S. K.; and Dey, A.
(2018). Finite element modeling of embankment
resting on soft ground stabilized withprefabricated vertical drains, GeotechnicalEngineering, 49 (4) 63-72.

228

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

LABORATORY MODEL TEST ON CONTACT EROSION BETWEENSAND AND SILTY CLAY LAYERS
Pradeep Pokhrel1, Jiro Kuwano1
Department of Civil and Environmental Engineering, Saitama University, Japan

1

ABSTRACTInternal erosion is one of the major causes of the failure of hydraulic structures, road pavement and the naturaldeposits ground as well. Except suffusion and piping, contact erosion is also a type of internal erosion that canmostly occur at the foundation of embankment dams and dikes, in the road pavement and the alluvial fan deposition
valley. This phenomenon occurs when the seepage flow exists at the interface of fine and coarse soil layer wherefine particles are detached and transported through the voids of coarse soil layer. During 2013-2017 numerous
sinkholes have been observed in Armala area of Pokhara valley in central Nepal. The locations of the sinkholes
were observed mostly at paddy fields in the alluvial fan deposit. Sinkhole affected area was investigated twice ina year of 2015, 2016 and 2017. Based on the subsurface ground condition and the location of hidden cavity, it isassumed that an internal contact erosion was occurred, and this phenomenon leads to the formation of subsurface
hidden cavity and collapse sinkholes in the Armala area. Mechanics of contact erosion was studied in the laboratory
by performing a series of small-scale parallel flow contact erosion test. Physical model of the experiment was
prepared with the soil having the similar grain size and physical properties to the onsite granulometry. A cavity
was observed at the interface when the DL clay is overlying the coarse sand. In addition, the amount of discharged
soil decreases with the increase of overburden pressure.
Keywords: Sinkhole, Bore-hole, Contact erosion, Flow velocity, Piping

INTRODUCTION

through the void of coarse soil. In the past, theresearch mainly focuses on the mechanics of contact
erosion and the threshold flow velocity for the
initiation of contact erosion. This study focuses on theinternal contact erosion to understand thephenomenon that leads to sinkhole formation using
the soil material having the similar physical andgeometrical properties of the onsite soil. The
maximum grain size of the coarse soil used in thisstudy is significantly finer than the coarse soils usedby the previous researcher. The effect of overburdenpressure on the erosion rate was studied.

Internal erosion is one of the major causes of the
failure of hydraulic structures, road pavement and thecollapsed sinkholes in the natural deposits ground as
well. Except suffusion and piping, contact erosion is
also a type of internal erosion that can mostly occur
at the foundation of embankment dams and dikes, inthe road pavement and the alluvial fan deposition
valley. This phenomenon occurs when the seepage
flow exists at the interface of fine and coarse soil layer
where fine particles are detached and transported
through the voids of coarse soil layer. Various
research has been conducted on contact erosion in thecontext of dikes [1], embankment dams [2], [3], [4]and road pavement [5]. Previous authors [1], [2], [3][4] conducted a parallel flow contact erosion test
simulating the internal contact erosion that mostfrequently encountered in the hydraulic structures. A
perpendicular flow contact erosion test was
performed to simulate the internal contact erosion
below the pavement layers due to the fluctuation ofwater table [5]. For the significant occurrence ofparallel flow internal contact erosion, two conditions
should be fulfilled [1], [2], [3], [4]. First one is thegeometrical condition where the size of the voids ofcoarse soil should be large enough than the grain sizeof the fine soil. So that all the fine particles can passthrough the voids of coarse soil. Second, thehydraulic condition where the seepage velocity ishigh enough to scour and transport the fine particles

Background of The Research
During November 2013 to 2017, numerous (morethan 200) sinkholes have been observed in Armala
area of Pokhara valley in central Nepal. The locationsof the sinkholes were observed mostly at paddy fields
in the alluvial fan deposit. An example of sinkhole in
the Armala area is shown in the Fig. 1. The subsurface
materials deposited in the sinkhole affected area areunconsolidated silty clay, sand, and gravel. Sinkhole
affected area was investigated twice in a year of 2015,2016 and 2017 and the results obtained from theinvestigation were reported in [6], [7], [8]. During theinvestigation, a bore hole logging with standard
penetration test (SPT) was conducted at one point
near the observed sinkhole. A thick white silty clay
layer was found at a depth of 4 m to the 20 m fromthe ground level [7], [8]. In addition, a hidden cavity
229

GEOMATE – Melbourne, Australia, 11-13 November 2020of 2.5 m height was discovered at a depth of 7.5 m to
10 m from the ground level.

not takes place at that location if there was only thepresence of silty clay in the subsurface of Armala.
The main source of groundwater flow in thesinkhole affected area is seepage flowing from the hillside to the main stream. According to the local
residents, excavation of aggregates from the riverbedof the main stream was started from 1990’s for thecommercial purpose. Due to the excavation of
aggregate, the riverbed of the main stream waslowered with time. It increased the hydraulic gradientin the ground and the subsurface water velocity. The
required hydraulic condition for the internal erosion
is fullfilled and therefore, contact erosion causing
cavities was accelerated. The path of seepage flowfrom the source to the exit shows that the groundwaterflows parallel to the interface of soil layers. All theseevidences show that, the presence of a sand seamwithin silty clay layers could be one of the factors totrigger parallel flow internal contact erosion andhidden cavity formation in the Armala area.
Therefore, a series of small scale parallel flow contact
erosion test was conducted in the laboratory toobserve the contact erosion and its phenomenon tolead the cavity formation. Physical model of theexperiment was prepared with the soil having thesimilar grain size and physical properties to the onsite
granulometry. The maximum grain size of the coarse
sand used in this study is 2.3 mm which issignificantly finer than the coarse soils used by theprevious authors [1], [2], [3], [4].

Fig. 1 A sinkhole in the Armala area.
To study the soil strata parallel to sinkhole cavity
discovered from bore hole survey, some scarp locatedon the right bank of the nearby river (Kali Khola) also
observed. It was identified from these scarp that a
0.05 m thick sand seams layer lies in between the silty
clay layers at a depth of 9 m from the ground surface
which is the same level as the sinkhole cavity
discovered from the borehole investigation. Figure 2
shows the outcrops just after the shaping (left),ground water discharged after 5 minutes (center) anderoded silty clay in the form of slurry after 11 minutes(right). The orientation of the soil strata at theoutcrops is illustrated in a schematic diagram in Fig.
3. At the outcrops, groundwater discharged at theinterface of sand and silty clay layers was observed
after the 5 minutes of shaping. Possibly, it is due tothe presence of high permeable sand layer in between
the silty clay layers. The discharge of groundwater
was taking place at both the upper and lower interface
of a sand layer which is attached with the silty clay
layers. In addition, more amount of discharged water
was observed at the upper interface of a sand layer i.e.
silty clay layer overlaying a sand layer at the outcrops.
However, internal erosion was observed at both theside of a sand layer and therefore, the presence ofsand layer could be one of the factors to trigger may
internal contact erosion on the site. Internal erosion

EXPERIMENTAL SETUP
The schematic diagram of parallel flow contact
erosion test apparatus is shown in Fig. 4. It consistsof two water tanks, inlet and outlet connected to a Ushape acrylic box containing a fine and coarse soil
combination. The overall inner dimension of the Ushape acrylic box is 30 cm long, 12 cm wide and 15
cm high. Base plate, Porous plate, DL clay layer, sandlayer, porous plate and piston is placed
simultaneously from bottom to the top. In each test,
10 cm thick DL clay layer is placed on the top of 1
cm thick sand layer in the case of fine soil overlying
the coarse soil and reverse configuration with theFig. 3 Illustration of soil strata at the outcrops.

Fig. 2 Groundwater oozing from sand layer.
230

GEOMATE – Melbourne, Australia, 11-13 November 2020

same thickness of soil was made in the case of coarse
soil overlaying the fine soil. An acrylic plate of 1 cm
× 12 cm opening was placed between the soilchamber and inlet -outlet box so that the openings ofthe soil specimen box coincide with the cross sectionof the coarse sand layer. Steel mesh of sieve No. 150
was placed at the opening of the plate. So that only
DL clay can pass through the mesh. When the valve
is open, water is flowing through the coarse sandlayer and parallel to the fine soil surface. A bellofram
cylinder is connected to the piston through a load cell
to apply the given overburden pressure to the soil
sample to reproduce the overburden pressure on thesite.

and grain size of silty clay found in the Armala area.
In this study, DL clay was considered as fine soil
whereas silica sand No. 3 and silica sand No. 5 wereconsidered as coarse material. Figure 5 shows thegradation curve of the tested soils. The median grain
size of silica sand No. 3, silica sand No. 5and DL clay
are 1.6 mm, 0.6 mm, and 0.028 mm, respectively. The
maximum grain size of the coarse sand is 2.3 mm
which is significantly finer than the coarse soils usedby the previous researchers for the contact erosion
test. The geometrical and physical properties of thetested soils are given in Table 1.

Fig.5 Particle size distribution of tested soils.
Fig.4 Schematic diagram of parallel flow contact
erosion test apparatus.

Table 1 Geometrical and physical properties of soils.

A series of experiment was performed with the
stepwise increase in water head difference at the timeinterval of 30 minutes at constant overburdenpressure. With the increase of water head difference,
the flow velocity increases as well. Flow velocity,
𝑉 (𝑐𝑚⁄𝑠) is calculated by using the formula 𝑉 =𝑄 ⁄𝐴𝑐 where 𝑄 ( 𝑐𝑚3 ⁄𝑠 ) is the discharged watermeasured which depends on coarse sand permeability
and 𝐴𝑐 (𝑐𝑚2 ) is the cross section area of the coarse
sand which is same as the cross section of the opening.
Turbidity of the discharged water was measured inevery 5 minutes of each interval to find theconcentration of DL clay in the discharged water.

Properties

DL
SilicaSilicaclay
Sand#3 sand#5
𝜌𝑠
g/cm3
2.654
2.56
2.56
𝜌𝑑𝑚𝑎𝑥
g/cm3
1.538
1.462
1.543
𝜌𝑑
g/cm3
1.384
1.388
1.383
𝐷15
mm
1.3
0.45
𝐷50
mm
0.0280
1.6
0.6
𝐷85
mm
0.0395
𝐶𝑢
1.935
1.417
1.553
𝐶𝑐
1.168
0.961
0.927
𝐾𝑠
m/s
6×10-7 5×10-4 1×10-4
Where, 𝜌𝑠 – density of the particle, 𝜌𝑑𝑚𝑎𝑥 –maximum dry density, 𝜌𝑑 – dry density, 𝐷15 – 15 %percentile of soil grain size, 𝐷50 – median grain sizeof soil, 𝐷85 – 85 % percentile of soil grain size, 𝐶𝑢 –coefficient of uniformity, 𝐶𝑐 – coefficient ofcurvature, 𝐾𝑠 – coefficient of permeability

TESTED SOILS
The white silty clay located in the hidden cavity isa non-plastic silty clay composed of 79% silt, 16%
clay and 5% sand. Grain size distribution of this whitesilty clay was analyzed by conducting hydrometer test
and sieve analysis method. The 𝐷10 , 𝐷30 and 𝐷60
grain size of this silty clay soil are 0.0014 mm, 0.005
mm, 0.023 mm respectively. Test material used in thisstudy comprised of DL clay, which is the commercialname of the soil and silica sand No.3 and silica sandNo.5. DL clay is a non-plastic silt composed of 90%
silt and 10% clay which shows the similar properties

Unit

In 1984, Sherard proposed 𝐷15 ⁄𝑑85 ratio as afiltering criterion, where 𝐷15 (mm) is the 15%
percentile of the coarse soil grain size and 𝑑85 (mm)is the 85% percentile of the fine soil grain size. Basedon the experiments, they found that filtering criteria
are 𝐷15 ⁄𝑑85 ≤ 4 and 𝐷15 ⁄𝑑85 ≤ 9 depending on thefine soil [9], [10]. The tested soils, in this study does231

GEOMATE – Melbourne, Australia, 11-13 November 2020

not validate this criterion. It means, the void size ofthe sand used in the test have large enough to pass thetotally or partially the grain of DL clay.
Fine soil was prepared at optimum water content
and compacted five successive layers of 2 cm thickon the soil chamber to achieve the 90% of the
maximum density by static compaction method. The
corresponding dry density of fine soil at 90% degreeof compaction is 1.384 𝑔⁄𝑐𝑚3 . Coarse sand wasplaced manually at the relative density of 50% and its
corresponding dry density of silica sand No. 3 andsilica sand No. 5 are 1.388 𝑔⁄𝑐𝑚3 and 1.383 𝑔⁄𝑐𝑚3
respectively. The permeability of the fine soil
6.68×10-7 m/s is significantly smaller than thepermeability of coarse soil 1.26×10-4 m/s. So thatwater will flow only thorough the coarse soil layer.

EXPERIMENTAL RESULTS
Observation of Erosion at the Interface
When the valve is open, water flows through thecoarse sand at the interface of DL clay and sand layers.
All the experiments were started with a small flow
velocity. At low flow velocity, no erosion at theinterface was observed. Also, the measured turbidity
value of the discharged water corresponding to thesmall flow velocity was small. It means, the shear
force generated by the water flow is smaller than the
stress exerted by the fine particles at the interface. So
that, the fine soils are in stable state and shows nonerosion behavior at low flow velocity. With theincrease of flow velocity, continuous erosion wasobserved at the interface.
In the case of coarse sand overlying the DL clay,
collapse of the coarse sand grain was observed evenin small erosion rate. Probably, it is due to the noncohesive behavior of coarse sand. At the time ofcontinuous erosion, both the fine and coarse soils
particles at the interface are in unstable state. Fine
particles at the interface were detached andtransported through the voids of coarse material due
to flow. At the same time, the rate of erosion tended
to increase but the unstable coarse particles are notable to transform the overburden stress. Indeed, theunstable coarse particles were collapsed due to theeffect of gravity and clogging effect was observed. So,
the amount of eroded soil may have been affected bythis clogging effect. The observation result shows that,the contact erosion phenomenon in this configuration
leads the surface settlement if the coarse material is a
non-cohesive soil. The propagation of erosion withtime is shown in Fig. 6.
In the case of DL clay overlying the coarse sand,a cavity was observed at the interface. The
propagation of erosion with time when DL clay
overlaying the silica sand is shown in Fig. 7. The archof the cavity formed by the erosion was seems to bestable due to the cohesion of DL clay. During thecontinuous erosion, the fine soil at the interface was

EXPERIMENTAL PROGRAM
In this study, six parallel flow contact erosion tests
were performed, considering the configuration of fineand coarse soil combination, types of coarse material
and the applied overburden pressures. Two types ofsoil configuration, coarse sand overlaying the DL
clay and DL clay overlaying the coarse sand, wereused in the tests. The collapse sinkholes that
discovered in the Armala area were usually shallow
depth of range 1 to 5.5 m. To reproduce the same
depth of cavity and sinkhole discovered at the site, 40
kPa and 70 kPa overburden pressure were applied in
the tests. Test conditions are summarized in Table 2.
Table 2 Test conditions
Test
No.
1
2
3
4
5
6

Soil configuration
SS #3 ⁄ DL clay
SS #3 / DL clay
DL clay / SS #3
DL clay / SS #3
DL clay / SS #5
DL clay / SS #5*

Overburdenpressure (kPa)
40
70
40
70
40
70

*SS-Silica sand(a) t = 1 min (0.47 cm/s)(b) t = 90 min (0.9 cm/s)(c) t = 92 min (1 cm/s)(d) t = 120 min (1 cm/s)

Fig. 6 Propagation of erosion with time (Coarse sand overlying the DL clay).

(a) t = 1 min (0.55 cm/s)(b) t = 120 min (1.16 cm/s) (c) t = 131 min (1.3 cm/s)

Fig. 7 Propagation of erosion with time (DL clay overlying the coarse sand).
232(d) t = 120 min (1.3 cm/s)

GEOMATE – Melbourne, Australia, 11-13 November 2020

flowed out with water where a piping phenomenon
was observed. The density of the DL clay decreases
at the interface with continuous erosion and resulting
the concentration of flow at one location. With thepassage of time, the flow concentrated at the unstablepart and the cavity propagate in the horizontal andvertical direction. The observation result shows that,the contact erosion phenomenon in this configuration
leads the formation of subsurface cavity and finallycollapse sinkhole.

This threshold flow velocity is called the critical flow
velocity. The turbidity value and the sampling eroded
mass shows that continuous erosion initiated after 120
minutes that correspond to the flow velocity of 1.3
cm/s for the coarse silica sand No. 3 and 0.91 cm/s forthe silica sand No.5. The critical flow velocity
depends on the grain size of coarse soil and its value
decreases with the decrease in grain size of coarse
material [1]. The fluctuation pattern of measured
eroded mass and the turbidity of the discharged water
are similar.

Concentration of DL Clay in the Discharged
Water and Critical Flow Velocity
Concentration of the soil particles in the
discharged water is the key factor to analyze thebehavior of erosion with time. The flow velocity
corresponding to the non-zero turbidity considered as
threshold velocity to initiate the contact erosion offine soil [2], [3], [4]. Turbidity of the discharge water
was measured in every 5 minutes of each interval of
30 minutes. The relationship between the turbidity
and the concentration of DL clay was obtained fromthe several measurements of turbidity by samplingfrom predetermined quantities of soil particles. The
eroded fine particles may sediment in the discharge
pipe which can affect the turbidity measurement andthe erosion rate. The tendency of flow velocity andthe eroded mass in the discharged water was alsomeasured through sampling to cross check thevalidity of turbidity measurement. In this method,discharge sample was collected in every 5 minutes ofeach interval for 5 seconds. The weight of all thesamples are measured and placed in an electric ovenfor dried. The result of flow velocity, eroded massmeasured through sampling and turbidity with time atdifferent overburden pressure are shown in Fig. 8 andFig. 9.

Fig. 9 Measurement of flow velocity, turbidity, anderoded mass with time. (DL clay / silica sand #5).
The critical flow velocity obtained fromexperimental results was slightly smaller than thecritical flow velocity proposed by Guidoux in 2010refer to “Eq. (1)” [2]. Probably, it is due to the effectof coarse soil grain size and the properties of DL clay.
The coarse soil grain size used in this study has
significantly smaller than the material used byprevious researcher.
𝜌 −𝜌𝑤

𝑢𝑐𝑟 = 0.7𝜂𝐹 √( 𝑠𝜌𝑤

𝛽)𝑔𝑑𝐻 (1 + 2 )𝑑𝐻(1)Where, 𝑢𝑐𝑟 (m/s) - critical flow velocity, 𝜂𝐹 porosity of coarse soil, 𝜌𝑠 (kg/m3) - density of finesoil grains, 𝜌𝑤 (kg/m3) - density of water, 𝑔 (m/s2) gravity acceleration, 𝑑𝐻 (m) - effective diameter offine soil, 𝛽 (m2) - adhesive properties of particles.
Effect of Overburden Pressure on Erosion Rate
Amount of discharge water in each interval wascollected in a container to measure the eroded mass.
The effect of overburden pressure on the amount oferosion or erosion rate was clearly seen from thegraph of cumulative eroded mass in Fig. 10. It is seenthat the amount of erosion is less in the case of 70 kPa
compared to the 40 kPa overburden pressure in allcases of experiments. In real, coarse soil layer iscompressed with the increase of overburden pressure.
The contact surface between fine soil and coarse soil
increases with compression. It means, the total

Fig. 8 Measurement of flow velocity, turbidity, anderoded mass with time. (DL clay / silica sand #3).
The concentration of the DL clay in the
discharged soil was zero at low flow velocity. The
concentration increases significantly with theincrease of flow velocity from certain threshold value.
233

GEOMATE – Melbourne, Australia, 11-13 November 2020

volume of voids in the coarse soil layer decreases. It
results in the less amount of the fine particles
transported through the voids. The permeability of thecoarse soil also decreases with the compression.
Therefore, overburden pressure has an inverse effecton the erosion rate.

3) The surface area prone to erosion and total volume
of voids decreases with increase of overburdenpressure. Thus, overburden pressure has an inverse
effect on the erosion rate.
REFERENCES[1] Bezuijen, A., Klein Breteler, M., and Bakker, K.J.
Design criteria for placed block revetments andgranular filters, in Proceedings of 2nd International
Conference on Coastal and Port Engineering in
Developing Countries, Beijing, China, 1987, pp.
1-15.
[2] Guidoux, C., Faure, Y.-H., Beguin, R., and Ho,
C.-C., Contact erosion at the interface between
granular filter and various base-soils with
tangential flow, J. Geotech. Geoenviron. Eng.,
Vol. 136, No. 5, 2010, pp. 741-750.
[3] Beguin, R., Faure, Y.-H., Guidoux, C., andPhilippe, P., Hydraulic Erosion along theInterface of Different Soil Layers, in Proceedingsof 5th International Conference on Scour and
Erosion, San Francisco, USA, 2010, pp. 387-396.
[4] Philippe, P., Beguin, R., and Faure, Y. H. Contact
erosion, In Erosion in Geomechanics Applied toDams and Levees, Wiley/ISTE, 2013, pp. 101192.
[5] Jegatheesan P., Sothilingam P., Arulrajah A.,
Disfani M. M., and Rajeev P., Laboratory Model
Test on Contact Erosion of Dispersive Soil
Beneath Pavement Layers, Geotechnical Testing
Journal, Vol. 38, No. 6, 2015, pp. 1-9.
[6] Pokhrel, R. M., Kiyota, T., Kuwano R., Chiaro, G.,
Katagiri, T., and Arai, I., Preliminary Field
Assessment of sinkhole damage in Pokhara,
Nepal, International Journal of Geoengineering
Case histories, Vol. 3, No. 2, 2015, pp. 113-125.
[7] Kuwano, R., Kiyota, T., Pokhrel, R. M., Katagiri,T., Ikeda, T., Yagiura, Y., Yoshikawa, and T.,
Kuwano, J., Investigation into the multiple recent
sinkholes in Pokhara, Nepal, in Proceedings of 8thInternational Conference on Scour and Erosion,UK, 2016, pp. 1117-1122.
[8] Yagiura, Y., Takemasa, M., Yoshikawa, T.,
Kiyota, T., Katagiri, T., Ikeda, T., and Pokhrel, R.
M., Follow-up survey of sinkhole damage in
Pokhara, Nepal – Riverbed Lowering andSubsequent Groundwater Lowering Causing
Numerous Sinkholes in Pokhara, Nepal Since
2013-, in Proceedings of 7th Japan-Taiwan
Workshop on Geotechnical Hazard from Large
Earthquake and Heavy Rainfall, Taiwan, 2017.
[9] Sherard, J. L., Dunnigan, L. P., and Talbot, J. R.,
Basic properties of sand and gravel filters, J.
Geotech. Eng., Vol. 110, No. 6, 1984a, pp. 684700.
[10] Sherard, J. L., Dunnigan, L. P., and Talbot, J. R.,
Filters for silt and clays, J. Geotech. Eng., Vol.
110, No. 6, 1984b, pp. 701-718.

Fig. 10 Cumulative eroded mass with flow velocity.
Possible Mechanism of Internal Erosion in
ArmalaIn fact, contact erosion was initiated at theinterface of silty clay and sand seam in the Armala
area. When the groundwater flowing through the sandseam, not only the sand but the surficial part of thewhite silty clay at the interface gets saturated. The
saturated part of the silty clay loses their stability andis detached and transported through the coarse sandseam. Experimental results show that, the contact
erosion is highly accelerated with piping formation
when the fine soil overlying the coarse sand. Similar
phenomenon was anticipated in the Armala area.
Large amount of subsurface fine materials was eroded,
and subsurface cavities were formed. With time, thesubsurface cavities increase width and height andfinally collapse as a sinkhole.
CONCLUSIONSA series of contact erosion tests was conducted
and the mechanism of internal erosion in Armala area
was studied. Following conclusions were drawn fromthe study.
1) At low flow velocity, fine and coarse soil particles
at the interface are in stable state and thus contact
erosion is insignificant. Erosion is significant when
the flow velocity is greater than certain thresholdvelocity called as critical flow velocity.
2) A cavity was observed at the interface when the DL
clay layered above the coarse sand layer. Contact
erosion following piping phenomenon wasobserved in this configuration which leads the large
amount of erosion rate.
234

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

UNIAXIAL COMPRESSION TEST OF BENTONITE
UNDER VARIOUS CONDITIONS
Masaki Yanai1, Shinichi Kanazawa1, Rie Suzuki1 and Osamu Yoshino2
1

National Institute of Technology, Fukushima College, Japan; 2Nishimatsu Construction CO.,LTD
ABSTRACTAt present, underground geological disposal at depths greater than 300 m are considered a viable disposal
option for high-level radioactive waste generated from the reprocessing of spent fuel used in nuclear power plants.
In geological disposal, bentonite is employed as the primary component of buffer material filling gaps between
waste and geologic rock. Bentonite has remarkable water absorption swelling capability and low permeability
characteristics. Bentonite buffers are generally mixed with silica sand to improve workability and reduce costs.
However, specification details have yet to be completely determined. It is anticipated that the buffer material will
be exposed to high temperature due to the heat generated by the vitrified solid. In this study, uniaxial compression
tests were carried out on specimens made of a mixture of bentonite and silica sand. The purpose of this study is toclarify the mechanical properties of bentonite silica sand mixed specimens by comparing the results of this study
with those of the previous study [1~5]. In addition to using the temperature change by water tank curing, as carriedout in the previous study, the uniaxial compression test was also carried out while continuously applying
temperature to the specimen.
Keywords: Bentonite, Geological disposal , temperature, Uniaxial compression test

1.

summarizes the physical and chemical properties ofKunigel V1 and silica sand.

INTRODUCTION

At present, the use of bentonite as a buffermaterial for geological disposal is being examined.
Bentonite fills gaps due to its water intake swelling
properties and low permeability, thus preventing theleakage of groundwater contaminated with highlevels of radiation. In Japan, various tests, such as
swelling pressure tests and uniaxial compression tests,
have helped to characterize swelling performance andmechanical properties of bentonite [6]. During thedisposal period, the bentonite buffer is exposed tovarious conditions, It is feared that backfill materials
and buffer materials may permeate and saturate fromthe surrounding bedrock due to heat generation fromthe vitrified material and infiltration and saturation ofgroundwater during the disposal period. In this study,
in order to understand the mechanical properties ofbentonite buffer materials, the uniaxial compression
test was carried out with varying degrees of saturationand specimen temperatures. In addition to tests using
temperature change by the water tank curing, tests
with continuously applied temperature were also
carried out.

Table 1 Physical and chemical properties of Kunigel
V1 and silica sandKunigel V1Silica sand No.5

soil particle
density(Mg/m3)
Montmorillonite
content(%)
Silica sand No. 5
particledensity(Mg/m3)

2. TEST METHOD
The initial conditions were set at a dry density of
1.6 Mg/m3 and a saturation of 30% and 90%.
Temperatures of 30 ℃, 50 ℃, 70 ℃ and 90 ℃ wereapplied to bentonite (Kunigel V1) with No. 5 silica
sand ratios of 30%. The test with 90% saturation was
carried out at 30 ℃, 60 ℃ and 90 ℃. Table 1

Silica sand No. 5
particle size(mm)

235

2.61
51
2.62
0.3~0.8

GEOMATE – Melbourne, Australia, 11-13 November 2020

2.1 Sample Preparation Method
A mixed sample of bentonite and silica sand was
prepared at a ratio of 7:3 for Kunigel V1 and silica
sand particle size no. 5. The properties of each sample
are shown in Tables 2, 3, and 4. Equation (1) was used
for the effective clay density[7] of JAEA.
𝜌𝑒 =𝜌𝑑 (100 − 𝑅𝑠 )
𝜌 𝑅(100 − 𝑑 𝑠 )𝜌𝑠(1)(1) where, 𝜌𝑒 : effective clay density ， 𝜌𝑑 :dry
density，𝜌𝑠 ：soil particle density of silica sand，𝑅𝑠 ：silica sand mixing ratio
Fig. 1

Table 2 Properties of each sample at the 7:3 mixing
ratio
silica sand No.
Mixed soil particle
density(Mg/m3)
Void ratio
Effective clay density(Mg/m3)

Methylene blue adsorption test

2.3 Water Content Adjustment

No.5

The water content of the mixed samples was
adjusted to a saturation of 30% and 90%. The
procedure is shown below.
(1) The sample was mixed using a spray and mixer
as shown in Fig. 2 (1).
(2) For the water content adjustment at highsaturation, the water content of the sample wasadjusted using a humidifier and a tent to shorten
the time, as shown in Fig. 2 (2).
(3) The water content was measured using a
microwave oven (500 W, 15 min) to shorten the
time.

2.610
0.631
1.371

2.2 Methylene Blue Adsorption Test
The montmorillonite content of bentonite in Table
1 was measured with reference to the Methylene blue
adsorption measurement method [8] of AIST.
The methylene blue adsorption test is a test to
estimate surface area and cation adsorption capacity
using the adsorption of methylene blue bymontmorillonite, which is a main component ofbentonite. In this test method, methylene blue isadded to a solution in which bentonite is dispersed,
and a drop of the solution is dropped on filter paper
to make a dark blue spot of 1 cm in diameter. Fig.1
shows the adsorption amount of methylene blue.
When the amount of added methylene blue is large,
blue bleeding, called a halo, appears around the spot.
The halo is considered to be the methylene blue thatthe bentonite could not adsorb. In this study, theappearance of a halo is judged to be the maximum
adsorption of methylene blue on bentonite.
The calculation of the amount of adsorbed
methylene blue is given by the following equation:
Adsorbed amount of methylene blue (MB) per 100 gof sample [mmol] = concentration of MB solution[mmol/l] × amount of MB solution added [ml] ×(1/1000) × (100 [g]/Weight of the sample [g])As a result of the test, the montmorillonite content
of the Kunigel V1 used in this study was 51%.

Humidifier(1) Mixer and sample (30%) (2) Humidifier andsampleFig. 2 Water content adjustment
2.4 Specimen Molding
The sample was divided into five layers, eachplaced in the mold and compacted. Afterwards, staticcompaction (40 MPa, 10 min) was applied with ahydraulic jack to produce the test specimen.
Equations (2) and (3) were used for the mass of thespecimen, assuming that the volume of the specimen
was constant.
236

GEOMATE – Melbourne, Australia, 11-13 November 2020

e=

𝜌𝑠
−1
𝜌𝑑

(2)

𝑤𝜌𝑠
𝑒𝜌𝑤

(3)

S𝑟 =

minutes to reach the set temperature (30℃, 50℃,
70℃, 90℃). After this, the uniaxial compression test
was carried out under continuous heating. It was
confirmed that the curing time allows the specimen toreach the set temperature in advance.
Figure 4 shows the uniaxial compression test with
continuous temperature.

(2) where, e : void ratio, 𝜌𝑠 : soil particle
density, 𝜌𝑑 : dry density : (3) and where, Sr : degreeof saturation, w : water content, 𝜌𝑠 : soil particle
density e : void ratio, 𝜌𝑤 : water density (1.00Mg/m3).
2.5 Temperature Change

specimen(1) Temperature change by water tank curing
In order to avoid water contact with the specimen,
the temperature change was applied to the specimen
by attaching an impervious sheet to the specimen andcuring for 24 hours in the water tank (at 30℃, 50℃,
70℃, 90℃), during which time the temperature was
kept constant. After curing, the impervious sheet wasremoved from the specimen; the volume wasmeasured again; the compression auxiliary
equipment was installed; and the uniaxial
compression test was carried out. Figure 4 shows thetemperature curing of the specimen in the water tank.

Water tank with heater

Fig. 4 Uniaxial compression test in a temperatureenvironment
2.6 Uniaxial Compression Test

specimen

The compression test was carried out at a fixedloading speed of 0.4 (mm/min). An electric screw
jack system of 10 kN capacity was employed as atesting machine. The test method was carried out
referring to the uniaxial compression test method of
the JIS standard.[8]3. TEST RESULTS AND DISCUSSION

water tankFig. 3Figure 5 shows the results of the uniaxial
compression test in which the temperature waschanged by curing in a water tank, and Fig. 6 shows
the results of the uniaxial compression test in atemperature environment. Figure 7 shows the
distribution of water content by curing in a water tank,and Fig. 8 shows the distribution of water content ina test conducted under a temperature environment.

Temperature change by water tank curing(2) Uniaxial compression test in a temperatureenvironment

3.1 Results of Tests in Which the Method of
Applying the Temperature Change was Varied

A rubber sleeve was attached to the specimen toprevent water from coming into contact with the
prepared specimen. Next, a water tank container
capable of keeping the temperature constant was
installed in the main body of the uniaxial compression
tester, and the test piece with the compression assist
device was installed in the water tank container for 25

Figures 5 and 6 confirm that the maximum
compressive strength decreased linearly in the watertank curing in the uniaxial compression test in which
the temperature was changed. In the uniaxial
compression test under the temperature environment,
it was confirmed that the compressive strength tended
to decrease rapidly with the rise in temperature from237

GEOMATE – Melbourne, Australia, 11-13 November 2020a certain threshold. With the rise of the temperature,
the factors causing the reduced compressive strengthappear to be multiple. The first factor is the expansionof specimen volume. It is possible that pore water andair expanded by heat were difficult to discharge dueto the low permeability and air permeability ofbentonite. According to Charles's law, as the volume
of gas is proportional to temperature, the expansionof air is about 1.07 times at 50℃, about 1.13 times at
70℃, and about 1.20 times at 90℃, compared to atemperature of 30℃. The volume expansion of water
is about 1.01 times at 50℃, about 1.02 times at 70℃,
and about 1.03 times at 90℃, based on the density ofwater at 30℃. Therefore, it is considered that theoccurrence of minute cracks due to the expansion ofwater and air reduce strength characteristics. In
addition, the effects of cracks generated by the drying
shrinkage of the specimen surface is also considered.
Figure 7 shows the distribution of water content
before and after the test in the water tank. In addition,
Fig. 8 shows the distribution of water content in thetest conducted under the thermal environment. In
both cases, it was confirmed that the water content
after the test tended to decrease with increasing
temperature. It is considered possible that the higher
the temperature applied to the specimen, the higher
the evaporation from the surface or the inside of thespecimen and the lower the strength characteristics
due to the cracking caused by drying shrinkage. At
this time, cracking in the inside of the specimen could
not be confirmed, but cracks by drying shrinkage
were confirmed in the test after curing on the surface.

700
maximu m compressive
strength(kN/m2 )

600
500

400
300
200
100
0
0

20

40

60

80

100

Temperature(℃)

Fig. 6 Uniaxial compression test in a temperatureenvironment

8.0

Water content(%)

7.5

7.0
6.5

6.0
pre-test

5.5

after test

5.0
0

20

40

60

80

100

T empearture(℃)

700Fig. 7 Distribution of water content in water tankcuring test

maximu m compressive
strength(kN/m2 )

600
500

400
300
8.0

200

7.5
Water content(%)

100
0

0

20

40

60

80

100

Temperature(℃)

Fig. 5curingUniaxial compression test by water tank

7.0
6.5

6.0
pre-test

5.5
after test
5.0

0

20

40

60

80

100

T emperature(℃)

Fig. 8 Distribution of moisture content in the test ina thermal environment

238

GEOMATE – Melbourne, Australia, 11-13 November 2020

3.2 Results of Uniaxial Compression Tests at High
Saturation
compressive stress（kN/m2）

400

Figure 9 shows the results of uniaxial
compression tests with varying temperature andsaturation. Figure 10 shows the stress-strain curve at
30℃, and 90% saturation, and Figure 11 shows thestress-strain curve at 90℃ and 90% saturation. Figure
12 shows the distribution of water content.
Figure 9 shows that the results of the uniaxial
compression test under a saturation degree of 90%
could not confirm increasing strength with a rise intemperature. However, the stress-strain curves in Figs.
10 and 11 demonstrate different fracture patterns.
Ductile fracture with large plastic deformation until
fracture is observed in Fig. 10, and brittle fracturewith rapid fracture is observed in Fig. 11. The change
in water content is likely related to the cause of such
fractures. The water content distribution in Fig. 12
confirms that the water content before and after thetest did not change under the condition of 30℃, but
the water content tended to decrease rapidly under thecondition of 90℃. The test environment of 90℃ is
considered to have become a factor in decreasing thewater content, and the fracture form exhibited brittle
fracture with the lowering of the water content.
In addition, according to the uniaxial
compression test conducted by Nishimura underhigh-temperature action, uniaxial compression
strength was reported not to change with temperature
rise.
These results suggest that the uniaxial
compressive strength of bentonite specimens at highsaturation did not change with increasing temperature.

350
300
250
200
150
100

0
0.00

5.00

10.00

15.00

compressive strain（%）

Fig. 10

Stress-strain curve (30℃)

compressive stress（kN/m2）

400
350
300
250
200
150
100

50
0
0.00

Fig. 11

5.00

10.00

15.00

compressive strain（%）

Stress-strain curve (90℃)

22.0

450
400
350
300
250
200
150
100
50
0

21.5
Water content(%)

maximum compressive strength(kN/m2 )

50

21.0
20.5
20.0
pre-test

19.5
after test
19.0
0

20

40

60

80

0

100

T emperature(℃)

20

40

60

T emperature(℃)

Fig. 9Uniaxial compression tests varying
temperature and saturation

Fig. 12239

Distribution of water content

80

100

GEOMATE – Melbourne, Australia, 11-13 November 2020

4. CONCLUSION
In this study, the uniaxial compression test of
bentonite mixed with silica sand was carried out
considering temperature and degree of saturation. The
findings from this study are as follows:(1) The maximum compressive strength of
bentonite-silica
sand
mixed
specimens
decreased linearly with increasing temperature.
(2) Since the water content of the specimen
decreased after the test, cracks likely occurredon the surface and interior of the specimen,
leading to the decrease in strength.
(3) In the uniaxial compression test at highsaturation, the effect of the strength bytemperature could not be confirmed, but thedifference in the fracture form was apparent.
In future studies, the uniaxial compression testin which the degree of saturation is varied will be
continuously carried out, and the data will becollected cumulatively.
REFERENCES[1] Ichikawa N., Kanazawa S., Hayashi H., andIshiyama K,. Study on the mechanical behavior of
bentonite buffer considering temperature andsaturation changes in geological disposal
facilities., Journal of JGS (CD-ROM) ., 2017.
[2] Muto N., Kanazawa S., Hayashi H., Ishiyama K.,
and Iizuka A., Dynamic Characteristics ofBentonite
BufferMaterial
Considering
Temperature, Density and Saturation in Stratum
Disposal Facility, Environmental GroundEngineering Symposium., Journal of JGS,Vol.12,
2017, pp567-570,.
[3] Muto N., Kanazawa S., Hayashi H, Hoshi T., andKoji Ishiyama., Experimental study onmechanical properties of bentonite bufferconsidering various conditions., Journal of
JSCE(CD-ROM)., 2018.
[4] Yanai M., Kanazawa S., Ichikawa N., Muto N.,
Kobayashi S., and Ishiyama K., Uniaxial
compression test of bentonite buffer material withvarying silica sand content and temperature.,
Journal of JSCE., (CD-ROM), VII-133, 2019.
[5] Yanai M., Kanazawa S., Ichikawa N., Muto N.,
Kobayashi S., and Ishiyama K., Experimental
study on mechanical properties of bentonite
mixed with silica sand considering temperaturechange., Environmental Ground Engineering
Symposium, Journal of JGS., Vol.14., 2019.,
pp467-470.
[6] Japan Nuclear Cycle Development Institute(1999): Technical confidence on the disposal
facilities for high level radioactive wastes 2ndReport.
[7] Horiuchi Y., Takagi T., Method of the methylelene blue adsorption test for bentonite at AIST.,
Research Materials of the Geological Survey ofJapan., No.555(2012).
[8] JIS A 1216:2009, Method for uniaxial
compression test.
[9] Japan Nuclear Cycle Development Institute(1999): Engineering technology for geologicaldisposal[10] Nishimura T., Effect of saltwater and hightemperature on the engineering properties ofcompacted bentonite., Journal of JSCE., CS7013., 2018., pp.25-26.

240

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STUDY ON SOIL-BENTONITE VERTICAL CUTOFF WALLSWITH EARTHQUAKE-PROOF PERFORMANCE
Koji Watanabe1, Shinya Matsushita2, Daisuke Ueno3and Masaaki Hasegawa3
1

Department of Civil Engineering, Aichi Institute of Technology, Japan; 2Matsushita M&C Lab Co. Ltd.,
Japan; 3SeikoTone 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, laboratory-scale fundamental tests and in-situ full scale test were
performed to evaluate the hydraulic performance and the quality of cutoff wall. The following findings wereobtained from this study. Firstly, the hydraulic conductivity showed the high value and decreased with theconfining pressure from the triaxial hydraulic conductivity tests. The hydraulic conductivity values of soilbentonite cutoff wall have the excellent correlation with the confining pressures due to overburden pressures.
Secondly, according to the in-situ full scale test, it is confirmed from the results of CPT that the soil-bentonite
vertical cutoff wall which is developed in this study can be constructed the constant quality along to the depth
direction by using TRD (Trench cutting Re-mixing Deep wall) method. In addition, it is examined that the soilbentonite vertical cutoff wall has the high hydraulic conductivity from hydraulic conductivity test for the coresampling specimen of in-situ full scale test.
Keywords: Vertical cutoff wall, Earthquake-proof performance, Hydraulic conductivity, CPT test

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 walls as bentonite and in-situ soil are mixed.
2. The damage of cutoff walls does not occur when

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.
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.
In this study, the construction of vertical cutoff
walls is supposed by mixing of heavily
concentarated specialty bentonite slurry and in-situ
soil. The laboratory-scale fundamental tests and insitu full scale test were performed to evaluate the241GEOMATE – Melbourne, Australia, 11-13 November 2020

hydraulic performance and the quality of cutoff wall.
CONSTRUCTION METHODBENTONITE CUTOFF WALLSOFSpecialty bentonite slurry
Verticalcutoff wall

SOIL-

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 shapeconnects the base machinery and this auger inserts
into in-situ ground.
2. The specialty bentonite slurry injects into the insitu ground and the chainsaw type auger agitates insitu ground as the chainsaw type auger moves tohorizontal direction.
It may be suggested that the homogeneous cutoff
wall is constructed by using TRD method.

Specialty
bentoniteslurryLABORATORY-SCALL TESTS

Fig. 2Concept of vertical cutoff walls.

Fig. 3

Construction machinery for TRD method.

Table 1 Compounding condition.

The laboratory-scale tests are carried out toconfirm the fundamental performance of cutoff wallin this chapter. The fundamental study which is
described in this chapter is used the artificial cutoff
wall material to clarify the performance.

Soil
type
Case 1Case 2Making Process of Cutoff Wall Material

Sand
(g)

Sandy
soil
Composite
soil

1713
1486.6

Kaolin
clay
(g)
165.2

Water
(g)

CaIonbentonite exchange(g)material (g)

480.4

122.6

4.4

541.6

122.6

4.4

Table 2 Physical properties of vertical cutoff wall.
Case 1Liquid limit wL (%)
Plastic limit wp (%)
Plastic index Ip

The compounding condition of artificial cutoff
wall material is proposed in Table 1. The
constitutional materials are sand (Silica sand No. 5),
Kaolin clay (only Case 2), Ca-bentonite, water andion-exchange material. The making process of cutoff
wall is described as 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 are
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.

47.1
16.0
31.1

Case 2Liquid limit wL (%)
Plastic limit wp (%)
Plastic index Ip

38.8
10.6
28.2

100

Grain passing (%)

80
60
40
Case 1Case 220
00.001

Fig. 40.01

0.1
1
Grain size (mm)

10

100

Grain size distribution.

Water Absorption Expansive Pressure Test
Physical Properties
The water absorption expansive pressure test for
the vertical cutoff wall material is carried out toevaluate the confining pressure for the triaxial
compression test and the triaxial hydraulicconductivity test. The confining pressure which isacted in the triaxial compression test and the triaxial
hydraulic conductivity test is considered based on

The physical properties of vertical cutoff wallmaterial are shown in Table 2. The plastic index ofmaterial for Case 2 is smaller than that for Case 1.
Figure 4 indicates the grain size distribution ofvertical cutoff wall material. According to Fig. 4, thefine content of Case 2 dominates that of Case 1.

242

GEOMATE – Melbourne, Australia, 11-13 November 2020

the expansive pressure obtained from this test. The
time history curves of expansive pressure aresuggested in Fig. 5. From Fig. 5, the expansive
pressure does not occur in both Case 1 and Case 2.
Therefore, it is said that the target confining pressureis acted in the triaxial compression test and thetriaxial hydraulic conductivity test.

water pressure shows the negative pressure in theCase 1 and the positive pressure in the Case 2
respectively. The strength parameters are evaluatedfrom the results of triaxial compression tests as
described as follows;
Case 1: c=124.1 (10.9) kN/m2, =33.1 (37.1) deg.
Case 2: c=3.7 (1.4) kN/m2, =24.5 (32.6) deg.
Where, c: cohesion, : internal friction angle.
Here, the strength parameters are shown by total
stress and effective stress. The values shown inparentheses are obtained from effective stress.

Triaxial Compression Test
The triaxial compression tests are carried out toobtain the strength and deformation characteristicsof the vertical cutoff wall material. The condition of
triaxial compression tests are CU bar (Consolidated
Undrained) and strain rate 0.1%/min. Three kinds ofconfining pressure such as r’=50, 100 and 200kPa
are adopted in the triaxial compression tests.
The results of triaxial compression tests areshown in Fig. 6 and Fig. 7 including therelationships between deviator stress and axial strain
and the relationships between pore water pressure
and axial strain. According to Fig. 6 and Fig. 7, themaximum deviator stress increases as the confiningpressure increases. The trend indicates that thedeviator stress reaches the maximum value in theCase 1. However, it is said that the deviator stressincreases because the deviator stress does notreaches the maximum value in Case 2. The pore

Triaxial Hydraulic Conductivity Test
The triaxial hydraulic conductivity tests are2

Expansive pressure (kN/m2)

0
0

2000

4000

6000
8000
Time (min)

-0.5
-1

-2

Time history
pressure.

curvesofexpansive

300
Confining pressure:50kPa
Confining pressure:100kPa
Confining pressure:200kPa

250
Compressive strength (kN/m2)

Compressive strength (kN/m2)

0.5

Fig. 5

800

600

400
Confining pressure:50kPa
Confining pressure:100kPa
Confining pressure:200kPa

200

200

150

100

50

0

0
0

3

6
9
Axial strain (%)

12

15

0

60

2

4

6

8
10
Axial strain (%)

12

14

16

140
Confining pressure:50kPa
Confining pressure:100kPa
Confining pressure:200kPa

20
0

0

3

6

9

Confining pressure:50kPa
Confining pressure:100kPa
Confining pressure:200kPa

120

Pore water pressure (kN/m2)

40

Pore water pressure (kN/m2)

1

-1.5

1000

-20

Case 1Case 2

1.5

12
15
Axial strain (%)

-40
-60

-80
-100

100
80
60
40

20

-120

Axial strain (%)
0

0

-140

Fig. 6

Results of triaxial compression test for
Case 1.

Fig. 7
243

2

4

6

8

10

12

14

16

Results of triaxial compression test for
Case 2.

GEOMATE – Melbourne, Australia, 11-13 November 2020

carried out to estimate the effect of confiningpressure on the hydraulic conductivity for thevertical cutoff wall material. The triaxial apparatus
is used to prevent the water leakage from the sidesurface of the specimen. The results of hydraulicconductivity obtained from the triaxial hydraulicconductivity tests are suggested in Fig. 8. According
to Fig. 8, the trend indicates that the hydraulicconductivity decreases as the confining pressure
increases. It is observed that the obtained
experimental values are fitted as the function ofconfining pressure. The hydraulic conductivity
values of soil-bentonite cutoff wall have theexcellent correlation with the confining pressuresdue to overburden pressures.

1.6E-09

Case 1
Hydraulic conductivity (m/sec)

1.4E-09

Case 2

1.2E-09y = 2E-09e-0.011x
R² = 0.9871

1.0E-09
8.0E-10
6.0E-10

4.0E-10
y = 9E-10e-0.011x
R² = 0.9999

2.0E-100.0E+00
0

Fig. 8100
150
Confinig pressure (kPa)

200

250

Relationships
betweenhydraulicconductivity and confining pressure.

IN-SITU FULL SCALE TESTThe vertical cutoff wall in this in-situ full scale
test is constructed by using TRD method. The detailsof construction method and process are described in
the chapter of CONSTRUCTION METHOD OF
SOIL-BENTONITE CUTOFF WALL. The in-situ
full scale test is carried out to evaluate the quality of
constructed cutoff wall and the applicability of RICPT (Radio Isotope-Cone Penetration Test) for thequality management after construction.

50

No.2 (RI-CPT)

No.1 (RI-CPT)
No.3

Buried SPT N-value
50
soil 0Loam
Tuffaceous
clay

0
5

Depth

G.W.L

Condition of In-situ Full Scale Test

10

15

The condition of in-situ full scale test is shownin Fig. 9. The constructed cutoff wall is also inserted
in Fig. 9. The ground consists of buried soil, loam,
tuffaceous clay and fine sand. The dimensions ofeach tested cutoff wall are 8000mm in length,
550mm in width and 20m in depth. The
compounding condition of injection slurry ischanged at the middle section of cutoff wall in theplaner direction. Thus, the quality of cutoff wall isconfirmed two different conditions on the one cutoff
wall. Table 3 shows the compounding conditions ofinjection slurry.
RI-CPT is carried out to examine the quality of
cutoff wall and the applicability of quality
management. RI-CPT can be measured wet densityof ground and three components (tip resistance, shaft
resistance and pore water pressure). In this in-situ
full scale test, the measurement of RI-CPT along thedepth direction is carried out at the middle section of
cutoff wall. Moreover, the core sample along thedepth direction is obtained from the wall of No. 1 toexamine the performance of cutoff wall.

Fine
sand

20

8000

8000

23.36
（m）

Fig. 9Condition of in-situ full scale test.

Table 3 Compounding condition.

No.1
No.2

Ca-Bentonite
consentration
(%)
50
45

Injection ratio (%)
Ca-Bentonite
30
30

Ion-exchangematerial
55Dispersing
agent
(%)
0.33
0.33

which are shown in Fig. 10 (a)~(c) increase in thedepth direction as the effective stress increases. It is
confirmed that the values of tip resistance and shaft
friction at the depth of GL-6.0 shows the large
values in the Case 1. It is also observed that the tip
resistance and the shaft friction increase rapidly at
the depth of GL- 4.0, 6.0, 9.0, 12.0 and 15.0m in theCase 2. This fact implies that there is the thin soil
layer which includes the sand component at therelevant depth as mentioned the above sentence. The
inclined angle distribution as shown in Fig. 10 (d)indicates the large value at the deeper depth.
However, it is seen that the vertical accuracy of RI-

Results of RI-CPT and Laboratory Test
Figure 10 indicates the results of RI-CPT
obtained from the in-situ full scale test. The values
244

GEOMATE – Melbourne, Australia, 11-13 November 2020

Tip resistance qt (MPa)
2
4
6
8

0

10

0

0

5

5

Depth (GL-m)

Depth (GL-m)

0

10

15

Inclined angle q (deg.)
3
6
9
12

10

15

No.1
No.2

No.1
No.2
20

20

25

25

(d) Inclined angle(a) Tip resistance0

Shaft friction fs (kPa)
10
20
30
40

1

50

0

0

5

5

Depth (GL-m)

Depth (GL-m)

15

10

15
No.1
No.2

2.5

10

15

20

20

Wet density rt (g/cm3)
1.5
2

No.1
No.2
No.1
No.2
No.3

25

25

(e) Wet density
Fig. 10 Results of RI-CPT(b) Shaft resistance0

Pore water pressure u (kPa)
100 200 300 400

500

Hydraulic conductivity k (x10-10 m/sec)
0
5
10
15
20

0

0

5
10

Depth (GL-m)

Depth (GL-m)

5

15
No.1
No.2
20

25

10

15

20

(c) Pore water pressure
CPT is kept along the depth direction because thevalues of inclined angle is small. Figure 10 (e) plots
the wet density distribution obtained from the RICPT and the laboratory test of core sample. Here,the solid lines and the circular plots indicate thevalue of RI-CPT and the value of laboratory test
respectively. It is observed that the wet density is thealmost constant value along the depth direction.

No.1
No.2
No.3

25

Fig. 11 Results of hydraulic conductivity tests
Compared to the results of RI-CPT and that oflaboratory test, the wet density from the RI-CPT
agrees almost with that of laboratory test. Thus, it
may be concluded that the quality of cutoff wall
which is constructed by TRD method indicates theconstant quality in the depth direction.
245

GEOMATE – Melbourne, Australia, 11-13 November 2020

Copressive strengtyh qu (kN/m2)
0
20
40
60
80
100

Figure 11 suggests the hydraulic conductivity
distribution for the core sampling specimen obtained
from the test cutoff wall. The triaxial hydraulicconductivity test is carried out the specimen No. 1
and No. 2. On the other hand, the one dimensional
hydraulic conductivity test which is injected thepressured water is carried out the specimen No. 3. In
Fig. 11, the obtained hydraulic conductivity values
show the ranges of 6.1x10-11~1.7x10-9 (m/s).
Therefore, it is said that the constructed test cutoff
wall is impermeable and that has the high barrier
performance.
The results of unconfined compression test forthe core sampling specimen is plotted in Fig. 12. The
values of unconfined compression test indicate theranges of 12~28kN/m2. This fact implies that thecutoff wall is constructed the constant quality in thedepth direction.

0

Depth (GL-m)

5

10

15

20
No.1

Laboratory test

25

Fig. 12 Results of unconfined compression test
along to the depth direction as TRD (Trench cutting
Re-mixing Deep wall) method was used. In addition,
it is examined that the soil-bentonite vertical cutoff
wall has the high hydraulic conductivity fromhydraulic conductivity test for the core sampling
specimen of in-situ full scale test.

CONCLUSIONSThe 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. In
this study, laboratory-scale fundamental tests and insitu full scale test were performed to evaluate thehydraulic performance and the quality of cutoff wall.
The following findings were obtained from thisstudy.
1) The hydraulic conductivity showed the highvalue and decreased with the confining pressure
from the triaxial hydraulic conductivity tests. The
hydraulic conductivity values of soil-bentonite
cutoff wall have the excellent correlation with theconfining pressures due to overburden pressures.
2) According to the in-situ full scale test, it isconfirmed from the results of CPT that the soilbentonite vertical cutoff wall which was developedin this study was constructed the constant quality

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.

246

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

FUNDAMENTAL STUDY ON TRENCH STABILITY OF
DIAPHRAGM WALL
Yuki Sasaki1, Koji Watanabe1, Makoto Arakawa2 and Tadahisa Yamamoto2
1
Department of Civil Engineering, Aichi Institute of Technology, Japan; 2 Obayashi Corporation, Japan
ABSTRACTThe diaphragm wall construction method is used as the retaining wall in the large excavation and the adjacentconstruction because the diaphragm wall has the high stability in the excavation. It is important to be stable thetrench of diaphragm wall in the construction process from the excavation to the concreting. There are someprevious studies on the trench stability of diaphragm wall. However, it is needed to evaluate the trench stabilityon the complicated conditions such as the adjacent construction. The purpose of this study is to evaluate thetrench stability of the diaphragm wall on the simple construction condition. The centrifuge model tests and thenumerical analysis were carried out to evaluate the trench stability of the diaphragm wall. In the centrifuge
model tests, the model ground was prepared the medium dense sandy ground (Relative density Dr=40 and 60%).
All the tests were conducted under 50G. It is examined that the failure mode of trench is influenced by the model
ground conditions. The FEM numerical analysis was carried out to simulate the failure mode based on thecentrifuge model tests. The SSR (Shear Strength Reduction) method was used to the numerical analysis.
According to the numerical analysis, it is concluded that it is possible to simulate the failure mode which was
obtained from the centrifuge model tests.
Keywords: Diaphragm Wall, Trench Stability, Centrifuge Model Test, FEM, Shear Strength Reduction Method

test is to simulate the equivalent stress and strain
field for the actual ground under the centrifugal
acceleration field. Katagiri et al.(2000) carried out
the centrifuge model tests to confirm the trench
stability of diaphragm wall [5]. They suggested thatthe stability condition of slurry trenches wasdependent on the length of trench, and the lateralconfining force around the sliding brock decreased
at the yield condition. Ishii et al. (2001) carried out
the three dimensional elasto-plastic FEM
simulations including the shear strength reduction
method to examine the trench stability of diaphragmwall based on the centrifuge model tests [6]. It may
be concluded that the global safety factor which was
obtained from FEM simulation could be evaluatedaccurately. However, this study is subjected to thepartially stability problem such as only the
excavation process.
The purpose of this study is to develop theevaluation method for the trench stability by theexperimental approach and the numerical approach.
The centrifuge model tests were carried out toexamine the failure mechanism. Moreover, the shear
strength reduction FEM (here after, SSR-FEM) was
performed to simulate the experimental results andto confirm the applicability of SSR-FEM for the
trench stability of diaphragm wall.

INTRODUCTION
The diaphragm wall method is used as aretaining wall in the large excavation and theadjacent construction because the diaphragm wall
has the high stability during the excavation. Figure 1
indicates a series of construction process for thediaphragm construction method. The diaphragmwall method is also shown in Photo 1. It is important
to be stable the trench of diaphragm wall in theconstruction process from the excavation to
concreting. There are some previous studies on the
trench stability of the diaphragm wall. The
diaphragm wall is generally stable that the fluidpressure such as the slurry in the trench supports theearth pressure of ground around the trenchsummarized in JGS (1988) [1]. The previous
evaluation methods of stability on the trench
proposed by Sato et al. (1975), Kanatani et al.
(1984) and Higuchi et al. (1994) based on the above
described concept [2]–[4]. The global safety factorof trench stability is calculated by the limit
equilibrium method assuming the slip surface.
However, there is the difference of calculated global
safety factor because the assumed slip surface on the
trench according to the trench evaluation method
differs. The different evaluation method is used in
the practical engineering because the shape of slip
surface on the trench of diaphragm wall is unclear.
The centrifuge model test is adopted as experimental
approach to solve the problems as mentioned theabove sentence. The purpose of centrifuge model

CENTRIFUGE MODEL TESTThe centrifuge model test which can be
simulated the equivalent stress and strain field for247

GEOMATE – Melbourne, Australia, 11-13 November 2020

Concrete

SlurryTremie pipe
Excavation
machineExcavation

Fig. 1

Reinforced cage
Install the reinforced cage

Concreting

Construction process for the diaphragm construction method(a) Excavation process(b) Concreting process

Photo 1 Diaphragm wall methodtarget centrifuge acceleration of 50G reaches. The
mud membrane to stable the model trench issimulated by plastic package which has a thicknessof 0.1mm. The model stable liquid which is pouredin the model trench is used the salty water (Specificgravity, 1.05). Here, the specific gravity of stableliquid used in the experiment is equivalent to that ofactual stable liquid such as bentonite slurry. Silica
sand No.7 was used in the centrifuge model test.
Figure 3 shows the grain distribution curve of Silica
sand No. 7, and the physical properties of that are
shown in Table 1. The model ground was preparedby air-pluviation method using Silica sand No. 7 toachieve a relative density of Dr=40% and Dr=60%
when the model trench equipped with the spacer
between the plastic package and the model ground
was installed in the soil container. The thin soil layer
made by color Silica sand No. 7 was installed atevery 50mm intervals of depth direction in themodel ground to observe the failure mode. Moreover,
the target in front of the soil container such as theacrylic window to conduct the image analysis forthe observation of ground deformation was installedaround the model trench. After preparing the model
ground, it is poured the model stable liquid into themodel trench approximately 20mm above theground surface. Then, the model ground is saturated
by pouring the water from the bottom of soil
container. The spacer between the model trench andthe model ground is pulled out when the model
groundissaturated.
Here,
the
stablethe actual behavior by considering the similarity
laws is the experimental approach to apply this study.
The beam type centrifuge apparatus was used tothis study. The beam type centrifuge apparatus
consists of the whirling arm, the driving device, themeasuring device installed in the arm and the system
to record the video.
Method of Centrifuge Model Test
The test name of centrifuge model test such as
Case 1 and Case 2 show the test case of relative
density Dr=40% and Dr=60% respectively. The
purpose of centrifuge model test is to grasp theeffect of model ground conditions on the trench
stability for the model diaphragm wall. The
centrifuge model tests were carried out by using two
kinds of model ground conditions such as therelative density Dr=40 and 60% to focus the failure
mechanism of model trench.
The schematic view of centrifuge model is
proposed in Figure 2. The shape of model trench has
the three-dimensional shape with a width 48mm, a
length 80mm, and a height 500mm. It is possible toobserve the deformation behavior when the grounddeformation occurs in the experiment because thefront of soil container which is installed model
trench is made by the acrylic window.
The centrifuge model tests were conducted under
50G of centrifuge acceleration field. The model
stable liquid in the model trench decreases when the248

GEOMATE – Melbourne, Australia, 11-13 November 2020

100

48

Model trench
80

20

80

Model ground
Relative density
Dr=40, 60%

Grain passing (%)

350

Plan view

60
Elevation view

60
40

500

650

20
0
0.001

TargetColor sand layer

Model trench

Fig. 3

Laser displacementtransducer

0.01

0.1
1
Grain size (mm)10Grain size distribution curve
Stable liquid level in model trench (m)

930

0

1

2

3

500

Fig. 2

Schematic view of centrifuge model

0
Case 1

3

Settelement (mm)Table 1 Physical properties-500

Case 2-1000

Soil particle density ρ s (g/cm )2.654

Mean diameter D 50 (mm)0.185

Maximum void ratio e max

1.193

-2000

Minimum void ratio e min

0.698

-2500

-1500

(a) 1m from model trenchliquid can be drained by controlling the solenoid
apparatus. The pore water pressure transducer wasused to measure the stable liquid level in the model
trench. The laser displacement transducer was used
to measure the settlement of ground surface.

Stable liquid level in model trench (m)
0

1

2

3

40

Settelement (mm)0Results of Centrifuge Model Test
In this chapter, the results of centrifuge model
tests are described in the prototype scale.
Figure 4 indicates the relationships between
settlement and stable liquid level in model trench.
Here, the result as shown in Figure 4 (a) is Case 1such as the case of relative density Dr=40%, and thatas shown in Figure 4 (b) is Case 2 such as the caseof relative density Dr=60%. The measurement
locations at the ground surface are 1.0m and 4.0m
from the model trench respectively. According to
Figure 4, the settlement on the ground surface
increases as the stable liquid level in the model
trench decreases. Especially, Figure 4 (a) has apronounced tendency to decrease, and the settlementon the ground surface begin to increase since
immediately after the drawdown of model stableliquid, the settlement of ground surface eventually
showed a large increase of approximately 2m.
Compared with Case 1 and Case 2, it is seen that

-40
-80
-120
Case 1-160

Case 2-200(b) 4m from model trench
Fig. 4 Relationships between settlement andstable liquid level in model trench
the settlement of Case 1 occurs when the stableliquid level in the model trench is small. This result
implies that the relative density of Case 1 is small.
The settlement and the stable liquid level in themodel trench are almost alike up to 1.3m decrease as
proposed in Figure 4(b). After that the settlement ofCase 1 shows a large increase.
The target to obtain the deformation of model
249

GEOMATE – Melbourne, Australia, 11-13 November 2020

300

Plan view

Y direction (mm)

250

4m

200

2.4m

150

45.5m

Elevation-view

100

G.W.L. is equal to G.L.

50
0The stableliquid level
decrease 0.5m
intervals of
depth direction

-50
-50

0

50

100

150

200

250

300

350

X direction (mm)Stable liquid
25m
31m
Ground(a) Case 1 (Dr=40%)
300

2.4m

Y direction (mm)

250

45.5m

Fig. 6 The schematic view of the numericalanalysis

200
150
100

Table 2 Scales on analysis model

50
0

Scales

-50
-50

0

50

100

150

200

250

300

350

X direction (mm)(b) Case 2 (Dr=60%)
Fig. 5Ground model Trench model Guide wallx (width)

45.5

2.4

0.5

y (depth)444z (height)

25

25

2.5
Unit: (m)Results of image analysis (Case1 and 2)

because the elasto-plastic FEM simulations take up
deformation problems in SSR-FEM.

ground was installed along the acrylic window, andthe image analysis was carried out by tracking thetarget movement sequentially. Figure 5 shows theresults of image analysis. According to Figure 5, it ispointed out that the settlement at the location of
50mm in the X-direction from the model trenchshows the large displacement in both Case 1 andCase 2. Compared with Case 1 and Case 2, Case 1
shows a large displacement near the model trench. It
is said that the ground deformation of model groundnear the model trench become noticeable in Case 1
because the relative density is small and model
ground is loose. In this experiment, the failure ofmodel ground was observed at near the groundsurface because the self-weight of model groundabove the water level acts to the model ground as thesurcharge pressure due to the change of the groundwater level.

Method of FEM Numerical Analysis
The purpose of this analysis is to simulate theresults of centrifuge model tests as described theprevious chapter. The SSR-FEM simulation of Case
2 was carried out by using Midas GTS-NX. The
model trench is one fourth of the trench in the
prototype scale by considering symmetry on the
trench. The failure criterion of soil is defined by
Mohr Coulomb's equation. The iteration process isbased on the modified Newton Raphson method.
The upper value of iterations is set in 500. The
convergence criterion is given by the ratio divided
displacement increment by the total displacement,
10-5 is set in this study. It is judged the FEM isdiverged when the criterion is not achieved less than
10-5 within 500 iterations. Figure 6 indicates theschematic
viewofnumerical
analysismodel, ∆H=+1.0m is set as the initial state. Here,∆H=+1.0m means the stable liquid level is 1.0m
higher than the groundwater level (i.e. the groundsurface). The stable liquid level decrease 0.5m
intervals of depth direction at every analytical stepfrom the initial state. The deformation behavior was
also confirmed by calculating the global safety
factor at every analytical step. Table 2 shows thescales on the analytical model which are classified

FEM NUMERICAL ANALYSISThe SSR-FEM which is the elasto-plastic FEM
simulations including the shear strength reduction
method can be evaluated the global safety factor
which is defined by limit equilibrium methods
without assuming slip surface. In addition, there aresome advantages to deal with the complicatedconditions and to be considered the confiningpressure which acts to a sliding earth mass naturally
250

GEOMATE – Melbourne, Australia, 11-13 November 2020

ratio is obtained from the result on K0 consolidation
test. The density of model ground is calculated byusing void ratio e and specific gravity Gs. In theboundary condition, the displacement on the bottom
surface, back surface and right side surface isconfined in the all direction (X, Y and Z). The
displacement in front of mesh is confined in the only
the X direction. The pressure difference between thestable liquid and the groundwater act to the left side.

as model ground, model trench and model guidewall in the prototype scale. Here, the guide wall toprevent the failure of model trench is used to theactual construction site. The X, Y and Z in Table 2 iswidth, depth and height respectively. Figure 7
indicates the mesh division and the model trench
located at left side in the finite element mesh. The
mesh division in X direction was finely at the model
ground near the model trench, but the otherdirections are equally divided. The analyticalparameter of ground and stable liquid are shown in
Table 3. The material parameters of cohesion c’,internal friction angle φ’ and elastic modules E50 isdetermined based on the triaxial compression tests.
The cohesion c' obtained from the triaxial
compression tests is 2.90kN/m2. However, cohesionc' used in numerical analysis is set as 0kN/m2
because the results of centrifuge model test areconsidered. The elastic modulus regarding the
ground sand have characteristics which depend on
confining pressure. However, the elastic modules inthis analysis is adopted as the constant value such as
the smallest value in the triaxial test. The Poisson's

Results of FEM Numerical Analysis
The results of numerical analysis are described in
the prototype scale in this chapter.
Figure 8 (a) and (b) show the contour of Xdirection displacement when the difference of waterlevel ∆H is +0.0m and -1.0m. Both Figure 8 (a) and(b) show the deformation immediately after the
failure. According to the contour of X-direction
Plan viewｘｚ

Table 3 Analytical parameter of ground and stableliquidProperties

Ground3

Density, ρ (g/cm )1.90

Elastic modules, E' (kPa)8837

Poisson's ratio, ν

0.277

Cohesion, c' (kPa)0Ιnternal friction angle, φ ' (deg)38.8

Dilatancy angle, ψ ' (deg)38.8

Elevation view

Stable liquid

1.05

ｚFig. 7

Finite element mesh used for simulation(a) Displacement(x) ∆H=+0.0m(b) Displacement(x) ∆H=-1.0m(c) Maximum solid shear strain ∆H=+0.0m(d) Maximum solid shear strain ∆H=-1.0m

Fig. 8

Displacement (X) and maximum solid shear strain
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Global safety factor:Fs

1.10

test and the numerical analysis. The following
findings were obtained from this study.
1) The centrifuge model tests were carried out toexamine the difference of model condition.
Compared with Case 1 and Case 2, Case 1 such as
Dr=40% shows a large displacement. It is concluded
that the ground deformation of model ground near
the model trench become noticeable in Case 1
because the relative density is small and model
ground is loose.
2) It is seen that the noticeable deformation occur atthe shallow location near the ground surface in boththe centrifuge model tests and the numerical analysis.
It is suggested that the deformation behavior for thecentrifuge model test can be simulated by thenumerical analysis of SSR-FEM.
3) The global safety factor is equal to approximately
1.0 when the difference in the water level ∆H is+0.0m. After that, the global safety factor has apronounced tendency to decrease. It was guessedthat the global safety factor can be also simulatedby the numerical analysis of SSR-FEM.

1.05
1.00
0.95
0.90
0.85
1

0.5

0

-0.5

-1

The difference in the water liquid level ∆H (m)Fig. 9 Relationships between global safety factor
Fs and the difference in the water level ∆Hdisplacement, the deformation toward to the trench
was confirmed at the shallow location on the groundnear the trench. In addition, the deformation toward
to the ground was also examined at the deeper zonesof the ground near the trench. Figure 8 (c) and (d)show the contour of maximum shear strain analyzed
when the difference of water level ∆H is +0.0m and-1.0m. According to Figure 8 (c) and (d), it is seenthat the noticeable deformation occurs at the shallow
location near the ground surface. The purpose of the
SSR-FEM is simulated the ground on relative
density Dr=60%. It corresponds to Figure 5 (b) of
the results on image analysis. According to Figure 5(b), the similar behavior was obtained at the shallow
location near the ground surface. Therefore, it issuggested that the deformation behavior for thecentrifuged model test can be simulated by SSRFEM as described in this chapter.
The relationships between global safety factor
and the difference of the water level ∆H are shownin Figure 9. Here, the difference in the water level∆Η means the difference between the water level
and the groundwater level. From the results of
Figure 9, the global safety factor decrease as thestable liquid decrease. These relationships can be
expressed the secondarily approximated equation.
The global safety factor is equal to approximately
1.0 when the difference in the water level ∆H is+0.0m. After that, the global safety factor has apronounced tendency to decrease. In the centrifuge
model test, it was confirmed the ground failure at thestate among approximately ∆H=-0.5m. Therefore, it
was guessed that the global safety factor can also be
simulated.

REFERENCES[1] Japanese
GeotechnicalSociety(1988).
Diaphragm Wall Construction Method,
Japanese Geotechnical Society (in Japanese).
[2] Sato, H. & Akino, N. (1975). “Stability
Analysis for Slurry Trench”, Proceedings of
10th Annual Conference of Japanese
Geotechnical Society, 1023-1026 (in Japanese).
[3] Kanatani, Y. & Akino, N. (1984). “Full-sized
Test on Stability of Vertical Sides for Slurry
Trench Excavation”, Soils and Foundations,Vol. 24, No. 4, 171-182 (in Japanese).
[4] Higuchi, Y., Touda, J., Nakura, K. & Kawasaki,
K, (1994). “A New Stability Analysis Method
for Slurry Trenches Constructed in Sandy
Ground”, Tuchi-to-Kiso, Vol. 24, No. 3, 7-12
(in Japanese).
[5] Katagiri, M., Ishii, T., Saitoh, K., Masuda, T.,
Aizawa, F. & Ugai, K, (2000). “Observationand Evaluation on Failure Mechanism of Slurry
Trenches in Sandy Ground”, Journal ofJapanese Society for Civil Engineering, No. 666,
Ⅲ-53, 127-143 (in Japanese).
[6] Ishii, T., Saitoh, K., Kuwabara, K. & Ugai, K,(2001). “Stability Analysis by 3-D ElastoPlastic Finite Element Method for Slurry
Trenches Constructed in Sandy Ground”,
Journal of Japanese Society for Civil
Engineering, No. 673, Ⅲ -54, 1-14 (inJapanese)

CONCLUSIONSThe purpose of this study is to confirm the trench
stability of diaphragm wall by the centrifuge model252

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A PRACTICAL APPROACH TO DETERMINE THE TEMPERATURECORRECTION FOR EC-5 MOISTURE SENSORS EMBEDDED IN
VERTOSOL
Rajitha Shehan Udukumburage1, Chaminda Gallage2 and Les Dawes3
1,2,3

Science & Engineering Faculty, Queensland University of Technology, Australia
ABSTRACTThe expansive soils are predominant in arid and semi-arid regions, and they are highly moisture sensitive. The
investigation of the climate-ground interaction of expansive soils is extremely important to understand the hydromechanical responses of such soils. During the past decade, the low cost and simple methods to reliably monitor
the soil volumetric water content have been popular among the geotechnical practitioners. As a result, EC-5
moisture sensors have been widely used in agricultural and scientific projects due to the minimized salinity andtextural effects on the final volumetric water content readings. The changes in soil volumetric water content profile
can be effectively monitored using soil-specifically calibrated EC-5 sensors. However, due to the temperaturesensitivity of EC-5 sensors, a suitable correction factor is essential to determine the actual volumetric water content
of the expansive soil. In this study, the authors present a simple and practical approach to calibrate EC-5 sensors
incorporating the temperature effect. This simple calibration approach enhances the accuracy of the EC-5 moistureprofiling and eventually leads to safe decision making of geotechnical practitioners.
Keywords: Unsaturated soil, Soil moisture, Temperature correction, EC-5 moisture sensors

The monitoring of the soil water content in
expansive soils has been conducted based on both
destructive and non-destructive methods; however,the non-destructive methods are widely accepted due
to minimum disturbance to long-term in-situ
monitoring [17-19]. The radioactive non-destructive
methods such as gamma-ray attenuation and neutron
scattering have been mostly avoided due to possible
health hazards. As a result, the non-destructive
methods based on the dielectric constant of the media
have been trending during the past decade to monitor
the volumetric water content profile in expansive
clays [20-23]. EC-5 moisture sensors can be
identified for its wide application in research as nondestructive, dielectric sensors based on capacitanceprinciple. The simplicity, cost and the ability to takepoint measurements due to small size have been
beneficial for geotechnical practitioners whencompared to the other commercially available options[24-28].
According to Decagon, EC-5 sensors use 70 MHz
frequency which minimizes the textural and salinity
effects [8,9,15,16]. The volumetric water content
measurement of EC-5 sensors depend on thesurrounding soil volume of the probes. The change involumetric water content in expansive clays occurs
due to the volume changes in both the surrounding
soil and water [8,9,29,30]. Therefore, additional
considerations for embedment and calibration of EC5 sensors are required. Generally, these sensors
capable of measuring the volumetric water contents
of saturated soils up to 60%; however, is highly
dependent on the soil [10,11,15,18,20,22].

INTRODUCTION
The unsaturated soil behaviour and the soil
properties are significantly dependent on the soil
moisture conditions [1-4]. Therefore, it is imperative
to identify accurate methods and instrument toinvestigate the moisture variations in soil profiles [17]. These investigations include laboratory-based
element tests, model tests and field-based
investigations. The moisture sensitivity of expansive
clays are considerably greater compared to nonreactive soils and hence, much attention has been
given to these soils lately [3-11].
Expansive soils are widely distributed in arid andsemi-arid regions in the world [8-9]. The surface soils
in Queensland, Australia alone consists of more than
40% of these reactive soils. The swell-shrink
characteristics of expansive soils when subjected into
alternate wet-dry cycles have caused severe
repercussions for the light-weight structures foundedon these clays [10-11]. Therefore, the geotechnicalpractitioners and researchers have identified theimportance of the climate-ground interaction in
expansive soils and investigated their behaviour
under field and laboratory conditions during the past2 decades [8-14]. Based on the current research, thevariations in soil moisture causes changes in soil
suction which eventually results in surface and subsoil movements in expansive soil strata [8-10], [1516]. Therefore, it is utmost important to determine thechanges in soil moisture profile to investigate theexpansive soil behaviour under different climaticconditions [8,9,15].
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GEOMATE – Melbourne, Australia, 11-13 November 2020

EC-5 sensors are mostly soil-specificallycalibrated for volumetric water content without
incorporating the temperature effect due to thecomplexities involved in expansive soils [31-34].
This study presents a simple and practical approachto determine the temperature correction function fora given expansive clay. These findings may be usefulfor geotechnical practitioners and researchers to soil
specifically calibrate EC-5 sensors maintaining highaccuracy and thereby obtain a reliable moistureprofile for their project-related decisions.

TEST MATERIAL
Natural expansive grey clay collected from
Sherwood, Queensland, in Australia was used in thisstudy. These grey Vertosol soils are widespread in thesouth-east Queensland region and representative of
subsoil conditions in Brisbane. Infiltration in thesesoils is moderate-to-low and known for swell-shrink
responses due to climate changes [35]. For laboratorymodel and element tests, 250 kg (dry soil mass) ofexpansive grey Vertosol was extracted and carefullytransported to Queensland University of Technology(QUT) with in-kind support from ‘The SoilTesters’.
The basic soil classification of the test material
was conducted according to Australian Standards (AS1289.3. 6.3, 2003; AS 1289.3.5.1, 2006; AS
1289.3.4.1, 2008; AS 1289.3.1.1, 2009a; AS
1289.3.2.1, 2009b; AS 1289.3. 6.1, 2009c). The grain
size distribution of the test material was obtained
from the combination of sieve and hydrometer
analyses. It contains 77.3% fines (particles finer than
75 microns) and 50.2% of clay (particles finer than 2
microns). Other basic properties of the clay, including
liquid limit (LL), plasticity index (PI), linear
shrinkage (LS), and the specific gravity were
determined in accordance with Australian Standard
test methods, and they were 67%, 37.2 %, 13.4%, and2.68, respectively [29-34]. The soil was classified as
CH (Clay of High Plasticity) according to the Unified
Soil Classification System (USCS). The compaction
properties (MDD = 1.75g/cm3, OMC = 18%) were
determined from the modified proctor compaction
test. X-ray diffraction (XRD) analysis carried out on
the test material identified smectite minerals, which
predominantly contribute to the expansive nature of
the soil.

Fig. 1 – EC5 moisture sensor calibration and dataacquisition

METHODOLOGY
Fig. 2 – EC5 moisture sensor embedment in
expansive soil

EC-5 sensors are ideal to measure soil moisture
content in localized areas due to the small sensor
length (50 mm) and its small area of influence. Thesesensors need to be properly calibrated for thetemperature variations to obtain a precise
representation of the sensor output due to themoisture variations within the range of 0-60% vwc.
The independency to salinity and soil texture
accompanied with low cost build-up makes EC-5s

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GEOMATE – Melbourne, Australia, 11-13 November 2020

preferable to use in research subsequent to rigorous
soil specific calibration, resulting in 1-2% accuracy.
In this study, soil samples of five different known
gravimetric moisture contents (i.e. 15%, 20%, 25%,
30% & 35%) were static compacted to achieve dry
density of 1.2g/cm3. The entire sensor body of EC-5
sensors were embedded on the compacted soil asshown in fig. 1. The minimum sample height for thesensor calibration was maintained more than that ofsensor body (i.e. 50 mm) to ensure the entire area iscontacted by the soil to obtain a precise correlation
between volumetric water content (%) and the sensor
output voltage as shown in fig. 2.
Since EC-5 moisture measurements are very
sensitive to temperature, temperature correction
factors were developed by measuring soil moisture atdifferent temperatures. Calibrated temperature sensor(Therm-EP) was embedded to acquire thetemperature variation throughout the measuring
duration. Soil moisture was maintained constantduring the experimentation by applying a thick grease
layer on the topsoil surface. Final gravimetricmoisture content of the soil was measured by ovendrying the test sample to make sure the moisture is
successfully maintained. Likewise, the sameprocedure was adopted for all the moisture contents
to derive a correlation between the volumetric watercontent (%), temperature (°C) and the sensor voltage
output (V).

controlled temperature variation inside theenvironmental chamber. Relative humidity inside thechamber was maintained at a reasonable value of 60%
during the investigation. The experimental results
indicated a significant variation of dielectric
responses of EC-5 moisture sensors (vwc) when thetemperature varies from 15°C to 33°C during heating
and cooling cycles.

The volumetric water content of grey Vertosol at
a given soil temperature can be derived with respect
to 20 °C. Equations 1 and 2 demonstrate therepresentative calibration equation for EC-5 moisturesensor.
𝛉𝛉𝐓𝐓 = 𝛉𝛉𝟐𝟐𝟐𝟐 − 𝛆𝛆𝐓𝐓 (𝐦𝐦, ∆𝐓𝐓)

Eq. 1

𝛉𝛉𝐓𝐓 = (𝟏𝟏. 𝟑𝟑𝟑𝟑𝟑𝟑 × 𝐀𝐀𝐄𝐄𝐄𝐄𝐄𝐄 − 𝟏𝟏. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎) − [𝐦𝐦 × (𝐓𝐓 −𝟐𝟐𝟐𝟐)]
Eq. 2

Where;𝛉𝛉𝐓𝐓 = Volumetric water content at T° C temperature𝛉𝛉𝟐𝟐𝟐𝟐 = Volumetric water content at 20° C temperature
𝛆𝛆𝐓𝐓 (𝐦𝐦, ∆𝐓𝐓) = Temperature correction factor

m = Change in volumetric water content for unitchange in temperature corresponds to volumetricwater content of sample at 20° C

RESULTS & DISCUSSION
The results of this study investigate the volumetricwater content variations of grey Vertosol at different
temperature conditions. Figure 3 illustrates thevariation of EC-5 moisture sensor responses underFig. 4 – Sample slope variation with respect to thevolumetric water content at reference temperature

Fig. 3 – EC5 moisture sensor responses withtemperature variations

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GEOMATE – Melbourne, Australia, 11-13 November 2020

The temperature calibration was conducted withrespect to T = 20° C and ‘m’ value for Eq. 3 can be
determined from Fig. 4 provided that volumetricwater content of the soil at 20°C is known. Equation
3 presents the statistical relationship obtained
between the ‘volumetric water content of soil at 20°C’
and ‘change in vwc for unit change in temperature’
that can be used to derive Eq. 4; temperature
calibrated volumetric water content of EC5.
𝐦𝐦 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 × 𝛉𝛉𝟐𝟐𝟐𝟐 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎

ACKNOWLEDGMENTS
Authors gratefully acknowledge the technicalstaff at Queensland University of Technology (QUT)
for providing the on-campus & off-campus (Banyo
Pilot Plant) laboratory facilities to conduct the test
series. Further, the gratitude should be extended to
Steve Hackworth from THE SOILTESTERS for
providing the in-kind support for the project. The firstauthor acknowledges the scholarship for doctoral
degree received from QUT, Australia.

Eq. 3

θT = (1.383 × AEC5 − 1.0868) − [{0.0074 × θ20

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Eq.4

Where;

AEC5 = Sensor voltage output from Soil DAQ
CONCLUSIONSThe climate-ground interaction of expansive
clays is imperative to monitor to understand the soil
responses due to climate variations. The volumetricwater content of expansive soils has been identified
as one of the critical parameters to investigate thestress state variables (i.e. soil suction anddisplacement) in such soils. The use of EC-5 moisturesensors to monitor the changes in volumetric watercontent profile has been trended due to low cost andsimplicity of the sensors. However, the use of thesesensors for expansive soil investigations requires theincorporation of temperature calibration. The
complexities involved in the current calibration
methods have restricted the use of these low-cost
sensors.
This study introduced a practical and simple
approach to accurately calibrate EC-5 sensors by
incorporating the temperature effect. The method has
been successfully validated for grey Vertosol and canbe used to soil specifically calibrate any EC-5 sensor
embedded in expansive clays. This approachenhances the applicability of low-cost EC-5 moisturesensors to accurately monitor the volumetric watercontent profile in expansive soils. Consequently, thequality of the data acquired can be maintained at areliable state for the decision making of the end-user(i.e. geotechnical practitioners). As a result, thequality of the geotechnical application and eventually,
the safety of human lives may be improved.

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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EXISTENCE OF SILT SEAM LAYER TRIGGERINGFLOW LIQUEFACTION ON THE PALU MW 7.5 EARTHQUAKE 2018
1,2

Dandung Sri Harninto1 and Widjoyo A. Prakoso2
Civil Engineering and Environmental, Indonesia University, Indonesia

ABSTRACT
Palu Mw 7.5 earthquake on 28 September 2018 caused extra ordinary geotechnical event in the world. Not only
building damage, tsunami caused by submarine landslide, but also the most dramatically event was huge scale offlow liquefaction. In the area studied, flow movement was more than 100 meters. Another location, the flow wasmore than 1,000 meters. This must be extra ordinary phenomena of liquefaction, which never happen at other sites
in the world so far. Geological formation of Palu city has been formed from young alluvial fan which have sourced
from mountain at east-west of the city. The formation is dominated by loose non-cohesive material, and fully
saturated due to very shallow ground water table. Sedimentation has been occurred layer by layer for long timeprocess, then it formed layers with variety of density and hydraulic properties. Moreover, during these processes
there were chance of weathering, and this could form series of layer with low permeability called silt seam. In
order to find out presence of silt seam layer inside deposit we have conducted trenching observations. There were
three pits of trenching, with depth 5-meters and 30-meters length in one location of flow liquefaction called Lolu
Village. Appreciation for non-commercial map application which can help make superimpose before-after
earthquake map with quite good precision. By the superimpose this trenching successfully dug cross between flow
and non-flow liquefaction area and cut one sand boil structure. So that we could differ layers of soil between theseareas correctly by finding existence of silt seam interlayer. By using void redistribution/water film theory from
Kokusho [24], this report will explore possibility facts as triggering huge magnitude of flow liquefaction.
Keywords: Palu Earthquake 2018, Liquefaction, Trenching, Silt Seamof earthquake magnitude data in table fig. 3 from
BMKG, other ground accelerations record should besignificantly contributed for generating extreme
disaster, especially liquefaction. BMKG web site
(link: http://shakemap.bmkg.go.id) shown spread out
earthquake magnitude data (as table at fig. 3).
Recognition from level of catastrophic occurred [2],USGS reported a much larger peak ground motion
acceleration and reached magnitude of 0.7g and thisvalue might be possible (fig. 2).
Before the Palu 2018 earthquake -for example
devastating liquefaction in Niigata, Japan (1964) andAlaska, USA (1964)-, lateral spreading hadmagnitude of teen meters or even less and prompted
some differential settlement which generated tiltingof buildings or houses. However, Palu earthquake
generated not only common liquefaction occurred,
but also hundred meters either lateral spreading orsoil mass flow which brought all objects moving
downslope with very gentle grade (2-3 %).
Liquefaction itself so far has never made fatalities,
but the liquefaction spawned thousand fatalities,
buried, and revolved them under deposit of sandwhich changed from solid transformed to mud(liquid) for some period.
Palu City, Indonesia is located at complex tectonic
formation where collision of three plates comprising

INTRODUCTION
Mw 7.5 earthquake struck Palu and Donggala City,
Central Sulawesi, Indonesia on 28 September 2018,as consequence of releasing energy of Palu-Kore fault
where aligned west side of Palu City. This event
created several catastrophic disasters, and caused
large number of casualties, destroyed housings,
buildings, and many infrastructures such roads,
power lines, bridges, and water channels, etc.
Tsunami, ground shaking, landslide arrived almost at
the same time and the most extreme liquefaction
disaster ever on earth caused by earthquake.
Indonesia Meteorology, Climatology andGeophysical Agency [4] reported that epicenter of themainshock was about 72 km north of the city withdepth 10 km. Series of significant foreshocks (Mw >4.4) occurred four hours before and main shock
arrived with magnitude Mw 7.5, and it was followed
also by series of aftershocks (Mw >5.7) for the periodof 48 minutes since main shock. Epicenters ofsignificant foreshocks-mainshock-aftershocks were
documented sweep over area Palu City (fig. 1).
BMKG and JICA (Japan International
Corporation Agency) [1] have released time history
data of ground acceleration of mainshock from one
survival seismograph station near Palu City (Balaroa
Station) as fig. 2. Unfortunately, for other shocks
writer have no access to get them. If we studied series
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Eurasian, Indo-Australia, and Philippines Sea.
Besides. There were at least three features of Palu city
associated to liquefaction disaster:
1. Palu city is just located across Palu-Kora fault,aligned from north to south of the city. This fault
has slip rate around 58 mm/year [13]. This
tectonic landscape will make Palu city become
very susceptible to high earthquake event..
2. Palu low land mostly consist of young alluvium
formed by sediment transport from east and westside of mountains [2]. In general, deposit mostly
originate from boulder, gravel, and sand withloose consistency. Natural water stream called
Palu river divide the city at centre with slope ofvalley around 1-6 %.
3. The valley area is located at middle of mountains;annual rain intensity is extremely low at most 500mili meter annually. Conversely, ground watertable was very shallow and there were some water
springs blown up ground surface and possibility
artesian spring. There was unlined water irrigation
canal system at eastern part of the city which has
been operating since 80’, many experts allegedthat this canal had contributed significantly
saturated sand deposit at east side of Palu [1], [5],[12].
LIQUEFACTION

Fig. 2 Time history of ground acceleration
BMKG&JICA (2018) [1] & USGS PGA data [2]Liquefaction is a phenomenon where saturated
soil deposit reduces stiffness and strength due to thegeneration of high excess pore pressures during
earthquake shaking. In certain condition the excess
could be the same with total overburden pressure(ru=1), and then effective pressure practiced zero, itmeans solid soil deposit for the time being behaves asliquid. Evidence of liquefaction in the event had been
onsite, we found number of sand boils footprint (fig.
5). Sand boil structure is formed by sand ejecta fromsand deposit of underlayer as releasing excess porewater pressure which brought some fraction of sand.

Fig. 3 Magnitude of foreshock, mainshock andaftershock of Palu earthquake 2018 [4]As Bradley [12] reported that mostly downhill
area of unlined canal occurred lateral spreading(figure 4) with variation of magnitude. The crown ofslide was generally along irrigation canal, however,at uphill of canals were still good, no evidence eitherliquefaction or lateral spreading at all. However, inseveral areas such as at Petobo, Lolu Village and JonoOge (Sidera) where occurred in hundred-meter of
spreading and it engendered huge number ofcasualties especially at Petobo area (fig. 4).
Basic mechanism could be the same for all
locations -even though the thesis is being still
debatable-. It started with fluidization of certain areaof sand deposit which initiated by liquefaction, withgentle slope of land commenced a long-distancelateral spreading caused by gravitational force. At theupper part of the area started to retrogressive collapse
which caused by lost their counterweight at toe,Fig. 1 Epicenter of the earthquake [2]260GEOMATE – Melbourne, Australia, 11-13 November 2020

underwent high excess pore water pressure createdwater sprang out (seepage) huge volume of water on
surface and perhaps followed by land subsidence asconsequently of sand deposit contraction process.
Water mixed with fine sand-silt formed mud flow andsparked more destructive process. Finally, all erodedand debris material with very high-water content
glided to downstream or stacked create deposition fill.
The hypothetic mechanism has not firmed yet, many
researchers are still working to discover accurate one.

Fig. 5 One of sand boil at Lolu Village site
Hall [5] and Bradley [12] that construction andoperation an unlined agriculture canal (fig. 4 & fig.6)
had played a large role in the slope failure. Yearlywater seepage from the canal increased height ofwater table accordingly saturated the underlaying
loose soil layer and caused of it making themliquefaction prone.
To know magnitude of lateral displacement, we
also have measured magnitude of lateral spreadinguse Google Earth Pro. At time being, we have been
only focusing at Lolu Village site with some reason
which will be discussed later. We use figure
published by Bradley [12], to help describe
magnitude of lateral spreading. The longest distancewas 155-meters (yellow arrow line), and relative long
distance occurred at northern part of area. At southernpart, displacement distance was average 70-meters.
At eastern part or upper hill, lateral displacement was
average 25-meters. Outside landslide area, many
objects were measured as well where lateral
movement with magnitude less than 10-meters.

Fig. 4 Disaster footprints: (a) Balaroa, (b) Petobo,(c) Petobo Irrigation Canal, (d) South Sibalaya,(e) Jono Oge, (f) Lolu Village
The above mechanism proposed by writer was
similar with Manson [2]. This suggestion is without
any contribution of breakage of confined aquifer.
Since JICA [9] suggested that there was contributedof confined aquifer breakage which maintenanceexcess pore water pressure kept high, this was argued
by Ishihara [11] as well. This hypothesis must bequite difficult to be proven, even though that will bepossibility happened. However, all the above mention
BH-3
BH-1
PTPP
BH-4
BH-5

BH-9

stopper zone

stacking
zone

TRENCHING OBSERVATIONSThere must be distinct onsite sand deposit
characteristic to make as discussed above could
BH-10

BH-6
Irrigation canaltrenching location

raft-initiation zone

retrogressive zone

tension zone

Fig. 6 Alienation of flow and affected area
happen. Understanding of this mechanism isresearchers have agreed including Watkinson and261

GEOMATE – Melbourne, Australia, 11-13 November 2020

surface was carefully cleaned to have clearer surface
with smaller equipment such as brush, hoe, trowel,
and geology hammer. Kind of deposit based on graincomposition are defined to ease clustering with either
eye or hand feeling. We created grid line 50x50 cm
over the wall surface with synthetics rope, these grids
will help to draw cluster according to graincomposition. We have defined into seven fractions:sand-gravel-boulder (SGB), sand-gravel (SG),course-sand (SC), fine-sand (FS), sandy-silt (SS),clay-silt-plastics (CSP), and top-soil (TS). Perhaps
these clustering were not precisely caused defined
only by eye or hand-feeling. Even though, thismethod needs extra work and extra care to draw
cluster correctly, if we only define from photo taken,
stratigraphy created by excavator bucket will confuse
to differ one cluster with another cluster.

important since the flow liquefaction areas were mostdeadly compare to outside area. It should be valuable
for mitigation hazards purposes for next communal
settlement planning. Lolu Village was exceptionalsite and thought as main mechanism of purely
liquefaction without any involvement from otherforce such as water inundating from breakage of main
water canal. Except Balaroa, other big lateral
spreading including Petobo, Jono Oge and South
Sibalaya, all of them involved huge volume of waterfrom breakage canal since top-crown of landslides
were also started at breakage of canal. Understandingat Lalo Village will be useful to recognize mechanism
for other areas, because magnitude moving objects atground surface were triggered purely by liquefaction.
We have divided Lolu Village liquefaction area
into two area; called “flow area” and “affected area”.
Flow area is inside blue line (fig. 5) where lateral
spreading generated more than 10-meters till
maximum 153 meter and affected area is outsidewhere less than 10-meter. As discussed above aboutbasic mechanism argued by writer at long direction(east to west), the area has been alienated into several
zones (fig. 6): tension, retrogressive, raft-initiation,
stacking and stopper zone.

Fig. 8 Interpreted trench log trenching#1 south wall
We fortunately disclosed cross section of sandboil footprint which had reservoir of deposit started at
sand-gravel-boulder (GB layer), please refer to fig. 8.
At first one meter, composition of sand boil at throat
was still the same with reservoir resource. And thennext 0.5-0,75 bigger grain fraction had left
underneath only finer sand was able to be flowed up,

Fig. 7 Orientation of trenching at Lolu Village
slide area
In order to understand basic mechanism between
two kind of area, we have conducted trenchingobservation to recognize layering of soil at two areas.
Trenching were excavated of three pits at raftinitiation zone (fig. 6). First was at flow area
alongside with movement direction called trenching#1. Second was at affected area called trenching #2
and third was perpendicular with slide movement,
cross flow and affected area called trenching #3.
These trenching were excavated in April 2019 or six
months aftermath. Dimension of trenching pit wereapproximately 5-meters wide, 5-meters depth and 30
meters long. Orientation of trenching plotted overarea can be seen at fig. 7.
Log clustering of trenching wall have been donesoon after preparing clear surface. After trenching
excavation with heavy equipment (excavator), wallFig. 9 Interpreted trench log trenching#2 south wall

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GEOMATE – Melbourne, Australia, 11-13 November 2020

1-meter rest was fully of fine sand at vent and createdcrater at surface.
This log fig. 9 is at affected area, we did not finda continue clay silt plastic layer. Layering of deposit
was observed not too regular if it compares to flow
area. Clay-silt-plastics fraction was also presence,
however not too significant, and formed in-regularformation. Mostly the deposit consists of sand graveland some interbedded sandy silt. Possibility thisdeposit experienced random movement instead ofblock (raft) movement. Unfortunately, we did notdiscover any sand boil at this section, even this area
also had number of sands boil. But if we look at
layering deposit form of sand gravel (SG) which
punched the above layer indicated there was highexcess pore water pressure which try to push up to
surface.

abFig. 11 Long continue sand boil at boundary line
affected area and flow area (a) one day aftermath(b) four months aftermath
significantly to understand this mechanism. Void
redistribution which generated during and after
shaking will become basic theory to develop otherfactors. Trenching observation has been conducted asadvise from him to knowing what real mechanism
inside sand deposit.
Simple mechanism for the stable water film
generation schematically indicated in fig. 12. The
series of test results clearly indicates that a water film
is readily formed just after the onset of liquefaction
beneath a sandwiched sublayer with smaller
permeability and stays there much longer than the resedimentation time of the liquefied sand particles.
This indicates that the liquefied sand is actually in thedrained condition locally, allowing the void

Fig. 10 Interpreted trench log trenching #3 east wall
At cross section between flow area and affected
area, at slide surface there was a thin fine sand pathstarted from reservoir of sand-gravel-boulder
continue till surface (fig. 10). The footprint of thisphenomena can be seen from photo taken (fig. 11),along the boundary there were huge quantity of finesand which blown up and spread over this boundaryline. During releasing excess pore water pressure, thiszone supposed to be conduit “long sand boil”.
From our trenching work, we have concluded thatat elevation -3-meters from ground surface on we
have found clay silt plastic layer which havepermeability much lower than both above and under
their layer with thickness 0.3 up to 0.5-meters on botharea of trenching at the same level. We predict thatthickness of significant movable layer was about 3.0meter at this trenching point, it is also just confirmed
by fact result at trenching #1. However, if we lookmagnitude of lateral deformation at southern part offlow area with longer distance and then the thicknessof movable layer could be much thicker.

Fig. 12 Water film/void redistribution generation
mechanism [24]DISCUSSION
redistribution to occur. Soil layering structures
providing this mechanism seems to be abundant in thefield, introducing multiple water films at different
depths [24].

Kokusho [24] has proposed useful theory to
explain such kind of phenomena. Series of laboratory
and onsite research work done by him contribute
263

GEOMATE – Melbourne, Australia, 11-13 November 2020

contraction during shear loading. Contraction
inside saturated sand deposit will generate excess
pore water pressure. High excess pore waterpressure will maintain soil in liquid phase.
3. Trenching observations has provided new term ofliquefaction. Sand boil so far only have point ofcrater, however now we recognize long continue
sand boil plane where fine sand blown up along
the boundary line of sliding.
4. Many “obstacles” from silt seam for releasing
excess pore pressure. This condition had
maintained deposit in liquid phase for relative
long period and gave enough time to glide or flowlong distance of displacement to downhill till
deposit came back to solid state and stopped.
5. Because limitation depth of trenching withconsideration of safety and elevation of groundwater level, the exact presence of silt seam is only
limitation in range of 5 meters depth. For otherdepth we need other method, CPT will be reliable
test to know in detail presence of silt seam at otherdepths.
Writer advice that further research should be
conducted to understanding more accurate regarding
the mechanism. As fact found at this trenching, writer
recommend other method to get un-disturb sample. If
possible, a continue sample so that we will know
exactly presence of silt-seam at other depths let say
till 20- or 30-meters depth. Because with long
magnitude of lateral movement must involve very
thick liquefied layer of deposit.

From three pit trenching observations which have
explored already, we argue that void redistribution/water film had been engendered during or especiallyjust after earthquake shaking. This argument has been
proven by presence of seam silt or clay silt plastics atelevation -3 meters from ground surface. We cannot
explore more deeper layer because either stability oftrenching consideration or we had reached groundwater level.
We realized that trenching has successfully
explored only small and shallow part of either huge
movement part or right mechanism, it still could becannot catch up whole mechanism yet. However,from trenching data taken can be concluded an
important point: that seam silt was exist at least single
layer at elevation app. 3-meters and this is become
one of boundary the releasing excess pore waterpressure trap. During earthquake and aftermath, there
were possibility that underneath this layer generated
thick water film which have zero shear strength, thislayer was being one of slide surface of the deposit. If
there were series of silt seam, and all of them werecreating series of water film layer at the same time,
and then it can be understood that long distancegliding of mass soil could be happen.
If we learned from sand fraction inside conduit ofsand boil, we have speculated there was a big excesswater pressure bult up under silt seam layer. Because
not only fine fraction which forcing up but also gravelalso flown up from reservoir of sand-gravel-boulder(SGB) underneath. Deeper elevation of liquefied
layer should have higher excess pore water pressure
and stayed inside the deposit for long time caused
many “obstacles” from silt seam for releasingpressure and created water film for long time as well.
This condition had maintained deposit in liquid phasefor relative long period and gave enough time to glideor flow long distance of displacement to downhill.

ACKNOWLEDGMENT
We have many parties to thank for giving
encouragement of this report: our team in University
Indonesia especially Prof. Widjoyo A. Prakoso, Past
immediate Chairman of Indonesia Society for
Geotechnical Engineering (HATTI) Prof. Masyhur
Irsyam and team, and GEER (Geotechnical Extreme
Events Reconnaissance), USA team including H. Ben
Banson, Aron P. Gallant, Jack Montgomery, Joe
Wartman, and Daniel Hutabarat for worth moment
along with all them for site reconnaissance joint
survey in Palu. We would like also to thank for many
valuable discussion and lecture from Kenji Ishihara,
Susumu Yasuda, Takeji Kokusho, Ikou Towhata andHisashi Furuichi (JICA) during they visited on site inPalu and series of lecture in Jakarta, Indonesia.

CONCLUSION
Liquefaction disaster caused by Palu Earthquake2018 have been still generating questions for many
researchers in the world. Geology and geotechnicalmechanism have been developing to know what
exactly occur inside the sand deposit. Writer have
tried also contributing to enrich knowledge byconduct trenching observations and other series ofseries to explore possibility scenario. This report has
concluded some important as follow thought:
1. Presence of silt-clay-plastics layer have played
important contribution to product long distance oflateral spreading, and by trenching observation
this thesis has been proven at location we conduct
the test.
2. From interpretation log, there are some cluster ofsand-silt, this content called non-plastic fine grain.
Existence of such kind of deposit as proposed by
Kokusho [24] cause soil deposit underwent

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266

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EXPERIMENTAL RESEARCH ON THE INFLUENCE OF DISTANCEBETWEEN CFG PILES ON THE FOUNDATION TREATMENTRESULT IN VIETNAM
Doanh Phu Bui1, Tung Hoang1, Phuong Viet Nguyen1, Duc Viet Ngo1 and Cuong Phu Cao1
1
Faculty of Road and Bridge, National University of Civil Engineering, Hanoi, Vietnam

ABSTRACT
According to government statistics, there are currently about 19 thermal power plants under operation,
emitting about 20 million tons of fly ash annually which is classified as hazardous waste, an urgent
environmental problem. In the context of lack of construction materials, this research aiming at promoting
consumption of the fly ash through application of CFG (cement - fly ash - gravel) pile - a type of pile using a
mixture of cement, fly ash, aggregate to handle the foundation for construction works that may help consume
part of the fly ash. This paper introduces the results of an experimental research on the influence of distance
between CFG piles on soft soil treatment. The optimal fly ash component is about 25% of binder content, and thereasonable distance between the piles is from 4d to 6d (d- diameter of the pile). This can be an effective solution
for soft soil treatment since it is reasonable both in terms of price and construction time. It is also easy for quality
control in Vietnam.
Keywords: CFG (cement fly ash gravel); composite foundation, experimental research, distance effectthere is no theoritical study and experiments in
Vietnam on CFG pile application on foundation
treatment in construction works. So with advantages
in foundation treatment in construction works andlarge amount of fly ash consumption for thermo
electricity plants, CFG piles application is perfectly
efficient and suitable for works in cost saving andsimple construction, quality assurance and easy inquality control while fly ash pollution is mitigated,
solving the waste issue for thermal power plants.
Hence, the research on CFG application in
foundation treatement in basic construction works
plays pivotal role with high practical and feasible
meaning in order to improve quality as well as toreduce cost of basic construction works in Vietnam.

INTRODUCTION
As a developing country, Vietnam has been
facing the shortage of electricity and there will more
and more new thermal power plants in order tosatisfy the related increasing demands. Thesethermal power plants have been emitting a large
number of hazardous waste, ash (comprising 75% of
fly ash), which causes environmental pollution. Thisis considered one of the consequences of thedevelopment, causing a headache for Vietnamese
authorities [1]. In this circumstance, the researches
on applying fly ash as a construction material havebeen conducted since 80s of this century. However,
the objective of those studies was mainly for
generating high-quality fly ash mixture as anadhesive for concrete. Recently, studies in Vietnam
have focused on the consumption of fly ash, such as
putting into roadbed [2], geopolymer concrete [3],and pervious concrete [4]. In this trend, theapplication of a large amount of fly ash to produce
low-cost, low-strength concrete mixture forfoundation treatment is very necessary.
The CFG (Cement Flying-ash Gravel) pile is a
type of pile using mixture of cement, fly ash,aggregate of stone (gravel) to form a low-strength
concrete pile for foundation treatment in basic
construction works. Through pile length, distance
between piles and pile cap cushion layer as well as
the right mixture ratio of pile materials, the pile cansatisfy the required load capacity of treatedfoundation, achieving high load capability, small
variation and simple construction. The low-cost
CFG piles have been applying in mass construction
works in some countries in the world. However,

CFG PILES IN SOFT SOIL FOUNDATIONTREATMENTCFG Development
CFG pile application is classified as anembankment measure on pile system according to
the list of embankment treatement methods on soft
soil. CFG piles together with soil between piles andpile cap cushion layer form a combination
foundation to ensure stability for embankment
foundation on soft soil.
In 1985, Foundation Institute of China Academyof Building Research studied CFG pile application
based on foundation with ballast pile added in
cement and fly ash to generate high-strength piles.
CFG piles were made by specialized machine andmixture of CFG pile materials were poured in place
into pre-drilled empty tube and finally the piles were
267

GEOMATE – Melbourne, Australia, 11-13 November 2020

formed in the ground. With advantages of high load
capability, small variation, simple construction andlow cost, this method has been popularly applied forconstruction of houses, car roads, railways and yards
in China and some other countries in the world.
Researches in China on theorical models, calculation
theory, technical requirements on materials andconstruction technology have been conducted.

Length of pile is designed to touch the geologicallayer with good load capability to transport down theload from embankment. In other cases, pile length
can be calculated according to the friction load.
Because pile strength is larger than embankment
strength so the stress on piles is also higher than theone on soil between piles (Figure 1).
The pile cap is usually designed with concrete inorder to distribute more load from embankment on
pile, sizing down pile diameter, increasing distance
between piles and saving more materials. Size ofpile cap affecting the load distribution capability ofcap cushion layer is designed to be around 10% ~30% working area of piles [7].
The cushion layer on pile cap is designed toadjust stress of piles and soil between piles, reducing
stress at pile cap, reinforcing load capability offoundation. Design of cushion layer is an important
technique in treatment for the CFG combined
foundation, which is pivotal task to ensure the load
capability of piles and foundation. Structure ofcushion layer comprises medium size sandstone,coarse-grained sand, selected sand, ballast aggregate
or pebble gravel with size of sand stone no larger
than 30mm. A geotextile layer can be spread on pile
cap to form a cushion to distribute load on piles. The
cushion layer should be 150÷500mm thick,compression strength is not greater than 0.9. The
thickness of this cushion layer increases in par withthe rise of pile diameter and distance between piles[8].
Based on theoretical study and in laboratory
experiment on design CFG pile for foundation
treatment with distance between piles which is equal
to 6 times of pile diameter, authors have done
experiment on site to verify the working ability of
combined foundation and piles, of which, distance
between piles is set to be equal 4d and 6d of piles (dis pile diameter) for embankment foundation modelwith height of 4m.

Structure, Specifications and Working Principleof CFG piles
CFG piles with diameter from 350mm to 600mm.
By changing cement content and ratio in the CFG
pile mixture, pile strength varies from C10 to C25,
forming the intermediate piles between two forms ofhard and soft piles [5], [6]. CFG piles works withsoil in between through its cap cushion arrangement,
which form the combined foundation so that thefoundation can utilize load capability of soil between
piles and also transport the embankment load down
to good geological layer to increase stability andreducing settlement risk for the embankment,
bringing in high technical and economic efficiency.
Furthermore, the pile mixture takes good use of fly
ash from thermal power plant without reinforcementsteel frame to save material cost, extending higher
economic efficiency.
CFG pile method is calculated and designed
based on working principle of the combined
foundation. The main factors covering ingredientsand ratio of pile materials, pile length, distance
between piles, size of pile cap, structure andthickness of pile cap cushion and construction
technique need carefully calculating, designing,
experimenting, analyzing to satisfy requirements offoundation strength treatment.

EXPERIMENT IN ROOM DEFINES RATIOOF FLY ASH IN COMBINED CFG MIXTURE
Aggregate Design for CFG Mixtures
Authors has conducted experiments in laboratory
with three different fly ash types from Pha Lai
thermal power plant, Nghi Son thermal power plant,
and Ninh Binh introgenous fertilizer plant to mix
aggregate to find proper ratio of fly ash/adhesives in
CFG mixtures.
Within experiment scope, authors selected theratio water/cement =0.65 to study. The designed
aggregate is 7 and ratio of fly ash replacing adhesivecontent from 0%~35%. Through absolute volume
calculation method, calculation for aggregate for
1m3 CFG mixtures as follow:

Fig. 1 Stress of embankment on the combined
foundation.
Distance between piles must be defined throughload capability of the combined foundation, physicalcharacteristics of geological layers and construction
techniques. The experimental studies on centrifugal
acceleration model show that the distance between
piles should be equal to 3-6 times of pile diameter[7]. In order to reduce cost, distance between piles
should be set as largest as possible, however, there is
no experiment study on the distance between piles
which is equal to 6 times of pile diameter.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Calculation result of aggregate concrete
using fly-ash additives for 1m3
F/C(%)

Cement
(Kg)

0
10
15
20
25
30
35

280
252
238
224
210
196
182

Fly
Ash
(Kg)
0
28
42
56
70
84
98

Sand
(Kg)

Stone
(Kg)

Water
(Liter)802.67799
796
793.33790.67788.67786

989.33982.67980
977.33
974.33972.67970.67194.67194.67194.67194.67194.67194.67194.67

EXPERIMENTON
CONSTRUCTION

SITE

CFG

PILE

Experimental Site and Geological conditions
Experimental site is at the Experiment Site
within the National University of Civil Engineering
at Ha Nam province.
Using approved geological parameters [10], in
accordance with the geological survey report
LKBS02, surveyed depth at 15m with geologicallayers: Soil covering layer: Pond and field mud,brown gray and dark gray (1). 0.5m thickness;
Mixed clay layer with organic soil, gray-brown andsoft clay (2) 3.9m thickness; Mixed clay layer withfine sand bed brown gray, dark gray, soft clay (3)7,3m thickness; Mixed sand layer, brown gray, darkgray and soft clay (3) thickness covering the rest of
surveyed height. Use of data of physical properties
of soil layers follows its appendix. Depth ofunderground water: 0.00m.
Density of embankment material: 1.8 T/m3
(compacted black sand embankment with K85).

Experiment Result
Experimental sample number is prepared inaccordance with standard “TCVN 3118:1993
Concrete – Compression strength” [9].
Number of samples: 6 cube samples: 10 x 10 x
10 (cm) to define compression strength Rn;
Combined samples 7x6x3=126.

Technical Scale of Experiment

Aggregate Design Result

Section 1: Design with different load capacity,each 20m section with pile arrangement as shown in
the photo, pile diameter 0.5m, lenght 12m, pile cap
diameter 1.1m with distance between piles 3m,average embankment height 4m.
Section 2: Design with larger load capability ofsection 1 with length of 15m for the works to be
used as container warehouse, foundation for low-risebuildings (5 storeys and below), pile diameter 0.5m
and length 12.0m, pile cap diameter 1.1m with
distance between piles of 2m and average
embankment height 4m.

The selected compression strength value is theaverage values from 6 samples. Rn values at 3 days;
7 days, 14 days and 28 days. Experimental results
show that the CFG mixtures using fly ash from Nghi
Son plant and other fly ash with replacing ratio of
35% the adhesive objects still satisfy compression
strength value Rn >10MPa, which are suitable to be
used in CFG pile construction.
Table 2. Compression Strength Rn values at 28
days of fly ash from Nghi Son plant.
Ri at 28th day (MPa)

F/
C

Rn1

Rn2

Rn3

Rn4

Rn5

Rn6

Rntb6

0%

15.5

15.7

15.8

15.6

15.9

15.7

15.7

10%

14.6

14.8

14.7

14.8

14.9

14.7

14.7

15%

13.2

13

13.1

13.2

13

13.2

13.1

20%

11.7

11.6

11.5

11.6

12

12

11.7

25%

10.9

10.8

10.6

10.7

10.8

10.9

10.8

30%

10.6

10.7

10.8

10.6

10.7

10.5

10.7

35%

10.6

10.7

10.5

10.8

10.5

10.6

10.6

Hence, from selected materials (PC40 But Son
cement, Red River black sand, fly ash from Pha Lai,
Nghi Son thermal power plants, and Ninh Binh
introgenous fertilizer plant; gravel and water),authors aim to figure out optimal aggregate ratio,
ensuring cost effectiveness in manufacturing CFG
pile production for foundation treatment inconstruction works.

Fig. 2 ½ Section 1 experiment embankment crosssection
Construction Technique
Construction Materials

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Installation of measuring equipment

CFG pile material: gravel 1x2, from Ha Nam
quarry, Red River black sand, Nghi Son thermo
electricity plant fly ash and But Son PC40 cement;
Pile cap cushion material: Aggregate gravel type2 from Ha Nam quarry; Geotextile with tensile
strength 50kN/m. Embankment material: Red River
black sand γ = 1.8 T/m3. Other materials: Genetubes; Settlement plate.

Authors arranged 6 soil pressure cells with
2.0MPa cell at pile cap, 0.2 MPa at soil between
pipe cap. Cell position as show as diagram below.

CFG Pile Aggregate Construction
The design parameters of CFG piles are resultsof survey with fly ash/adhesive ratio (Flyash/cement) with replacing fly ash content ratio of
25%, Water/cement ratio is 0.65
Table 3. Selected
construction
F/C
(%)
25

Cement(Kg)
210

aggregate

Fly ash
(Kg)
70

Sand
(Kg)
790.67

for

CFG

Stone
(Kg)
974.33pile
Water(Liter)194.67

Fig. 5 Soil Pressure Cell diagram at 2m pile distance

CFG Pile Construction TechniqueFig. 6 Soil Pressure Cell diagram at 3m pile distance
ANALYSIS OF EXPERIMENT RESULT
Settlement Monitoring Result by Settlement Plate

Fig. 3 CFG pile construction process

Settlement monitoring result is shown in the
below tables:
Table 4. Settlement monitoring results at 2m pile
distance position
Settlement Value by Day (cm)Plate Day Day Day Day Day Day
1
3
5
10
30
60
#1
1.7
2.3
2.3
2.3
2.4
2.4
#2
5.2
6.3
6.4
6.5
6.5
6.5
Findings:
Foundation is completely stable, settlement
measurement results show the settlement value atsoil between piles is higher than the one on pipe.
Settlement measurement results at 3m pile distanceis higher than the ones at the 2m pile distance.
Settlement values of soil between piles are small
at beginning and gradually increasing. After the 3rd

Fig. 4 Pile site after construction
Based on the geological survey results, authors
decided to select the mini drilling stuffing techniqueto conduct construction and form piles according to
the design.
270

GEOMATE – Melbourne, Australia, 11-13 November 2020

days, the values become stable, reaching maximum
at 9.2cm.

Cell

Day 5

142945 0.13
142921 0.053
142948 0.028
Cons.Process (CP)Pressure value (MPa)Day
Day
Day 601030
0.141 0.143 0.144
0.05
0.048 0.049
0.027 0.026 0.026

/
/
/
/

Pressure measurement values on soil and soil
between piles show that the value didn’t vary clearlyat the beginning of construction process until theembankment reached height of 4m where the values
vary significantly and pressure of load graduallyshift to piles through pile cap cushion layer. The
maximum value at pile cap is 0.143MPa, minimum
value at soil between 4 piles is 0.026MPa.
Foundation works on principle of combination andon vertical then pressure also varies and graduallystable.

Fig. 7 Settlement measurement results at 2m pile
distance position
Table 5. Settlement measurement results at 3m pile
distance position
Settlement Value by Day (cm)Plate Day
1

Day
3

Day
5

Day
10

Day
30

Day
60

#3

2.2

3.2

3.2

3.3

3.4

3.5

#4

6.2

8.8

8.9

9.0

9.1

9.2

Fig. 9 Soil pressure results at 2m pile distanceposition
Table 7. Pressure measurement results at pile cap
and soil between piles at 3m distancePressure Measurement Results at Pile Cap andSoil between Piles
Pressure measurement results at pile cap and soil
between piles as show in table 6:

Cell142945142921
142948

CP
0.028
0.022
0.015

0.016

142970

0.024

0.049

0.066

0.067

0.068

142956

0.017

0.036

0.071

0.074

0.077

/

534082

Day
5
0.119

Pressure value (MPa)
Day
Day
Day
10
30
60
0.131 0.136 0.137

142970

0.066

0.064

0.062

0.064

/

142956
0.075
Cons.Process (CP)

0.072

0.07

0.07

/

Cell

Table 6. Pressure measurement results at pile cap
and soil between piles at 2m distancePressure value (MPa)
CP
CP
Day 1
0.042 0.077 0.108
0.033 0.061 0.054
0.019
0.03
0.027

534082Pressure value (MPa)
Day
CP
CP
1
0.039 0.069 0.097

Cell

Fig. 8 Settlement mesurement results at 3m pile
distance position

Day 3
0.119
0.058
0.029

271

CP

Day
3
0.107

/

GEOMATE – Melbourne, Australia, 11-13 November 2020

REFERENCES[1] Prime Minister of the Socialist Republic ofVietnam (2017), Decision No. 452/QD-TTg
approving the scheme to promote treatment anduse of ash, slag, plaster of thermal power plants,
chemical plants, fertilizers as raw materials forconstruction materials production and inconstruction works.
[2] Hoang T., Nguyen V.P., and Thai H.N, Use ofcoal ash of thermal power plant for highway
embankment construction, in CIGOS 2019,
Springer,
Singapore,
pp.433-439.
DOI:
https://doi.org/10.1007/978-981-15-0802-8_67[3] Pham T.T., Nguyen T.T., Nguyen N.L., Nguyen
V.P., A neural network approach for predicting
hardened property of geopolymer concrete.
International Journal of GEOMATE, 2020,
Vol.19,Issue74,
pp.193–201.
DOI:
https://doi.org/10.21660/2020.74.72565[4] Nguyen T.T., Phan Q.M., Pham T.T., Nguyen
V.P., Experimental study on mechanical andhydraulic properties of porous geopolymer
concrete. International Journal of GEOMATE,
2020, Vol.19, Issue 74, pp.66–74. DOI:
https://doi.org/10.21660/2020.74.41280.
[5] Ministry of Construction of the People’s
Republic of China (2002), Code for Design of
Building Foundations GB 50007-2002.
[6] Ministry of Housing and Urban-Rural
Development of China (2012), Technical codefor ground treatment of buildings - JGJ 79-2012.
[7] Doanh, BUI Phu, Qiang LUO, Liang ZHANG,
and Xing SU, Analysis on pile spacing effect ofcomposite foundation with high strength piles
by centrifugal model test. Journal of the China
Railway Society, Vol.31, Issue 6, 2009, pp.6975.
[8] P. D. Bui, Q. Luo, L. Zhang, Y. Yang,
Geotechnical centrifuge experiment and force
analysis of reinforced cushion with pile cap net
structure embankment. Proceedings of the 4th
Asian Regional Conference on Geosynthetics.
China (Shanghai), 2008, 185-190. ISBN: 978-3540-69312-3.
[9] Vietnam
Standard
TCVN
3118:1993.
Heavyweight
concrete
Methodfordetermination of compressive strength.
[10] Approved Design drawings of Experimental in
National University of Civil Engineering. Ha
Nam Province, 2017.

Fig. 10 Soil pressure results at 3m pile distanceposition
CONCLUSION AND SUGGESTIONS
Conclusion
Through experiment in room and on site the soft
foundation treatment by CFG pile with different
distance between piles, the following findings havebeen gained:
1) Authors designed CFG mixtures using fly ash
from 3 different sources, experiment results show
the fly ash ratio in the mixture between 20%~35%
then adhesive can assure quality for CFG tocontribute to the consumption of fly ash emissions
from thermal power plants.
2) CFG pile is the low-strength concrete
C10~C15 with the designed pile distance of
2.0m~3.0m equal to 4d~6d, combining with the pilecap covering 12%~27% pile working area then themethod can handle soft foundation with highstability at small settlement value.
3) Soft foundation treatment method with CFG
pile brings economic-technical efficiency and easy
control in quality as well as help contribute toconsumption large amount of fly ash and thismethod should continue researching and evaluatingin the coming time.
Recommendations
The following recommendations are raised
during experiments:
Foundation treatment with CFG pile forconstruction works can be popularly deployed in
Mekong Delta region where thermal power plants
with a high amount of fly ash emission exist. The
treatment method should be piloted in the region
before application.
With the advantages of low cost and easy quality
control comparing with normal ballast piles, it issuggested that standards for design and constructionof foundation treatment with CFG pile should be
developed to serve as a basis for mass infrastructure
construction in Vietnam.
272

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DESIGNING GABION STRUCTURES UNDER MULTI CRITERIAOBJECTIVES WITH GOAL PROGRAMMING
Vuttichai Chatpattananan1, Tanadol Kongsomboon2, and Vatanavongs Ratanavaraha3
1,2

Department of Civil Engineering, King Mongkut's Institute of Technology Ladkrabang
3

Suranaree University of Technology, Nakhon Ratchasima, Thailand
ABSTRACT

Gabion structure is a set of stacked prefabricated cages filled with rocks. Theses gabion cages are made ofprefabricated steel wire, polypropylene or polyethylene or nylon. Rearranging theses gabion cages mainly follows
the suppliers’ guidelines or governmental design drawings. Designing this gabion structure, at a minimal, mustsatisfy many design criteria in passing external stability in sliding, overturning or eccentricity, and bearing capacity.
Good gabion design should also balance the toe bearing stress and heel bearing stress. By nature of this multicriteria objectives in designing this gabion structure, this paper proposes a goal programing. Mixed integer
optimization model is also combined with goal programing to set optimization models in designing gabion
structures. A 3-meter gabion retaining wall is used to compare the design of using the proposed optimization
approach with the design provided by an available online supplier guideline. The finding of this paper shows thatusing goal programming techniques that is multicriteria in nature gives better optimum results in designing gabion
structure than using only optimization technique with only one objective.
Keywords: Gabion, Goal programming, Multi criteria decision making, Retaining wall

Arranging different sizes of gabions can facilitate
the design of gabion wall. The gabion wall can beshaped in front slope, rear slope, or trapezoidal shape
as shown in Fig.2. Flexibility in gabion shapes andsizes leave a room to utilized optimization techniques
in setting gabion wall.

INTRODUCTION
GabionGabions are cages made of hexagonal mesh fabricor steel wire mesh. These cages or boxed are widelyused in construction works as a retaining structure, anerosion protection, coastal protection, pipe protection,and other usages such as Araki & Hirakawa, 2019 [1] .
As a gravity retaining structure, this gabion retaining
structure uses its self-weight to resist the lateral earthpressure behind it and also support vertical surcharge
resting on top of the gabion structure.
The gabion cages typically are 2 meters long, 1
meter wide, and 1 meter high as shown in Fig 1.
ASTM A975 [2] provides gabion sizes with 1 meterin width, varying lengths of 2, 3, 4 meters, andvarying heights of 1, 0.5, 0.3 meters. BS8002 [3] also
gives gabion shapes in 0.5 meter box in lengths of 2
m to 6 m, 1 m to 2 m widths and 0.3 m, 0.5 m and 1
m in depths.

Front Slope

Rear Slope

Trapezoidal

Fig. 2. Gabion Wall Shapes, BS8002 [3]Designing Gabion Structures
Designing gabion structure as a gravity wall asshown in Fig. 2 needs to meet external stability insliding, bearing, and overturning. There is no need ininternal stability checking except using gabion as afacing unit and combined with other reinforcements.
The reinforcement can be geogrid or even anchors asshown in Fig. 3. Global stability checking is also
omitted since the global stability depends on theactual site location and the global stability can bechecked either using limit equilibrium method (LEM)

Fig. 1 Gabion (left) and Revet Mattress (Right),
ASTM A975-97 [2]Gabion Structures

273

GEOMATE – Melbourne, Australia, 11-13 November 2020

or finite different method (FEM) by using softwares.

Fig. 4. Dimension of Gabion Structure from
Environmesh Designed Example [Enviromesh,2007])

Fig. 3 Gabion Facing with Geogrid Reinfocement(left), FHWA-NHI-10024 [4] Compared with
Traditional Gabion Gravity Wall (right) [FHWASA-96-038 [5]Design Parameters

Optimization Techniques used in Designing
Gabion Structures

From Environmesh [6] example, the design
parameters are as follows.

Hence, designing a gabion structure is aconstrained optimization problem by nature since thedesign needs to be the most economical as possible
while needs to satisfy all stability constraints.
Also designing gabion structure faces many
mutiticriteria by nature as aforementioned. Of course,
the first objective is the design the most economically.
Another objective, for example, is to equalize thevertical stress at toe and the vertical stress at heel ofthe gabion structure as recommended byEnvironmesh 2007 [6].
Some papers apply optimization techniques indesigning retaining walls such as Saribas 1996 [7]that uses nonlinear optimization model in designing
reinforced concrete-cantilever retaining walls.
Basudhar 2008 [8] applies nonlinear cost
optimization model in designing mechanicallystabilized earth (MSE) walls by using Sequential
Unconstrained Minimization Technique (SUMT)
instead of directly solving nonlinear optimization
model which may not give the exact solution.
However, there is no paper directly using
optimization technique in designing gabion wall.
This paper proposes mixed integer nonlinear
programming combined with goal programing
approach to solve gabion design problem which is amulticriteria problem in nature.

Geometry
Slope Wall height, H = 3.1 m where 𝒚𝒚𝟏𝟏 = 1.0 meter,𝒚𝒚𝟐𝟐 = 𝒚𝒚𝟑𝟑 = 𝒚𝒚𝟒𝟒 = 0.7 meter and 𝒃𝒃𝒘𝒘 = 1.7 meter, 𝒃𝒃𝟐𝟐 =1.4 meter, 𝒃𝒃𝟑𝟑 = 1.0 meter, 𝒃𝒃𝟒𝟒 = 0.7 meter.
Slope angle of the retained soil, 𝜺𝜺, is 15 degree.
Wall inclination, 𝜶𝜶, is 10 degree.
Materials
Soil friction angle, φ, is 28 degree.
Soil density, 𝜸𝜸 = 19 kN/m3.
Gabion density, 𝜸𝜸𝒈𝒈 = 16 kN/m3.
Foundation soil density, 𝜸𝜸𝒇𝒇 = 19 kN/m3.
Foundation soil internal friction angle, ∅𝒇𝒇 , is 30
degree.
Bearing capacity parameters,(1.1)
𝑵𝑵𝒒𝒒 = 𝒆𝒆𝝅𝝅𝝅𝝅𝝅𝝅𝝅𝝅∅ 𝒕𝒕𝒕𝒕𝒕𝒕𝟐𝟐 (𝟒𝟒𝟒𝟒 + ∅/𝟐𝟐) = 18.4
𝑵𝑵𝒄𝒄 = 𝒄𝒄𝒄𝒄𝒄𝒄∅(𝑵𝑵𝒒𝒒 − 𝟏𝟏) = 30.1
(1.2)
𝑵𝑵𝜸𝜸 = �𝑵𝑵𝒒𝒒 − 𝟏𝟏�𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕. 𝟒𝟒∅ = 15
(1.3)
Loading
Surcharge, P0 = 10 kN/m2.
Earth Pressure

DESIGN EXAMPLE
Environmesh, 2007 [6] gives a design example of
3.1 gabion wall with a total cross section of 3.87 m2
as shown in Fig. 4.

Fig. 5. Coulomb Active Earth Pressure
Environmesh, 2007[6]274GEOMATE – Melbourne, Australia, 11-13 November 2020

𝑭𝑭𝒓𝒓 = (𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵 + 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻)𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕.
(3.9)
Driving force,(3.10)
𝑭𝑭𝒅𝒅 = 𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻𝑻 − 𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵.
Safety factor against sliding,
𝑭𝑭𝑭𝑭𝒔𝒔 = 𝑭𝑭𝒅𝒅 /𝑭𝑭𝒓𝒓.
(3.11)
Reaction eccentricity,𝒃𝒃
𝑴𝑴 −𝑴𝑴𝒆𝒆 = 𝟐𝟐𝒘𝒘 − 𝒓𝒓𝑵𝑵 𝒐𝒐 .
(3.12)
e is a free variable which is unrestricted in sign.
Note that negative eccentricity causes reverse
overturning to the back side (earth filled) of the wall.
Vertical stress at toe,𝑵𝑵𝟔𝟔𝟔𝟔
𝝈𝝈𝒕𝒕 = �𝟏𝟏 + �(3.13)

Fig. 6. dh and bv, Environmesh, 2007 [6]Inclination angle to vertical plane, 𝜷𝜷, is 94.46
degree.
Retained Wall friction reduction by geotextile, δ, is
28 degree where δ = φ if no geo-textile or 0.9φ with
geotextile.

𝒔𝒔𝒔𝒔𝒔𝒔𝟐𝟐 (𝜷𝜷+𝝓𝝓)𝐬𝐬𝐬𝐬𝐬𝐬(𝝓𝝓+𝜹𝜹)𝐬𝐬𝐬𝐬𝐬𝐬(𝝓𝝓−𝜺𝜺)�𝐬𝐬𝐬𝐬𝐬𝐬(𝜷𝜷−𝜹𝜹)𝐬𝐬𝐬𝐬𝐬𝐬(𝜷𝜷+𝜺𝜺)

𝒔𝒔𝒔𝒔𝒔𝒔𝟐𝟐 𝜷𝜷𝐬𝐬𝐬𝐬𝐬𝐬(𝜷𝜷−𝜹𝜹)�𝟏𝟏+�

𝒃𝒃𝒘𝒘

𝒃𝒃𝒘𝒘

𝒃𝒃𝒘𝒘

(3.14)

Allowable soil bearing stress,
𝒒𝒒𝒂𝒂 = 𝑷𝑷𝟎𝟎 𝑵𝑵𝒒𝒒 + 𝟎𝟎. 𝟓𝟓𝜸𝜸𝒔𝒔 𝒃𝒃𝒘𝒘 𝑵𝑵𝜸𝜸 .
(3.15)
Safety factor against bearing,
𝑭𝑭𝒃𝒃 = 𝝈𝝈𝒕𝒕 /𝒒𝒒𝒂𝒂.
(3.16)

Active earth pressure coefficientม Bowles [9]), Ka =0.364 from
𝑲𝑲𝒂𝒂 =

𝒃𝒃𝒘𝒘

where vertical stress at heel,𝑵𝑵𝟔𝟔𝟔𝟔
𝝈𝝈𝒉𝒉 = �𝟏𝟏 − � .

(2.1)

Design Output

The triangular pressure acting on the wall, Pa = 43.4
kN
(2.2)
𝑷𝑷𝒂𝒂 = 𝟎𝟎. 𝟓𝟓𝑲𝑲𝒂𝒂 𝜸𝜸𝑯𝑯𝟐𝟐 + 𝑷𝑷𝟎𝟎 𝑲𝑲𝒂𝒂 𝑯𝑯.
Horizontal component Ph = 39.8 kN, as shown in
Fig. 5.
𝑷𝑷𝒉𝒉 = 𝑷𝑷𝒂𝒂 𝐜𝐜𝐜𝐜𝐜𝐜(𝟗𝟗𝟗𝟗 − 𝜷𝜷 + 𝜹𝜹)(2.3)
Vertical component Pv = 17.3 kN from
𝑷𝑷𝒗𝒗 = 𝑷𝑷𝒂𝒂 𝐬𝐬𝐬𝐬𝐬𝐬(𝟗𝟗𝟗𝟗 − 𝜷𝜷 + 𝜹𝜹).
(2.4)

In fact, Environmesh [6] provides two
approaches in designing gabion systems. One iscalled “gabion 27 system”. Another is called “gabion
39 system”. For this 3-meter height gabion structure
example. The 27 system has 4 layers and the 39
system has 3 layers. In fact, the gabion 27 system with4 layers is already illustrated in the calculation
previously. The output for these two systems iscompared and later shown in table 1.

Variables
Vertical distance to Pa, dh = 0.852 meter. From𝒅𝒅𝒉𝒉 = 𝑯𝑯/𝟑𝟑(𝑯𝑯 + 𝟑𝟑𝑷𝑷𝟎𝟎 /𝜸𝜸)/(𝑯𝑯 + 𝟐𝟐𝑷𝑷𝟎𝟎 /𝜸𝜸) − 𝒃𝒃𝒘𝒘 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔
𝒅𝒅𝒉𝒉 = 𝒅𝒅𝒉𝒉′ − 𝒃𝒃𝒘𝒘 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔(3.1)
as shown in figure 4. This is from moment
equivalent, ((𝟏𝟏/𝟐𝟐)𝜸𝜸𝒔𝒔 𝑯𝑯 + 𝑷𝑷𝟎𝟎 )𝒅𝒅𝒉𝒉′ = (𝟏𝟏/𝟐𝟐)𝜸𝜸𝒔𝒔𝑯𝑯(𝑯𝑯/
𝟑𝟑) + 𝑷𝑷𝟎𝟎 (𝑯𝑯/𝟐𝟐).
Horizontal distance from toe to Pa, 𝒃𝒃𝒗𝒗 = 1.741 meteras shown in Fig. 4.
𝒃𝒃𝒗𝒗 = 𝒃𝒃𝒘𝒘 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 − 𝒅𝒅𝒉𝒉 /𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕(3.2)
Horizontal distance to 𝑾𝑾𝒈𝒈 as shown in Fig. 6.
𝑿𝑿𝒈𝒈 = 𝒙𝒙𝒈𝒈 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 + 𝒚𝒚𝒈𝒈 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔(3.3)
Resisting moment,
𝑴𝑴𝒓𝒓 = 𝑷𝑷𝒗𝒗 𝒃𝒃𝒗𝒗 + 𝑾𝑾𝒈𝒈 𝑿𝑿𝒈𝒈.
(3.4)
Overturning moment,
𝑴𝑴𝒐𝒐 = 𝑷𝑷𝒉𝒉 𝒅𝒅𝒉𝒉 .
(3.5)
Safety factor against overturning,
𝑭𝑭𝑭𝑭𝑶𝑶 = 𝑴𝑴𝒓𝒓 /𝑴𝑴𝑶𝑶.
(3.6)
Normal Force on plane of sliding
𝑵𝑵 = 𝑾𝑾𝒈𝒈 + 𝑷𝑷𝒗𝒗 .
(3.7)
Tangential Forces,
𝑻𝑻 = 𝑷𝑷𝒉𝒉 .
(3.8)
Sliding resistance

OPTIMIUZATION MODELS
Optimization techniques are applied here. Threemodel are proposed. The first model is a mixed
integer nonlinear programming (MINP) model. The
other two are goal programming models. One ispreemptive goal programming model and another isweighted goal programming model. The design
parameters for the three models are as follows:
Design Parameters
Geometry Constants and Soil Parameters
Environmesh [6] design example is borrowed
here. The example is a 3.1 meter gabion structure.
The following constants are used here.
Slope angle of the retained soil, 𝜺𝜺, is 15 degree.
Inclination angle to vertical plane, 𝜷𝜷, is 94.46
degree.
Soil cohesion, c is 0 kPa.
Soil friction angle, φ, is 28 degree.
Soil density, 𝜸𝜸 = 19 kN/m3.
275

GEOMATE – Melbourne, Australia, 11-13 November 2020

Retained Wall friction reduction by geotextile, δ, is
28 degree.
Wall inclination, 𝜶𝜶, is 10 degree.
Surcharge, P0 = 10 kN/m2.
Instead of using original Environmesh example with4 layers gabion as mentioned before, this model uses
6 gabion layers with 𝒚𝒚𝟏𝟏 = 𝒚𝒚𝟐𝟐 = 𝒚𝒚𝟑𝟑 = 𝒚𝒚𝟒𝟒 = 𝒚𝒚𝟓𝟓 = 𝒚𝒚𝟔𝟔
= 0.5 meter

Width Constraint Group𝒃𝒃𝟐𝟐 = 𝒃𝒃𝒘𝒘 − 𝒙𝒙𝟐𝟐𝟐𝟐 − 𝒙𝒙𝟐𝟐𝟐𝟐
𝒃𝒃𝟑𝟑 = 𝒃𝒃𝟐𝟐 − 𝒙𝒙𝟑𝟑𝟑𝟑 − 𝒙𝒙𝟑𝟑𝟑𝟑
𝒃𝒃𝟒𝟒 = 𝒃𝒃𝟑𝟑 − 𝒙𝒙𝟒𝟒𝟒𝟒 − 𝒙𝒙𝟒𝟒𝟒𝟒
𝒃𝒃𝟓𝟓 = 𝒃𝒃𝟒𝟒 − 𝒙𝒙𝟓𝟓𝟓𝟓 − 𝒙𝒙𝟓𝟓𝟓𝟓
𝒃𝒃𝟔𝟔 = 𝒃𝒃𝟓𝟓 − 𝒙𝒙𝟔𝟔𝟔𝟔 − 𝒙𝒙𝟔𝟔𝟔𝟔
Integer Width Group𝒃𝒃𝒘𝒘 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝒘𝒘
base width)𝒃𝒃𝟐𝟐 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝟐𝟐
2 width)𝒃𝒃𝟑𝟑 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝟑𝟑
3 width)𝒃𝒃𝟒𝟒 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝟒𝟒
4 width)𝒃𝒃𝟓𝟓 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝟓𝟓
layer5 width)
𝒃𝒃𝟔𝟔 = 𝟎𝟎. 𝟓𝟓𝒊𝒊𝟔𝟔
layer6 width)

Calculated Constants
Active earth pressure coefficient, Ka = 0.364.
The triangular pressure acting on the wall, Pa = 43.4
kN.
Horizontal component Ph = 39.8 kN.
Vertical component Pv = 17.3 kN.
Bearing capacity parameters, 𝑵𝑵𝒒𝒒 = 18.4, 𝑵𝑵𝒄𝒄 = 30.1,
𝑵𝑵𝜸𝜸 = 15.
Vertical centroid
𝒚𝒚𝒚𝒚𝟏𝟏 = 𝒚𝒚𝟏𝟏 /𝟐𝟐 = 0.25,(4.1)
𝒚𝒚𝒚𝒚𝟐𝟐 = 𝒚𝒚𝟏𝟏 + 𝒚𝒚𝟐𝟐 /𝟐𝟐 = 0.75
(4.2)
𝒚𝒚𝒚𝒚𝟑𝟑 = 𝒚𝒚𝟏𝟏 + 𝒚𝒚𝟐𝟐 + 𝒚𝒚𝟑𝟑 /𝟐𝟐 = 1.25
(4.3)
𝒚𝒚𝒚𝒚𝟒𝟒 = 𝒚𝒚𝟏𝟏 + 𝒚𝒚𝟐𝟐 + 𝒚𝒚𝟑𝟑 + 𝒚𝒚𝟒𝟒 /𝟐𝟐 = 1.75
(4.4)
𝒚𝒚𝒚𝒚𝟓𝟓 = 𝒚𝒚𝟏𝟏 + 𝒚𝒚𝟐𝟐 + 𝒚𝒚𝟑𝟑 + 𝒚𝒚𝟒𝟒 + 𝒚𝒚𝟓𝟓 /𝟐𝟐 = 2.25
(4.5)
𝒚𝒚𝒚𝒚𝟔𝟔 = 𝒚𝒚𝟏𝟏 + 𝒚𝒚𝟐𝟐 + 𝒚𝒚𝟑𝟑 + 𝒚𝒚𝟒𝟒 + 𝒚𝒚𝟓𝟓 + 𝒚𝒚𝟔𝟔 /𝟐𝟐 = 2.75
(4.6)

(C 2.1:(C 2.2: layer(C 2.3: layer(C 2.4: layer(C 2.5:(C 2.6:

Non-negativity Integer Width Group𝒃𝒃𝒘𝒘 ≥ 𝟏𝟏, 𝒃𝒃𝟐𝟐 ≥ 𝟏𝟏, 𝒃𝒃𝟑𝟑 ≥ 𝟏𝟏, 𝒃𝒃𝟒𝟒 ≥ 𝟏𝟏, 𝒃𝒃𝟓𝟓 ≥ 𝟏𝟏, 𝒃𝒃𝟔𝟔 ≥ 𝟏𝟏
(C 3.1 - 3.6: integer width)
Weight Constraint
𝑾𝑾𝒈𝒈 = 𝜸𝜸𝒈𝒈 (𝒚𝒚𝟏𝟏 𝒃𝒃𝒘𝒘 + 𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 + 𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 + 𝒚𝒚𝟒𝟒 𝒃𝒃𝟒𝟒 + 𝒚𝒚𝟓𝟓 𝒃𝒃𝟓𝟓 +𝒚𝒚𝟔𝟔 𝒃𝒃𝟔𝟔 )(C 4: gabion weight)

Mixed Integer Nonlinear Programing

Horizontal Centroid Constraint Group(C 5.1: base layer)
𝒙𝒙𝒙𝒙𝟏𝟏 = 𝒃𝒃𝒘𝒘 /𝟐𝟐
𝒙𝒙𝒙𝒙𝟐𝟐 = 𝒙𝒙𝟐𝟐𝟐𝟐 + 𝒃𝒃𝟐𝟐 /𝟐𝟐(C 5.2: layer 2)
𝒙𝒙𝒙𝒙𝟑𝟑 = 𝒙𝒙𝟐𝟐𝟐𝟐 + 𝒙𝒙𝟑𝟑𝟑𝟑 + 𝒃𝒃𝟑𝟑 /𝟐𝟐(C 5.3: layer 3)
𝒙𝒙𝒙𝒙𝟒𝟒 = 𝒙𝒙𝟐𝟐𝟐𝟐 + 𝒙𝒙𝟑𝟑𝟑𝟑 + 𝒙𝒙𝟒𝟒𝟒𝟒 + 𝒃𝒃𝟒𝟒 /𝟐𝟐(C 5.4: layer 4)
𝒙𝒙𝒙𝒙𝟓𝟓 = 𝒙𝒙𝟐𝟐𝟐𝟐 + 𝒙𝒙𝟑𝟑𝟑𝟑 + 𝒙𝒙𝟒𝟒𝟒𝟒 + 𝒙𝒙𝟓𝟓𝟓𝟓 + 𝒃𝒃𝟓𝟓 /𝟐𝟐(C 5.5: layer 5)
𝒙𝒙𝒙𝒙𝟔𝟔 = 𝒙𝒙𝟐𝟐𝟐𝟐 + 𝒙𝒙𝟑𝟑𝟑𝟑 + 𝒙𝒙𝟒𝟒𝟒𝟒 + 𝒙𝒙𝟓𝟓𝟓𝟓 + 𝒙𝒙𝟔𝟔𝟔𝟔 + 𝒃𝒃𝟔𝟔 /𝟐𝟐(C 5.6: layer 6)

Mixed integer nonlinear programming (MINP)
model minimizes the minimum gabion weight whilesatisfying the external stability constraints in sliding,overturning, and bearing stability. This is done byadjusting the decision variables
Objective Function
Min 𝑊𝑊𝑔𝑔(C 1.1: layer 2)(C 1.2: layer 3)(C 1.3: layer 4)(C 1.4: layer 5)(C 1.5: layer 6)(O 1: MIP)

Horizontal Moment Group𝒙𝒙𝒙𝒙𝒙𝒙𝟏𝟏 = 𝒚𝒚𝟏𝟏 𝒃𝒃𝒘𝒘 𝒙𝒙𝒙𝒙𝟏𝟏
𝒙𝒙𝒙𝒙𝒙𝒙𝟐𝟐 = 𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 𝒙𝒙𝒙𝒙𝟐𝟐
6.2: layer 2)𝒙𝒙𝒙𝒙𝒎𝒎𝟑𝟑 = 𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 𝒙𝒙𝒙𝒙𝟑𝟑
6.3: layer 3)𝒙𝒙𝒙𝒙𝒙𝒙𝟒𝟒 = 𝒚𝒚𝟒𝟒 𝒃𝒃𝟒𝟒 𝒙𝒙𝒙𝒙𝟒𝟒
6.4: layer 4)𝒙𝒙𝒙𝒙𝒙𝒙𝟓𝟓 = 𝒚𝒚𝟓𝟓 𝒃𝒃𝟓𝟓 𝒙𝒙𝒙𝒙𝟓𝟓
6.5: layer 5)𝒙𝒙𝒙𝒙𝒙𝒙𝟔𝟔 = 𝒚𝒚𝟔𝟔 𝒃𝒃𝟔𝟔 𝒙𝒙𝒙𝒙𝟔𝟔
6.6: layer 6)

Decision Variables
The decision variables are as follows:𝒃𝒃𝒘𝒘 : gabion width (m) at the base layer𝒃𝒃𝒊𝒊 : gabion width (m) at layer i, i = 2, 3, 4, 5, 6
𝒙𝒙𝑭𝑭𝑭𝑭 : offset (m) of gabion at the front at layer i, i = 2,
3, 4, 5, 6
𝒙𝒙𝑩𝑩𝑩𝑩 : offset (m) of gabion at the back at layer i, i = 2,
3, 4, 5, 6
𝒊𝒊𝒘𝒘: positive integer variable of gabion wall width at
the base.
𝒊𝒊𝒊𝒊 : positive integer variable of gabion wall width atlayer i, i = 2, 3, 4, 5, 6

Vertical Moment Group
𝒚𝒚𝒚𝒚𝒚𝒚𝟏𝟏 = 𝒚𝒚𝟏𝟏 𝒃𝒃𝒘𝒘 𝒚𝒚𝒚𝒚𝟏𝟏
𝒚𝒚𝒚𝒚𝒚𝒚𝟐𝟐 = 𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 𝒚𝒚𝒚𝒚𝟐𝟐
7.2: layer 2)Constraints
276(C 6.1: base layer)
(C
(C
(C
(C
(C

(C 7.1: base layer)(CGEOMATE – Melbourne, Australia, 11-13 November 2020

𝒚𝒚𝒚𝒚𝒚𝒚𝟑𝟑 = 𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 𝒚𝒚𝒚𝒚𝟑𝟑
7.3: layer 3)
𝒚𝒚𝒚𝒚𝒚𝒚𝟒𝟒 = 𝒚𝒚𝟒𝟒 𝒃𝒃𝟒𝟒 𝒚𝒚𝒚𝒚𝟒𝟒
7.4: layer 4)
𝒚𝒚𝒚𝒚𝒚𝒚𝟓𝟓 = 𝒚𝒚𝟓𝟓 𝒃𝒃𝟓𝟓 𝒚𝒚𝒚𝒚𝟓𝟓
7.5: layer 5)
𝒚𝒚𝒚𝒚𝒚𝒚𝟔𝟔 = 𝒚𝒚𝟔𝟔 𝒃𝒃𝟔𝟔 𝒚𝒚𝒚𝒚𝟔𝟔
7.6: layer 6)(C

Suggested by Ortigo and Sayao, 2004 [10]that the base width should occupy about 0.4H to
0.6H. Hence, b/H lower bound for all 6 layers is
set at 0.4 in constraint 12.1 to 12.6 while the b/Hupper bound is set at 0.6 in constraint 13.

(C
(C

𝒃𝒃

𝒃𝒃𝒘𝒘 /𝑯𝑯 ≥ 𝟎𝟎. 𝟒𝟒 , 𝑯𝑯𝟐𝟐 ≥ 𝟎𝟎, . 𝟒𝟒 , 𝒃𝒃𝟑𝟑 /𝑯𝑯 ≥ 𝟎𝟎. 𝟒𝟒 , 𝒃𝒃𝟒𝟒 /𝑯𝑯 ≥𝟎𝟎. 𝟒𝟒, 𝒃𝒃𝟓𝟓 /𝑯𝑯 ≥ 𝟎𝟎. 𝟒𝟒, 𝒃𝒃𝟔𝟔 /𝑯𝑯 ≥ 𝟎𝟎. 𝟒𝟒(C 12.1 – 12.,6: minimum width)
𝒃𝒃𝒘𝒘 /𝑯𝑯 ≤ 𝟎𝟎. 𝟔𝟔(C 13: maximum width)(C

Center of Gravity
𝒚𝒚 𝒃𝒃 𝒙𝒙𝒙𝒙 +𝒚𝒚 𝒃𝒃 𝒙𝒙𝒙𝒙 +𝒚𝒚 𝒃𝒃 𝒙𝒙𝒙𝒙 +𝒚𝒚 𝒃𝒃 𝒙𝒙𝒙𝒙
𝒙𝒙𝒈𝒈 = 𝟏𝟏 𝟏𝟏 𝒚𝒚𝟏𝟏 𝒃𝒃 𝟐𝟐+𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 +𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 +𝒚𝒚𝟑𝟑 𝒃𝒃 𝟒𝟒 𝟒𝟒 𝟒𝟒
𝒚𝒚𝒈𝒈 =

𝟏𝟏 𝒘𝒘

𝟐𝟐 𝟐𝟐

𝟑𝟑 𝟑𝟑

𝟒𝟒 𝟒𝟒

MINP solution(C8.1: horizontal centroid)𝒚𝒚𝟏𝟏 𝒃𝒃𝟏𝟏 𝒚𝒚𝒚𝒚𝒚𝒚𝒚𝒚𝟏𝟏 +𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 𝒚𝒚𝒚𝒚𝟐𝟐 +𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 𝒚𝒚𝒚𝒚𝟑𝟑 +𝒚𝒚𝟒𝟒 𝒃𝒃𝟒𝟒 𝒚𝒚𝒚𝒚𝟒𝟒

By mainly adjusting the decision variables ingabion layer widths 𝑏𝑏𝑤𝑤 , 𝑖𝑖𝑤𝑤 , 𝑏𝑏𝑖𝑖 , 𝑖𝑖𝑖𝑖 , i = 2, 3, 4, 5, 6, thesolution of this mixed integer nonlinear programming
model 1 gives the minimum weight 𝑊𝑊𝑔𝑔∗ = 52 kN/m.
The eccentricity 𝑒𝑒 = 0.242 meter as shown in table 1.

𝒚𝒚𝟏𝟏 𝒃𝒃𝒘𝒘 +𝒚𝒚𝟐𝟐 𝒃𝒃𝟐𝟐 +𝒚𝒚𝟑𝟑 𝒃𝒃𝟑𝟑 +𝒚𝒚𝟒𝟒 𝒃𝒃𝟒𝟒

(C8.2: vertical centroid)𝒙𝒙𝒙𝒙𝒈𝒈 = 𝒙𝒙𝒈𝒈 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 + 𝒚𝒚𝒈𝒈 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔(C 8.3: centroid)

Sliding Stability
𝑵𝑵 = 𝑾𝑾𝒈𝒈 + 𝑷𝑷𝒗𝒗

(𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵+𝑷𝑷𝒉𝒉𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔)𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕
𝑷𝑷𝒉𝒉 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄−𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵𝑵(C 9.1: reaction)≥ 𝟏𝟏. 𝟓𝟓

Preemptive Goal Programming(C 9.2: sliding factor of safety)

Preemptive goal programing is used in this modelby sequential assign the two goals as shown below.

Overturning Stability
𝒅𝒅𝒉𝒉 = 𝑯𝑯/𝟑𝟑(𝑯𝑯 + 𝟑𝟑𝑷𝑷𝟎𝟎 /𝜸𝜸𝒔𝒔 )/(𝑯𝑯 + 𝟐𝟐𝑷𝑷𝟎𝟎 /𝜸𝜸𝒔𝒔 ) −𝒃𝒃𝒘𝒘 𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔(C 10.1: 𝒅𝒅𝒉𝒉 )(C𝒃𝒃𝒗𝒗 = 𝒃𝒃𝒘𝒘 𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 − 𝒅𝒅𝒉𝒉 /𝒕𝒕𝒕𝒕𝒕𝒕𝒕𝒕
10.2: 𝒃𝒃𝒗𝒗 )𝑴𝑴𝒓𝒓 = 𝑷𝑷𝒗𝒗 𝒃𝒃𝒗𝒗 + 𝑾𝑾𝒈𝒈 𝒙𝒙𝒙𝒙𝒈𝒈(C 10.3: resisting
moment)𝑴𝑴𝒐𝒐 = 𝑷𝑷𝒉𝒉 𝒅𝒅𝒉𝒉
(C 10.4: overturning moment)
𝑴𝑴𝒓𝒓 /𝑴𝑴𝑶𝑶 ≥ 𝟐𝟐
(C 10.5: moment factorof safety)

Goal 1: minimum gabion weight, 𝑊𝑊𝑔𝑔
Goal 2: minimum eccentricity, e
The first preemptive goal is done by assigning thefirst objective function as:
Min 𝑊𝑊𝑔𝑔(O 2.1: preemptive goal 1)

The set of constraints is the same as in model 1.

Eccentricity𝒃𝒃
𝑴𝑴 −𝑴𝑴𝒆𝒆 = 𝟐𝟐𝒘𝒘 − 𝒓𝒓𝑵𝑵 𝒐𝒐(C 11.1: eccentricity)|𝒆𝒆| ≤ 𝒃𝒃𝒘𝒘 /𝟔𝟔(C 11.2:
eccentricity limit)
By replacing e which is a free variable with 𝒆𝒆+ and𝒆𝒆− which are positive variables by using therelationship 𝒆𝒆 = 𝒆𝒆+ − 𝒆𝒆− .
This constraint is converted to 𝒆𝒆+ − 𝒆𝒆− ≤ 𝒃𝒃𝒘𝒘 /𝟔𝟔
Where
𝑒𝑒 + : positive eccentricity. In this case, the wall willlean toward facing.
𝑒𝑒 − : negative eccentricity. In this case, the wall willlean toward the retained soil.

The solution of the first preemptive goal ofmodel 3 gives the minimum weight 𝑊𝑊𝑔𝑔∗ = 48 kN/m
where the eccentricity 𝑒𝑒 = 0.233 meter as shown intable 1.
From this optimum solution in goal 1, the second
preemptive goal is assigned with this second
objective function. This second goal is trying toequalize the vertical stress at the toe to be the same as
the vertical stress at the heel. In other words, thisobjective function is trying to minimize theeccentricity to be zero. Since the vertical stress at toe

Bearing𝑵𝑵
𝟔𝟔𝟔𝟔
𝝈𝝈𝒕𝒕 = �𝟏𝟏 + �

𝑒𝑒 = 0 . This is done by adjusting the decisionvariables of the offsets 𝑥𝑥𝐹𝐹𝐹𝐹 and 𝑥𝑥𝐵𝐵𝐵𝐵 , i = 2, 3, 4, 5, 6 of
the gabion layers. The second objective function is asfollows.

𝒃𝒃𝒘𝒘

𝒃𝒃𝒘𝒘

is 𝜎𝜎𝑡𝑡 =𝜎𝜎ℎ =(C 12.1: vertical bearing at toe)𝒒𝒒𝒂𝒂 = 𝑷𝑷𝟎𝟎 𝑵𝑵𝒒𝒒 + 𝟎𝟎. 𝟓𝟓𝜸𝜸𝒔𝒔 𝒃𝒃𝒘𝒘 𝑵𝑵𝜸𝜸
(C 12.2: soil bearing capacity)
𝝈𝝈𝒕𝒕 /𝒒𝒒𝒂𝒂 ≥ 𝟐𝟐. 𝟓𝟓(C 12.3.: bearing factor of safety)

𝑁𝑁

𝑁𝑁

𝑏𝑏𝑤𝑤

𝑏𝑏𝑤𝑤

�1 +

�1 −

𝑏𝑏𝑤𝑤

Min 𝑒𝑒 + − 𝑒𝑒 −Width/Height Ratio

277

6𝑒𝑒

𝑏𝑏𝑤𝑤
6𝑒𝑒

� and the vertical stress at heel is� , Equalizing 𝜎𝜎𝑡𝑡 = 𝜎𝜎ℎ implies that

(O 2.2: preemptive goal 1)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Except one constraint is added to satisfy the first goal,
the other constraints are the same as in model 1. The
added constraint is as follows:
𝑾𝑾𝒈𝒈 = 𝟒𝟒𝟒𝟒

minimum weight and forcing eccentricity to be close
to zero gives a better design result. This gives a lower
gabion weight, as seen in column G-2 in table 1, also
gains a lesser eccentricity at 0.002 m which is close
to zero.
The weighted goal programming model, which
subjectively assigns the objective weights of 2 and 1
to the first goal in minimum weight and the second
goal in forcing eccentricity to be zero, also gain thesame result as in using preemptive goal.

(C 14: satisfying the first goal)

The solution of the second goal of model 3,
which is the optimum solution of this model, gives theminimum eccentricity 𝑒𝑒 ∗ = 0.002 meter which is lessthan the eccentricity in the previous first preemptive
goal model at 𝑒𝑒 = 0.233 meter. Of course, theminimum weight 𝑊𝑊𝑔𝑔∗ = 48 kN/m which is the same as
the previous first preemptive goal model since thisminimum weight is enforced as a constraint in thesecond preemptive goal model. The detail of thesolution is shown in table 1.

Table 1 Comparison of the Designs
Model
27
39
MINP
G-1
G-2
H, m
3.1
3
3
3
3
Wg, kN
61.92
72
52
48
48
e, m
0.086
0.172 0.242 0.233 0.002
FSO
2.72
2.87
2.00
2.00
2.45
FSS
1.87
1.83
1.60
1.52
1.52
FSB
7.14
6.18
4.48
4.89
9.37
Note:
Column description
27 – Environmesh 27 system design
39 – Environmesh 39 system design
MINP – Mixed integer nonlinear programing design
G -1 – Preemptive goal programing, 1st goal: min Wg
G-2 – preemptive goal programming, 2nd goal: min e
G-3 – Weighted goal programing
Row description
H – Wall height in m
Wg – Gabion Weight in kN/m
e – Eccentricity in m
FSO – Overturning Factor of Safety
FSS – Sliding Factor of Safety
FSB – Bearing Factor of Safety

Weighted Goal Programming
Weighted goal programming is used in this modelby assigning a set of weights to handle 2 objectives asfollow.
Goal 1: minimum gabion weight, 𝑊𝑊𝑔𝑔
Goal 2: minimum eccentricity, e
The weights assigned to the five goals are 2 togoal 1 and 1 to goal 2. The reason behind assigning
such weights is largely based on the importance ofeach goal, Taylor, 2006 [11].

The calculation details including the widths andoffsets of all the models are shown in table 2.

Hence the objective function is:
Min 2𝑊𝑊𝑔𝑔 + 𝑒𝑒 + + 𝑒𝑒 −

G-3
3
48
0.25
2.45
1.52
9.37

(O 3: weighted goal)
Table 2 Calculation Details of the Designs

The set of constraint is the same as in model 1:
Model
H, m

The solution of the first preemptive goal ofmodel 3 gives the minimum weight 𝑊𝑊𝑔𝑔∗ = 48 kN/m
where the eccentricity 𝑒𝑒 = 0.233 meter as shown intable 1.

α

bw, m
b2, m
b3, m
b4, m
b5, m
b6, m
x2F, mx2B, mx3F, mx3B, mx4F, mx4B, mx5F, mx5B, mx6F, mx6B, mWg, kN
e, m
Mr,
MO,
FSO
Fr, kNFd, kNFSSCOMPARISON OF THE MODELSThe comparison of the all the designs are shown
below. Note that although the MINP model, whichtries to minimize that gabion weight, yields a lesser
gabion weight than the original Environmesh 27
system and 39 system, the MINP gives a larger valueof eccentricity than the original Environmesh 27
system and 39 system. The result from using MINP
model does not follow the good suggestion in thedesign that tries to equalize vertical stress at the toewith the vertical stress at the heel by setting theeccentricity to be close to zero.
The preemptive goal programing model whichtries to combine the two goals in achieving the
278

27
3.1
10
1.7
1.4
1.0
0.7

36
3
6
2.0
1.5
1.0
-

0
0.3
0.4
0
0.3
0
61.92
0.086
96.4
35.5
2.72
49.4
26.4
1.87

0
0.5
0
0.5
72
0.172
101.9
35.5
2.87
53.3
29.1
1.83

MINP
3
10
1.5
1.5
1
1
1
0.5
0.066
0.434
0.007
0.493
52
0.242
70.3
35.2
2.00
43.4
27.0
1.60

G-1
3
10
1.5
1.5
1
1
0.5
0.5
0.145
0.355
0.048
0.452
48
0.233
67.0
33.5
2.00
40.7
26.7
1.52

G-2
3
10
1.5
1
1
1
1
1
0.5
0.5
48
0.002
82.0
33.5
2.45
40.7
26.7
1.52

G-3
3
10
1.5
1
1
1
1
0.5
0.5
0
0.006
0.494
0.5
48
0.25
82.0
33.5
2.45
40.7
26.7
1.52

GEOMATE – Melbourne, Australia, 11-13 November 2020

Qa, kNσt, kN
FSB

437.1
61.2
7.14

481.7
78.0
6.18

407.3
90.9
4.48

407.3
83.3
4.89

407.3
43.5
9.37

407.3
43.5
9.37

REFERENCES[1] Araki H, Hirakawa D., 2019, Effects of Thrust

Note: table 2 uses the same column description androw description as those in table 1.

Protecting Method for Buried Pipe Using Geogrid
Gabion of Different Sizes. International Journal of
GEOMATE, June 2019, Vol.16, Issue 58, pp.62 – 68.
[2] ASTM A975-97, ‘Double-Twisted Hexagonal Mesh
Gabions and Revet Mattresses (Metallic-Coated Steel
Wire or Metallic-Coated Steel Wire with Poly (Vinyl
Chloride) (PVC) Coating)’, America Standard Testing
Association, 2003.
[3] BS8002, ‘Code of Practice for Earth Retaining
Structures’, British Standards Institution, 2015.
[4] FHWA-NHI-10024, Federal Highway. Administration,
2001. Mechanically Stabilized Earth Walls andReinforced Soil Slopes Design and Construction
Guidelines.
[5] FHWA-SA-96-038, Federal Highway Administration,
1996. Geotechnical Engineering Circular No. 2. Earth
Retaining Systems.
[6] Enviromesh, 2007. Designing with Gabions Volume 1.
A Reference Guide for the Designing of Mass Gravity
Gabion Walls.
[7] Saribas A., Erbatur F., Optimization and Sensitivity of
Retaining Structures, Journal of GeotechnicalEngineering, 1996, pp. 649-656.
[8] Basudhar, P. K., Vashistha, A., Deb, K. and Dey, A.
(2008): Cost optimization of reinforced earth walls,
Geotechnical and Geological Engineering, Vo1.26,
No.l, February 2008, pp. 1 - 12.
[9] Bowles J. E. 2001, Foundation Analysis and Design’,
McGraw-Hill.
[10] Ortigao A. R., Sayao A. S., 2004. ‘Handbook of Slope
Stabilisation, Springer.
[11] Taylor B. W., 2006. Introduction to Management
Science, Ninth Edition, Prentice Hall.

CONCLUSIONS
Although optimization techniques such as mixed
integer nonlinear programming (MINP) is a good toolin helping the design in choosing the goodcombination of many decision variables. This MINP
has a limit in achieving only one objective. In thiscase, goal programming is a good alternative inachieving more that one objectives.
Also, many nonlinear programming problems
does not guarantee the optimum exact solution but
will give only the approximated solution thatsometimes may not be a good solution. This can beseen in table 1 that although MINP can give a lesser
weight than the original Environmesh 27 system and39 system but MINP model gives a larger eccentricity
than the 27 system and 39 system. In table 1,preemptive goal programing gains a lesser weight anda lesser eccentricity than the MINP model.
Note that the weighted goal model gives thesame result as the preemptive goal model although
the weights for the two goals are subjectively
assigned as 2 and 1. Hence, there is a room for a futherwork to investigate the effects of the weights assign
to the design output.
Also, this goal programming approach can beused in other geotechnical, especially other retaining
wall structures, that needs yto achieve many
objectives and design criteria at the same time.

279

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INTRODUCTION OF ARTIFICIAL INTELLIGENCE SYSTEM FOR
SUPPORTING SOIL CLASSIFICATION
Shinya Inazumi1, Kazuhiko Kojima2,
Hiroyuki Hashida3 and Ken-ichi Shishido4
1

Shibaura Institute of Technology, Japan; 2Otowa Co. Ltd., Japan;
3
Sumice Kenzai Co. Ltd., Japan; 4Tomec Corporation, Japan
ABSTRACT

From the perspective of soil engineering, soil is uncertain and heterogeneous. Therefore, if an attempt is madeto determine the soil classification of a soil without a precise test, for example, an engineer’s individual judgement
is often involved in making the determination based on his/her own experiences. In relation to acquiring vast andvaried knowledge which is easily influenced by individual experiences, the purpose of this research is to gather
the know-how of engineers and to create a certain index for use in making on-site judgments that are likely to be
more inclusive of various data than those of individual engineers. In order to expand the application of artificial
intelligence (AI) in the field, the possibility of image recognition by artificial intelligence is shown through theuse of machine learning. This paper discusses the potential of image recognition by artificial intelligence, using amachine learning technique called deep learning, for the purpose of expanding the cases which employ artificial
intelligence. In this research, it is shown that artificial intelligence, along with deep learning, can be applied to soil
classification determination by performing simple deep learning with a model using a neural network.
Keywords: Soil classification, Machine learning, Neural network, Deep learning, Image recognition
INTRODUCTION

addition, although a digital camera was used in the
research by Kiso-Jiban Consultants Co, Ltd. [3], we
will examine here whether a smartphone withdifferent features from a digital camera can be applied
to an AI program.

As an example of previous studies in geotechnicalengineering and related fields, Ito [1], [2] prepared
150 sample photographs for three types of soil,
namely, organic soil, gravel-mixed sand, and siltmixed sand. As a result, it was shown that the rate ofaccuracy obtained by the model (the predictive valueof the model against the real phenomenon) improved
as the number of learning sessions was increased to30, 100, 300, and 1000 times. On the other hand,when the 150 sample photographs were converted tograyscale, the black organic soil became almost blackand the features of the images were lost. This led tothe problem of the black organic soil images being
erroneously recognized as silt-blended sand with few
features. In addition, in the research by Kiso-Jiban
Consultants Co., Ltd. [3], an AI program using deeplearning was developed for three types of rock images,
namely, granite, andesite, and mudstone, taken with adigital camera instead of a high-precision camera,such as a single-lens reflex camera. As a result, therate of accuracy of the AI program was 88% for a totalof 100 rock images consisting of 60 granite, 20
andesite, and 20 mudstone pieces. Furthermore, it
was shown that the accuracy of this AI program
determination exceeded that of geologists and soil
engineers.
In this study, it is verified that the model
misrecognition caused by the loss of image features
reported in Ito's study [1], [2] occurs even whenanother soil type is set as the discrimination target. In

CONDUCT MODEL LEARNING
The purpose of this research is to create the basis
for a model to be used to make judgements on soil
classification that can be utilized on a site-by-sitebasis. For this reason, the soils to be classified weredivided into three types: clay (D50 = 0.008 mm), sand
(D50 = 0.7 mm), and gravel (D50 = 4 mm) with thewater content adjusted to 0 for simplicity. Clay, sand,and gravel, whose particle sizes were adjusted byconducting a sieving test, were put in a transparent
plastic cup as shown in Figs. 1, 2, and 3, considering
this practice as a difference from the previous studies[1], [2], [3]. The images were taken with asmartphone (iPhone 7) camera. Comparing theperformances of the smartphone camera and thedigital camera (FUJIFILM X-T4) released at the timeof the experiment, the iPhone 7 has 12 million pixels
and the digital camera has 26 million pixels. In
addition, the size of the image sensor related to image
noise (the larger the image, the more faithful theimage to the subject) is 4.8 x 3.6 mm for the iPhone 7
and 23.5 x 15.6 mm for the digital camera. It is seenthat the photographs were shot indoors. As for theclay, 200 photographs were taken both with andwithout lighting (400 in total). As for the sand, 200
280

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Types and numbers of images taken
With lights
Without lights(Total)

Clay
200
200
400

Sand
200
200
400

Gravel100
100
200(Total)
500
500
1000

Table 2 Parameters used
Learning coefficient
Batch size
Number of learning
Image size during learning (px)

1e-06
20
70
56×56

Fig. 1 Image of clay taken

photographs were taken with and without lighting(200 in total). In order to see the influence of thedifference in the amount of data on the learning
results, only the gravel had a different amount of data.
In order to secure the data variation, adjustments suchas switching the presence or absence of lighting
during shooting and adding vibration or rotation to
the cup to change the appearance of the surface wereadded. Vibration was applied so that the arrangementof the particles on the sample surface would becompletely different, and rotation was performed in
90-degree units. These processes were combined toprevent the data from becoming uniform. This isbecause, even if a large number of identical images
are copied and prepared, the effect of the learningmodel will be weak.
Table 1 presents a list of prepared images. These
1000 images were used as the learning data, and a
total of 60 images of each randomly selected 20
images were used as model accuracy verification data.
In this research, the soil images were learned as amodel using deep learning by the convolutional
neural network and by the steepest descent method.
At that time, each parameter was set as shown in
Table 2.

Fig. 2 Image of sand taken

LEARNING RESULTS
Figure 4 shows the transition of accuracy withrespect to the number of learning sessions. The
transition of the error is shown in Fig. 5. From thesefigures, it can be seen that the accuracy improves andthe error decreases as the number of learning sessions
increases. The error referred to here is the one definedby Eq. (4). Finally, an accuracy of about 86% wasrecorded for the image data for learning and about
77% for the data for verification. Here, the accuracy
is the rate at which the images of clay, sand, andgravel used for learning can be accurately identified.
In other words, out of a total of 1000 learning data
images, about 86% (about 860) of the images werecorrectly classified.
In order to confirm whether the model can

Fig. 3 Image of gravel taken

photographs were also taken with and without
lighting (400 in total). As for the gravel, 100
281

GEOMATE – Melbourne, Australia, 11-13 November 2020

correctly recognize the training data, Table 3 shows
the judgment results for each soil type among thetraining data. The table shows how the model
determined each soil type. For example, as a result of
inputting 400 learning images showing clay as a true
value into the model, 313 of the images were
determined to be clay. At the same time, 87 of theimages had the true value of clay, but the model
misidentified them as sand. From the table, it can beseen that all the sand was correctly recognized andonly about half of the gravel was correctly recognized.
In particular, the gravel and clay were often mistaken
for sand.
In addition, learning and classification were
performed in this study by targeting only three typesof soil. However, considering further development,
not only the adjustment of parameters but the
expansion of corresponding soil is the first issue. As
shown in Figs. 6 and 7, clay and sand, which lookvery similar to human eyes, can be discriminated by
the model. Therefore, like the fine sand in Fig. 8, there

Fig. 6 Clay that has been identified correctly

Fig. 4 Transition of accuracy with learning coefficient
1e-6
Fig. 7 Sand that has been identified correctly

Fig. 5 Transition of error in learning coefficient 1e-6

Table 3 Judgment results for each soil type
True value
Class
Judgment
resultFig. 8 Example image of fine sand

Clay

Clay
Sand

Gravel

Clay

Sand
Sand

GravelClay

GravelSand

Gravel

313

87

0

0

400

0

10

83

107

282

GEOMATE – Melbourne, Australia, 11-13 November 2020are various soil types. It is possible to obtain ageneralization performance for various types of soils
by preparing several types, securing a sufficientamount of data for learning, and conducting thelearning sessions with a sufficiently large number ofpixels. It is thought that this will soon be possible.

of pixels in the image during the learning sessions is
too small, the features of the images cannot be
detected sufficiently and the accuracy decreases. If
the number of pixels is increased, the features can bedetected and the accuracy increases.
REFERENCES

CONCLUSIONS[1] Ito, M (2017): Possibility of automated soil
quality judgment by machine learning,
Proceedings of ZENCHIREN Technology
Forum 2017 Asahikawa, (http://www.takuhokuchika.co.jp/machinelearning/resume.pdf).
[2] Utsuki, S., Nakaya, M. and Tsuruta, R. (2018):
Construction of a geological information
management system using AI, CIM and image
processing technology, Journal of Japan Societyof Engineering Geology, Vol. 58, No. 6 pp. 408415.
[3] Kiso-Jiban Consultants Co.,Ltd. (2019):
https://www.kisojiban.com.

In this study, deep learning was performed with amodel using a neural network. For three types of soil,
namely, clay, sand, and gravel, an AI model was
created that was conscious of the practical simplicityof the images used. It was shown that this AI model
can be applied to make judgments on soil
classification. As a result, a high recall rate of 1 was
obtained for sand. This means that all the sand images
could be identified as sand. On the other hand, a highmatching rate was obtained for clay and gravel. Thismeans that images of clay and gravel can be carefully
discriminated without much mixing of different typesof images. Regarding the parameters, if the number283

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

UNSATURATED SHEAR STRENGTH OF EXPANSIVE SOILS FROM
NATURAL SLOPES IN QUEENSLAND
Tharindu Abeykoon1, Chaminda Gallage2 and Jessica Trofimovs3
1,2,3

Science and Engineering Faculty, Queensland University of Technology, Australia
ABSTRACT

Expansive soils exhibit significant changes in the swell and shrink behaviour upon variations of moisture
content. Majority of soils in Queensland are expansive; consequently, failures in natural slopes with expansive
characteristics are commonly reported under climate-induced soil moisture variations. In order to understand thefailure mechanism of such soil slopes, it is vital to analyse slope stability under unsaturated conditions. The current
study employed undisturbed soil samples from natural slopes in Queensland, and determined the basic soil
properties, including the soil-water characteristic curve (SWCC), and unsaturated shear strength to facilitate theanalysis of rainfall-induced slope failure mechanism. A series of conventional direct shear tests were conducted
for different moisture contents under various vertical stresses. The test results were analysed to obtain the variationof the apparent cohesion and the effective friction angle of the soil with moisture content and suction. The study
presents an empirical equation to predict the cohesive component in the shear strength of unsaturated expansive
soils as an exponential function of suction. The formulation originated from multiple linear regression analysis fordata sets obtained from shear tests using undisturbed soils with varying moisture contents. The empirical equations
can realistically predict the reduction in soil cohesion due to wetting (R2 = 0.8791). The study provides analternative to the quantitative estimation of unsaturated shear strength of expansive soils.
Keywords: Unsaturated soil, unsaturated shear strength, Expansive soil, slope stability, Direct shear test

required to reach the failure of the soil specimen
compared to the triaxial test. The time to failure in the
direct shear test is significantly reduced because thespecimen is relatively thin. However, a lengthy
testing period is expected for unsaturated soils due tothe low coefficient of permeability of the soil. In thispaper, an empirical equation is suggested for theprediction of unsaturated shear strength as a functionof suction.
The equation was obtained from a multiple linear
regression analysis of the results of direct shear tests
using undisturbed expansive soils with varying
moisture contents. Even though the equation lacks atheoretical validity from a physical standpoint, but
has practical advantages, especially for geotechnicalengineering purposes.

INTRODUCTION
The understanding of soil interface shear strengthis vital in designing and analysing the stability ofgeotechnical structures [1-6]. The cohesive strengthof unsaturated soils plays an important role in thestability of both natural and artificial soil slopes, [7,8] in which the reduction of the soil cohesion due towetting can cause shear deformation of the slopes at
a previously unsaturated shallow layer. Therefore,
determination of the critical condition for sliding
requires a slope stability analysis, including an effectfor the loss of cohesion.
As shear strength tests on unsaturated soils arecostly and time-consuming, simple and indirect
methods, have been developed to obtain the shear
strength of unsaturated soils for engineering purpose.
Numerous researchers suggested that the shear
strength of unsaturated soils could be predicted using
different equations [9]. Based on the soil-water
characteristic curve (SWCC), various theoretical andempirical models were proposed for prediction of
unsaturated shear strength. However, minimum
compelling attempts were made on the analysis of thecharacteristics of unsaturated shear strength, and therelationship between the shear strength parametersand suction of unsaturated expansive soils.
This paper presents the results of a series of direct
shear tests on unsaturated soils from natural slopes in
Maleny, Queensland, Australia. Direct shear testingof unsaturated soils is desirable since less time is

STUDY AREA
The study area is located in Lake Baroon
catchment, Maleny, Queensland, Australia (26.72 ºS
152.87 ºE). Mapleton - Maleny plateau, which has
been documented as a highly susceptible area for
rainfall-induced slope failures since the mid - 1950s.
(e.g. [10]). Slope failures and mass movements of
sediment into the waterways within the Lake Baroon
catchment are recognised as a significant risk to water
quality and the water storage capacity of Lake Baroon,
which is used to supply water to South East
Queensland. Approximately 170 mass movement
landforms have been identified within the Lake

284

GEOMATE – Melbourne, Australia, 11-13 November 2020

Baroon catchment, and the study area is one suchhigh-risk slope. This landslide site hosted a
voluminous, single-failure rotational landslide in2008 following heavy rainfall.

sampling location. The soil samples were used todetermine SWCC, swelling characteristics andunsaturated shear strength.
A dewpoint potentiometer (Fig. 2) was employed
in determining SWCC, which is the graphicalrelationship between soil suction and water content(gravimetric or volumetric) or degree of saturation.
The dew point potentiometer measures water
potential from 0 to -300 MPa with an accuracy of ±0.1
MPa from 0 to -10 MPa and ±1 % from -10 to -300
MPa. The high total suctions measured from WP4C
Dewpoint potentiometer were assumed to consist of
negligible osmotic suction effect and hence, used as
matric suctions. These samples were initiallyprepared for known water contents and placed in
enclosed containers to prevent any moisture loss prior
to testing. Further, to obtain the SWCC, a best fitting
relationship was established using the logarithmic
model proposed by Fredlund and Xing [11] aspresented Eq. (1) and (2).

TEST MATERIAL
The test series herein employed soils from theLake Baroon catchment. Undisturbed samples fromseven locations of the study area were collected atvarious depths ranging from the surface down to 4 m.
Initially, a trench was created to the desired depthwith an excavator and then 500 mm long samplingtubes (diameter 75 mm) were inserted into the soil byconnecting a specially modified adaptor to the
excavator's arm as in Fig. 1.

Fig. 2 WP4C Dewpoint potentiometer for suction
measurement

Fig. 1 Insertion of soil sampling tube by the modified
adaptor to the excavator's arm

𝐶𝐶 (𝜓𝜓) 𝜃𝜃𝑠𝑠𝜓𝜓 𝑛𝑛
{ln [ 2.718 + � 𝑎𝑎 � ]}𝑚𝑚𝜓𝜓− ln (1 + 𝜓𝜓 )𝑟𝑟
𝐶𝐶 (𝜓𝜓) =+1ln ( 1 + 106 /𝜓𝜓𝑟𝑟 )𝜃𝜃 (𝜓𝜓, 𝑎𝑎, 𝑛𝑛, 𝑚𝑚) =Table 1 summarises the results of the laboratory
tests conducted to determine the index properties ofthe soil according to Australian standards.
Table 1 Index soil properties
Classification Test
Grain size Distribution
Atterberg Limits
Linear Shrinkage
Specific Gravity
X-ray diffraction
(XRD)

(1)

(2)

where a is equal to the air-entry suction; n is thetangent to the curve at the inflexion point (in thetransition zone); m is related to the residual watercontent; C(ψ) is a correction function; ψi is the suction
corresponding to a given water content, and θs is thesaturated volumetric water content.
A series of oedometer based one-dimensional
consolidation tests (Fig. 3) were conducted asprescribed in AS 1289.6.6.1-1998 for soil samples forvarious initial water contents and dry densities under
different surcharges to determine the rate of shearing
for each direct shear test [12]. At the completion of
the primary consolidation, the time to reach 50%
consolidation (t50) and following empirical equations
were used to determine the shearing rate (Eq. (3) and(4)). Taylor's square root of time fitting method wasused to determine t50 during the study. The selectedsurcharge values for this study were 50, 100, 150, 200
and 250 kPa. Samples were tested for different initial

Results% finer than 75 μm > 79%
Clay % = 41.0 %LL = 67.2 %PI = 28.2 %LS = 13.4 %Gs = 2.67
Presence of Smectite
minerals (> 30 %)Note: LL = Liquid limit; PI = Plastic index; LS = Linear shrinkage

TEST PROCEDURE
Undisturbed soil samples were collected from
Lake Baroon catchment. Soil sampling tubes were
employed in collecting undisturbed samples fromseven locations at various depths. In-situ density andthe moisture content of soil were determined for each285

GEOMATE – Melbourne, Australia, 11-13 November 2020

volumetric water contents from 25% to 43% toreplicate in-situ water contents. The samples were
cured for four days prior to the oedometer tests. The
soil samples prepared for known initial gravimetricmoisture contents were then statically compacted toachieve the target density to replicate in-situ density.
Subsequently, a representative sample was cut into a
consolidation ring, followed by the placement of filter
papers and porous disks, at the top and the bottom of
the sample. The samples were then subjected to theaforementioned surcharges.

contents, using the SWCC, which was measured forthe same density, the suction corresponding to thevolumetric water content was obtained and ultimately
the variation of c with the suction was adopted todetermine ɸb.

Fig. 4 Direct shear apparatus and the soil sample
compacted into the cutting ring for direct shear test
RESULTS AND DISCUSSIONThe SWCC
The total suction (ψ) was measured based on therelative humidity concept, and the SWCC equation
proposed by Fredlund and Xing [11] was used in thisstudy. However, due to the negligible osmotic suction,
the total suctions were assumed as the matric suctions.
Figure 5 depicts the SWCC for the test material.

Fig. 3 Conventional oedometer apparatus used for the
shear rate determination
𝑡𝑡𝑓𝑓 = 50𝑡𝑡50
𝑅𝑅 = 𝑑𝑑𝑃𝑃 /𝑡𝑡𝑓𝑓

(3)
(4)

where tf is the time to failure in minutes, dp the shear
displacement at which peak strength is likely to bereached, in millimetres and R is the required shearingrate.
To obtain cohesion (c) and friction angle (φˊ) for
the same in-situ water content of soil as in oedometer
tests, five direct shear tests on five identical soil
samples (the same density and water content) wereconducted with variation in the normal stresses 50,
100, 150, 200 and 250 kPa as prescribed in AS
1289.6.2.2 - 1998. The maximum failure shear
stresses were then plotted with the corresponding
normal stress to obtain c and φˊ for the given watercontent. The shearing rate determined from theoedometer tests were adopted for the direct shear tests.
The same procedure was repeated for five different
water contents to replicate in-situ water contents.
The direct shear apparatus (Fig. 4) accommodated
samples with a diameter of 63.5 mm and a height of
25 mm. The soil samples prepared for thepredetermined initial moisture contents were
statically compacted to achieve the target density.
Subsequently, a representative sample was cut into ashear ring, as shown in Fig. 4, followed by theplacement of filter papers and porous disks, at the top
and the bottom of the sample.
After determining c and φˊ for different water

Fig. 5 The SWCC for the test material
The air-entry suction is about 85 kPa, and theresidual degree of saturation 8.2%. The SWCC isclearly step-phased with the boundary effect zone andthe transition zone well defined while the residualzone is not well defined. In the boundary effect zone,
the soil is almost saturated, and the water content isnearly independent of suction, while the shear
strength develops linearly with suction. In thetransition zone, the water content decreases
drastically as the suction increases, and the shear
strength varies nonlinearly with suction. In theresidual zone, the water content has a residual value.
For sandy and silty soils, the shear strength isindependent of suction; by contrast for clayey soils, it
286

GEOMATE – Melbourne, Australia, 11-13 November 2020

kPa, the stress-strain relationship of the specimenchanges from strain-softening to strain-hardening.

increases slightly with the suction increase [9].
Shearing rate
The t50 determined using Taylor's square root oftime fitting method was adopted in calculating theshearing rate. Initially, compression vs square-rootof-time plot was produced as depicted in Fig. 6.
Figure 6 presents the compression vs square-root-oftime plot for the volumetric water content of 34.6%
under normal stresses 50, 100, 150, 200 and 250 kPa.
The construction as prescribed in AS 1289.6.6.1-1998
was then employed in determining shearing rates.
Table 2 summarises the shearing rates determined forthe volumetric water content of 34.6%, and a similar
procedure was followed in determining shearing rates
for soil specimens with other moisture contents.

Fig. 7 Variation of shear stress with horizontal
displacement under different normal stresses for thevolumetric water content of 34.6%
Shear strength
The shear strength of the soils decreased with theincrease in moisture content. Average volumetricwater contents ranged from 0.25 to 0.43 for the test
material (44.0 - 75.4% saturation). Figure 8 shows theresults with a simple linear regression for each of thevolumetric water content. Table 3 lists the values ofthe y-intercept and the inclination of the regression
lines (i.e. cohesive strength and angle of shearing
resistance in terms of simple linear regression), alongwith the corresponding suction values as per theFredlund and Xing [11] fitting curve presented in Fig.
5.

Fig. 6 The compression versus square-root-of-timeplot for the volumetric water content of 34.6%
Table 2 Shearing rates determined for the volumetricwater content of 34.6%,
Normal stress
(kPa)
50
100
150
200
250

t50 (min)
7.247
6.317
5.898
4.628
6.450

Shear rate
(mm/min)
0.055
0.063
0.069
0.086
0.062

Direct shear tests
Direct shear tests were conducted for all the fivemoisture contents under the same normal stresses 50,
100, 150, 200 and 250 kPa. Figure 7 presents thevariation of shear stress with horizontal displacement
for the soil specimens with the volumetric watercontent of 34.6%. Figure 7 depicts that the peak shear
stresses were increased with the normal stress andcurves are clearly step-phased. In the beginning, theshear stress increases very rapidly with the increaseof shear displacement. Then, after a certain stressvalue, the slope of the curve declines. It also suggeststhat when the normal stresses increase from 50 to 250

Fig. 8 Results of the direct shear tests (The solid lines
indicate the simple linear-regression lines for eachspecimen group)
Table 3 Shear strength parameters obtained bysimple linear regression with suction

287

GEOMATE – Melbourne, Australia, 11-13 November 2020

Volumetric watercontent (m3/m3)
0.251
0.319
0.346
0.385
0.432

Cohesion
(kPa)
86.28
81.01
65.25
56.52
56.93

Friction angle(Degree)
31.42
14.32
18.24
14.97
12.02

Suction(kPa)11258.74364.83043.51827.7991.8

𝜏𝜏 = 𝜎𝜎 ′ tan 𝜙𝜙 ′ + 𝐶𝐶𝑒𝑒 𝜇𝜇𝜇𝜇

(5)where τ is shear strength, σˊ is net normal stress, ψ issuction, φˊ is the effective angle of shearing
resistance, C is a hypothetical minimum value ofcohesion (when ψ = 0), and μ is a coefficient related
to the susceptibility of strength increment (μ>0).
To determine the values of unknowns (i.e. φˊ, Cand μ), Eq. 5 should be linearised as in below Eq. 6:

The inclinations of the regression lines are largest
in the driest conditions and drastically decreases forthe wetter samples, converging at 12–18° (Fig. 8;
Table 3). In other words, the angle of shearing
resistance of the moist soils seems to be constant,independent of volumetric water content, except in
the driest condition.
The y-intercepts of the regression lines decreased
with increasing moisture content and approached aminimum value at the more saturated condition(Table 3). Figure 9 shows the relationships between
the corresponding suction values for the averagevolumetric water content in each group and the yintercept of the regression lines. The values of the yintercept tend to increase linearly on the semi-log
scale (i.e. the cohesive strength of the soils
exponentially increase with an increase in suction).

ln [𝜏𝜏 − 𝜎𝜎 ′ tan 𝜙𝜙 ′ ] = 𝜇𝜇𝜇𝜇 + ln 𝐶𝐶(6)Using the linearised Eq. 6, multiple regression
analyses were conducted by considering arbitrary φˊ.
For arbitrary φˊ values, simple linear regression
analyses were conducted for data sets of ln [𝜏𝜏 −𝜎𝜎 ′ tan 𝜙𝜙 ′ ], and ψ to determine the φˊ with the highest
coefficient of determination, as shown in Fig. 10.

Fig. 10 Variation in square of correlation coefficientof Eq. 6 with respect to the varied value of φ′Fig. 9 Relationship between the corresponding
suction values for the average volumetric watercontent and cohesive strength
Formulation of shear strength as a function ofsuctionOn the basis of the shear strength reduction
characteristics (Figs. 8 and 9), it was assumed that:(1) the angle of shearing resistance takes a constantvalue; (2) an exponential function is valid to express
the relationship between apparent cohesion andsuction.
From this, the regression function can be
postulated as follows:

Fig. 11 The relationships between the logarithm ofcohesive shear strength and suction

288

GEOMATE – Melbourne, Australia, 11-13 November 2020

The shear strength of test material was well
represented by the below regression function𝜏𝜏 = 𝜎𝜎 ′ tan 14.90 + 56.61𝑒𝑒 3.89𝜓𝜓 as depicted in Fig.
11 (R2 = 0.8791). The regression variables in theequations include the liquefaction characteristics ofthe soils, effects of lubrication and susceptibility tochanges in pore-water pressure during the shear
deformation. For this reason, the values should beconsidered purely as empirical parameters for thesoils, each of which has inherent geotechnicalbehaviours.

"Effects of reclaimed asphalt materials on
geotechnical characteristics of recycled concrete
aggregates as a pavement material," Road
Materials and Pavement Design, vol. 20, pp. 754772, 2019.
[4] C. Gallage, S. Jayakody, and T. Uchimura,"Effects of slope inclination on the rain-induced
instability of embankment slopes," inProceedings of the Second International
Conference on Geotechnique, Construction
Materials and Environment, 2012, pp. 196-201.
[5] C. Gallage, J. Kodikara, and T. Uchimura,"Laboratory
measurementofhydraulicconductivity functions of two unsaturated sandy
soils during drying and wetting processes," Soils
and Foundations, vol. 53, pp. 417-430, 2013.
[6] C. Gallage, R. Udukumburage, T. Uchimura, and
T. Abeykoon, "Comparison of direct and indirect
measured soil-water characteristic curves for asilty sand," International Journal of GEOMATE,
vol. 13, pp. 9-16, 2017.
[7] T. Abeykoon, C. Gallage, and J. Trofimovs,"Optimisation of sensor locations for reliable andeconomical early warning of rainfall-induced
landslides," in Ninth International Conference on
Geotechnique, Construction Materials andEnvironment (GEOMATE 2019), Japan, 2019,
pp. 69-74.
[8] T. Abeykoon, C. Gallage, B. Dareeju, and J.
Trofimovs, "Real-time monitoring and wirelessdata transmission to predict rain-induced
landslides in critical slopes," Australian
Geomechanics Journal, vol. 53, pp. 61-76, 2018.
[9] S. Vanapalli, D. Fredlund, D. Pufahl, and A.
Clifton, "Model for the prediction of shear
strength with respect to soil suction," Canadian
Geotechnical Journal, vol. 33, pp. 379-392, 1996.
[10] W. F. Willmott, Slope Stability and Its
Constraints on Closer Settlement on the
Mapleton-Maleny
Plateau,Southeast
Queensland: Geological Survey of Queensland,
1983.
[11] D. G. Fredlund and A. Xing, "Equations for thesoil-water characteristic curve," Canadiangeotechnical journal, vol. 31, pp. 521-532, 1994.
[12] R. S. Udukumburage, C. Gallage, and L. Dawes,"Oedometer based estimation of verticalshrinkage of expansive soil in a large
instrumented soil column," Heliyon, vol. 5, p.
e02380, 2019.

CONCLUSIONSThe cohesive strength of an unsaturated
expansive soil was formulated as an exponentialfunction of suction. In the formulation, shear strengthτwasexpressedas𝝉𝝉 = 𝝈𝝈′ 𝐭𝐭𝐭𝐭𝐭𝐭 𝝓𝝓′ + 𝑪𝑪𝒆𝒆𝝁𝝁𝝁𝝁 ; where σˊ is net normal stress,φˊ is the effective angle of shearing resistance, C isminimum cohesion, μ is a susceptibility coefficient,and ψ is suction of soil. An advantage of thisformulation is that all the parameters required areavailable without any extensive soil testing. The
variables can be obtained by a basic shear test and asubsequent regression analysis. It is considered that
this empirical method provides a convenient
alternative for engineering practice.
ACKNOWLEDGMENTS
Authors gratefully acknowledge the technicalstaff at Queensland University of Technology (QUT)
for providing the laboratory facilities to conduct thetest series. Further, gratitude should be extended to
Steve Hackworth from THE SOILTESTERS for
providing the in-kind support for the project. The firstauthor acknowledges the scholarship for the doctoral
degree received from QUT, Australia.
REFERENCES[1] C. Gallage and T. Uchimura, "Direct Shear
Testing on Unsaturated Silty Soils to Investigate
the Effects of Drying and Wetting on Shear
Strength Parameters at Low Suction," Journal of
GeotechnicalandGeoenvironmental
Engineering, vol. 142, 2016.
[2] M. P. Amarasinghe, L. I. N. De Silva, and C.
Gallage, "The effect of lateral confinement on
the settlement characteristics of shallow
foundations on sand," International Journal, vol.
15, pp. 258-265, 2018.
[3] S. Jayakody, C. Gallage, and J. Ramanujam,289

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A LABORATORY METHOD FOR ACCURATE CALIBRATION OF
STRAIN-GAUGE TYPE SOIL PRESSURE TRANSDUCERS

1

Chamara Prasad Gunasekara Jayalath1 and Chaminda Gallage1
School of Civil and Environmental Engineering, Queensland University of Technology, Australia

ABSTRACT
Strain-gauge type soil pressure transducers are widely used in health monitoring and laboratory and field
investigation on the performance of geo-structures to accurately measure the soil pressure. Even though thesepressure plates are sold with the factory-measured calibration factors, these sensors should be re-calibrated in thelaboratory before using them because the calibration can be affected by the data-logging system and the length ofthe cable used. Therefore, in this study, a laboratory calibration procedure for strain-gauge type soil pressure plates
was proposed. The soil pressure transducer was embedded in a uniform-fine- sand medium in a specially designed
pressure cell, and the pneumatic pressure was applied into the system as gradual increments. After that, thecalibration chart of the pressure gauge was developed based on the sensor outputs for different pressures applied.
Then the calibrated soil pressure transducer was used in the laboratory pavement model test to measure soilpressure at the base-subgrade interface under a surface loading area. The measured soil pressure values werecompared with the estimated vertical stresses from elastic theories to validate the pressure measurements and thecalibration process of the soil pressure transducer. The test results revealed that there is a satisfactory agreement
between the pressure measured by the soil pressure transducer and theoretical estimations. Thus, the calibration
process of the soil pressure transducer and its outputs are proven to be accurate.
Keywords: Vertical stress in soil, Geotechnical Instrumentation, Pavement subgrade, Soil pressure transducers

recommended compared to the DWC method as theformer method provides more accurate results [12]. In
this method, the calibration is performed in acalibration chamber by applying hydrostatic or airpressure in increments. However, this methoddemands advanced laboratory facilities and technicalknowledge, which are not available in mostlaboratories. As an alternative, the soil-calibration
method can be used in the laboratory to calibrate soilpressure cells. Therefore, in this study, a laboratory
calibration procedure for strain-gauge type soilpressure plates using sand is proposed. Then thecalibrated soil pressure transducer was used in alaboratory pavement model test to measure soilpressure at subgrade-base interface under a circulararea loading applied on the top surface. The measured
soil pressures were compared with the estimatedvertical stresses from elastic theories to validate thepressure measurements and the calibration process ofthe soil pressure transducer.

INTRODUCTION
The accurate measurement of soil or earthpressure is essential in health monitoring and,laboratory and field investigation on the performanceof geo-structures [1]-[9]. Vibrating wire and straingauge type pressure plates are widely used to measuresoil pressures. Strain-gauge type soil pressure plates
are commonly used in laboratory model tests due toits high accuracy, less sensitivity to temperature, andavailability in different sizes. Even though thesepressure plates are sold with the factory-measured
calibration factors, it is vital to re-calibrate thesetransducers in the laboratory before using them [10]as the calibration can be affected by several factors,
for instance, the data-logging system and the lengthof the cable used. Therefore, as a common practice,several laboratory methods are used to calibrate soilpressure sensors.
The most common calibration methods of soilpressure cells are dead weight calibration (DWC),
fluid/pneumatic calibration and soil calibration. In the
DWC method, the calibration is performed by placing
known dead weights directly on the sensing area ofthe pressure cell in increments. Majority of theresearchers prefer to calibrate pressure sensors by
DWC method as it is less time consuming,
economical and easy to perform without muchadvanced equipment and technical experience [11].
Generally, fluid/pneumatic calibration is highly

Soil Pressure Transducer
A soil pressure transducer (See Fig.1) with 1MPa
capacity was used in this experimental study. The
outside diameter and the thickness of the pressure
plate are 200mm and 25.5mm, respectively. The
percentage of rated output (%RO) of the pressuregauge has been specified as 1%, and the diameter ofthe sensing area is 166mm. It is designed with a dual-

290

GEOMATE – Melbourne, Australia, 11-13 November 2020

diaphragm structure that can minimise thedisplacement of a sensing area, and thereby can keep
the stress distribution undisturbed under pressure.
This type of soil pressure gauges is widely used tomeasure the pressure in soil and to monitor the
behaviour of embankments.

transducer in the cellPressure above 750kPa (i.e. 785kPa) was applied
into the setup for 24 hours to allow the particle
rearrangement of the sand under high pressuresapplied. From the initial trials conducted before thisexperiment, it was found that the required pressure to
overcome the friction between the piston and the wallof the cell is negligible. Since the sand thickness
above the top surface of the sensor is thin (i.e. 50mm),
and the friction is negligible, it is reasonable toassume that the pressure applied on the sensor is equal
to the applied pressure into the setup. The pressure
into the system was increased approximately up to
750kPa in 50kPa increments. The pressure gauge wasconnected to a data logger, and the sensor readings
were recorded in parts per million (ppm) for eachpressure increment. Based on the sensor outputs fordifferent applied pressures, the calibration chart of thepressure gauge was developed.

Fig.1 Soil pressure transducer
METHODOLOGY
A specially designed pressure cell [13, 14] (SeeFig.2 (a)) made of acrylic was used to calibrate thesoil pressure transducers. The internal diameter andthe height of the cell are 360mm and 400mm
respectively. Uniform-fine sand was used in thiscalibration. The particle size distribution andproperties of the sand are given in Fig.3 and Table 1,
respectively. The sand was filled up to 250mm, andthe sand surface was properly levelled. After that, thesoil pressure transducer was placed on top of the sandlayer. The level of the top surface of the sensor waschecked with a spirit level (bubble level), as shown in
Fig.2 (b). Then the sand was filled to have a 50mm
thick sand layer above the top of the sensor. The sandsurface was levelled, and the inner wall of the cell was
cleaned to remove all the attached sand and dust. Oil
was applied on the inner wall of the cell to minimise
the friction between the wall and the piston. After that,the piston was placed inside the cell and valves wereconnected to supply the pneumatic pressure into thesetup.

100
100

Percentage passing (%)

80
80

60
60

40
40

20
20

00
0.01
0.01

0.1
0.1

11

10
10

Paricle size (mm)Fig.3 Particle size distribution of sand used in thecalibration of soil pressure transducers
Bubble level

Table 1 Properties of sand used in the calibration ofsoil pressure transducers
Property
D10 (mm)
D30 (mm)
D60 (mm)
Coefficient of curvature (Cc)
Coefficient of uniformity (Cu)

(a)

Value
0.165
0.220
0.340
2.06
0.86

(b)
This soil pressure transducer was used to measure
the pressure applied at the base-subgrade interface ofunbound-granular pavement-models during a cyclic

Fig.2 calibration of soil pressure transducers; (a) The
pressure cell; (b) Embedding the soil pressure291

GEOMATE – Melbourne, Australia, 11-13 November 2020

loading test series. The schematic diagram of theexperimental setup of the cyclic-loading pavement
test is shown in Fig.4. A granular pavement with
200mm unbound-granular base and 500mm thicksubgrade with 2.5% unsoaked-CBR was selected in
the present study to verify the pressure measurements
given by the pressure gauge. The pressure gauge wasembedded in the subgrade close to the base-subgradeinterface and below the centre of the loading area asshown in Fig.5. In this experiment, the maximum loadof 17.31kN was applied through a 25mm thick and
200mm diameter steel plate to create a tyre-contactpressure of 550kPa with a frequency of 0.33Hz. Moredetails of the conducted test can be found in [15] and[16]. The pressure sensor was connected to the datalogger, and the readings were recorded for every fiveseconds. After that, the captured sensor outputs wereconverted to pressure readings using the developed
calibration equation.

550 kPa
1/3 HzThe measured soil pressure values were comparedwith the calculated values from two elastic-theories
methods, namely the Boussinesq method [17] andFox L. method [18], to validate the measured values
and the calibration process of the soil pressure gauge.
Even though the Boussinesq equation for verticalstresses under uniformly loaded circular area has been
derived for homogeneous, elastic, and isotropic
mediums, it was used for this two-layer system for arough estimation of vertical stresses at the basesubgrade interface. The pressure applied at the basesubgrade interface was also computed based on thechart for the vertical stresses on the interface stresses
developed by the Fox L. method.
RESULTS AND DISCUSSION
The applied vertical pressure values were plottedagainst the corresponding sensor readings to develop
the calibration chart of the soil pressure transducer, as
shown in Fig.6. The equation for the regression lineof the plotted points was adopted as the calibration
equation of the sensor. The obtained calibration
equation perfectly fits the data as the coefficient ofdetermination is equal to one (R2=1). Later, thiscalibration equation was utilised to determine thevertical stresses applied at the base-subgradeinterface of the laboratory-scale unbound-granularpavement model.
Fig.7 illustrates the variation of the vertical stressat the base-subgrade interface of the laboratory-scaleunbound-granular-pavement model. Three curves
were produced depending on the technique (e.g.
pressure gauge, Boussinesq method and Fox L.
method) used to measure/estimate vertical stresses
during the test. All three curves were plotted
approximately up to 120,000 cycles where the cycling
loading test was stopped. Results show that theestimated vertical stresses from the Boussinesq
method are always higher than the Fox L. method.
Soon after the commencement of the loading, thevertical stress applied at the base-subgrade interface
was measured as 170kPa whereas the estimatedstresses were 169kPa and 144kPa respectively fromthe Boussinesq equation and Fox L. method. The
measured vertical stresses follow the similar shapeand magnitudes of the vertical stress developmentthat has been computed by the Boussinesq method in
the initial stage of the test, approximately up to
27,000 cycles where the measured vertical stress liesat 260kPa. Then, the measured vertical stresses by thesoil pressure transducer fluctuate in between thevertical stress values that have been estimated by thetwo elastic-theory methods up to 100,000 cycles.
Afterwards, the measured vertical stresses follow thesimilar shape and magnitudes of the vertical stressvariation that has been estimated by the Fox L.

Loading
plate
200

200

Unbound granular base
Pressure
gauge

200

500
Subgrade (CBR 2.5%)1000
Note: Not to scale
All dimensions are in millimetresFig.4 The schematic diagram of the experimental
setup of the cyclic-loading pavement test

Fig.5 Installation of the soil pressure transducer
292

GEOMATE – Melbourne, Australia, 11-13 November 2020

method until the end of the test where the measured
vertical stress was 313kPa.

estimations is apparent. Thus, the calibration processof the soil pressure transducer and its outputs areproven to be accurate. Therefore, the proposed
calibration method can be used to calibrate soilpressure cells in the laboratory accurately.

800
800
yy =
= 0.8064x
0.8064x -- 128.03
128.03
R²
= 11
R² =

400
400

300
300

400
400

Verticalstress (kPa)(kPa)
Vertical stress

Verical stressstress (kPa)(kPa)
Verical

600
600

200
200

Pressure guage
guage
Pressure
Boussinesq method
Fox L.
Fox
L. method
method

200
200

100
100

0
0
00

1000
600
800
200
400
1000
400
600
800
200
Output of the pressure sensor (ppm)

1200
1200
00

Fig.6 Calibration chart of the soil pressure transducer

00

40000
80000
40000
80000
Number of cycles

120000120000

Fig.7 Development of the vertical stress at the basesubgrade interface

As mentioned in the methodology section, theBoussinesq equation has been developed to estimate
vertical stresses of homogeneous, elastic andisotropic mediums but the tested pavement section
has two layers (i.e. subgrade layer and granular baselayer) which have completely different material
properties. Each layer alone can be considered ashomogeneous and elastic; however, both subgrade
and granular materials are cross-anisotropic. The
referred chart from the Fox L. method for the verticalstresses on the interface has been developed for twolayer pavement systems considering the influence ofthe ratio of the elastic modulus of the top and thebottom layers (E1/E2). As the subgrade CBR value is
2.5%, the elastic modulus of the subgrade has been
estimated to be 25MPa based on the approximationthat elastic modulus of pavement material is ten times
its CBR value as has been specified in Austroads
Guide to Pavement Technology Part 2: Pavement
Structural Design [19]. Based on the mechanisticpavement design principals, the average elastic
modulus of the granular base, which was limited by
the weak subgrade was estimated as 51MPa.
Therefore, the E1/E2 of the pavement section isapproximately two (i.e. E1/E2 = 2). However, in the
Boussinesq case, E1/E2 ratio must be assumed as one(i.e. E1/E2=1) even when it is used for a two-layer
system. These assumptions may result in slightdifferences
betweentheactualpressuremeasurements and estimated vertical stresses fromthese theoretical methods. Overall, it is evident that areasonable agreement between the pressures
measured by the sensor and the theoretical

CONCLUSIONS
Based on the test results of the calibration of thepressure transducer, and the comparison of measured
pressures with theoretical estimations, the following
conclusions can be drawn:•The developed calibration equation perfectly fits
the data as the coefficient of determination is equal toone (R2=1); therefore, it can be assumed as accurate.
•A satisfactory agreement between the sensor
measurements and theoretical estimations is evident.
Thus, the calibration process of the soil pressuretransducer and its outputs are proven to be accurate.
•The presented calibration method can beeffectively used as a laboratory calibration
methodology to calibrate soil pressure cells
accurately.
ACKNOWLEDGMENTS
The authors acknowledge the continued supportof the Department of Transport and Main Roads(TMR) and Australian Road Research Board (ARRB)
by providing the required funding for the instruments
including data logger and pressure sensors for thisresearch study and supplying the required testingmaterials. Thanks are also extended to the technicalstaff of Banyo Pilot Plant Precinct of Queensland
University of Technology (QUT) for their great
support and assistance during the pavement model
test. Further, the first author would like to thank hiscolleagues and friends who helped in preparing the293

GEOMATE – Melbourne, Australia, 11-13 November 2020

pavement model, and for QUT for providing him witha scholarship to pursue his postgraduate studies.

measurement of the behaviour of an in-service
water reticulation pipe buried in reactive soil
(Altona North, Victoria), Monash University,
Australia, Research report RR10. 2008.
[10] Chen Q., Abu-Farsakh M., and Tao M.,
Laboratory evaluation of geogrid basereinforcement
andcorresponding
instrumentation program. Geotechnical testing
journal, 32(6), 2009, pp.516-525.
[11] Gade V. K., and Dasaka, S., Calibration of Earth
Pressure Sensors. Indian Geotechnical Journal,
2017, pp.1-11.
[12] Ramírez A., Nielsen J. and Ayuga, F., On the useof plate-type normal pressure cells in silos: Part
2: Validation for pressure measurements.
Computers and electronics in agriculture, 71(1),2010, pp.64-70.
[13] Gallage C.P., Mostofa M., Vosolo DA,
Rajapakse J.P., A new laboratory model of aslaking chamber to predict the stability of on-sitecoal mine spoils, International Journal of
GEOMATE, 10 (22), 2016, pp.2065-2070[14] Gallage C.P., Mostofa M., Vosolo D.A.,
Rajapakse J.P., Laboratory investigation on theeffects of overburden pressure, water, and timeon slaking induced material property degradationof coal mine spoil, Geotechnique, Construction
Materials and Environment, 2015, pp.395-400[15] Jayalath. C.P.G, Gallage C., Dhanasekar M.,
Dareeju B., Ramanujam J., and Lee J. Pavement
model tests to investigate the effects of geogrid
as subgrade reinforcement. In Liu, H, Mills, P,
Ruxton, N, & Mazengarb, C (Eds.) Proceedingsof the 12th Australian and New Zealand Young
Geotechnical
Professionals
Conference.
Australian Geomechanics Society, Australia,
2018, pp.1-8.
[16] Wimalasena K. and Jayalath C.P.G., Effect ofgeogrid reinforcement in weak subgrades.
International Journal, 18(65), 2020, pp.140-146.
[17] Boussinesq J., Application des Potentials à
L’Etude de L’Equilibre et du Mouvement desSolides Elastiques, Gauthier-Villars, Paris, 1883.
[18] Fox L., Computation of traffic stresses in a
simple road structure. Proc. 2nd ICSMFE, 1948,
1, 1948, pp.236-246[19] Austroads Guide to Pavement Technology Part
2: Pavement Structural Design. Austroads Inc.:
Sydney, Australia, 2012.

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Proceedings of the Institution of Civil Engineers:
Geotechnical Engineering, 165 (1), 2012, pp.4557[2] Chan D., Gallage C.P., Rajeev P., Kodikara J.K.,
Field performance of in-service cast iron water
reticulation pipe buried in reactive clay,
Canadian Geotechnical Journal, 52 (11), 2015,
pp.1861-1873[3] Chan D., Rajeev P., Kodikara J.K., Gallage C.P.,
Field performance of in-service cast iron gas
reticulation pipe buried in reactive clay, Journalof Pipeline Systems Engineering and Practice, 7(2), 2016, pp.1-15[4] Chan D., Gallage C., Gould S., Kodikara J.K.,
Bouazza A., Cull J., Field instrumentation ofwater reticulation pipe buried in reactive soil, In
Ozwater'09: Australia’s National Water
Conference and Exhibition, Melbourne, Victoria,
Australia, 2009, pp.9-16[5] Gallage C.P., Kodikara J.K., Chan D., Davis P.,
A comparison of the results of the numericalanalysis and the physical behaviour of a pipeburied in reactive clay, in Proceedings of the
12th International Conference of the Association
for Computer Methods and Advances inGeomechanics (IACMAG), 2008, pp.1-9.
[6] Kodikara J., Davis P., Gallage C., Chan D.,
Gould S. and Zaho X.L., Behaviour of apolyethylene pipe buried in reactive soil.
Proceedings of the 7th National ASTT
Conference, Sydney, Australia, 2008 (CD ROM
publication).
[7] Gallage C., Chan D., Gould S., Kodikara J.K.,
Behaviour of an in-service cast iron water
reticulation pipe buried in expansive soil, In
Ozwater'09: Australia’s National Water
Conference and Exhibition, 16-18 March 2009,
Melbourne, Victoria, Australia, 2009, pp.1-8[8] Gallage C.P., Chan D., Kodikara, J.K., Ng P.C.,
Discussion: Response of a plastic pipe buried in
expansive clay, Proceedings of the Institution of
Civil Engineers: Geotechnical Engineering, 166(3), 2013, pp.328- 330.
[9] Gallage C., Gould S., Chan D., Kodikara J., Field294

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

NUMERICAL SIMULATION OF INHIBITORY EFFECT OF
SUBSIDENCE FOR THE LEANING PAGODA BY GROUNDIMPROVEMENT
Yuko Ishida1 ，Hajime Ito2 and Ryoichi Fukagawa3
1

Civil Engineering and Environment Cource, Osaka Prefecture University Colledge of Technology, Japan;
2
JIP Techno Science Corporation., Japan;
3
Department of Science and Engineering, Ritsumeikan University, Japan

ABSTRACTThe Wat Krasai pagoda in Ayutthaya, Thailand, leans mainly toward the north. The main cause of deformationof the pagoda is soft clay sediments and one of the other causes is the deterioration of bricks and mortar which
compose the pagoda. In 2013, the surface of the lower part of the pagoda was covered with new bricks tosuppress weathering as the restoration work. Additionally, the original bases were reconstructed by new bricks
piled around the pagoda up to a height of approximately 2.3 m above the ground. We conducted field surveys
twice a year from 2014 and confirmed that the ground surface deformed during the rainy season and cracks andirregularities generated in the reconstructed structures. In 2019, we found a gap was created between the restoredbase and the pagoda at the southside. As a result of the measuring of inclination and these surveys, we thoughtthat the pagoda has been continuously sinking toward the north and it has possible of subsidence in the future.
Therefore, a countermeasure to stop the inclination of the pagoda is needed. In this study, ground improvement
is assumed as a countermeasure work for an unequal settlement and the numerical simulation was conducted tograsp the effect of ground improvement. The results indicate that the weight of new bricks due to restoration
work in 2013 promote subsidence and inclination of the pagoda. In addition, ground improvement is effective forsettlement, decreased the amount of subsidence with increasing the thickness of ground improvement.
Keywords: Consolidation analysis, Differential settlement, Soil improvement, World heritage, Inclined Pagoda
improvement is conducted as a countermeasure forunequal settlement.

INTRODUCTION
Ayutthaya in Thailand is located approximately
90 km north of Bangkok and was the capital of the
Ayutthaya dynasty, which lasted 417 years from 1350.
The “Historic City of Ayutthaya” was recognized asa World Heritage site in 1991 owing to the following
criteria: “The historic city of Ayutthaya is an
excellent testimony for the development of true Thainational art.” The Ayutthaya ruins are characterizedby tall pagodas and monumental proportions of
Buddhist monasteries [1]. Some historical buildings
made of bricks, such as pagodas and walls of temples,
have tilted or collapsed, as shown in Fig. 1. This study
focused on the conservation of the leaning pagoda at
Wat Krasai from the viewpoint of cultural property
protection. The objective of this study is to stop thetilting of the pagoda and enable the inheritance ofhistorical assets in a good condition for the future. We
observed the condition of the pagoda and surrounding
assets twice a year from 2014, started monitoring
groundwater fluctuation 2016 onward, and measured
the angle of the pagoda from 2018. These results
indicated that the pagoda was still sinking and leaning.
Therefore, a countermeasure to stop the inclination of
the pagoda is needed. In this paper, the simulation
results are reported for cases wherein the ground？

Fig. 1

Brickwork at Wat Mahathat(Photograph taken on March 1, 2016)

HISTORY OF WAT KRASAI
According to the signboard at the Wat Krasai site,
there is no evidence of its historical information or
period of construction. However, it can be surmised
295

GEOMATE – Melbourne, Australia, 11-13 November 2020

that Wat Krasai was built at least prior to 1767 owing
to its use as a Burmese army base during the second
Burmese–Siamese War. Additionally, Wat Krasai has
been mentioned in the Late Ayutthaya Period as theburial site of Phra Si Sin, who was charged withtreason to King Na Rai the Great, who ruled between
1656 and 1688. Assuming that Wat Krasai waserected in the days of King Nar Rai, it can be inferredthat it has been approximately 360 years since it wasconstructed.
When the Wat Krasai temple was built, it was
surrounded by walls. The principal pagoda, Chedi,
was bell-shaped, built on an octagonal foundation,
which was a popular Chedi style in the early
Ayutthaya period. The principal pagoda wassurrounded by four auxiliary Chedis. In front of theprincipal pagoda, a building is presumed to be a
Ubosot. However, we cannot confirm the existence of
the walls, four auxiliary Chedis, and the Ubosot as of2010, based on the data from Google Earth [2]. In
2013, extensive restoration work was conducted at
Wat Krasai. Before restoration, the surface bricks ofthe principal pagoda had deteriorated and theoctagonal foundation had collapsed or was buried
underground, as shown in Fig. 2. As part of therestoration, a lower part of the deteriorated surface ofthe principal pagoda was covered with new bricks,
and a new octagonal foundation and square base werereconstructed, as shown in Fig. 3. Additionally, thewalls surrounding the precinct and the foundation of
the auxiliary Chedis were reconstructed. A groundsurvey was conducted ahead of the restoration in thesoutheast and northwest regions of the pagoda. The
ground investigation confirmed that the thickness ofthe second layer, which is composed of soft cohesive
soil, was thicker in the northwestern part than in thesoutheastern part. One of the possible causes for the
pagoda leaning toward the north is the second clay
layer. Many bricks were used during this restoration
work, wherein a new weight was loaded onto theground.

Fig. 2

Photograph of principal pagoda before
restoration

Fig. 3

Principal
pagodaafter
restoration(Photograph taken on February 22, 2014 )PROBLEMS OF BRICKWORK BUILDINGS IN
AYUTTHAYA
Bricks are mainly made of clay or mud and shaped
into a rectangular parallelepiped and then sun-driedor fired in a kiln. Thereafter, owing to fluctuations intemperature and repeated humidity, they degrade andreturn to the soil. Such deterioration of bricks is acommon problem in historical brick buildings aroundthe world. Several studies on historical brickstructures have focused on the deterioration of bricks.
Nishiura et al. confirmed the progression of brickdeterioration in Egyptian remains during theirconservation study [3].
Because some stucco remained on the surface ofthe historical brick buildings in Ayutthaya, it ispossible that the entire surface was protected by astucco at the beginning of construction. However,when the plaster on the surface wore away due todeterioration over time, it exposed a means for water
penetration that melts the clay mortar and weakens
the structure, possibly leading to an eventual collapse.
Therefore, ICOMOS Thailand points out that thedeterioration of bricks and mortar due to water is oneof the risks of Thai monuments [4]. Ishizaki et al.
carried out moisture measurements, precipitation salt
surveys, and meteorological observations on
historical brick buildings in Ayutthaya anddetermined the relation between brick deteriorationand moisture migration in brick materials [5]. In
addition, Ishizaki et al. conducted a moisturemigration analysis before and after the surface layerwaterproof strengthening treatment of the Sukhothai
Brick Buddha. The results of the analysis showed thatthe water content of the sediment decreased due to the296

GEOMATE – Melbourne, Australia, 11-13 November 2020

from these surveys and measurements, we speculatethat the main body of the pagoda has been sinking
toward the north separately from the reconstructedbase surrounding the main body. We thought that oneof the causes of subsidence of the pagoda is adding
the weight of new bricks by the restoration, and oneof the other causes is without countermeasuresagainst the soft clay layer such as ground
improvement.

waterproof treatment, consistent
with themeasurement results [6]. Yoshida et al. created ananalytical model for heat and moisture transfer in the
Sukhothai Brick Buddha. Additionally, they showedthat the possible cause of discoloration was the
movement of the components of the stucco and bricks
to the surface associated with the internal moisture,
leading to evaporation [7].
We have been observing the restored pagoda,
reconstructed wall, and basements of Chedis at Wat
Krasai twice a year from 2014. Deterioration of thebrick block of pagoda restored in 2013 was identified
in 2017, as shown in Fig. 4. The restored bricks are
piled up according to the inclination of the original
bricks; therefore, water gathers through joints on thenorth side. In fact, bricks on the northwest side,having a larger inclination, are significantly
deteriorated. When the brick deteriorates andcollapses, the voids of the brick block are compressed
and the volume decreases. The pagoda has a
distortion. We infer that partly breaking bricks bydeterioration can possibly affect the deformation of
the pagoda.

Fig. 5

Deformation of brick wall at Wat Krasai(Photograph taken on May 17, 2017)

InclineGap
Fig. 4

Deterioration of bricks of the Wat Krasai
pagoda (Photograph taken on May 17, 2017)
Main body of
Wat Krasai
pagodaHistorical brick buildings have other issues owing
to soft ground. Kuchitsu indicated that the structuralproblem of brick buildings due to uneven settlement
was greater than that owing to the weathering of salts
and biodeterioration by moss and lichens [8]. In a
study that nondestructively investigated thedeterioration of stone materials at Angkor ruins,
Uchida indicated that the uneven settlement of theground and the deformation of the platform had asignificant influence on the structure [9].
The deformation of the reconstructed wall owing
to the softening of the ground during the monsoons of2017 was confirmed at Wat Krasai, as shown in Fig.
5. Several cracks were recognized at thereconstructed base of the Chedi and the pagoda. In
addition, we observed a gap between the main bodyof the pagoda and the adjoining reconstructed squarebase in 2019, as shown in Fig. 6. An increasing thisgap width was confirmed in February 2020. Resulting

Reconstructedbasement
Fig. 6 The gap between the main body andreconstructed basement of the pagoda (Photographtaken on August 20, 2019)INHIBITION OF INCLINE OF STUPA BY
GROUND IMPROVEMENT
Groundimprovement
isacommoncountermeasure method against the soft ground.
There are many problems that should be solved in theactual construction if this method is chosen for297

GEOMATE – Melbourne, Australia, 11-13 November 2020

countermeasure work of Wat Krasai. However, weonly examine the effect of ground improvement by
numerical simulation in this paper.

Thickness Surface

1st Layer 2nd Layer

Line A (m)

0.5

2.350

2.890

Line B (m)

0.5

3.48

6

Thickness 3rd Layer 4th Layer 5th Layer
Line A (m)2.42
10.84011.000Line B (m)
3.99
6.26
9.77

Analysis models for the numerical simulation
Bricks of the Wat Krasai pagoda are covered withweak binding by soft mortar, and it is assumed that
individual blocks move flexibly. In addition, sandwas filled inside the reconstructed square brick baseof the pagoda. Therefore, the pagoda behavesdifferently from a completely rigid body. However,the pagoda was treated as a structural body in theanalysis.
A nonlinear finite element analysis was
performed with the plane strain condition using
PLAXIS, which is a finite element program software.
It is assumed that it took two years to build the Wat
Krasai pagoda based on inquiries with thearcheologists of the Ayutthaya historical park. After
construction to restoration in 2013, it was set to 360
years. In 2013, restoration bricks were added to the
pre-restoration model of the pagoda. Additionally, theend of analysis was set to after 100 years, from 2013.
The composition formula of the sandy soil wasused as a linear elastic model, and that of the clay soil
was used as an elasto-plastic model as per the
Sekiguchi–Ohta model. This is widely prevalent inJapan because it considers the occurrence of plasticstrain accompanying the anisotropy of naturally
deposited clay. A procedure to determine the input
parameters of the Sekiguchi–Ohta model waspreviously developed by Iizuka et al. [10].

Table. 2 Soil parameters of each layers
Layer

Surface

1st Layer

2nd Layer

Constitution
Elasto-plastic
Elasto-plastic
Mohr-Coulomb
rule of soil
(Sekiguchi-Ohta) (Sekiguchi-Ohta)
M

-

1.035

1.009

Λ

-

0.59150.5764D-

0.07260.0726

ν'

0.2

0.358

0.362

K0

-

0.559

0.568
1.324

e0

-

1.223

E [kN/m 2]

8540

-

-

γunsat [kN/m 3]

19.2

19.2

17.1

γsat [kN/m 3]

19.2

19.2

17.1

k [m/day]

2.40E-06

2.40E-06

3.60E-06

3rd Layer

0.3

4th Layer
Elasto-plastic(Sekiguchi-Ohta)
1.128
0.64440.0611
0.344

K0

-

0.525

e0

-

0.940

-

E [kN/m 2]
γunsat [kN/m 3]

15050

-

28560

19.0

19.2

19.0

γsat [kN/m 3]
k [m/day]

20.0
2.00E-04

19.2
3.60E-06

20.0
1.20E-03

Layer
Constitution
rule of soil
M
Λ
D
ν'

Linear elastic

5th Layer
Linear elastic
0.3-

Parameters and analysis cases
Table. 3 Material properties of bricks
Wat Krasai pagoda is hollow, with anapproximate height of 26.9 m. The cross-section ofanalysis is shown in Fig. 7. The soil layers comprised
five layers. The thicknesses of the layers and eachparameter are shown in Table 1 and 2. The parametersof the brick are listed in Table 3.

After
2013

Case1
Case2
Case3

23.6m

3

1.546 g/cm
5

Poisson ratio
Unitweight

15.15 kN/m3

Density

1.546 g/cm3

The area of ground improvement is set to thereconstructed square basement, 1.0 m on the outside.
The depth of ground improvements are set for eachcase as follows:
Case.1: No ground improvement
Case.2: GL-3.0 m
Case.3: GL-6.0 m
Case.4: GL-10.0 m

26.9 mSurface Layer
Layer1
Layer2
Layer3Layer4

Lineare elasticity
5.0×10 g/cm2
0.07

Young's modulus

North side

South side

Before 2013

Material modelDensity

30 m

Layer5
Line BLine ARESULTS AND DISCUSSION

100 m

Fig. 7 Closs-section of the Analysis

The temporal change in the inclination of thepagoda in Case 1 is shown in Fig. 8. The inclination

Table. 1 Thickness of soil layers and parameters
298

GEOMATE – Melbourne, Australia, 11-13 November 2020

was calculated from the south and north points, as
indicated in Fig. 9. The result indicates that a rapid
incline occurred during the construction of the pagoda.
It is assumed that consolidation was promoted at thesecond soft clay layer at an early stage. After rapid
inclination, only a slight tilt occurred. Furthermore,because the additional weight was loaded to theground by the restoration work in 2013, theinclination was promoted. This is consistent with thesituation in which a gap occurs between the main
body of the pagoda and the reconstructed squarebasement. The inclination of the pagoda will
gradually increase in the future. The measured
inclination of the pagoda was approximately 2° to 11°[11]; therefore, the result may be underrepresented.

Fig. 10 Temporal change of settlement (all cases)

CONCLUSIONSIn this study, several changing conditions of thepagoda and surrounding ground were described basedon observation. It is thought that the main body of thepagoda has been sinking toward the north separatelyfrom the reconstructed base surrounding the main
body, and necessary of countermeasure forsubsidence of the pagoda is also described.
In addition, the simulation was carried out,
wherein cases were considered for ground
improvement as a countermeasure work for anunequal settlement. The results indicate thefollowing:
1. The settlement and inclination of the pagoda
tend to increase in the future.
2. The possibility that the settlement andinclination of the pagoda are promoted by the
restoration work, which uses many bricks is
confirmed. This is consistent with the situation
wherein the gap occurs between the main body of
pagoda and the reconstructed square basement.
3. The amount of inclination of the pagoda from theanalysis results has possibility was underestimated,because it is not equal to the actual situation.
4. The effect of preventing settlement of ground
improvement was confirmed by comparing with andwithout ground improvement. Additionally, the effectof prevention for subsidence increases with the
thickness of ground improvement.
For the better conservation of Wat Krasai
pagoda, we should notice not only the behavior of theinclination of the pagoda but also structural
deterioration such as crack, gap, deformation, brickdeterioration. Moreover, a restoration work ofhistorical brickwork buildings that need to adding
many bricks should consider influence by its newweight and deterioration by the condition of gathering
water.

Restoration(2013)

Fig. 8 Temporal change of inclination (Case 1)

Center
South

North

Under

Fig. 9 Output reference points
The temporal change in the settlement of thepagoda 300 years after construction in all cases isshown in Fig. 10. The settlement of the pagoda also
tends to gradually increase in the future. However, theresults indicate that ground improvement prevents
subsidence. In addition, the effect of prevention for
subsidence increases with the thickness of the ground
improvement.

ACKNOWLEDGMENTS
We would like to sincerely thank the Third
Regional Office of Fine Arts and the Ayutthaya
Historical Park for their cooperation and information.
Additionally, we are grateful to Dr. Weerakaset
Suanpaga and Mr. Chalermchai Trakulphudphong ofKasetsart University for helping with the Wat Krasai
investigations. This research was partially funded by
the Muching Fund of the Institute of Disaster
Mitigation for Urban Cultural Heritage, Ritsumeikan
University.

Case.4
Case.3
Case.2
Restoration(2013)

Case.1299

GEOMATE – Melbourne, Australia, 11-13 November 2020

[6] Takeshi I. Tadateru N. Jiri S. Martinus G.,
Numerical Analysis of the Water Regime of
Buddha Monuments in Skhothai, Thailand,
Science for conservation, No.39, 2000, pp.43-50.
(in Japanese)[7] Yuri Y. Shunichi H. Daisuke O., Degradation of a
Big Buddha in Skhothai ruins, J. Environ. Eng.,AIJ, Vol. 78, No.689, 2013, pp. 561-567.
[8] Nobuaki K., Survey Report on the Stones andTheir Weathering at Historic Monuments in
Thailand, Science for conservation, No.37, 1998,
pp.59-68. (in Japanese)[9] Etsu U., Stone materials and their non-destructive
examination in the Angkor monuments,
BUTSURI-TANSA, Vol.60, No.3, 2007, pp.223234.
[10] Atsushi I. Hideki O., A determination procedureof input parameters in elasto-viscoplastic finite
element analysis, Soils nad Foundations, Vol.27,
No.3, 1987, pp.71-87.
[11] Yuko I. Ayaka O. Weerakaset S. Chalemnchai
T. Chaweewan D. Masamitsu F. Ryoichi F.,
Estimation of initial void ratio of consolidated
clay based on one-dimensional consolidation
theory, International Journal of GEOMATE,
Vol.14, Issue 46, 2018, pp.51-56.

REFERENCES[1] UNESCO., Historic City of Ayutthaya,https://whc.unesco.org/en/list/576/,2019.5.25Acc
ess.
[2] Yuko I. Ayaka O. Weerakaset S. Chalemnchai
T. Chaweewan D. Masamitsu F. and Ryoichi F.,
Estimation of initial void ratio of consolidated
clay based on one-dimensional consolidation
theory, International Journal of GEOMATE, Vol.
14, Issue 46, 2018, pp.51-56.
[3] Tadateru N. Hiroyuki K. Sakuji Y., Preservation
and utilization of ruins -From the preservation
process of the Egyptian adobe remains -,Conference proceedings, in Proc. 21th
conference on Japan society for science studies
on cultural properties, 2004, pp.42-43. (inJapanese)[4] ICOMOS Thailand., Heritage at Risk, Thailand,
https://www.icomos.org/risk/world_report/2000/thail_2000.htm,2019.5.25Access.
[5] Takeshi I. Nobuaki K. Tadateru N. Jirka S.,
Analysis of Water Regime in Historical Brick
Buildings in Ayutthaya, Thailand, Science forconservation, No.38, 1999, pp.154-163. (inJapanese)300

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

APPLICATION OF ABAQUS PROGRAM TO INVESTIGATE THE
EFFECT OF VARIATION IN SUBGRADE LAYER PROPERTIES ON
THE DAMAGE OF FLEXIBLE PAVEMENT STRUCTURE
12

3

Saad Farhan Ibrahim AlAbdullah , Maryam Hussain Hassan , Suha Aldahwi
1,3
2 Faculty of Engineering, Isra University, Amman, Jordan;

Faculty of Engineering, Al.Mustansiriyah University, Iraq
ABSTRACTThe major purpose of the pavement structure is to reduce stresses in the subgrades so that there is little or no deformationin the subgrade. Therefore, the more the subgrade is resistant to deformation, the thinner the pavement will be, thus
reduction in the construction cost of the road. Good quality subgrade soils are preferable for durable roads but they are
not always available for highway construction. Finite Element techniques can be effectively applied to simulate different
pavement problems that could not be modeled using the simpler multi-layer elastic theory. Thus, finite element Abaqus
6.14-1 program is used in this study. The laboratory tests are carried out on both the natural and stabilized soil samples
that used as subgrade layer to determine the properties of subgrade that will be used as inputs for the finite element Abaqus
program. The natural samples tested with three percentages of moisture contents (11.5%, 13.5%, and 15.5%). Different
types of stabilizers are used in this study in form of mixture of two types of stabilizers. So, mixture of lime-kaolin (3%,4%), mixture of silica fume-Poly Vinyl Alcohol (2%, 2%), mixture of Rice Husk Ash-Poly Vinyl Alcohol (3%, 2%), and
2% of silica fume are used as stabilizers. The pavement application by Abaqus program indicating that, the characteristics
of subgrade layer have a big influence on the vertical deformation of whole pavement. The combination of (3% of Rice
Husk Ash and 2% of Poly Vinyl Alcohol) as a stabilizer to the subgrade layer reduces the deformation about (9.5 %). While
the addition of (3% of Lime and 4% of Kaolin) led to regard the deformation about (7.8%).
Key word: Subgrade, Strength, Deformation, Finite element method, Shear strength, Elastic modulus.
INTRODUCTION
Finite Element Method is the dominate discretization
technique in the structural mechanics. The basic concept in
the physical interpretation of finite element method is thesubdivision of the mathematical model for disjoint
components of simple geometry called finite element. (Kim,
2007) studied the non-linear modulus and deformation
behavior of the fine grained soil that used as a subgrade
layer and the unbounded aggregate for the base/subbase
layers with respect to repeated wheel loads by using Abaqus
program. Abaqus is a commercial finite element modelingprogram. This program is suite of engineering analysis
software packages used around the world for the simulationof the physical response of structures and soil bodies forloads, impact, contact, temperature and the otherenvironmental conditions (Abaqus Theory Manual, 2009).
(Shafabakssh et al., 2013) used the Abaqus software toinvestigate the dynamic response of pavement in term ofelastic behavior. The results of this work indicating that,
there is a reduction in the longitudinal strain in the asphalt
layer. Also the comparison between the strains computed at
the field and those of model is a suitable accommodation.
(Yassenn et al., 2015) suggested the using of linear elastic
modeling to determine the expected fatigue and rutting
damage in flexible pavements due to the traffic loads.

Three dimensional finite element method is the best
approach to analyze more accurately critical pavement
response through the minimizing or eliminating theassumptions of the two dimensional analysis. The analysis
in (3D) method is more complicated as compared with theaxisymmetric because of the extra coupling that will occur
in additional direction.
The responsing of each element is expressed in terms of afinite number of degrees of freedom that characterized as
the value of unknown function at a set of nodal points. For
typical finite element analysis by any software, it's very
important to provide the following information:







The geometry of the structure.
Elements connecting.
Materials properties.
The boundary conditions or the restraints.
Load or the details of forcing function.
The options of analysis process.

The solution process in finite element method can besummarized in the following steps:
 Dividing the structure into pieces (the meshing step).
 Connecting the elements at the nodes form on
approximate system of equations for the whole
structure.

METHODOLOGY301

GEOMATE – Melbourne, Australia, 11-13 November 2020
 Solving the equations that involve the unknown
quantities at the nodes.
 Calculating the desired quantities such as stresses,
strains at the selected element.

120

Materials and Tests

80%Passing

100

The soil used in this research is brought from AL- Sadercity at the east of Baghdad. The sample is taken from (24m) depth below the existing ground surface. The soiltransferred in plastic bags to soil mechanics laboratory of
the highway and transportation engineering department.
Tests are carried out on the soil samples to determine thephysical and chemical properties. The tests included
particle size distribution, soil classification and description,
specific gravity, Atterbirg limits.
Tables (1) and (2) represent the physical properties andchemical composition of the soil sample used in this study.

60
40
20

0.001

0.01

0.1

0

1

10

Particle Size (mm)Figure (1): Grain Size Distribution of Soil Sample.

Table (1): Physical Properties of Soil Samples.
Lime-Kaolin Mixture
Index Property
Liquid Limit (L.L.)
Plastic Limit (P.L.)
Plasticity Index (P.I.)
Specific gravity (Gs)
%Gravel
%Sand
%Silt
%Clay
AASHTO Classification
Unified Soil Classification System(U.S.C.S.)

Value
Both of these materials are brought from the local market.
In this study hydrated lime Ca (OH)2 is used. in this work
2.65
0
20
24
56
A-7-6
CL

3% of lime and 4% of kaolin are mixed and added to thenatural soil. The additives and soil are mixed until
obtaining homogeneous mixture in color. Physical andchemical properties for both lime and kaolin are shown intables (3), (4), (5) and (6).
Table (3): Physical Properties of Hydrated Lime

Table (2): Chemical Properties of Soil Samples.
Index Property
Total Soluble Salts (T.S.S.)
Organic Matter Content

Value
10.8
2.4%

Total Sulphate Content
PH Value
Gypseous Content

1.45%
7.9
3.57

Figure (1) shows the grain size distribution of the soil
sample.

Properties

Lime

Form

Fine dry white powder

Color

White

Specific Gravity

2.3

PH (250 C)
Finess (m2/Kg)

12.93
657

Table (5): Physical Properties Kaolin

Table (4): Chemical Properties of Hydrated Lime

Property

Index

Composition

Percent by Weight

Specific Gravity

2.58

CaO

71

Liquid Limit (L.L.)

57

Al2O3
SiO2
Fe2O3

0.5
4
0.4

Plastic Limit (P.L.)

27

Plasticity Index (P.I)

30

MgO
SO3
Active CaO

2.5
0.3
52

Soil Symbols (USCS)

CH

302

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table (6): Chemical Composition of Kaolin.
Composition

Percent by Weight

SiO2
Al2O3
CaO

51.6
33
1.5

Fe2O3
MgO
L.O.I.***

1.4
0.6
12.1

(2% of PVA) the added to the natural soil to study theeffective of this additive on the soil. Tables (9) and (10)show the physical and chemical properties of RHA.
Table (9): Physical Properties of RHA.
Property

Silica-FumeGrain Size
Distribution (% Finerthan)

Silica-fume is brought from the local market. It’s a highly
effective material because of the high silica content and theextreme fineness. This stabilizer is available in two colors
weight and dark grey. In this study 2% of silica-fume isused as a percent of the dry weight of the natural soil.
Physical and Chemical properties of this stabilizer are
shown in tables (7) and (8) respectively.

4.75 mm100

2.0 mm98

0.595 mm82

0.425mm

56

0.149
mm
0.075 mm

25
9Specific Gravity

Table (7): Physical Properties of Silica-Fume
According to (ASTM C1240-03).
Physical Property%Retaining on 45-μm(No. 325) sieve, Max.
Specific Surface, Min.,
m2/g
Specific gravity

Value

2.03

Table (10): Chemical Composition of RHA (after
Oyetola and Abdullah, 2006).

S.F.
7

Composition

21

SiO2

Percent by
Weight
86

2.231

Al2O3

2.6

Table (8): Chemical Properties of Silica-Fume
According to (ASTM C1240-03).

Fe2O3

1.8

CaO

3.6

Oxide Composition

S.F.

MgO

0.27

Sio2, Min. percent

90

Moisture Content, Max.
percent
Loss of Ignition, Max.
percent

0.68

Silica-Fume and Poly Vinyl Alcohol (PVA)

2.86

In this study a mix of (2% Silica-Fume) and (2% PVA) isused. Before the using of this mixture, the three additives
are mixed together and then added to the natural soil.

Mixture of Rice Husk Ash (RHA) and Poly Velyin
Alcohol (PVA)

MATERIALS PROPERTIESFor understanding and investigating the distribution of

RHA is obtained from the mailing of rice ash which isbrought from Al-Meshkhab in Al-Najaf city. The rice ashes
are burn at (600 C) for 2 hours. In controlled temperaturethen the by-product is mailed to obtain the RHA in powderform. Plate (3.1) shows the rice husk and rice husk ash.
PVA is a white to yellow powder that obtained from thelocal market. In this research (3% of RHA) is mixed withstresses, strains and displacement in the pavement layerswith the variation in subgrade layer properties, 3D models
are created by finite element Abaqus program. The
simulation of subgrade layer in this case included the usingof drucker prager to produce the elasto-plastic behavior.
The angles of friction (ø) and cohesion stress (C) were
determined through the direct shear test while the values of303

GEOMATE – Melbourne, Australia, 11-13 November 2020

modulus of elasticity (E) are obtained from the results ofthrough the using of time-amplitude tabular. Step by step

U.C.S.

loading is used to simulate the motion of wheel in a
Layer

DensityE(Kg/cm

(Mpa

3

)

)

Asphalt Surface

2310

1200

-

0.35

Subbase

2200

110

-

0.35

-

1640

18.9

38.7

0.4

0.08

S.F.

1692

10.5

42.1

0.4

0.07

Stabilized

(RHA

1680

21.5

46.6

0.4

0.05

Subgrade

and

1660

12.9

40.3

0.4

0.03

Lime

ø

determined speed. The loading place must be moved in a
C form in order to have a complete wheel turn. Figure
gradual(Mp
ratio (υ) *(2) a)
represents the steps of loading used in finite element
Poisson's

Abaqus program. The simulation of repeated load by using
time-amplitude
tabular is shown in figure (3).

andKaolin

PVA)
(S.F.
and
PVA)
Figure (2): Steps Used to Simulate Moving Load in Finite
Element Abaqus Program.

*Poisson's ratio from Huang (2004).
SIMULATION OF MOVING LOAD

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Amplitude

The application of layered theory for the design offlexible pavement included the assumption of circular
contact area for each tire. This assumption is not correct
but the error is very small. So, for simplification theanalysis of flexible pavement, a single circular with same
contact area as the duals is used to simulate a set of dual
tires instead of using two circular areas. The contactpressure on the pavement is equal to the tire pressure. The

0

5

10

Time (Sec)contact area can be in the form of square and two half
circles (Huang, 2004). According to (Yoder and Witczack,Figure (3): Simulation of Moving Load by Using TimeAmplitude Tabular.

1975), the wheel load that applied on the pavement ranging
from (80 KN to 90 KN). In this study the load of one tire isconsidered equal to (96 Kg) and it distributed uniformly

BOUNDARY CONDITIONS

over the total contact area. The pressure used in thisresearch equal to (550 Mpa).The simulation of moving
load on the pavement structure included division the
pavement into (12) Steps to simulate the path of tire on theasphalt surface layer, each step consist of number of cycles

The boundary conditions of the model have important
effect in the prediction of response of the model. Steel box
is assumed to be used, so the bottom of subgrade and thesides of each layer is assumed to be fixed. In other word,
there is no horizontal or vertical movement in this case. The
304

GEOMATE – Melbourne, Australia, 11-13 November 2020

nodes along the edges of pavement are assumed free tomove vertically but forced in the horizontal direction.
Figure (4) represents the boundary conditions of the model.

Figure (5): Meshing of all Parts of the Model.
INTERACTION MODELING TECHNIQUES
To contact the parts of model together by using Abaqus

Figure (4): Boundary Conditions of the Model Simulated
by Finite Element Abaqus Program.

program, it's very important to define the surfaces ofinteraction between two

FINITE ELEMENT TYPE
All the parts of the model that created by finite element
Abaqus 6.14-1 are modeled using (8) nodes continuum
three dimensional brick element (C3D8R) with reducedorder numerical integration available in Abaqus program.
This element has the ability to represent large deformation,geometric and material nonlinearities. Solid element(C3D8R) has three degrees of freedom at each node,translations in the nodal X, Y and Z directions. The layersof pavement are simulated with the same shape to reserve
the continuity of the nodes between the sequential layers.

layers. There is several

formulation of contact in Abaqus program. Eachformulation is depending on the choice of contact
discretization, tracking approach and assignment of(master) and (slave) roles for the contact surfaces. Theinteraction technique permits merging two regions together.
In this study, (Surface to Surface) contact with small
sliding is used for all the contact interactions in the model
which assumed a comparatively small sliding but could
submit to an arbitrary rotation of the bodies. The
formulation of interaction between the layers of model isshown in figure (6).

MESHING SIZE
The mesh of model is considered in way to provide best
and more accurate results. So, (8) nodes linear brickreduced integration elements (C3D8R) meshing is used to
improve convergence rate. (C3D8R) elements are ofquadratic type. Quadratic elements have better results thanthe linear interpolation element (Hadi and Bodhinayake,2013). Fine mesh size is used around the loading area,
along the wheel path and at the interface between layers
Figure (6): Formulation of Interaction between the Layers

where the gradients of stresses and strains are higher.

of the Model.

Figure (5) shows the mesh size for all layers of the model.
305

GEOMATE – Melbourne, Australia, 11-13 November 2020

RESULTS AND DISCUSSIONSTo study the influence of variation in subgrade layer
properties on the permanent deformation of the flexible
pavement structure, seven models are built by using finite
element Abaqus program. In this research, the comparison
between these models based premilary on the permanent
deformation. Figures (7) through (13) show the verticalpermanent deformation for the natural and stabilized
models.

Figure (9): Vertical Deformation for Natural Model
Compacted at Wet Side.

Figure (7): Vertical Deformation for Natural Model
Compacted at Dry Side

Figure (10): Vertical Deformation for Stabilized Modelwith (3% of Lime and 4% of Kaolin).

Figure (8): Vertical Deformation for Natural Model
Compacted at O.M.C.
Figure (11): Vertical Deformation for Stabilized Modelwith (2% of Silica Fume).

306

Deformation (mm)GEOMATE – Melbourne, Australia, 11-13 November 2020

31.5
31
30.5
30
29.5
29
28.5
28
27.5

Dry Side

O.M.C.

Wet Side

Moisture Content
Figure (12): Vertical Deformation for Stabilized Modelwith (3% of RHA and 2% of PVA).

Deformation (mm)Figure (14): Vertical Deformation for Elasto-Plastic
Models with Natural Subgrade Compacted at Different
Moisture Contents.

Figure (13): Vertical Deformation for Stabilized Modelwith (2% of Silica Fume and 2% of PVA).
For the models with untreated subgrade layer it's noted that,the vertical deformation reduced about (5.3%) due to theincreasing of moisture from (11.5%) to (13.5%). The
increasing in moisture to (15.5%) results in increasing thesurface deformation about (7.8%).

28.5
28
27.5
27
26.5
26
25.5

Lime and Silica RHA and Silica
Kaolin Fume
PVA
fFume
and PVA
Model Type

Figure (15): Vertical Deformation for Elasto-Plastic
Models with Stabilized Subgrade Compacted at Optimum
Moisture Contents.
The percentage of improvement in vertical deformation
due to using stabilized subgrade layer with different
stabilizers is shown in figure (16) below.

The using of (3% of RHA and 2% of PVA) as stabilizers
for the subgrade layer led to improving the deformation to(9.5%) as compared with untreated model. The otherstabilizers improved the deformation with different
percentages. Figures (14) and (15) illustrated the values ofvertical deformation for the natural and stabilized models
respectively.

Figure (16): Percentages of Improvement in Vertical
Deformation Due to Using Stabilized Subgrade Layers.
307

GEOMATE – Melbourne, Australia, 11-13 November 2020

CONCLUSIONSThe untreated subgrade layer that compacted at the wet side
resulting in high vertical deformation due to the applied
traffic loading as compared to the subgrade layers
compacted at dry or O.M.C.
All types of stabilizers used in this research improved thedeformation but the addition of combination of (RHA and
PVA) gives a less deformation as compared to the othertypes of stabilizers.
REFERENCES[1] Abaqus Theory Manual, (2009): Hibbitt, Karlsson and308

Sorenson, Inc, INC.
[2] Kim, M., (2007): "Three-Dimensional Finite Element
Analysis of Flexible Pavements Considering
Nonlinear Pavement Foundation Behavior", Ph.D.
Thesis, College of Engineering, Department of Civil
Engineering, University of Illinois, UrbanaChampaign.
[3] Shafabakhsh, G.A., Taleb, M., Motamedi, M., andBadroodi, S.K., (2013): "Validation of Load
Movement on Flexible Pavement Using Pennsylvania
Field Test", Technical Journal of Engineering andApplied Sciences. 3(22): pp. 3125-3130.
[4] Yassenn, O.M., Hafez, M.A., Endut, I.R., Ishak, S.Z.,
and Yasseen, H.M., (2015): Finite Element Modelingof Flexible Pavement", Journal of Multidisciplinary
Engineering Science and Technology (JMEST), Vol.
2, Issue. 1, pp.115-120.

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DEVELOPMENT OF A COMBINATION OF A ROTARYPENETRATION TYPE SAMPLER*Katsuya Iwai1, Naoaki Suemasa1 and Tsuyoshi Tanaka1
1

Faculty of Engineering, Tokyo City University, Japan

ABSTRACT: The Swedish Weight Sounding (SWS) test is a ground survey method often used in residential
areas. However, it is difficult to determine soil properties and understand soil quality accurately only by the
SWS results. If it becomes possible to collect undisturbed samples by inexpensive and simple sampling, it
could be applied to a wide range of construction designs such as pile foundation works. The purpose of thisstudy is to establish an inexpensive, simple and accurate ground survey method by a combination of a rotary
penetration type sampler (Screw Driving Sampler), and an improved SWS tests, the Screw Driving Sounding
test (SDS test). In this report, after carrying out the SDS test and sampling by a SD Sampler (SD sampling) inactual ground, the laboratory test results were compared with the boring test results obtained from neighboring
ground. As results of an unconfined compression test, since an unconfined compressive strength was large but
a deformation coefficient was small, it was revealed that the sample was disturbed at some parts. However,when comparing the result of SD sampling with that of boring test, contamination was not observed in thephysical test on the sample obtained by SD sampling. As a result, it was confirmed not only that sedimentation
status of the ground could be estimated through the SDS test but also more detailed soil properties could be
investigated with SD sampling.
Keywords: Sampling, sampler, sounding, boring test, unconfined compression test

tube to reduce frictional resistance between thesampling tube and a wall of borehole during
sampling. The penetration of the sampling tube isbased on a ball screw mechanism. The test
procedure was as follows; first the sampler was
penetrated through a borehole pre-excavated by asounding test to a selected position with no rotation,and then the rods were rotated and lifted off forcollecting a sample. There are three types ofsamplers with different diameters, including a large
sampler to collect an undisturbed sample used for
an unconfined compression test and a small and a
medium sampler for physical tests.

1. INTRODUCTION
The Swedish Weight Sounding (SWS) test,
which is more inexpensive and simpler than theboring test, is commonly used for ground survey inresidential areas. However, the SWS test candetermine only soil hardness but not soil properties.
If it becomes possible to collect samples by an
inexpensive and simplified sampling method, it
could be feasible to assess liquefaction potential by
grain size analysis on the collected samples. In
addition, if a collected sample is undisturbed, it
could be applied to a wide range of construction
designs such as pile foundation works. The purposeof this study is to establish an inexpensive, simple
and accurate ground survey method by a
combination of a rotary penetration sampler (Screw
Driving sampler), and an improved SWS test, theScrew Driving Sounding test (SDS test). In thisreport, after carrying out the SDS test and samplingby a SD sampler (SD sampling) in the actual ground,the laboratory test results were compared with theboring test results obtained from neighboring
ground.

Fig. 1 SD samplers
Table 1. Summary of SD samplersouter

2. SD SAMPLER

innerdiameter diameter

Figure 1 and Table 1 show a photo and asummary of SD samplers, respectively and Fig. 2
shows a SD sampling procedure. The SD sampler iscomposed of a piston, blades and double tube
structure including an outer tube and a sampling309

maximumusesample(mm)(mm)

length(mm)

S

32

18

350

M

36

25

350

L

44

36

350

physical (soil)
test(clay)

mechanics test

Start the test
Stop the penetrate
Increasing 250N
vertical load andpenetrating one
rotation
Lifted off by 2cm
The amount ofpenetration is
25cm

Adding with
125N vertical loadand penetrating
one rotation

YES

NO

Gauging torqueafter one rotation

The amount of allrods is 1000N

NO

YES

Penetrating with rotatingof 1000N till getting to
25cm

Fig. 2 SD sampling procedure

Pushing 2cm to backon the position

Fig. 4 Test procedure

3. SDS TEST
Fig. 3, Fig. 4 and Fig. 5 show a photo of the SDS
test machine, a test procedure and a summary of
torque and load applied on the rod, respectively.
The SDS test results were studied based on theplastic analogy model obtained from theconstitutive law of soil stress and strain. The rotary
penetration was regarded as deformation behavior
caused by a combined load of a vertical load andtorque. During the test, a vertical load was addedwith 125N at each rotating in seven stages from
250N to 1000N, and after 25cm penetration, therods were lifted off by 2cm and the torque wasgauged to reduce peripheral frictional resistance(Fig. 4). There were 9 measurement items:
penetrate
depth,
load,
averagetorque,maximum/minimum torque, settlement speed, half
rotation amount and settlement amount. In addition,
to reduce peripheral frictional resistance, a verticalcomponent Wf and a horizontal component appliedon the rods Tf were subtracted from practicallygauged load Wa and torque Ta (Figure 3),
respectively.

𝑇𝑎

𝑊𝑎

𝑇𝑓

T

𝑊𝑓

𝑊𝑎 : real gauged load
𝑇𝑎 : real gauged torque𝑊𝑓 : vertical load operating on rods
𝑇𝑓 : torque operating on rods
W : vertical load operating on screw point
W T : torque operating on screw point

Fig. 5 Summary of torque and load on rods
Depth
Soil
Ground(m) Description water(m)

1

clay

2
3
4

clay

8

20

30

0.5

4.0

clay

5

7

10

1.9

sandy clay

6

1.70

SPT "N"0

tuffaceous
clay

0.6

0.7
2.0

sandy silt

4.7

sandy silt

7.7

9

sandy silt
10

46.0
43.0

organic silt

Fig. 3 SDS test machine

Fig. 6 Results of SPT and soil profile
310

40

50

60

4. SD SAMPLING IN ACTUAL GROUND

Natural Water Content (%)
50
100

0

150

0

Fig. 6 shows the SPT test results and a soil
profile. While the boring test was carried out except
for the part from the ground surface toapproximately 2.5m depth, SD sampling was
carried out at the same time, where samples werecollected with a SD sampler from designated eight
points between 3.00m to 8.00m depth on the clay
ground. A series of tests were carried out on thecollected
samples
includingunconfinedcompression tests, water content tests andliquid/plastic limit tests.

TWS(wn)2

SDS(wn)Depth (m)46

8

4-1. SAMPLING RESULTS

10

Fig. 7 shows the water content test results ofcollected samples by thin-walled sampling in theboring test (TW sampling) and SD sampling (wn),and Fig. 8 shows the liquid/plastic limit test resultsof TW and SD samplings (wl, wp), respectively.
First, focusing on the water content test results ofboth samples, the values were close at 4m, 6m and7m depths. Moreover, the values from theliquid/plastic limit tests on both samples were close
as well. From the results that both samples at each
depth were likely to be equivalent in the physicalproperties, it was revealed that contamination was
not caused during SD sampling.
Fig. 9 shows the unconfined compression test
results of TW and SD samplings (qu). Compared theunconfined compressive strengths of both samples
at 4m depth, the values were very close. At deeper
depths, on the other hand, the unconfinedcompressive strengths of SD sampling werebasically same but relatively larger than those of
TW sampling. Then, the deformation modulus was
focused on to confirm sample disturbance. Fig. 10shows the deformation modulus (E50). At 4m depth,
the both deformation moduli were close, but atdeeper depths, the deformation moduli of SD
sampling were smaller than those of TW sampling.
From the results of the unconfined compression
tests, we considered that the unconfinedcompressive strengths were affected by the
positional relationship between the sampling andthe structure. Since the deformation moduli were
small, it appeared that disturbance occurred in thesamples of SD sampling at depths deeper than 4m.

Fig. 7 Water content test results
Liquid & Plastic Limit (%)
50
100

0

150

0
TWS (wl)
TWS (wp)
2
SDS (wl)

Depth (m)SDS (wp)
4

6

8

10

Fig. 8 Liquid/plastic limit test results
Unconfined Compression Strength(kN/m²)
50
100
150

0
0

TWS(qu)2

SDS(qu)Depth (m)

4

6

8

10

Fig.9 Unconfined compression test results311

above sea level, the ground at depths deeper than
6m used to be under sea level centuries ago so thatthe stratum was assumed to be marine clay.
According to the torque results, the ground of 210.25m was clarified into three stratums: 2~4m,
4~6m and 6-10.25m depths.
SD sampling was carried out at additional four
points with different torque values between 3mand 6m depths to understand stratum boundaries at
4m and 6m depth, respectively.

Deformation Modulus (MN/m2)
0
3
6
9
12
0

TWS(E50)2Depth (m)SDS(E50)46
0E0.25 (Nm)
0.5

1

0

8
2

10

SDS
SPT

4
Depth (m)Fig. 10 Deformation modulus
5. SDS TEST AND SD SAMPLING
To understand accumulation conditions of theground, the SDS test and SD sampling werecarried out. After the SDS test was conducted in
the ground approximately 20m away from theboring test position and up to 10.25m depth, thedepth for SD sampling was determined from theresponse of measured torque. Then, samples werecollected by a medium sampler and applied towater content tests and liquid/plastic limit tests toinvestigate their physical property.

6

8

10

0

5
N-value

10

Fig. 11 E0.25 and N-value
5-1. SDS TEST RESULTS
0

Fig. 11 shows E0.25 obtained from the SDS test
and N-value from the boring test, where E0.25
represents an energy amount when penetrating
25cm and calculated from torque, the number ofrotation, load and depth. As seen in Fig 11, thebehavior of E0.25 tended to be similar to that of Nvalue. Therefore, there is a possibility that E0.25
could be an alternative of N-value.
Compared torque from the SDS test with Nvalue as shown in Fig 12, similar tendencies wereseen in both behaviors, same as E0.25. In addition,
focusing on only torque behavior, it was assumed
that the stratum of 0-2m depth was a reclaimed
ground because the torques fluctuated significantly.
Focusing on 2-4m and 4-6m depths, the torques
increased according as depth increased and asudden decrease was observed when passing 4m
and 6m parts, respectively. While, N-value
decreased at depths deeper than 6m as well. From
the results, there is a possibility that these two parts
were assumed to be stratum boundaries.
Furthermore, since the test ground was 11.0m TP

Torque (Nm)
10
20

30

0

2
SDS
SPT

Depth (m)

4

6

8

10

0

5
N-value

Fig. 12 Torque and N-value
312

10depth with a large torque was the lowest of the four
samples (Fig. 16). As one of the reasons, the test site
was adjacent to a structure so that the ground may
have been affected by it and accordingly theintensity became high around 4m depth.

5-2. SD SAMPLING RESULTS
Fig. 13 shows the water content test results (wn)and Fig. 14 shows the liquid/plastic limit test results(wl, wp). There was a tendency that the watercontents increased as depth increased, however, awider difference was seen between 4m and 5m
depth, compared with the difference between 3mand 4m and that between 5m and 6m. Similarly, theliquid limit and the plastic limit increased as depth
increased. It resulted from increasing of sand in thestratum at depths shallower than 6m by recession of
the sea or deposit of sediment.

120
110
100
90

Plastic Index

80

(CH)

70
60

3m

50
4m

40

5m

30

0

Natural Water Content (%)
40
80
120
160

(CL)

20
10

0

(MH)

6m

(ML)

0

0 10 20 30 40 50 60 70 80 90 100110120130140150160170180190200
Liquid Limit (%)

1

wn

Fig. 15 Plasticity chart

Depth (m)

2
3

0

4

0

5

1

6

2

Liquid Index
0.5

1

IL

Depth (m)7

Fig. 13 Water content test results045

Liquid & Plastic Limit (%)
40
80
120
160

0
1

3

6

wl

7

wp

Fig. 16 Liquidity index

Depth (m)2
3

6. CONCLUSION
4

It was confirmed from the physical test resultsthat SD sampling was capable to collect samples
from a target depth without disturbing a borehole.
The unconfined compression test results also
revealed that little disturbance was observed in thesamples collected by a SD sampler on the whole,although the quality was not as high as that of thinwalled sampling.
In addition, there was a possibility for E0.25
obtained from the SDS test results to be analternative to N-value, by evaluating an
accumulation state from a torque response.
Properties of soil stratums and accumulation states
were investigated in more detail by SD samplingbased on the SDS test results.

5
6
7

Fig. 14 Liquid/plastic limit test results
Fig. 15 shows a plasticity chart and Fig. 16
shows a liquidity index calculated fromliquid/plastic limit test. Differences were seen in thephysical properties between 3m and 4m depth withclay and 5m and 6m depth with silt (Fig. 15). The
liquidity indexes of 5m and 6m depth were close toone, while those of 3m and 4m depth were lower
than one. In particular, the liquidity index of 4m
313

In conclusion, a better understanding was
obtained on usability of SD sampling in
combination with the SDS test in this study. In orderto improve sampling accuracy, more SD samplings
will be carried out on various types of grounds aswell as a consolidation test and an unconfinedcompression test with measuring suction.

[2] Tanaka, Tsuyoshi and Naoaki Suemasa, Kazuya
Itoh, Naoko Ishida, Shinichi Yamato,“Development of the new soil sampler”,
Proceedings of the japan national conference on
geotechnical engineering, 2016[3] Tsuji, Kouhei and Minoru Yamashita, Naoaki
Suemasa, Tsuyoshi Tanaka, Yasuo Sugano,
Shinichi Yamato, “An application of new
sounding method (SDS) which measures torqueto the river embankment survey”, Proceedingsof the japan national conference on geotechnicalengineering, 2016

7. REFERENCES[1] General Incorporated Foundation Better Living,“Utilization technology of ground survey resultsby SDS test”, 2013.

314

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

BASE-ROCK ELEVATION MEASUREMENT USING SINGLE AND
ARRAY MICROTREMOR INVESTIGATION

1

Windu Partono1, Sukamta1, Frida Kistiani1, Undayani Cita Sari1 and Kukuh Cahya Adi Putra1
Civil Engineering Department, Engineering Faculty, Diponegoro University, Semarang 50275, Indonesia

ABSTRACT
Site-specific ground motion analysis is a method for developing surface ground motion and surface spectral
acceleration. The analysis is performed by conducting propagation analysis from base-rock to surface elevation.
The important information needs for site-specific ground motion analysis is the position of base-rock or bedrock
below the surface level. This paper presents the investigation results of base-rock elevation at the Diponegoro
University Semarang, Indonesia. The background reasons why the base-rock investigation of this university area
shall be performed because this area is located closed to three seismic sources (Semarang, Lasem and Demakshallow crustal faults). The base-rock elevation prediction at the study area was performed using a combination of
microtremor test and soil boring investigations. The base-rock elevation prediction developed using microtremor
test was verified using soil profile developed from soil boring investigation. Based on soil boring and microtremor
investigations, the base-rock elevation at the university area is predicted at 30 m to 60 m below the surface level.
Keywords: Base-rock, Ground Motion, Propagation Analysis, Microtremor, Shallow Crustal Fault

on the New Indonesian Seismic Hazard Map 2017[2], the university area is located closed to threedifferent seismic sources, Semarang, Lasem and
Demak crustal fault traces. The base-rockinvestigation at this area is part of seismic
microzonation research for the university area.
Seismic vulnerability analysis due to a specificscenario earthquake motion study is still on going.
The study is performed because most of the buildings
within the university area were designed andconstructed following the old version of Indonesian
seismic code. The base-rock prediction level was
conducted using a combination of single and arraymicrotremor test and soil boring investigations.
Figure 1 shows the position of the university area
against three closed seismic sources. All seismic
sources are located less than 10 kilometers to the
University area.

INTRODUCTION
Site-specific ground motion analysis is a methodfor developing surface ground motion, usually interms of displacement, acceleration and velocity time
histories. Surface response spectral acceleration andamplification factor can also be calculated or obtainedusing this method. The analysis is conducted as acomplementary analysis for buildings and otherstructures resistance design and evaluation against
seismic forces. Surface spectral acceleration forbuildings design in Indonesia usually performs using
amplification or site factor and multiplying this site
factor with spectral acceleration at base-rockelevation. ASCE/SEI 7-16 [1] introduce therequirement of site-specific ground motion analysis
for buildings located at soft soil (site class SE) andmedium soil (site class SD) having a specificminimum Risk Targeted Maximum Considered
Earthquake (MCE R ) S S and S 1 spectral acceleration.
ASCE/SEI 7-16 states that all structures located onsite class SD and SE having an MCE R -S 1 minimum
or equal to 0.2 g shall be evaluated using site-specificground motion analysis. One of the important
information for performing site-specific groundmotion analysis is obtaining the base-rock elevation.
The base-rock elevation can be obtained or predictedusing typical invasive soil boring investigation andnon-invasive microtremor test, either using single orarray microtremor investigation.
This paper explains the base-rock investigation
research at Diponegoro University area. The study
area is located at the southern part of Semarang, thecapital city of Central Java Province, Indonesia. BaseFig. 1315

Seismic

sources

positions

and

the

GEOMATE – Melbourne, Australia, 11-13 November 2020

corresponding distance to the university area

examples of HVSR curve developed at the study area.
The dominant frequency (Fo) obtained from HVSR
curve used for base-rock prediction [9, 10, and 13].
The base-rock prediction elevation is calculated
following the empirical formulae developed by [14,15]. Equation 1 shows the formula used for predicting
base-rock elevation [14, 15]. The variable “Z” at Eq.
1 represents the thickness of the soil sediment above
the base-rock elevation. Table 1 shows the “m” and“n” constant values used for sediment thickness
calculation. The Z values obtained at all single
microtremor positions surrounding the study area areused for developing the base-rock elevation map of
the study area.

Based on the New Indonesian Seismic Code 2019
(SNI 1726:2019) [3], the MCE R -S S and MCE R -S 1
spectral response acceleration of the study area are in
between 0.87-0.891g and 0.373-0.38g respectively.
Figure 2 shows the 2019 MCE R -S S map. Figure 3
shows the 2019 MCE R -S 1 map. Based on those two
spectral MCE R acceleration, a site-specific groundmotion will be analyzed at the university area as acomplementary analysis using surface response
spectral acceleration.

Fig. 2

Fig. 3

MCE R -S S 2019 map of the university area
Fig. 4Single station seismometer investigation at
Semarang

Fig. 5

Two example HVSR analysis results at two
point within the university area

MCE R -S 1 2019 map of the university area

METHODOLOGY

Z = mFo n

Base-rock elevation prediction at the university
area is performed as part of the same research
conducted at the whole area of the city. This researchis part of the seismic microzonation research of
Semarang City and already started from 2015 and stillon-going [4]. The base-rock investigation for thewhole city area is started by conducting 241 single
station seismometer investigation and following thesame method proposed by [5, 6]. The purpose of thisinvestigation is to obtain the dominant frequency ofsoil sediment at the microtremor equipment position.
Figure 4 shows the position of 241 single station
microtremor investigations. The dominant frequency
for each microtremor position is calculated using
Horizontal to Vertical Spectrum Ratio (HVSR)
formula developed by [7, 8]. Figure 5 shows two(1)

Table 1 m and n values used for sediment thickness
calculation
References
[14]
[15]

m
96
108

n
-1.388-1.551

Following the single station seismometer
investigation, array microtremor investigations was
performed at the university area. The purpose of thisinvestigation is to find the predicted soil profile interms of shear wave velocity (Vs) profile at the study
316

GEOMATE – Melbourne, Australia, 11-13 November 2020

area. Based on the Vs data or Vs profile, the base-rockelevation can be predicted following the standard
criteria describes by [1, 3]. SNI 1726:2019 [3] andASCE/SEI 7-16 [1] states that the minimum Vs = 750m/sec (rock) and minimum Vs=1500 m/sec (hardrock) can be used for adjusting rock properties. The
basic concepts of array seismometer analysis almost
equal with single station seismometer analysis. Alldata developed using single seismometer is analyzed
as an array data.
Eight boring investigations are conducted at the
study area. The purpose of this investigation is to findthe soft rock or hard rock elevation at the boring
position. All boring investigation at the study area
were conducted until maximum 30 m depth. Figure 6
shows the array microtremor and soil boring
investigation positions.

Fig. 6

Array microtremor and soil
investigations at the study area

Figure 10 until Figure 12 show the three different
results of soil profile in terms of shear wave velocity(Vs) profile. Based on Fig. 10 base-rock elevation is
located minimum 40 m depth below the surface level.

Fig. 7

Dominant frequency (Fo) map of the study
area

Fig. 8Base-rock elevation map of the study area

Fig. 9Cross section positions

boringRESULTS AND DISCUSSION
Single station microtremor investigation
conducted at the university area produce a dominantfrequency (Fo) map. This map was performed byconducting spatial analysis. Figure 7 shows thespatial analysis results of dominant frequency map at
the study area. All dominant frequencies (Fo) dataobtained from single station seismometer test at the
study area are used for base-rock elevation prediction.
Figure 8 shows the base-rock elevation prediction
results conducted at the study area. It can be seen inthis figure the base-rock elevation at the study area
are divided into two different elevation 0-30 m (area
1, red area) and 30-60 m (area 2, green area). Due tothe restriction of soil boring investigations, the baserock elevation is divided into two different depths. Allboring investigations are executed until maximum 30
m depth. Three boring investigations are located atarea 1, however another five boring investigations arelocated at area 2.
Based on the information related with base-rockelevation, array microtremor analysis was thenperformed at the study area. Figure 9 shows threedifferent cross section positions for developing arraymicrotremor analysis and developing vs profile.

eda a a ( )

G
0

G

G3

G0

G

-10
-20
-30
-40
-50
0

10

20

30

40

50

60

70

80

90 100Jarak (m)Fig. 10 Vs profile at cross section no 1 developed
317

GEOMATE – Melbourne, Australia, 11-13 November 2020

from 5 seismometer position

4

10

30

50

Layer Tipe

sampleSymbol0.00

12Clayey Silt

6.00
6.45

35

Sandy Silt

9.00
9.45

56

12.00
12.45

58

15.00
15.50

>60

18.00

>60

4.50
4.00

8

2
3.00
3.45

4.00

5

5.00

2.00

10

10.00

10.00

11
12

9.50

Sandy Silt

4.00

13
14

14.00
1.00

15

15.00

16

2.00

17

14.50

Gravelly Sand

15.00

Sandstone

5

Gravelly Sand 18.45

3.00

19

19.50
1.00

21

9

11

21.00

Sandstone

Gravelly Sand

3.00

24

24.00

25.00

26
27
28

29.50

Silty Sand

14.50

15
16

Silty Sand

5.00

14

15.00

21.00
>60
21.45

19.50
`

22
23

27.00
>60
27.45

Silty Sand
24.50

25.00

29
30

30.00

>60

>60

22.00
>60
22.45

28.00
>60
28.45

28

29.50

30.00
>60
30.45

30.00

TE :

53

60

26.00
26.45 >60

27

(a)

14.00
14.50

27

24.00
>60
24.45

24
25

12.00
12.45

16

26

20.00
2045 >60

20.00

21

8.00
8.45
10.00
10.45

18.00
18.45

18
19

6.00
6.45

9

16.00
>60
16.50

16.00

17

30.00
>60
30.45

30.00

NOTE :

11.00

10.00

13

29
30.00

1.00

26

Sandstone

6.00

30

9.50

10.00

12

24.00
>60
24.45

24.50

25

Sandy Silt

5.00

8

10

20

20.00

22
23

5.00

7

`

20.00

4.50

5.00

6

2.00
2.45
4.00
4.45

4

17.00

18

20

Clayet Silt

3.00

3

8.00

9

5

N

1

Clayet Silt

4.00

3

Fig. 11 Vs profile at cross section no 2 developed
from 4 seismometer position

Depth (m)Layer Tipe

Depth (m)50

Field Test
SPT
Depth (m)30

Thickness (m)

10

0.00

2

7

(b)

Fig. 13 Two sample boring log results area 1 Bh1 (a)and area 2 Bh2 (b)220eDepthat o(m) ( )

N

1

6

8

Field Test
SPT
Depth (m)sampleSymbolThickness (m)Date

Depth (m)Based on three different cross section positions,the base-rock elevation can be proved using boring
investigation results at cross section no 3. From theVs profile the base-rock or hard-rock layer having
shear wave velocity minimum 1500 m/s can be foundat 30 m or maybe less than 30 m below the surface
level. The Vs profile developed at cross section no 3
will make base-rock level easy to predict. Based on
figure 10 or cross section no 1 the base-rock elevation
can be predicted at 50 m below the surface level.
However, it is difficult to predict the elevation of
base-rock based on cross section no 2.

Depth (m)Base-rock elevation were observed at three boring
locations at area 1. However, no rock sample obtained
at five boring positions at area 2. Fig 13(b) shows anexample of boring-log record at area 2 (cross section
no 1 and 2). Figure 14 shows the correlation of N-SPT(Standard Penetration Test) result and Vs profile
developed at boring no BH1. Figure 15 shows thecorrelation of N-SPT data and Vs profiles at boring
no Bh2.

200
N-SPT
0

180

0

160

5

10

20

30

40

50

60

10

0

20

40

60

80

100

120

140

160

180

Depth (m)

140

200

Distance (m)

15
N-SPT

20

Vs Loc. 14
Vs Loc. 18

25

Vs Loc. 22
Vs Loc. 25

30
0Fig. 12 Vs profile at cross section no 3 developed
from 4 seismometer position

200

400

600

800

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

Vs (m/sec)

Fig. 14 N-SPT and Vs correlation at cross section no3

To verify the base-rock elevation, 8 boring
investigation are performed at the study area. Figure
13 shows two different boring investigation results
collected from boring no Bh1 (Fig. 13 (a)) and Bh2(Fig. 13 (b)), two example boring-log data collectedfrom area 1 and area 2 respectively. A goodcorrelation between soil boring investigation andarray microtremor investigations results wasmeasured in this research especially at the university
area. As it can be seen in Fig. 13 (a) the base-rockelevation is easily to predict by conducting theminimum shear wave velocity of 1500 m/sec. The
base-rock elevation was observed at approximately
30 m below the surface level (cross section no 3).
Sand-stone sample was observed at the boring no
Lok01Bh1 at 25 to 30 m below the surface level.

N-SPT
0.0

10.0

20.0

30.0

40.0

50.0

60.0

1200

1400

0

Depth (m)

10

N-SPT
Vs Loc. 0
Vs Loc. 1
Vs Loc. 2
Vs Loc. 3
Vs Loc. 4

20

30
0

200

400

600

800

1000

Vs (m/sec)

Fig. 15 N-SPT and Vs correlation at cross section
318

GEOMATE – Melbourne, Australia, 11-13 November 2020

no1

Based on eight boring investigations results forverifying the base-rock elevation, the sand stone(hard rock) samples were observed at three boring
positions having a maximum of 30 m depth (firstarea). However, no hard rock samples observed atother five boring positions (second area).
Single and array microtremor investigations canbe used as a complement method in predicting baserock measurements. Boring investigations shall be
conducted to verify the microtremor investigation
results. A minimum of 1500 m/sec of shear wave
velocity values should be applied for base-rockelevation measurements. However, if the vs value
obtained on site less than 1500 m/sec, the predicting
base-rock elevation is difficult to perform. For
verifying base-rock elevation located at minimum
100 m depth, the soil boring investigation will spend
a lot of money and time.

Figure 14 shows the correlation of Vs profile
developed from array microtremor with 4 geophones
and soil boring investigation result. The analysis was
conducted at cross section no 3 position. Based on theVs profiles, the maximum Vs value obtained at 30 m
below the surface level is 2502 m/sec and theminimum Vs value at this level is 843 m/sec and theaverage Vs value is 1913 m/sec. The average Vs
value from 25 m to 30 m is greater than 1500 m/sec.
Following [1] and [3] the hard rock samples will beobtained at minimum 26 m below the surface level.
Based on the boring-log result conducted at this array
position as can be seen at Fig. 13(a) the sand stone
sample was obtained from 26 to 30 m below thesurface level. The N-SPT profile was also obtained
from the same boring position. However, it is difficult
to evaluate the hard rock position based on the N-SPT
data. Due to the capability of N-SPT spoon sampler
apparatus, for all N-SPT greater than 60, the boring
master will stop the blowing proses and write the NSPT record “>60” into the boring-log. Sand stone
samples are also observed at other two boring
investigation positions. A good output performanceof base-rock investigation was observed at area 1.
Figure 15 shows the same Vs and N-SPT
correlation conducted at array 1 position. Fivegeophones for ambient vibrations measurement were
installed at this position. In average no Vs value at 30
m below the surface level reach 1500 m/sec. The
average Vs value collected from five geophones isless than 1500 m/sec. Based on the average Vs data,
it is difficult to obtain the base-rock samples at thisarray investigation position. Base on the boring-log
report, no soft rock or hard rock samples obtained
from the soil boring investigation. As it can be seen
at Fig. 13 (b), the N-SPT values obtained from 12 m
to 30 m below the surface level are greater than 60.
Based on the microtremor and soil boring
investigations conducted at the University area, baserock elevation can be predicted using single and arraymicrotremor investigations. Base-rock elevation canbe predicted when the Vs data observed by
microtremor equipment reach minimum 1500 m/sec.

ACKNOWLEDGMENT
This research was financially supported by the
Ministry of Education and Culture, The Republic ofIndonesia through Research Grant 2019 and 2020.
REFERENCES[1] ASCE/SEI 7-16: Minimum Design Loads andAssociated Criteria for Buildings and Other
Structures. American Society of Civil Engineers,
2017.
[2] National Center for Earthquake Studies:
Indonesian Seismic Sources and Seismic Hazard
Maps 2017, Centre for Research andDevelopment of Housing and Resettlement,
Ministry of Public Works and Human Settlements,
ISBN 978-602-5489-01-3, 2017, pp. 1-377.
[3] SNI 1726:2019, Seismic Resistance Design Codefor Buildings and Other Structures, Jakarta, 2019,
pp. 1-238.
[4] Partono W., Prabandiyani S.P.R., Irsyam M. andMaarif S.: Seismic Microzonation of Semarang
Indonesia based on Site Response Analysis using
30 m Soil Deposit Model. Jurnal Teknologi, Vol.
78:8-5, 2016, pp. 31-38.
[5] Claudet S.B., Baise S., Bonilla L.F., Thierry C.B.,
Pasten C., Campos J., Volant P. and Verdugo R.,
Site effect evaluation in the Basin of Santiago deChile using ambient noise measurements,
Geophys. J. Int., 176, 2009, pp. 925-937.
[6] Johansson J.A.T., Mahecha E.A.L., Acosta A.T.T.
and Arellamo J.P.M.: H/V Microtremor
Measurements in Pisco, Peru after the 2007
August 15 Earthquake, The 14th World
Conference on Earthquake Engineering, Beijing,
China, (2008).
[7] Nogoshi, M. and Igarashi, T.: On the amplitude
characteristics of microtremor, Part 2, J. Seism.
Soc. Japan, 24, 1971, pp. 26-40.

CONCLUSIONS
Development of base-rock elevation is one of themost important data or information needs for seismic
hazard analysis and site-specific ground motion
analysis. Base-rock elevation can easily be predictedusing single and array microtremor or seismometer
investigation.
Followingthemicrotremor
investigation, in terms of base-rock elevation, thestudy area is divided into two different sub-area, thefirst area having a maximum of 30 m and the second
area is predicted having a maximum of 60 m baserock elevation.
319

GEOMATE – Melbourne, Australia, 11-13 November 2020

[8] Nakamura Y.: A Method for Dynamic
Characteristic Estimation of Subsurface Using
Microtremor on The Ground Surface, Quarterly
Report of Railway Technical Research Institute,vol. 30, 1989, pp. 25-33.
[9] Sun-Woo M., Palanidoss S., Yunhoa Z.,
Ganapathiraman V. and Taeseu K.: Bedrock depth
evaluation using microtremor measurement in
Singapore: empirical guidelines at weathered
granite formation in Singapore, Journal ofApplied Geophysics, 171, 2019, 103866, pp. 1-9.
[10] John W. L., Gregory V S., Eric A. W. and James
S. C.: Estimation of Bedrock Depth Using the
Horizontal-to-Vertical (H/V) Ambient-Noise
Seismic Method, Symposium on the[13] Koesuma S., Ridwan M., Nugraha A. D.,

Widiyantoro S. and Fukuda Y.: Preliminary
Estimation of Engineering Bedrock Depths
from Microtremor Array Measurements in
Solo, Central Java, Indonesia, Journal of
Mathematical and Fundamental Sciences,
Vol 49, No 3, 2017[14] Chun H. K., Chun-Te C., Che-Min L., KuoLiang W., Jyun-Yan H., Shun-Chiang C., S-wave
velocity structure and site effect parameters
derived from microtremor arrays in the Western
Plain of Taiwan, Journal of Asian Earth Sciences,Vol. 128, 2016, pp. 27-41.
[15] Ibs-von Seht, M. and Wohlenberg, J.:
Microtremors measurements used to map
thickness of soft soil sediments, Bulletin of the
Seismological Society of America, v.89, 1999,
pp.250-259.
[16] Parolai, S., Bormann, P., and Milkert, C.: New
relationships between Vs, thickness of sediments,
and resonance frequency calculated by the H/Vratio seismic noise for Cologne Area (Germany),
Bulletin of the Seismological Society of America,
v.92, 2002, pp. 2521-2527.

Application of Geophysics to Engineeringand Environmental Problems, September
2008,[11] Sunaryo, Harti Umbu Mala, and Anom Prasetio,
Earthquake Microzonation Study on Batubesi
Dam Nuha, East Luwu, South Sulawesi,
Indonesia, International Journal of Geomate, Vol
15, Issue 48, 2018, pp. 148 – 154.
[12] Yamanaka M., Hachimura T. and Hasegawa S.,
Distribution of Landfill by GeophysicalExploration Methods at Illegal Industrial Wastes
Disposal Site, International Journal of Geomate,Vol 9 (17), 2015, pp. 1342-1347.

320

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

OUTFLOW CHARACTERISTICS OF FINE PARTICLES IN TWOKINDS OF WATER-PASSING EXPERIMENTS USING A COLUMN
Tomoki Wakamatsu1, Motoyuki Suzuki1 and Taichi Ishimaru1
1
Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan
ABSTRACTIn embankment structures affected by seepage flow over a long period, such as river levees and reservoir
dams, soil particles flow into the voids and flow out of the embankments. The phenomenon whereby fine soil
particles move to the interior of embankments is called “internal erosion”. This progressive phenomenon in which
only the fine soil particles flow out, while the skeleton of the coarse soil particles remains, is called “suffusion”.
Suffusion causes a reduction in the strength of the embankment materials. Embankments affected by suffusion
may collapse due to heavy rain and earthquake events. However, the mechanism of suffusion remains unclarified.
In this study, two kinds of water-passing experiments were performed with our developed cylindrical column
apparatus in order to examine the progression and the continuity of suffusion, and the flow rate, turbidity of thedrainage water and mass of the discharged soil were continuously measured. Some experiments have been
performed in previous studies to reproduce suffusion artificially, but it was difficult to continuously collect andanalyze the discharged soil. This is because the amount of discharged soil was low and the particle size was small
in the cases of specimens of a general size. Thus, an attempt was made here to conduct a particle size analysis
using a turbidimeter in order to overcome these problems. As a result, the changes in the discharged soil particle
size could be traced, and it could be clarified that the soil particles with a small particle size were transported to
the lower part of the specimen and that a portion of the particles flowed out.
Keywords: Suffusion, Internal erosion, Embankment structure, Seepage

INTRODUCTION
In embankment structures affected by water
storage, such as riverbanks and reservoirs, sometimes
muddy water leaks from the ground surface. Whenthe leak is turbid, there is a risk of collapse, which isone of the important items to check during
inspections. As shown in Fig. 1, it is possible for thefine particles to flow out of an embankment while theskeleton of coarse soil particles remains. Thisphenomenon is called “suffusion” and it may reduce
the strength of the embankment materials used as theground material. Recent studies have revealed thatsuffusion also causes a decrease in the groundbearing capacity [1], [2]. In other words, if suffusion
continues, the stability of the embankment structure
will be lost over time. However, the process andmechanism of suffusion, as well as its effect on an
embankment’s stability, are unclear.
In this study, two types of downward waterpassing experiments, involving the discharge of fineparticles, were carried out with a cylindrical column
to examine the progress and continuity of suffusion.
In previous research, the discharged soil particles
were so few that a continuous analysis was difficult.
In this research, an attempt was made to analyze theparticle size of the discharged soil by measuring theturbidity of the wastewater as one method to solve theproblems. Thus, the progress of suffusion wasconsidered from the viewpoint of particle size.

Embankment

Seepage flow

Suffusion

Fine particles

Fig. 1

Image of suffusion

PARTICLE SIZE ANALYSIS USING A
TURBIDIMETER
It is difficult to continuously collect and analyze
discharged soil particles. This is because, in anexperiment, they are too small and too few in number
to reproduce suffusion. In this study, a particle size
analysis of the soil particles discharged in eachexperiment was performed using a turbidimeter. The
321

Turbidimeter information and features
The turbidimeter used in this study is turbidimeter
TR-55 manufactured by Kasahara Chemical Co. The
principle of turbidity measurement is the transmitted
and scattered light measurement method. The
transmitted light and scattered light of parallel lightfrom an LED light source are measured at the sametime, and the turbidity is converted from the ratio. The
feature of this method is that the scattering intensity
increases as the total surface area of suspended
substances in a unit volume increases. Thus, theturbidity value increases as the particle size of thesuspended substances decreases [3]. This feature of
turbidity was applied to the experiments.

1100
1000
900
800
700
600
500
400
300
200
100
0
0.0

Turbidity (degree)

particle size of the discharged soil was estimated bycomparing the turbidity of the pre-preparedcalibration suspensions with the turbidity of thedrainage in the experiment.

Fig. 2

Drawing reference lines
First, four calibration samples were prepared. The
samples were taken from Ube Masado soil using foursieves, with mesh sizes of 75 µm, 106 µm, 250 µm,
and 420 µm, and named C75, C106, C250, and C420,
respectively. Then, 80 different suspensions were
made, and their turbidity was measured. The 80
suspensions differed in calibration samples (fourtypes: C75, C106, C250, and C420), 𝑆𝑆 (five types:
0.2, 0.6, 1.0, 2.0, and 3.0 g/L), and volume (fourtypes: 0.05, 0.25, 0.50, and 2.5 L). Figure 2 shows therelationship between the turbidity of the suspensions
and concentration 𝑆𝑆 . Concentration 𝑆𝑆 is defined
here as
𝑆𝑆 =

𝑚𝑑𝑠
𝑉𝑑𝑤

(1)

where 𝑚𝑑𝑠 is the dry mass of the suspended soil

Reference line
1

Concentration

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Relationship between
turbidity of suspensions

concentration

and

100
Physical properties

Percentage finer by weight (%)

10 ml of sample water was collected from theagitated drainage and transferred to a measurement
cell using a pipette. After the bubbles on the wall of
the measurement cell were removed by shaking, thecell was set in the turbidimeter. After waiting about
10 seconds for the sample water to be stable, theturbidity was measured. The measured water was not
returned to the used suspension; the samemeasurement was performed four times using othermeasurement cells. At the start and end of eachexperiment, the turbidity of the purified water (usedas a control) and the tap water was measured in thesame way; the turbidity remained below 0.02 degrees
and 2 degrees, respectively. The turbidity of the water
was negligible.

Suspension
A
Rt

75μm
106μm
250μm
420μm

Turbidity ratioat the same
concentration

Concentration (g/L)

90

Procedure for turbidity measurement

Turbidity

GEOMATE – Melbourne, Australia, 11-13 November 2020

80
70
60

Sample ID

Fs-5

Fs-10

Fines content

(%)

5.0

10.0

Specific gravity

𝑠

2.625

2.625

Maximum void ratio

1.018

0.987

Minimum void ratio

0.664

0.574

Gap ratio

3.96

3.96

50
40
30

Ube Masado
Fs-5
Fs-10

20
10
0
0.001

0.01

0.1

1

10

100

Particle size (mm)Fig. 3

Physical properties
distribution curves

andparticlesize

particles and 𝑉𝑑𝑤 is the volume of water.
According to Fig. 2, the concentration andturbidity of the calibration samples showed a linear
relationship when the types of samples were the same.
Furthermore, the slope of the best fitting line became
smaller as the size of the soil particles contained in
the suspensions became larger. There was no effecton the turbidity measurement due to the differencesin the volume of the suspensions. Here, each best
fitting line is called the “reference line”. The
schematic diagram in Fig. 2 shows the ratio of theturbidity of suspension A to the turbidity on thereference line of the same concentration when therelationship between the concentration of this certain
suspension A and the turbidity is plotted. This ratio
was defined as the “Turbidity ratio at the same
concentration 𝑅𝑡 ” and used in the experiment as aparameter for the outflowing soil particles. The larger
the turbidity ratio at the same concentration, the larger
the distribution of smaller particle sizes than thecalibration samples.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

EXPERIMENTAL MATERIAL
The soil samples used in this study contained
decomposed granite soil, called “Ube Masado”,
because this type of soil is often used in reservoir
embankments. The “Ube Masado” was collected in
Yamaguchi, Japan. Figure 3 shows the particle size
distribution and the physical properties of the soil.
The Ube Masado was divided into four groups ofmore than 2 mm, 420 µm~2 mm, 106 µm~420 µm,
and less than 106 µm by sieving, and then mixed tohave the soil particle distribution shown in Fig. 3. The
two types of prepared soils were named “fs-5” (Finesand: 5%) and “ fs-10 ” (Fine sand: 10%). In thisstudy, the soil particles with a particle size of 420 µm
or more are called “coarse particles”, while those witha particle size of 106 µm or less are called “fineparticles”. Here, the percent of fine particles is the“fines content”. The particle size distribution in Fig.
3 implies that the coarse particles form a skeleton,
while the fine particles are present in the voids.
The stability of the internal erosion of this sample
was evaluated with the index proposed by Chang andZhang [4]. The many stability indexes related tointernal erosion were rearranged. Soil with an absent
portion of the particle size distribution, such as withthese samples, is called a “gap-graded soil”. It is said
that the internal stability of gap-graded soils withfines contents less than 10% may be dominated by the
gap ratio ( ). The
is defined as the ratio of the
maximum particle size, 𝑑, to the minimum
particle size, 𝑑 , of the severely under-represented
portion on the particle size distribution curve:
=

𝑑𝑚𝑎𝑥
𝑑𝑚𝑖𝑛

(2)

When the condition of ≦ 10% is satisfied, “ <3”is determined to be internally stable and “ >3” isdetermined to be internally unstable. All samples
used in this study satisfied ≦10% and the gap ratio
was 3.96 ( 𝑑=106 µm and 𝑑=420 µm).
Therefore, it can be said that the particle size
distribution is internally stable and that internal
erosion is likely to occur.
EXPERIMENTAL METHOD
Experimental Apparatus
Fig. 4 presents a schematic diagram of theexperimental apparatus used in this study. It was
produced by referring to the experimental apparatus
designed by [5]. At the bottom, where each specimen
was located, an acrylic plate, with 108 holes eachhaving a diameter of 5 mm, and mesh with a diameterof 420 µm, were installed. These parts allowed only
fine particles, smaller than the size of the sieve, toFig. 4

Schematic diagram of apparatus used in thisstudy

flow out of the specimen under the application ofwater flow. In addition, soil particles were easily
discharged by providing a funnel-shaped depression
in the bottom of the specimen.
Experimental Procedure
Two kinds of water-passing experiments wereconducted here. One was performed while
maintaining a constant water level; this experimentand its process are called the “constant water-passing
experiment” and the “constant water-passing
process”, respectively. The other was performed
under the conditions of repeated supplying anddraining; this experiment and its process are called
the “repeating water-passing experiment” and the“repeating water-passing process”, respectively.
Constant water-passing experiment
Specimens were prepared by tamping every four
layers of soil samples in order to adjust the watercontent to 10%. The target gap ratio was 0.85 or 0.80.
Before the experiments, water seeped into thespecimen until it permeated the surface. At this time,in order to prevent soil particles from flowing out,water was flushed from the surface of the porous
stone side by turning the specimen upside down.
After placing the specimen upside down andinstalling it in the apparatus, water was poured onto
the top of the specimen. The water surface position
was adjusted such that the hydraulic gradient was
1.125. At the start of the test, the specimen saturation
was about 70%.
When the valve at the bottom is open, water
permeation starts. After each beaker had filled to323

GEOMATE – Melbourne, Australia, 11-13 November 2020

about 100 ml, following the start of water permeation,
the beakers were collected and numbered from 1~14.
During the experiment, the sampling time for eachbeaker was measured. After each experiment, theamount of drainage and the turbidity of the drainage
were measured. Then, the mass of the discharged soil
particles was measured after being oven dried.
Repeating water-passing experiment
The repeating of the water-passing experiment
was continued using the same specimen as for thecases of fs-5_0.85 (2), (3), (4) and (5) in the constantwater-passing experiment. The repeating of thewater-passing process is shown in Fig. 5. When theexperiment was completed, the water supply onto the
top of the specimen was stopped. Simultaneously, thewater was drained from the bottom of it. In Pattern 1,
the lower valve was closed 5 minutes after stopping
the water supply. In Pattern 3, the lower valve wasclosed 24 hours after stopping it. After that, thespecimen was divided into six layers. On the other
hand, in Pattern 2, 500 ml of water was supplied onto
the upper surface of the specimen 5 minutes after
stopping the water supply. In Pattern 4, 500 ml ofwater was supplied onto the upper surface of thespecimen 24 hours after stopping it. In both patterns,
this process was repeated nine times. Finally, thelower valve was closed 5 minutes after the last supplyof water in Pattern 2 and 24 hours after it in Pattern 4.
After the lower valve was closed, the specimen brokeapart in 5 minutes in Pattern 2 and in 24 hours in
Pattern 4. The specimen was divided into six layers in
the height direction, and the water content for eachlayer was measured (Fig. 6). Each layer was washedwith water on a 106 µm sieve, and the residue wasoven dried. The mass of the residue was measured to
calculate the fines content. Furthermore, the soil
particles of the passing portion were collected
together with water, and 𝑅𝑡 was calculated bymeasuring the turbidity of the suspensions.

Water
level
drop

Repeatingwaterpassing
process

Pattern 1(fs-5_14_0.85(2)）

〇✕Breaking apartafter 5 min.

Pattern 2(fs-5_14_0.85(3)）

〇9 times watersupply every 5
min.

Breaking apartafter 5 min.

Pattern 3(fs-5_14_0.85(4)）

〇✕Breaking apartafter 24 hours.

Pattern 4(fs-5_14_0.85(5)）

〇9 times watersupply every 24
hour.

Breaking apartafter 24 hours.

Fig. 5

Flowchart ofexperimentrepeatingBreaking
apart ofspecimenwater-passing

Upper

6
5
4
3
2
1
Lower
Soil particles with 106µm or more•Amount of soil particles
measured••

Turbidity measured
Amount of soil
particles measured

Suspension containing soil particles
with 106µm or lessFig. 6

Breaking apart of specimen after theexperiments

18

Total amount of discharged soils (g)EXPERIMENTAL RESULTS
Results of constant water-passing experiments
Amount of discharged soil particles
Fig. 7 shows the relationship between the average
flow rate and the total amount of discharged soil
particles. Experimental IDs mean {sample ID_Initial
void ratio_(Number)}. Even if the experimental
conditions are the same, the average flow rates will
be different. Initial saturations were around 70%, and
it is possible that water preferentially flows throughthe parts with high saturation. It is thought that thedifferences in the flow rates were affected during theinitial saturation. In both fs-5 and fs-10, the higher the
average flow rate, the higher the total amount of

fs-5_0.85
fs-10_0.85
fs-5_0.80
fs-10_0.80

16
14
12
10
8
6
4
4

Fig. 7

324

6

8
10
12
Average flow rate (cm3/s)

14

16

Relationship between average flow rate andtotal amount of discharged soil particles

GEOMATE – Melbourne, Australia, 11-13 November 2020

7
fs-5_0.85
fs-5_0.85(2)fs-5_0.85(3)fs-5_0.85(4)fs-5_0.85(5)fs-10_0.85
fs-5_0.80
fs-10_0.80

Enlarged view from beaker No.5

6
Amount of discharged soils (g)

0.5

5
4
3
2

0.4

0.3

0.2

0.1

0.0
5

1
0

Fig. 8

Turbidity ratio at the same concentration

1

2

3

4

5

6

7

6 7 8
Beaker No.

8

9

9

10

11

12

13

14

10 11 12 13 14

Amount of discharged soil particles for each beaker(constant water-passing experiments)
4

Turbidity ratio at the same concentration Rt

Amount of discharged soils (g)discharged soil particles. In addition, the total
amount of discharged soil particles in the caseof fs-5 was greater than that in the case of fs10. Fig. 8 shows the discharge of soil particles
for each beaker in the constant water-passing
experiments. In all cases, the amount ofdischarged soil gradually decreased when thewater flow increased. In the five cases of fs5_0.85, the amount of discharged soil
particles became the maximum at the time ofbeaker No. 2. It is thought this is because thewater which had collected in the depressionof the lower plate flows out first. When thewater flow exceeded 600 ml (beaker No. 3),
the amount of discharged soil particles per
100 ml decreased to 0.2 g or less.

Fig. 9 shows the 𝑅𝑡 for each beaker. C106 wasselected as the reference line. The original particle
size distribution of the fines in the specimen is thesame as that for C106. If the fine particles aredischarged from the specimens with the same asoriginal particle size, 𝑅𝑡 should be about 1. The 𝑅𝑡 of
beaker 1 is about 1.7 to 2.5 in all cases. It is supposedthat many particles with a particle size of 106 µm orless were discharged from the specimens. Here, forthe brief explanation given below, the relatively small
particles among the fine particles are expressed as“clay”. In all cases, there is a tendency for 𝑅𝑡 to
increase to beaker No. 4-6 (water flow is about 400
ml to 600 ml) and then to decrease. It is thought that
400 to 600 ml of the clay is preferentially discharged
from the specimens, and thereafter, less clay isdischarged.

fs-5_0.85
fs-5_0.85(2)fs-5_0.85(3)fs-5_0.85(4)fs-5_0.85(5)fs-10_0.85
fs-5_0.80
fs-10_0.80

3

2

1

0

1

Fig. 9234

5

6 7 8 9 10 11 12 13 14
Beaker No.

𝑹𝒕 for each beaker (constant water-passing
experiments)

Cumulative amount of discharged soils (g)35

Repeating Water-Passing Experiments
Amount of discharged soils
Fig. 10 shows the discharge of soil particles fromeach beaker in the repeating water-passing
experiment. Although the discharge of soil particles
almost stopped during the constant water-passing
process, soil particles began to be discharged again
during the repeating water-passing process in the caseof Patterns 2 and 4.

25

Repeatingwater-passing

Dropped water level
Constant water-passing

20
15

Pattern 1
Pattern 2
Pattern 3
Pattern 4

10
50

Turbidity ratio at the same concentration
Fig. 11 shows the 𝑅𝑡 for each beaker in therepeating water-passing experiments. In Patterns 2
and 4, 𝑅𝑡 increased rapidly to about 2.5 after the firstsupply of water. Even after the first supply of water,
𝑅𝑡 remained at 2 or more. These values for 𝑅𝑡 weresimilar to the peak value for 𝑅𝑡 during the constantwater-passing process. In the experiments, the water
flowed under unsaturated conditions, so that, in the

30

2

4

6

8

10 12 14
Beaker No.

16

18

20

22

24

Fig. 10 Cumulative amount of discharged soil
particles for each beaker (repeating waterpassing experiments)
constant water-passing process, it is thought that thewater first flowed through the part where the initialsaturation had been higher. It is supposed that theroute where the water flow changed with eachpassage of water during the repeating water-passing
process caused the erosion of the part that was not
325

Breaking apart of specimen after the experiments
Fig. 12 shows vertical distributions of the fines
content and the rate of change in 𝑅𝑡 when the 𝑅𝑡 of
the uppermost layer was 1, for the specimen divided
after the experiment. The fines contents of Patterns 1
and 3 were below the initial fines contents of the alllayers. On the other hand, the fines contents ofPatterns 2 and 4 were larger from the uppermost layer
to the lowermost layer. It is thought that the fineparticles moved from the upper layer to the lower
layer, and that clogging occurred around the lower
part. The rates of 𝑅𝑡 in Patterns 2 and 4 became larger
from the uppermost layer to the lowermost layer. It isbelieved that the clay predominantly moved from theupper layer to the lower layer.

2.

3.

REFERENCES[1] Sato, M. and Kuwano, R., Laboratory testing forevaluation of the influence of a small degree ofinternal erosion on deformation and stiffness,

2

Pattern 1
Pattern 2
Pattern 3
Pattern 4

1

1

Fig. 11

3

5

7

9

11 13 15 17 19 21 23
Beaker No.

𝑹𝒕 for each beaker (repeating waterpassing experiments)
Pattern 1
Pattern 3

In this study, two types of water–passing
experiments were conducted to examine the progressand continuity of suffusion. The main results of thispaper are summarized as follows:
In the water-passing experiments under constantwater level conditions, more clay wasdischarged from the fine particles in thespecimen, and the clay was discharged first.
In the water-passing experiments under constantwater level conditions, the discharge of soil
particles almost stopped when the water flow
was about 400 ml to 600 ml. After that, thedischarge of soil particles restarted when thewater flow condition was repeated. The soil
particles flowing out of the newly eroded partcontained a large amount of clay that had not
flowed out during the constant water level flow
process.
Due to repeating the water-passing process, theclay content in the upper part of the specimen
moved to the lower part. In addition, since fineparticles in the lower part of the specimen werehigher than those at the initial stage, it waspossible that retention had occurred near themesh.

Dropped water level

30

CONCLUSIONS

1.

4

X layers from bottom of specimen

subjected to an outstanding flow or the movement ofsoil particles. Furthermore, the fact that 𝑅𝑡 increased
during the repeating water-passing process is thought
to have contributed to the fact that erosion occurredin new areas where clay had remained.

Turbidity ratio at the same concentration Rt

GEOMATE – Melbourne, Australia, 11-13 November 2020

Pattern 2
Pattern 4

6
5
4
3
2
1

1.0
1.5
2 3 4 5 6
Fines content Rate of change in Rt(%)(%)

Fig. 12 Fines content and rate of change in 𝑹𝒕 foreach layer
Soils and Foundations, Vol. 58, Issue 3, 2018, pp.
547-562.
[2] Ke, L. and Takahashi, A., Experimental
investigations on suffusion characteristics and its
mechanicalconsequencesonsaturatedcohesionless soil, Soils and Foundations, Vol. 54,
Issue 4, 2014, pp. 713-730.
[3] Yokoyama, K., The influence of particle size on
turbidity output and the instructions for using
turbidity sensor in the field, Journal of JSCE, Vol.
698, 2002, pp. 93-98 (in Japanese) .
[4] Chang, D. S. and Zhang, L. M.: Extended
internal stability criteria for soils under seepage,
Soils and Foundations, Vol. 53, Issue 4, 2013, pp.
569-583.
[5] Ke, L. and Takahashi, A., Triaxial erosion testfor evaluation of mechanical consequences ofinternal erosion, Geotechnical Testing Journal
Vol. 37, Issue 2, 2014, pp. 347-364.
326

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

APPLICATION OF COMPUTER VISION TECHNIQUE IN RAPID SOIL
CLASSIFICATION
Aw, Y.J.E.1, Koh, J.W.1, Chew, S.H.1, Tan, S.E.D.2, Cheng, L.M.2, Yim, H.M.A.2, Ang, L.J.L.2 and Chua,
K.E.2
1Faculty of Engineering, National University of Singapore, Singapore; 2Building & Infrastructure Group,
Housing & Development Board, Singapore
ABSTRACT
Singapore’s Staging Grounds (SGs) receive hundreds of truckloads of excavated soil from construction sites
daily and barge it to land reclamation sites as infill material. The excavated soil needs to be classified into two
broad types (i.e. “Good Earth” and “Soft Clay”), according to particle size distribution (PSD) and water content(w), for appropriate use and treatment before and after infilling. However, conventional laboratory tests for PSD
and w pose time and manpower constraints at SGs. Thus, computer vision technique is adopted for non-destructive,
non-intrusive and instantaneous classification of excavated soils upon delivery to SGs. A 4-stage procedure of
rapid soil classification with computer vision was established: (1) “soil image acquisition”, (2) “image processing”,(3) “image analysis”, and (4) “decision making”. A study was conducted with 40 soil images and samples collectedfrom SGs. Five parameters of Grey Level Co-occurrence Matrix (GLCM) were obtained for each image. PSD and
w of each sample were determined from conventional laboratory tests. 32 soil samples formed the training set toestablish an Artificial Neural Network (ANN) model using the back-propagation network, correlating GLCM
parameters with PSD and w results. The remaining 8 samples were used as the test set. This trained ANN model
is found to be able to differentiate the soil images in less than a minute, and thus able to classify soils into “Good
Earth” or “Soft Clay” with accuracy as high as 82.5%. This computer vision method will enable the SGs to achievemuch higher productivity with good accuracy in terms of classifying soil received.
Keywords: Computer vision technique, Soil classification, Rapid and non-destructive method, ANN, Training andtesting sets

INTRODUCTION
Singapore’s staging grounds (SGs) serve the dual
purpose of receiving excavated soil from theconstruction industry and supplying land reclamation
projects with a useful construction resource (Fig. 1).
Trucks transport unwanted excavated soil fromconstruction sites to staging grounds located along thecoast of Singapore. This excavated soil is loaded intobarges and is then transported to various land
reclamation sites for use as an infill material. It is
important to ascertain the quality and type of the soil
received at the staging grounds so that appropriate
land reclamation and soil treatment methods can beused. With hundreds of trucks passing through thestaging grounds daily, staging ground operators havebegun looking into ways to optimize operations andimprove the quality of checks on site.

Fig. 1

Transported by trucks to staging ground(left), then loaded onto barges fortransportation offshore (right)

According to the staging grounds’ classification
criteria, “Good Earth” soils must contain at least 65%
by weight of coarse particles (> 63μm) and have awater content of less than 40%. Soils classified as“Good Earth” generally have a higher percentage ofcoarse particles and relatively low water content,
making it a suitable material for constructing
revetment bunds. It also contains some percentage offine particles that makes it ideal for compaction – astandard and less costly ground improvement
technique – as the topmost layer of reclaimed land
above sea level.
On the other hand, “Soft Clay” soils have a higher
percentage of fine particles, or a high water content,
or both. These soils tend to have slow gain in shear
strength and large settlement over time, and hence

Definition of Classification Classes
Despite originating from different geologicalformations in various parts of Singapore, excavatedsoils received at SGs can be broadly classified into
two groups – “Good Earth” and “Soft Clay” – based
primarily on PSD and water content.

327

GEOMATE – Melbourne, Australia, 11-13 November 2020

require more costly ground improvement techniques,
such as vacuum consolidation, in order to beeffectively used as the bulk of infill material below
sea level.

an object or scene can be automatically extracted andanalyzed from an observed image, image set, or
image sequence [3]. Designed to replace human effort
in the performance of a given process, a typical
process in a computer vision involves: (1) image
acquisition; (2) image processing; (3) image analysis;and (4) decision-making. Some examples are listed inTable 1.

Current Classification Methods
The Housing & Development Board (HDB)
manages Singapore’s two largest staging groundswith the highest daily throughput. Their current
practice is to generally classify excavated soil byconstruction site, using information from the boreholelogs. At the SGs, the weigh-bridge operator does aquick inspection of the soil type in each truck via a
CCTV (closed-circuit television) camera – a “Good
Earth” soil may then be “reclassified” to “Soft Clay”
if the soil visibly has high water content i.e. is in aslurry state.
However, the current practice fails to consider
Singapore’s highly varying geology. Large volumesof “Good Earth” may be misclassified as “Soft Clay”,
such that coarser excavated soil that could have beenused for revetment bunds would be sub-optimallyused as infill material. Unnecessary costs may also be
incurred from using more expensive ground
improvement techniques for misclassified “Soft Clay”
soil.
This presents an opportunity for introducing an
additional layer of quality control at the SGs.
Performing this classification at the receiving point(i.e. at SGs) avoids the over-generalization apparent
in the current practice. This enhanced ability to carryout on-site classification of the excavated soils
received will translate into cost-savings in terms of
maximal recovery of “Good Earth” material andreduction in ground improvement costs.
The introduction of new methods in the field willneed to take into account the operational
considerations of the SGs. Any improvements to theoperations cannot compromise quality and efficiencyof the existing staging ground system that has been
optimized to cope with the high daily throughput oftrucks. Such requirements render traditional soil
investigation methods unsuitable. Testing methods todetermine in-situ water content and soil index
properties typically takes 2 to 3 days and require
samples of the soil to be tested in highly controlled
conditions. Rapid, non-destructive methods using
highly specialized spectrometers are costly to
implement for the classification of large volumes
daily. Therefore, this paper will focus on thefeasibility study of using computer vision in soil
classification on-site.

Table 1 Examples of computer vision applications
and their abilities
ExampleAbility
Medical Images Isolation and visualemphasis of a specificregion of interest

Food Images Detection of surface
properties (e.g.
irregularities) for nondestructive inspection
and testing
Satellite Images Image segregation
based on image
properties (e.g. color,
image texture)
Image Color
The most commonly used format is the byte image(each pixel brightness value stored as 8-bit integer) ofvalue between 0 and 255. This is similar to thewidely-used Red-Green-Blue (RGB) color model
which was used in many research.
A study by Chung et al. [4] demonstrated thatimage color, particularly RGB, can be used todevelop a soil texture classification algorithm.
However, in this study, preliminary trials using RGB
as input values showed that classification accuracy
was relatively low (48%). It may be highly probablethat it is not possible to establish clear color groups
with distinct soil properties, as the excavated soil usedin this study are not homogeneous in color, especiallyin residual soils that contain a mixture of colors but
have virtually indistinguishable soil properties.
Hence, an alternative scheme is needed for this study.

Computer Vision
Computer vision, also known as machine vision,is the science that develops the theoretical andalgorithmic basis by which useful information about
328

GEOMATE – Melbourne, Australia, 11-13 November 2020

accordance to BS 1377:1990) were conducted on the
40 samples retrieved from one of HDB’s staging
grounds. The soil images were captured by Nikon
D5300 digital single-lens reflex cameras mounted on
the roof of one of the weigh-bridge stations. The
cameras were set with aperture f/22.0 and ISO 100;
largest image size of 6000 x 4000 pixels were chosen
and images were stored in .jpeg format.

Image Texture
In soil investigation, soil texture refers to how thesoil feels to human touch (e.g. sandy, clayey, granular,fine etc.). However, image texture in computer vision,in terms such as fineness, coarseness, smoothness,and graininess are determined by the spatial
arrangement of pixel brightness values. For irregular
textures – such as that of excavated soils – it is mostideal to use statistical analysis methods.
One of the most popular methods are cooccurrence features obtained from a grey level cooccurrence matrix (GLCM) [3]. Conners and Harlow[5] found that GLCM is best in discriminating terrain
types from aerial photographs. Given that it possesses
the highest accuracy among statistical methods andhas been extensively used in other fields, GLCM is
adopted for this study.
In the GLCM method, 14 Haralick texturalfeatures can be obtained [3], and are listed in Table 2.

Fig. 2 Full-sized image (left) with a ROI of 1000 x1000 pixels cropped for analysis (right)
ImageJ, an open-source Java image processing
program developed by an international group ofcontributors from various institutions [6], was used inthis study. Due to constraints of truck sizes, a regionof-interest (ROI) of 1000 x 1000 pixels was cropped
from each image from any selected location (Fig. 2).
3D Color Inspector plugin was run on each ROI of the
40 samples (i.e. images), and the images were thenconverted to 8-bit greyscale using a weighted averageof the R, G, and B values. The GLCM Texture plugin
was used to calculate the five commonly used GLCM
Haralick textural features (ASM, CON, COR, ENT,
IDM).

Table 2 Grouping of 14 Haralick textura features
Contrast
Group

OrderlinessGroup

Descriptive
Statistics ofthe GLCM*Contrast (CON)*Inverse Difference Moment (IDM)
Dissimilarity*Angular Second Moment (ASM)
Energy*Entropy (ENT)
Maximum Probability
GLCM Mean
GLCM Variance / Standard
Deviation*Correlation (COR)

ANN Structure, Parameters and Training* The five most commonly used textural features considered in thisstudy.

The ANN models used were developed using
NeuralWorks Professional II/Plus, a software by
NeuralWare, Carnegie, USA [7]. All models wereexperimented using back-propagation networks(BPN) in supervised mode. Generalized delta rule
(epoch = 1) was adopted during learning and thesigmoid function was specified as the transfer
function. Learning coefficient values and momentum
were set to recommended values by the developer
NeuralWare [8].
40 samples collected were split (80-20 rule) into a“seen” data-set of 32 samples for training, and an“unseen” data-set of 8 samples to test the trained
network. Input parameters using three possible image
color (i.e. channel values of Red, Green and Blue)
fared poorly in preliminary trials and were hence
disregarded. Input parameters using five main image
texture feature values (i.e. ASM, CON, COR, ENT,
IDM) were employed instead. Individually, CON and
COR achieves a higher classification accuracy andhence a combination of CON and COR will be trialed
along with the use of all five parameters.
In determining the number of outputs, two general

Scaling of Water Content, w, by Liquid Limit
When classifying samples into water content (w)of “more than 40%” or “less than 40%” based on
HDB’s classification criteria, it was found that very
few samples fall into the latter group. It is not
appropriate to consider w alone as w value at the timeof inspection may not represent the true nature of soils.
Under non-raining weather conditions, most soils
would have been exposed to the atmosphere atstockpile pits and subjected to drying. Moreover, theappearance and behavior of soil fundamentallydepends on w relative to the Atterberg Limits (Plastic
Limit (PL) and Liquid Limit (LL)). Hence, w/LL wasselected to be used in place of w.
METHODOLOGY
Sampling of Data
Conventional soil index properties tests (in329GEOMATE – Melbourne, Australia, 11-13 November 2020grouping methods were explored. The first was a “2group” method that consists of two soil groups as
output: “Good Earth” and “Soft Clay”. The second
was a “3-group” method that consists of three soil
groups as output: “Good Earth”, “Soft Clay-A” and“Soft Clay-B”. The latter subdivided the “Soft Clay”
soils further based on the Percentage of Fines.

Table 3 Classification accuracy of the first roundTexturalFeatures
Soil
Property
Hiddenlayers2-group
3-groupRESULTS AND DISCUSSION
Soil Index Properties Tests
For the 40 samples tested, amongst those samples
that were classified as “Good Earth” by the current
practice, the Percentage of Fines ranged from 20.5%
to 72.0% (averaged 50.1%), with w ranging from
9.3% to 40.5% (averaged 20.6%). Those samples thatwere classified as “Soft Clay” by the current practice
had the Percentage of Fines ranged from 16.0% to
77.0% (averaged 44.7%), with w ranging from 11.4%
to 40.6% (averaged 24.3%) excluding an outlier of
83.5%. No distinguishable differences on thepercentage of fines between the two soil types
classified according to the current practice wereobserved.
HDB specifies that soils that contain more than
35% of fine particles would be considered “SoftClay”. This threshold not only concurs with thestandards in the British Soil Classification System [9],but can also be supported by a study on the effects offines content on some engineering properties of soil[10]. This study shows that the California Bearing
Ratio (CBR) – an indicator of the mechanical strength– of the soils tested decreased with increasing fines
content. It is of interest to note that the CBR decreases
rapidly when the percentage of fines reaches 35-40%,and stabilizes afterwards.

5: ASM, CON, COR, IDM, ENT
Percentage of
Fines

w/LL

1

2

1

2

87.5%
75.0%

87.5%
75.0%

87.5%
37.5%

37.5%
37.5%

The second round of experiments was to refine the
ANN model by reducing input parameters to only two
Haralick textural features (i.e. CON and COR), andpredicting according to only Percentage of Fines. The
results are summarized below in Table 4.
Table 4 Classification accuracy of the second roundTexturalFeatures
Soil
Property
HiddenlayersPercentage of
Fines
1

2

1

2

2-group

100%

87.5%

-

-

3-group

50.0%

50.0%

-

-

2: CON, COR
w/LL

Similar to the first round, classification according
to Percentage of Fines and into 2 groups yieldedhigher accuracy then classifying into 3 groups.
Comparing the results of first round and second
round, it was observed that for classification into 2
groups, classification accuracy increased when only
one hidden layer was used (from 87.5% to 100%), but
remains the same (i.e. 87.5%) when moved fromround 1 to round 2. On the other hand, for the 3-groupclassification, classification accuracy decreased from
75% to 50%, for either 1 or 2 hidden layers, fromround 1 to round 2. Since better classification
accuracy was achieved, the final model will make useof only two textural features (i.e. CON and COR) as
inputs for ANN model, run with one hidden layer andclassifying into 2 groups, based on Percentage ofFines.
In this ANN, the use of two Haralick texturalfeatures gave better prediction results than the use of
five Haralick textural features, possibly becausesome textural parameters within a given group arestrongly correlated (according to study by Hall-Beyer[11]). For examples, CON and IDM both belong in
the Contrast Group, containing the term |i - j|. The
same can be said for ASM and ENT, which bothbelong to the Orderliness Group and have p(i,j) intheir equations. Hence, for classification problems,
Hall-Beyer suggested using at most one parameter

ANN Classification Model
Two rounds of ANN experiments were conducted.
In the first round, the effectiveness of using ANN
models to predict different soil properties was tested.
Five Haralick textural features (ASM, CON, COR,
IDM, ENT) were used according to (1) Percentage ofFines and (2) w/LL. Five hidden layer nodes are used
as recommended [6]. The results are summarized inTable 3.
In general, higher classification accuracy wasobserved for classification according to Percentage ofFines. The results also indicate that using these fiveparameters to classify soils into 2 groups yield higher
accuracy than classifying into 3 groups, regardless ofthe number of hidden layers used in ANN model. It
was also noted that, in the classification according to
w/LL, better accuracy was obtained with 1 hidden
layer and classification into 2 soil groups. Using w/LL,
classification into 3 soil groups or using 2 hidden
layers yielded very poor accuracy.
330

GEOMATE – Melbourne, Australia, 11-13 November 2020

from each group. Results from this study seems toconfirm the findings by Hall-Beyer, i.e. the texture
features of CON and COR combination performs
better than using all five texture features.

The overall classification accuracy of the final
model is 82.5% (33 out of 40 samples). Furthermore,
it seemed to be able to predict better in terms of “SoftClay”, achieving 25/29 = 86.2% accuracy. For “Good
Earth” material, the prediction accuracy using this setof ANN Model is only 8/11 = 61.58%. It is noted thatthe 32 training samples were presented as random,
independent and unseen inputs to the trained ANN
model.
Table 5 Confusion matrix for ANN classification
with the selected ANN model

Actualresults
from PSD
and w

Fig. 3 RMS error vs. number of learning iterations(classification accuracy in brackets)“Good
Earth”“SoftClay”

ANN prediction results
“Good
“Soft
Earth”Clay”
8 √3
True Negative

False Positive

4

25 √

False Negative

True Positive√ indicates the number of samples with the same result “correctlypredicted” by both the ANN as compared to the actual results fromlaboratory tests.

With this ANN configuration, the effect oflearning iterations was studied and results shown in
Fig. 3. From Fig. 3, it can be seen that the model
achieved the highest classification accuracy at and15,000 to 20,000 iterations for training set. The sharp
increase in training root-mean-squared (RMS) error
after 20,000 iterations and subsequent large
fluctuations may be attributed to over-training of the
network. For testing set, iterations of more than
15,000 learning cycle will neither improve nor
worsen the RMS error.
The complete data-set of 40 samples were
presented to the ANN model (as shown in Fig. 4), andthe comparison between the predictions from ANN
and the actual results drawn from PSD and w inphysical tests are shown in Table 5.

In the case of SGs, a false “Good Earth” isarguably worse than a false “Soft Clay” as the former
could lead to a weaker material unknowingly being
used as revetment bunds, whereas the latter could
result in higher costs for more expensive ground
improvement techniques for clayey soils. Therefore,a true “Soft Clay” can be referred to as the True
Positive, while a false “Good Earth” can be treated as
the False Negative. Precision (Eq. 1) and recall (Eq.
2) are calculated as follows:
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 =𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 =𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃+𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃
𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑣𝑣𝑣𝑣+𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁

(1)
(2)

Using the result from all 40 samples, the precision
is calculated to be 25/28 = 0.89 while the recall is
calculated to be 25/29 = 0.86. With both values, theF1 score is calculated as 0.87 using Eq. 3:

𝐹𝐹1 = 2 ×

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 + 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅(3)CONCLUSIONS

Fig. 4

The final ANN model (with 1 hidden layer) isable to achieve a relatively high overall accuracy of
82.5% in classifying soils into two groups (“Good
Earth” and “Soft Clay”) according to Percentage ofFines, using two textural features of CON and COR
as input parameters. It has also achieved a decent
recall value of 0.86. In cases where a high cost is
associated with False Negative (false “Good Earth”),
recall may be a model matric to be used in comparing

Final ANN selected for this research

331

GEOMATE – Melbourne, Australia, 11-13 November 2020

models. The model is also able to “correctly” classify
some soils samples that were previously misclassified
based solely on borehole log data and visualinspection.
This study concluded that using GLCM pixel
values produces better results than using pure RGB
values in terms of ANN modelling. Furthermore,
results from this study seems to confirm the findings
by Hall-Beyer [11], i.e. the texture features of CON
and COR combination performs better than using all
five texture features.
However, several limitations include the low
number of samples available during the period ofstudy as well as only a single type of illuminance.
Further studies will include the direct extension ofcurrent work by collecting more samples for training
and testing, and the introduction of different
illumination conditions to confirm the possibility oferrors due to variation in illuminance. Parallel
repositioning of the camera and light source with thesoil surface will also be considered in future studies.
Further parametric studies can be performed on the
ANN model to identify the most suitable initial values
when the soil database is expanded.
The concept of relating image texture properties
to soil properties using artificial neural networks wasfound to be feasibile and useful in developing a rapid
classification method that is more accurate thanexisting methods. Morever, it is automatable and able
to complete the entire classification process on-sitewithin a minute. This method would in turn allow for
maximal recovery, recycling, and appropriate use andtreatment of excavated materials for land reclamation.
The use of computer vision meant that this quality
control measure is rapid, non-intrusive, andautomatable; it affords rapid classification without
imposing additional human labor and cycle time forthe staging grounds.

Singapore, through the project fund granted to the
National University of Singapore (NUS) for the
HDB-NUS Joint Study Agreement on the Reuse of
Excavated Material for Land Reclamation Works.
REFERENCES[1] R. M. Haralick and L. G. Shapiro, Computerand Robot Vision, 1st ed., Boston: AddisonWesley Longman Publishing Co., Inc. , 1992.
[2] S.-O. Chung, K.-H. Cho, J.-W. Cho, K.-Y. Jungand T. Yamakawa, "Soil texture classification
algorithm using RGB characteristics of soil
images," Faculty of Agriculture, Kyushu
University, vol. 57, no. 2, pp. 393-397, 2012.
[3] R. W. Conners and C. A. Harlow, "Atheoretical comparison of texture algorithms,"
IEEE Transactions on Pattern Analysis andMachine Intelligence, Vols. PAMI-2, no. 3, pp.
204-222, 1980.
[4] ImageJ, "Contributors," 2019. [Online].
Available: https://imagej.net/Contributors.
[5] NeuralWare, NeuralWorks Professional
II/PLUS, 2013.
[6] NeuralWare, "NeuralWorks Professional
II/PLUS: Getting Started (A Tutorial for
Microsoft Windows Computers)," NeuralWare,
Carnegie, 2001.
[7] British Standards Institution, British soil
classification system for engineering purposes,
1981.
[8] A. L. Ayodele, F. F. A and M. O. Ogendengbe,"Effect of fines content on some engineering
properties of lateritic soil in Ile-Ife," Journal ofEngineering Research , vol. 9, no. 32, 2009.
[9] M. Hall-Beyer, "The GLCM Tutorial," 2007.
[Online]. Available:
http://www.fp.ucalgary.ca/mhallbey/.
[Accessed 3 March 2016].

ACKNOWLEDGMENTS
The research was made possible with the supportof the Housing & Development Board (HDB),

332

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

UTILISATION OF RECYCLED MATERIALS IN STABILISATION OF
EXPANSIVE SOILS: A REVIEWJiasheng Zhu1, Mohammad Saberian1, Haiyun Luo1, Junkai Gong1, Manlin Liu1, Gang Ren1 and Jie Li1
1
School of Engineering, RMIT University, Melbourne, Victoria, Australia
ABSTRACTThe large and continuous consumption of natural aggregates is firmly against economic and environmental
sustainability due to the high exploiting costs and transportation costs. On the other hand, due to the growing
amount of waste materials and the running out of landfills, the disposal of waste materials has become anotherglobal issue. In recent years, the construction industry in the world has entered a stage of rapid development. The
occupation of natural resources by concrete as the largest human-made material and the negative impact on theenvironment has also triggered discussions on sustainable development. The amount of waste concrete
demolished in the world every year, waste concrete generated by new construction and waste concrete
discharged by concrete factories and prefabricated component plants, is huge. In addition, the generation of
byproducts and commercial and industrial waste materials is a global concern. Therefore, industries andresearchers have recently focused on the applications of recycled waste materials in construction and civilengineering. This paper provides a review on the utilisation of recycled materials for the improvement ofexpansive soils for geotechnical applications.
Keywords: Expansive soils; Construction and demolition materials; Comercial and industrial waste; Soil
improvement; Sustainability.

biggest issues with swelling clay is consolidation
settlement under long-term load, and another issue isthe considerable volume change of expansive soil
depending on the moisture content, which is related
to climatic conditions. Expansive soil would shrink
dramatically under dry conditions, and expands
significantly with the increase of humidity [5], [6].
The basic mechanism of swelling for clay soil isprimally relative to the amount and type of porespaces and their interaction with water. As a result,the swelling may occur within the early five to eight
years after the completion of the construction, whichcauses the deformation and cracking of the structure
and reduces the bearing capacity and service life of
the structure [7].
Nowadays, one of the major global
environmental problems is the disposal of wastematerials, including construction and demolition(C&D) wastes and commercial and industrial wastematerials. C&D wastes comprise primarily of solid
waste (such as asphalt, concrete, bricks, andplasterboard), metals (such as aluminium and steel),organics (such as timber), glass and other materials[8]. The worldwide generation of solid waste has
recently exceeded 17 billion tonnes per annum andis projected to hit 27 trillion tonnes by 2050 [9]. For
instance, around 9 million tonnes of C&D wastes areproduced in Australia annually that comprise 30.5%of overall wastes generated in Australia [10].
In helping to alleviate this problem, it should beconsidered necessary to recycle and reuse C&D
wastes. Fortunately, solid recycled waste materials

INTRODUCTION
Expansive soil refers to the clay soil that exhibits
considerable volume changes when the suction or
moisture content of the soil changes. Specifically,
the volume change is manifested by expansion underhumidity and shrinkage under drought [1]. The
failure of lightly loaded structures due to the
movement of the expansive soil has been reported
around the world. The problem is particularly acutein Australia, where about 20% of the land is coveredby expansive soil. Most of Australia's capital cities
and regional centres have issues related to expansive
soil [2], [3].
High concentrations of expansive soils have been
identified in Victoria, particularly in the northern
and western regions of Melbourne [2]. The swellingclay soil is the clay with the latter type of
expansibility. The swelling of clay can be classified
into two different types, one is the elastic rebound of
the compressed soil because of the decreasing incompressive force, and another one is the expansion
or contraction of soil caused by water content
change. The most important element to reveal theswelling characteristics of soil is clay minerals [4].
The major minerals of clay in the reactive soil
are montmorillonite, kaolinite, as well as illite [5].
Montmorillonite has the maximum amount ofexpansive potential [4]. The distinctive characteristicof expansive clay is its low strength and cohesion
when the soil is wet. Nevertheless, when the soil isshort of water, it becomes very stiff. One of the333

GEOMATE – Melbourne, Australia, 11-13 November 2020

can be used in a wide variety of civil andconstruction projects such as road pavement,
foundations, and backfilling of underground service
pipelines [11]. Using waste materials in roadpavement and other construction projects canremarkably reduce the consumption of natural
materials in quarries, reduce the disposal of landfills,
and reduce greenhouse gas emissions. Thus,
recycling and reusing waste materials in civilengineering applications, rather than using natural
resources, would significantly reduce the carbon
footprint of the projects, meanwhile improving thedefects of expansive soil [12].
This study aims to review the implications ofdifferent recycled waste materials for the
improvement of unsuitable expansive clays andexplore the influences of the waste materials on thegeotechnical properties and mechanical behavior of
expansive soils.

Table 1 Effects of RCA on the strength propertiesof clay [16].
RCA(%)

Cohesion
(kPa)

0
5
10
15

21.4
23.4
24.7
34.6

Frictionangle(degree)
27.5
29.2
34.4
36.9

UCS
(kPa)
171
205
258
275

USING WASTE GLASS FOR STABILISATION
OF EXPANSIVE SOIL
The manufacturing of glass and the landfilling ofwaste glass has been considered as the main
environmental issues in recent years. Over 300,000
tonnes of recovered glass were stocked in Victoria[18]. The two main products of glass reprocessing
are cullet and glass fines. Cullet is a higher-grade
product that used to produce new glass. However,the proportion of cullet used in glass manufacturing
is limited because of the introduction of impurities.
Glass fines are a lower grade product, mainly used
for civil construction, road construction, concrete
aggregate, building, and road aggregate [19]. The
waste glass is generally accepted as recycledmaterial for civil construction because the silica
content of the glass is high, and the use of glass does
not hurt the environment and human health [20].
[21]-[23] pointed out that the properties of clay
soil obtained an improvement after the addition ofglass powder or fibre. This improvement wasembodied in the increasing of California Bearing
Ratio values, shear strength, and internal friction
angle as well as the reduction in optimum watercontent and plasticity of the stabilised soil. [24]studied the effects of different proportions (2%, 4%,
6% and 8%) of the broken glass on the properties of
expansive soil. The laboratory results showed thatthe mixing of 4% crushed glass with expansive soil
could effectively improve the geotechnicalproperties of the soil. With the increase of theamount of crushed glass, the liquid limit and specificgravity of the composite specimen decreased, andthe plastic limit increased, due to the addition of
broken glass which is a granular material [24].
It was also observed that the bearing capacity
and shear strength improved by adding crushed glassin the soil [24]. [25] investigated the feasibility ofstabilising clay soil by the addition of crushed glass
waste. The result showed that when the content ofglass increased, the values of optimum moisture
content and cohesion of soil decreased, andmaximum dry density, California bearing ratio, andfriction angle increased.
[26] pointed out that with the addition of sodalime glass dust, with a size of less than 300 μm, both

ADOPTION OF RECYCLED CONCRETE
AGGREGATES FOR STABILISATION OF
EXPANSIVE SOIL
With the rapid urban development andpopulation growth, recently, the production anddisposal of C&D waste have become anotherenvironmental
issue
worldwide[13].
Consequentially, both the natural resource
consumption and landfilling spaces are facing
considerable challenges. As a substitution of natural
aggregates, recycled concrete aggregates (RCA) has
been studied and used as a choice for the reuse of
C&D wastes in road infrastructure.
Early studies show that the reuse of C&D waste
in civil projects could be applied against the highcost of natural aggregates and significantly reduce
the CO2 emission by 65% [14]. It was observed thatthe addition of RCA could improve the mechanicalproperties of expansive soil. The shear capacity of
the clay incorporating RCA was significantly
improved [15]. According to the studies of [16], theinclusion of RCA to clay resulted in increasing theunconfined compressive strength and shear strength
properties, as shown in Table 1. It was observed thatthe clay soil improved by 15% RCA was strong
enough to be adopted in pavement applications. [17]reported that the inclusion of RCA to clay leads todecreasing the energy absorption capacity andswelling of the clay.
However, in order to minimise the negative
effects due to the nature of RCA, further studies andtests are to be conducted including the appropriate
percentage of RCA content, aggregate size, andperformance with multiple materials.

334

GEOMATE – Melbourne, Australia, 11-13 November 2020

maximum dry density and plastic limit of clayey soil
increased, and both values of the compressive
strength and swell index reduced. [27] evaluated theeffects of waste soda-lime glass powder, with thesize of less than75 μm, on the geotechnicalproperties of expansive clay. It was concluded thatsoda-lime glass powder could improve thegeotechnical properties of reactive clay soil as theswelling of the soil decreased significantly when thepercentage of glass powder increased. Experimental
results illustrated that soda-lime glass powder had
positive effects on the optimum moisture content,
maximum dry density, and Plasticity Index of the
expansive clay. Also, California bearing ratio andunconfined compressive strength increased with theaddition of glass powder [27] (Fig. 1).

compressive strength was obtained with theinclusion of 50% steel slag in the clay soil sample.
However, due to the presents of free periclase andlime, it is found that steel slag has an issue ofvolume expansion in subgrade applications underhumidity, and the physical shape of steel slag will
also lead to a high void space which threats the longterm stabilisation of soil [33].
USING FLY ASH FOR THE STABILISATION
OF EXPANSIVE SOIL
Fly ash is a byproduct of coal-burning, which iscollected by electrostatic precipitators in thermal
power plants [4]. The essential components of fly
ash are silicon dioxide (SiO2), calcium oxide (CaO),
and aluminium oxide (Al2O3), and its particles arespherical in size, which make it easy to mix with soil
[34].
[35] reported the pH values of fly ash range from
1.2 to 12.5, and most of the fly ashes tend to bealkaline. Fly ash can be divided into three
categories: acidic ash, mildly alkaline ash with pH
8–9, strongly alkaline ash with pH 11–13 [36]. The
three most common types of fly ash are Class C,
Class F, and Class N fly ashes. Class F fly ash canbe produced by burning bituminous coal andanthracite [37]. Class C fly ash is produced byburning sub-bituminous coal and burning lignite[37]. Class F fly ash is less pozzolanic than Class C
fly ash due to a lower CaO content. [37] pointed outthat Class C fly does not need additional additives
for the pozzolanic reaction because it is selfcementitious; however, Class F fly ash needs cement
or lime for pozzolanic reaction due to it has a low
content of calcium oxide [37].
[38] Studied the effectiveness of using fly ash asa stabiliser in expansive soil. The results showed thatthe plastic limit and maximum dry unit weight of thesoil increased from 31% to 44% and from 13.91
kN/m3 to 14.3 kN/m3, respectively when the fly ash
content increased from 5% to 20%. The swellpotential and swelling pressure decreased with theaddition of fly ash to the clay [38] (Fig 2). [39]studied the effect of Class C fly ash on engineering
properties of expansive soil. It was pointed out that
adding fly ash to the soil led to reducing the swellpotential of highly plastic clays. Also, it was shownthat adding fly ash helped to reduce the amount ofclay size particle, plasticity index, and the swellpotential.
[40] evaluated the compaction and unconfinedcompressive strength of stabilised black cotton soil
by using fine and coarse fly ash mixtures. The
research found out that adding both sizes of fly ash
could improve the strength of the soil. Also, the peakFig. 1 Effects of soda-lime glass powder on thegeotechnical properties of clay [27].
USING STEEL SLAGEXPANSIVE SOILSTOSTABILISE

Steel slag is an accessory product of the
manufacture steel due to the reaction between the
impurities (CO2, SiO2, P, Mn etc.) and lime [28]. A
large amount of steel slag from boulder form to dust
form is generated accompanied by the enormous
need of steel annually around the world. Meanwhile,a large proportion of the steel slag is still treated bylandfills or dumpsites, which causes hazardous
impacts to the environment due to its metallic
ingredients [29], [30]. Nowadays, steel slag has beenused to enhance the mechanical performance of
expansive soils [31]. Generally, the major factor of
the stabilisation function is achieved by the cationexchanges between the steel slag and expansive soil.
It was shown that a comprehensive improvementin the strength is achieved by adding steel slag into
expansive soil. Specifically, when adding 8% ofsteel slag into the soil, it significantly increased theCalifornia bearing ratio of the clay by 40% [32]. It isalso found that both liquid limit and plasticity index
of expansive soil dropped dramatically by 33% and75%, respectively with the addition of 30% steelslag, which indicates that steel slag is acceptable as astabiliser or modifier of expansive soil [33]. Most
significantly, an improvement of 50% in unconfined335

GEOMATE – Melbourne, Australia, 11-13 November 2020

strength of the fine ash mixture was 25% higher thanthe coarse fly ash mixtures. [41] studied the behaviorof expansive soils stabilised with fly ash and fly ashlime blends. The results showed that when byincreasing the amount of fly ash and lime-fly ash
contents, the optimum water content and the
maximum dry unit weight decreased, and swellpotential and shrinkage of the soil reduced.
Furthermore, it was concluded that the inclusion of
9–12% fly ash to the soil is the optimum content forstabilisation of the expansive soil. Addition of fly
ash to expansive soil not only effectively reduced
the hydraulic conductivity, swell pressure, plasticity
index and swell potential, but also increased theshear strength, permeability and dry unit weight [34].

crumb rubber can provide suitable mechanical andgeotechnical properties to clay soils. Nonetheless,most studies only focus on the performance of one
or few specific percentages of crumb rubber, but inorder to make the best use of rubber, the specificbest rubber content and size are needed to be further
explored in practical as the specification when using
rubber-based applications.

Fig. 3 Effects of rubber on the geotechnicalproperties of reactive clay [45].
CONCLUSIONSThe current paper aimed to provide a review on
the possible applications of waste materials,
including construction and demolition wastes andcommercial and industrial wastes and theireffectiveness for the stabilisation of unsuitable
expansive clay soils. In this paper, the effects of
RCA, glass, steel slag, fly ash, and rubber on themechanical properties of expansive clay soils werereviewed. It can be concluded that the adoption ofwaste materials can provide promising results not
only for the stabilisation of clay soils, but also interms of the reduction of the negative environmental
impacts of the waste materials. Therefore, by the
inclusion of waste materials in expansive clay soils,
the geotechnical properties can be improved, thenegative environmental impacts related to thegeneration of waste materials would be reduced, andthe environmental footprints of construction projects
would be minimised.

Fig. 2 Effects of fly ash on the geotechnicalproperties of expansive clay [38].
INCLUSION OF RUBBER TO EXPANSIVESOIL
In Australia, the production of the waste tire ismore than 500,000 tons annually, and the treatment
and recycling of scrap tires have caused considerable
environmental issues since most of the scrap tires
are treated by landfilling and are barely degraded ina short time [42]. Meanwhile, accompanying withthe continuous consumption of natural material, thecrumb rubber has been used for road construction asa substitution of natural materials for pollution
reduction and cost reduction [43], [44].
Specifically, investigations on crumb rubber
revealed that it could provide low shrinkage but
better impact resistance, water resistance, acidresistance, and freezing & thawing resistance, which
explains that the crumb rubber is functional as astabiliser for expansive soils.
It was observed that the inclusion of rubber toexpansive clay leads to increasing the UCS andreduction in the swell-shrinkage capacity [45] (Fig3). [46] reported that the addition of rubber to clay
improved the ductility of the soil but decreased the
CBR. Also, [47] concluded that rubber could
provide higher frictional resistance and bettermechanical interlocking to the clay. To conclude,

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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

OPTIMUM UTILIZATION OF RICE HUSK ASH WASTE FORGROUND IMPROVEMENT
1

Najmun Nahar1, 2, Zakaria Hossain1 and Noma Tamaki3
Graduate School of Bioresources, Mie University, Japan,2Life and Earth Science, Jagannath University,
Bangladesh, 3Make Integrated Technology Company, Osaka, Japan
ABSTRACTThe disposal of the abundance of rice husk ash in rice-producing countries is creating problems inenvironmental contamination and health hazard. The usage of rice husk ash waste mixed with cement in the soil
is considered as one of the possible ground improvement techniques that are environmentally viable. The soil
sample of this study is classified as A-2-4 or SW-SM. In this study, five mix types of rice husk ash waste andcement in the soil are investigated for its effect on maximum dry density (MDD), optimum moisture content(OMC), bearing capacity, and permeability through the Standard proctor compaction test, California Bearing Ratio(CBR) test, and constant head permeability test. The test results illustrate that the soil with rice husk ash andcement improves the engineering properties of the soil. It is concluded that the soil-rice husk ash-cement
combination can be used for ground improvement.
Keywords: Soil texture; Optimum moisture content; California bearing ratio; Permeability coefficient; Ground
improvement

is not a new material for soil improvement. The
application of RHA in soil has shown dramatic
success in many types of soils around the world.
Moreover, the researches on the utilization of RHA
and OPC at a nominal dosage in soil samples werevery limited. Therefore, the present study attempts toassess the role of RHA as ground improvement andto investigate the optimum percentage combination ofsoil-cement-RHA.

INTRODUCTION
Rice husk is an agricultural by-product of rice andproduced from paddy during the milling process [1],which is plentifully available in many rice-producing
countries [2]. Rice Husk Ash (RHA) is formed when
rice husk is burnt under controlled temperature [3].
Every year about 20 million tons of RHA are
produced throughout the world [4]. Due to lack of its
utilization, the massive amount of RHA is discarded
at open places, riversides, and abandoned areas,
which causes environmental pollution [5] and also
poses a health hazard due to local air pollution.
However, RHA is a pozzolanic material, and it
contains around 85–90% amorphous silica, which isreactive as a pozzolan to replace part of Portland
cement [6-7]. It can potentially use as a construction
material for various geotechnical applications such asindustrial, commercial, housing projects, andinfrastructure construction for dams, tunnels, ports,
roadways, etc.
On the other hand, the production of cement, a key
binding component of concrete, is costly, consumes
high energy, depletes natural resources, and emits
enormous amounts of greenhouse gases [8] that also
causes environmental degradation, severe pollution[9-11], and even health hazards. So, if most of the
RHA were to be used in ground improvement, it
would not only get rid of dumping RHA but would
also have decreased the CO2 emissions to theatmosphere by bringing down the cement production.
As a pozzolanic material, usage of rice husk ash in
cement/concrete that brings about some advantages,
like improving strength, enhancing durability, or
decreasing carbon dioxide emissions, etc. [12]. RHA

MATERIALS AND LABORATORY TEST
Materials Used
In this study, testing specimen were comprised ofsoil, RHA, and cement. The soil sample was collectedfrom the Handa area, Tsu City, Mie Prefecture, Japan.
Table 1 Physical properties of soilParameters

ValuesSpecific gravity (ρs)
Maximum dry density (γ32.74 g/cm)
dry max

Optimum Water Content (W)

3

1.696 g/cm
17.50%

opt.

Sand (75 μm - 2 mm)
Silt (5 - 75 μm)
Clay < 5 μm
Liquid limit, LL
Plastic limit, PL
Plasticity Index, PI
AASHTO classification
ASTM D-2487 classification

339

89%
9%
2%
37.52%
28.97%
8.55%
A-2-4
SW-SM

GEOMATE – Melbourne, Australia, 11-13 November 2020

Soil particle size

(𝑪𝑪𝒖𝒖 ≥ 𝟔𝟔 𝐚𝐚𝐚𝐚𝐚𝐚 𝟏𝟏 ≤ 𝑪𝑪𝒄𝒄 ≤ 𝟑𝟑 ) besides with plasticity
index is greater than 7 (PI >7). According to theAmerican Association of State Highway andTransportation
Officials(AASHTO)
soil
classification system, the particular soil is Silty graveland sand (A-2-4), where percent passing no. 200
sieve ≤ 35%, LL ≤ 40 and PI ≤ 10 (Fig.1 and Table 1).
Readymade RHA was collected from Make
Integrated Technology (MIT) company, Osaka, Japan.
The basic properties of RHA are given in Table 2, andthe particle size distribution of RHA is shown withsoil particle size in Fig.1. Ordinary Portland cement(Type I) was used in this study, which is very
available in local markets and commonly used forconstruction purposes. The chemical properties ofthis type of cement can be found in other research [13].
The ordinary tap water in the laboratory was used in
all of the specimens.

RHA particle size

Percent Finer (%)

100
80
60
40
20
0
0.001

0.01

0.1

1

10

Particle diameter (mm)Fig. 1 Particle size distribution curve of soil and RHA
Table 2 Physical and chemical properties of RHA
Properties

Parameters

ValuesPhysicalproperties

Average Particle Size
Loss of Ignition
Specific Gravity
Burning Temperature
Burning Time

0.1 to 0.3 μm
85%0.12 g/cm3
600°C
24 hours

Chemicalproperties

Silica (SiO

91.10%Mix Designs of Specimen
For the chemical stabilization of soil, it is essential
to fix the proper mix design of specimens [14]. Somerelevant studies have been conducted using different
ratios of RHA with cement in soil. Soil with RHA 5%,
10%, 15%, 20% of and 6%, 8%, 10% of cement wereused for clayey soil stabilization [15]; RHA 4%, 8%,
12% and only 2% cement of soil weight wereconsidered for showing the effects of RHA on
geotechnical properties of soil from paddy field [16];some experiments were conducted with taking 0%,5%, 10%, 15%, 20%, 25%, 30% of RHA and 0%, 2%,
4%, 6%, 8%, 10%, 12%, 14% cement in residual soil[17]. In this study, 5%, 10%, 15% of RHA addition
with 2%, 4%, 6% cement of weight of SW-SM typesoil were used as the mixing design of the samples,
which is presented with index in Table 3.

2)

Carbon dioxide (CO )

4.35%

2

Potassium Oxide (K O)

2.40%

2

Calcium Oxide (CaO)

0.57%

Phosphorous Penta Oxide (P O
2Sulphur Tri Oxide (SO )

0.52%
5)

0.38%

3

Chlorine (Cl)

0.18%

Magnesium Oxide (MgO)

0.16%

Manganese Oxide (MnO)

0.14%

Iron Oxide (Fe O )0.05%

Mix design

Index

0.03%

Soil (Control)
Soil + 2% Cement + 5% RHA
Soil + 2% Cement + 10% RHA
Soil + 2% Cement + 15% RHA
Soil + 4% Cement + 5% RHA
Soil + 6% Cement + 5% RHA

Control
S+2C+5R
S+2C+10R
S+2C+15R
S+4C+5R
S+6C+5R

2

Alumina (Al O
2OthersTable 3 Investigated mixtures with indices

3

3)

0.12%

The physical properties of soil were determinedthrough a series of laboratory tests and presented inTable 1. From the graph of particle size distribution
by sieve and hydrometer analysis of soil, thecoefficient of gradation (𝑪𝑪𝒖𝒖 ) and the coefficient ofcurvature (𝑪𝑪𝒄𝒄 ) values are correspondingly 9.275 and
0.906, and from the Atterberg limit test, the plasticity
index (PI) is 8.55%. According to the Unified
Classification of Sandy soils by ASTM D-2487, theparticular soil is well-graded sand with silt, and thesymbol is SW-SM as the coefficient of gradation (𝑪𝑪𝒖𝒖 )of soil particles are greater than or equal to 6, and thecoefficient of curvature (Cc) value is between 1 and 3

Specimen Preparation
Soil-RHA-cement
mixtureswere
mixed
homogenously in a big tray, and water was gentle tothe dry mix. In the case of the California bearing ratio
test and permeability test, the amount of water was
mixed for each specimen below 2-3% from theoptimum moisture content (OMC), which was
calculated from the compaction test. Three specimens

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GEOMATE – Melbourne, Australia, 11-13 November 2020

were prepared and tested for each mix type. The
specimens were designed for those tests considering
the curing period for the same day (0 day) and sevendays (7 days). In the case of the same day, specimens
were tested after 30 minutes passed from thespecimen preparation time. For seven days curing
period, after setting the specimen into the mold, it was
wrapped with a plastic bag for maintaining the
moisture content of the specimens. The specimen
was taken out from the plastic after seven days ofcuring, and then the tests were performed.
Laboratory Test
Compaction test
Fig. 3 CBR test apparatus- A) Automated rammer,
and B) CBR machine

According to Japanese Industrial Standards (JISA-1210), the Standard Proctor compaction test was
conducted to determine the optimum moisture
content (OMC). Maximum dry density (MDD) of soil
control and other mix types. The applied apparatus for
performing the test was a 10 cm diameter cylindricalcompaction mold equipped with a base and a collar,rammer mass of 2.5 kg, and a falling height of 30 cm(Fig. 2). In the compaction mold, the soil wascompacted in three layers with 25 blows for eachlayer.

The CBR test specimens were prepared in steel moldswith an internal diameter of 15 cm and a height of
17.5 cm. For each mix type, Soil-RHA-cement was
mixed homogenously, where water was used below
2-3% from the OMC of the soil weight. The
specimens were compacted in the three layers under
67 blows for a per layer using an automatic rammer(Fig.3). The automatic rammer had a diameter of 5.0
cm, a mass of 4.5 kg, and a falling height of 45.0 cm.
The CBR values obtained by CBR testing calculated
from the following Eq. (1).
𝐶𝐶𝐶𝐶𝐶𝐶 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 (%) =𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ× 100 (1)𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎPermeability Test

The permeability tests were conducted by theconstant head permeability test method based on JIS
1218. The specimen for permeability tests wereprepared in the steel mold, which is 10.0 cm indiameter and 6.2 cm in height. The specimens werecompacted manually by using 2.5 kg rammer with afalling height of 30.0 cm. The permeability
coefficients were calculated using the Eq. (2) where
Q is the flow rate (cm³); L is the height (cm); h is thePressure (cm); A is the cross-sectional area (cm²); t is
the Time (s).

Fig.2 Standard Proctor compaction apparatusA) Base with mold; B) Collar; and C) Rammer
In the compaction mold, the RHA mixed soil wascompacted in three layers with 25 blows for eachlayer. For each mix type, Soil-RHA was manually
mixed homogenously in dry condition. Subsequently,
different
moisturecontents
were
added
homogeneously by manual mixing like the previous
step for getting the expected moisture content.

K (permeability coefficient) = QL / (hAt)(2)

RESULT AND DISCUSSIONS
Compaction Characteristics
The variations of MDD and OMC with RHA andcement addition with soil are presented in Figs. 4 and5. The figures illustrate that with increasing theamount of RHA and cement, OMC increases, but
MDD decreases significantly. After mixing the RHA
and cement with soil, the quantity of fines increases
in the soil that needs more water for hydration, andCalifornia bearing ratio test
All California bearing ratio tests were carried out
based on Japanese Industrial Standards (JIS-A-1211).

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GEOMATE – Melbourne, Australia, 11-13 November 2020

this trend also found in some researches [15-19]. The
declining direction of dry soil density specifies thatthe low compaction energy requires achieving the
MDD of a particular specimen that reduces thecompaction cost [20, 21].
S+2C+5R
S+4C+5R

S+2C+10R
S+6C+5R

Dry Density, ρd

1.7

S+2C+10R

S+4C+5R

S+6C+5R

0

5

250
200
150
100
50

1.6

0

1.5

2.5

1.4

7.5

10

12.5

Depth of penetration (mm)1.3

Fig. 6 Load penetration curve of Control and soilRHA-cement combination types for 0 day

1.2
1.1
8

13

18

23

28

33

38

Water Content, %

26

OMC
MDD

1.7
1.6

23

1.5

20

1.4

17

1.3

Dry Density, ρd29Applied load (KN)

Fig. 4 Compaction curves of Control and soil-RHAcement combination types

Water Content, %S+2C+5R

S+2C+15R

300
Applied load (KN)

Control
S+2C+15R

Control

Control
S+2C+15R

S+2C+5R
S+4C+5R0

5

S+2C+10R
S+6C+5R

400
350
300
250
200
150
100
50
0
2.5

7.5

10

12.5

Depth of penetration (mm)Fig. 7 Load penetration curve of Control and soilRHA-cement combination types for 7days

CBR Value, %Fig. 5 Variation of OMC and MDD of Control andsoil-RHA-cement combination types

3.1 Results of CBR tests
From Figs. 6 and 7, it can be noticed that all the
load-penetration curves of the CBR control specimen
and five different specimens of RHA and cement
mixed soils follow a typical trend considering thecuring period 0 and 7 days of CBR tests. Fig. 8
displays that the calculated CBR values for the
penetration depth of 2.5 mm of the specimens are
increased after seven days’ exception of Control. The
CBR value of the control specimen was obtained as
41.5%. The CBR value was decreased with increasing
the amount of RHA, particularly for the specimens
comprised of RHA 5%, 10%, 15%, and cement 2% in
the soil considering both cases without and with thecuring periods.

160
140
120
100
80
60
40
20
0

Same day

140

7 days108

76.5
41.5 41.5

68.5
54

49.5
38.5

43.5

48

29.5

Specimen Types
Fig. 8 Comparison among CBR values of Control andsoil-RHA-cement types for 0 and 7 days
On the contrary, with increasing the amount of
cement at 2%, 4%, 6%, with 5% RHA, the CBRvalues increased. The specimen of S+6C+5R reaches

342

GEOMATE – Melbourne, Australia, 11-13 November 2020the highest CBR value (140%) after seven days
curing, and this value increases 92% from 0 days to 7
days in this research (Fig. 8).

•

Results of the permeability test

Permeability coefficient, cm/s

Figure 9 represents the test results on thepermeability coefficient of soil-RHA-cement
combination types for 0 and 7 days of the curing
period. The permeability coefficient of all specimens
increases with increasing the age of the specimens’
exception of Control. The S+2C+15R specimen had
the highest permeability coefficient for the same day
test, and the S+2C+5R combination was achieved
with the highest permeability co-efficient, andS+6C+5R was showed the second-highest
permeability after seven days curing. The
permeability coefficient increases with increasing theamount of cement content in the soil that is similar toother studies [14].

ACKNOWLEDGMENT
We would also like to thank Ms. Toshichita Maime
for her assistance during the research at Mie
University.
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A. N., and Mirza, J., Reduction in environmental
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Construction and Building Materials, Vol. 30,
2012, pp. 360-365.
[9] Malhotra, V. M., Role of supplementary
cementing materials in reducing greenhouse gas
emissions, In Infrastructure regeneration andrehabilitation improving the quality of life
through better construction: a vision for the next
millennium, 1999, pp. 27-42.

1.40E-01
1.20E-01

0 daycuring and the S+6C+5R combination
exhibited the second highest permeability.
RHA can potentially improve the soil withcement and can replace the amount of
cement that reduces the construction cost,
particularly for rice-producing developing
countries.

7 days

1.00E-01
8.00E-02
6.00E-02
4.00E-02
2.00E-02
0.00E+00

Speceimen typeFig. 9 Variation of permeability coefficient of Control
and soil-RHA-cement types for 0 and 7 days
CONCLUSIONS
The following conclusions can be drawn based on the
results obtained from the experiment on soil-RHACement in the paper:• The OMC increases with increasing theamount of RHA and cement in the soil, but
MDD decreases.
• Considering the age of the specimen, theCBR values of all specimens increase after
seven days. The maximum CBR value wasfound for S+6C+5R combination after
curing, and this value increases 92% from 0days to 7 days.
• The permeability coefficient also increases
with time for all specimens. S+2C+15R
specimen had the highest permeability
coefficient for the same day test, andS+2C+5R was showed the highest
permeability co-efficient after seven days
343

GEOMATE – Melbourne, Australia, 11-13 November 2020[10] Sabir, B. B., Wild, S., and Bai, J., Metakaolin andcalcined clays as pozzolans for concrete: areview, Cement and concrete composites, Vol.
23, Issue 6, 2001, pp. 441-454.
[11] Worrell, E., Price, L., Martin, N., Hendriks, C.,
and Meida, L. O., Carbon dioxide emissions
from the global cement industry, Annual review
of energy and the environment, Vol. 26, Issue 1,
2001, pp. 303-329.
[12] Siddique, R., and Khan, M. I., Supplementary
cementing materials, 2011, Springer Science &Business Media.
[13] Hossain, M. Z., and Sakai, T., The effectivenessof nominal dosage of ordinary cement on
strength and permeability of clayey soil, Journalof the Japanese Society of Soil Physics, Vol. 110,2008, pp. 25-35.
[14] Nasiri, M., Lotfalian, M., Modarres, A., and Wu,
W., Optimum Utilization of Rice Husk Ash for
Stabilization of Sub-Base Materials in Construction
and Repair Project of Forest Roads, Croatian
Journal of Forest Engineering: Journal for Theory
and Application of Forestry Engineering, Vol. 37,
Issue 2, 2016, pp. 333-343.
[15] Kaur, I. and Jha, J. N., Effects of Rice Husk Ash Cement Mixtures on Stabilization of Clayey Soils,
IJCA Proceedings on International Conference on
Advancements in Engineering and Technology Vol.
8, 2016, pp.30-33.

[16] Ahmad, J., Rosli, M. I. F., and Rahman, A. S. A.,
The Effect of Rice Husk Ash (RHA) Mixtures on
Geotechnical Properties of Soil, In Regional
Conference on Science, Technology and Social
Sciences, 2018, pp. 345-352[17] Basha, E. A., Hashim, R., Mahmud, H. B., andMuntohar, A. S., Stabilization of residual soil
with rice husk ash and cement, Construction andbuilding materials, Vol. 19, Issue 6, 2005, pp.
448-453.
[18] Eberemu, A. O., and Sada, H., Compressibility
characteristics of compacted black cotton soil
treated with rice husk ash, Nigerian Journal ofTechnology, Vol. 32, Issue 3, 2013, pp. 507-521.
[19] Alhassan, M., Permeability of lateritic soil
treated with lime and rice husk ash, Assumption
University Journal of Thailand, Vol. 12, Issue 2,
2008, 115-120.
[20] Muntohar, A. S., and Hantoro, G., Influence ofrice husk ash and lime on engineering propertiesof a clayey subgrade, Electronic Journal of
Geotechnical Engineering, Vol. 5, 2000, pp. 1-9.
[21] Eisazadeh, A., Bhurtel, A. and Phai, H., May.
Compaction characteristics of Bangkok clay
stabilized using rice husk ash, bottom ash, andlime. In IOP Conference Series: Materials
Science and Engineering, Vol. 527, 2019, Issue
1, pp. 012039.

344

Construction Material345

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

FIELD EXPERIMENTS TO INVESTIGATE PULL-OUT MECHANISM
OF FLIP-TYPE GROUND ANCHORS INSTALLED IN CLAY SOIL
Shota Yoshida1, Tatsunori Matsumoto2, Mimu Yoshida3
1,2Graduate School of Science and Technology, Kanazawa University, Japan; 3Daisho Co., Ltd., Japan
ABSTRACTThe pull-out mechanism of plate, strip, or circular anchors other than flip anchors in sandy and clayey groundconditions has been mostly studied in laboratory experiments; however, there are few field studies on this subject.
For the widespread practical application of flip anchors, this study aims to provide results of field pull-out
experiments of actual flip anchors driven into clayey and sandy soils. A test site was selected comprising a top
sand layer with a clay layer underneath. Vane shear tests were conducted in the clay layer to measure the undrained
shear strength, cu. Moreover, a total of 27 flip anchors were driven into the position of 2 m pitch grid using apercussion driving device and pulled out using a hydraulic jack. All anchors were driven into the clay layer at two
different depths. The anchors in the sand layer had a greater pull-out force than the corresponding anchors in theclay. The pressure acting on the anchor increased with decreasing projected area of the anchor. In the clay layer,
the pressure was the same regardless of the project area of the anchor plate. The maximum pull-out forces measured
on in the clay layer were estimated using the interpretation method of the T-bar penetration test.
Keywords: Flip-type ground anchor, Field experiment, Pull-out experiment, Clay soil
Figure 1 shows the flip anchors. Fig. 2 shows an
installation process of the anchors. The flip anchors
are driven into the ground as percussion anchors withthe anchor head closed (Fig. 2a). After driving into adesignated depth, the anchor head rotates to open bypulling it with a jack (Fig. 2b) such that soil pressure
sufficiently acts on it. Because the grouting andcuring periods of grout are not required, flip anchors
are suitable for small-scale reinforcement or
restoration works in emergency situations. Although
the installation of flip anchors is simple and fast, themechanism of their pull-out resistance has not been
completely understood to date.

INTRODUCTION
Flip-Type Earth Anchors
Flip-type earth (ground) anchors (hereinafter,“flip anchor”) are effective for reinforcing slopes
such as soil nailing and ground anchors against slope
failures owing to heavy rains and for supporting
tower structures against strong winds. Because they
can be installed underwater, they are used as ananchor base for floating objects such as solar panels
and floating piers.

Objectives of this Research
The behaviors of square, rectangular, or circularanchors in sandy ground conditions have beenpreviously studied. Ground failure patterns andcertain methods for designing the pull-out capacity
have been proposed [2–10]. For clayey soil
conditions, several studies have proposed empiricalapproaches to estimate the uplift capacity of various
plate anchors [11–18]. However, there is no similar
research on flip anchors. Moreover, field experiments
have not been conducted to estimate the pull-out
capacity of actual flip anchors.
To widely use flip anchors in practicalapplications, this study aims to provide the results offield pull-out experiments of various sizes of actual
flip anchors driven into clayey and sandy soils.
Moreover, based on experimental results, a design
method for the pull-out capacity of flip anchors inclay is discussed.

Fig. 1 Flip type earth anchor [1].

a) Installation process(b) Pulling-out process

Fig. 2 Installation process of a flip type earth anchor
[1].

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GEOMATE – Melbourne, Australia, 11-13 November 2020

OUTLINE OF FIELD EXPERIMENT
Property of the Ground
The test site was located at Shiga Prefecture,
Japan. Figure 3 shows a total of 27 flip anchors, whichwere set at a position of 2 m pitch grids comprising
three rows: A, B, and C. Moreover, nine anchors were
installed in each row.

Fig. 5 Types of clay in the ground.

Depth from the ground surface, z (m)The former was located on the top portion of theclay layer. Vane shear tests were conducted in theclay layer to measure the undrained shear strength cu.
The black colored clay had a relatively larger cu thanthe blue colored clay.

Fig. 3 Ground used for the pull-out experiments.

Depth from the ground surface, z (m)The ground comprised a top sand layer overlyinga soft clay layer. Portable dynamic cone penetration
tests (DCPTs) were conducted at ten locations (Fig.
4). The DCPT device comprised a cone with adiameter of 25 mm, a drop hammer mass of 5 kg, anda hammer drop height of 500 mm. The converted SPT
N-values were empirically estimated from DCPT
results. The N-value converted to a depth of 2 mwithin the top sand layer was around 5, and increased
to around 15 near a depth of 3 m.

0.0
0.5

0

5

SPT N-value
10 15 20 25

2.0
2.5

A
Clay layer

35

2

5

4

7

Sand

50

location
A1
B5

1.0
Blue colored clay
1.5

Black colored clay

2.0

Figure 6 shows the water content W of the ground.
The water content of the black clay was nearly threetimes that of blue colored clay. No significant
difference was observed in the water content at eachlocation.

8

B No.1 No.3 No.6 No.9
C

0.5

Water content, W (%)
10
20
30
40

Fig. 6 Water content W of the ground.

No.1
No.3
No.6
No.9

Sand layer

1.0
1.5

30

0.0

0

10

Flip Anchors Used in the Experiments

GroundSand layer

3.0

Fig. 4 Converted SPT N-values of the ground.
Furthermore, the test ground was excavated at two
locations in the test site for direct soil observation.
The top sand layer was 1-m deep, followed by theclay layer to a depth of 1.8 m. Moreover, flip anchors
were driven in the top sand layer or clay layer.
As shown in Fig. 5, the clay layer comprised two
types of clay. The blue colored clay contained a small
amount of sand particles and the black colored clay
was pure sticky clay.

Fig. 7 Flip anchors used in the experiments.
347

GEOMATE – Melbourne, Australia, 11-13 November 2020

As demonstrated in Fig. 7, five types of flip
anchors were used in the experiments. The smaller
and larger ones are the H and HG series. The number
after H and HG denotes the width of the anchors.

RESULTS OF THE EXPERIMENTS
Pull-Out Force vs Upward Displacement
Figures 9–13 compare the relation between thepull-out force F and upward displacement w ofanchors planted in the sand and clay layers.
Figure 4 shows the sand and clay layers, whichwere separated at a depth of 1 m. In the early stagesof pull-out loading, the behaviors of all anchors
showed similar trends. For all anchors except the
HG320 anchor in the sand layer, the pull-out
resistance is mobilized very quickly with a small
upward displacement w and then levels off.

Experimental Cases and Procedure
As listed in Table 1, a total of 26 cases of pull-out
experiments were conducted. Depth z” denotes thedepth of the apex of the closed anchor plate.
Table 1 Experimental cases

A1
A2
A3
A4
A5
A6
A7
A9
B1
B2
B3
B4
B5
B6
B7
B8
B9
C1
C2
C3
C4
C5
C6
C8
C9
BC2.5

HG320
HG320
HG180
HG100
HG320
H110
H50
HG320
HG100
HG100
H50x2
H110
HG100
H110
H110
H50
HG100
HG180
HG320
HG180
HG100
HG180
H110
H50
HG180
H50

Depth,
z (m)
1.8
1.2
1.2
1.2
1.8
1.0
1.0
1.8
1.8
1.8
1.0
1.0
1.8
1.8
1.8
1.8
1.8
1.8
1.2
1.2
1.2
1.8
1.0
1.0
1.8
1.0

Soil
Clay
Sand
Sand
Sand
Clay
Sand
Sand
Clay
Clay
Clay
Sand
Sand
Clay
Clay
Clay
Clay
Clay
Clay
Sand
Sand
Sand
Clay
Sand
Sand
Clay
Sand

Max. force,
Fmax (kN)
*37.3
35.8
51.1
45.9
*16.2
26.0
14.0
*13.1
*7.9
16.2
20.5
*12.5
*6.1
*8.5
*4.5
*8.8
*17.1
66.1
64.3
62.7
*17.7
37.6
22.3
*12.5
10.7

80

H50

Pull-out force, F (kN)Anchor60

Sand z=1.0 m
Clay z=1.8 m

40
Sand

20

Clay
0

0
100
200
300
400
500
Upward displacement of the anchor, w (mm)Fig. 9 Pull-out force vs. upward displacement of H50
anchors installed in the sand and clay layers.
80

H110

Pull-out force, F (kN)

Case

Note: * Fmax between the depths of 1.8 and 1.3 m.

60
40

Sand z=1.0 m
Clay z=1.8 m
Sand

20
Clay
0

0
100
200
300
400
500
Upward displacement of the anchor, w (mm)Fig. 10 Pull-out force vs. upward displacement of
H110 anchors installed in the sand and clay
layers.
For anchors in the sand layer, the pull-out forces
began to increase again after this plateau and reacheda peak value at a relatively large w of 400–500 mm.
Therefore, the anchors in the sand layer weresignificantly influenced by the sand. By comparing
Figs. 12 and 13, the pull-out force of the 440-mm long
HG320 began to increase at about 50% of thedisplacement of the 340 mm long HG180 in the sandFig. 8 Percussion driving device and hydraulic jack.
Figure 8 shows that the anchors were driven using
a percussion device and pulled out using a hydraulicjack. The pull-out force F and upward displacement
w were measured while pulling out the anchors.
348

GEOMATE – Melbourne, Australia, 11-13 November 2020

80

layer. This is reasonable because HG320 had asmaller relative distance (the distance between thebottom of the overlying sand and anchor plate divided
by the anchor length).
In clay, the anchors maintained a plateau from theearly stage to the large upward displacement, asshown in Figs. 9 and 10 in which small-sized anchors
were tested. As shown in Figs. 11 and 12, HG100 and
HG180 anchors (length = 340 mm) required upward
displacement equal to their anchor length until thepull-out forces began to increase again. Subsequently,
with an additional pull-out displacement of around
100 mm, the anchors appeared to be fully open andreached their peak values in the clay layer.

Pull-out force, F (kN)60

Pull-out force, F (kN)

20

Therefore, if the anchors are installed in the clay
layer below the sand layer, the effects of the top sandlayer should be ignored in design of the pull-out
forces when the anchors do not reach the sand layer.
Clay

80

0

Pull-out force, F (kN)

0
100
200
300
400
500
Upward displacement of the anchor, w (mm)Fig. 11 Pull-out force vs. upward displacement of
HG100 anchors installed in the sand and clay
layers.
80
Pull-out force, F (kN)

HG180

Sand D=1.2 m
Clay D=1.8 m

20

60
40
20

Installation depth of anchors, z=1.8 m

A1 HG320
C1 HG180
B2 HG100
clay

A1

C1
sand
B2

0

0
200
400
600
800
Upward displacement of the anchor, w (mm)

60
40

Clay

Fig. 13 Pull-out force vs. upward displacement of
HG320 anchors installed in the sand and clay
layers.

Sand

20

Sand

40

0
100
200
300
400
500
Upward displacement of the anchor, w (mm)Sand z=1.2 m
Clay z=1.8 m

40

60

0

80

HG100

Sand z=1.2 m
Clay z=1.8 m

HG320

Fig. 14 Pull-out force vs. displacement of HG anchors
installed in the clay layer through the sandlayer.

Sand
Clay

Figure 14 shows the relation between F vs. w of
the anchors installed at a depth of 1.8 m. The F valuesof HG180 and HG100 began to increase rapidly when
the anchors reached the sand layer. In the sand layer,
the larger the anchor, the greater the pull-out force.
Figure 14 shows that when the anchors were pulled
out of the clay layer, the pull-out forces were notaffected by the overlying sand layer.
Figure 15 shows the relation between the pull-out
pressure, p = F/A (A = projected area of the flip
anchors), and w of the HG anchors. In sandy groundconditions, plate anchors such as rectangular andcircular anchors are well known to lift inverted
trapezoidal soil wedges over the anchor plates [6–8,
19–21]. This ground failure mechanism usually
decreases p as A increases. The measured trend of p

0

0
100
200
300
400
500
Upward displacement of the anchor, w (mm)Fig. 12 Pull-out force vs. upward displacement of
HG180 anchors installed in the sand and clay
layers.
However, the pull-out forces in the clay layer did
not increase similar to those in sand. As shown in Figs.
9–13, the anchors in the sand layer had significantly
greater F values than their clay counterparts, although
the overburden pressures on the anchors in the sandlayer were less.
349

GEOMATE – Melbourne, Australia, 11-13 November 2020

embedment ratios H/B or H/D, and overburden ratio,
H/cu. Several calculation steps are required to obtainthe Fc and Nc values, hindering their practical use.

in the sand layer conformed to the above mechanism.
However, the p values of HG100 and HG180
remained nearly constant, and their amplitudes were
almost similar during pulling out in the clay layer.
Thus, the p values of HG320 were smaller than theformer two anchors. Because HG320 had
approximately three times the area of HG100, a larger
displacement was required to open it sufficiently.
Therefore, at a displacement of 500 mm, the p valuesof HG320 were nearly equal to the p values of HG100
and HG180 at a 400 mm upward displacement.
Therefore, in the clay layer, the p values ofsufficiently opened anchors were nearly constantregardless of the size of the anchors.

Pull-out pressure, p (kPa)

1600
1200

A calculation method for the flip anchors in clay
The T-bar penetration test is used to estimate theundrained shear strength cu of clay ground. The cu
value is estimated using Eq. (3) [22] with themeasured value of pressure p on the T-bar, regardlessof the size of the T-bar:where Nb is the bearing factor of T-bar. Nb ranges
from 8.5 to 12.5 for various types of clay with anaverage value of 10.5 [23]. In this field experiment,
the range of cu values for the clay layer was estimatedusing the VSTs, as mentioned earlier. Whenestimating the cu values from soil tests other than the
T-bar test, the pressure of the anchor p can be
estimated using Eq. (4):Installation depth, z=1.8 m
2

A1 HG320, A=0.103 m
2
C1 HG180, A=0.049 m
2
B2 HG100, A=0.030 m

C1
B2

800
400

clay

(3)

𝑐 = 𝑝/𝑁

sand
A1

(4)

𝑝= 𝑁 𝑐0

In Fig. 16, the range of the calculated values of preasonably agrees with the measured values of p.

0
200
400
600
800
Upward displacement of the anchor, w (mm)Fig. 15 Pull-out pressures vs. displacements of HG
anchors installed in clay through sand layer.

Pull-out pressure, p (kPa)800

Calculation Method of Pull-Out Resistance Forceof Flip Anchors in Clay
Calculation methods for plate anchors in clay
First, the calculation methods for plate anchorsother than flip anchors are reviewed. The calculation
methods of the pull-out capacity for the square,
rectangular, or circular anchors in clay have alreadybeen proposed. Das [11] proposed Eq. (1) and
Merifield [17] proposed Eq. (2) for the ultimate
pressure acting on the anchor plates:
𝑞 = 𝑐 𝐹 + 𝛾𝐻(1)

𝑞 = 𝑄 /𝐴 = 𝑐 𝑁

(2)

600

Installation depth, z=1.8 m
2

A1 HG320, A=0.103 m
2
C1 HG180, A=0.049 m
2
B2 HG100, A=0.030 m

cu (kPa)
Nb=10.5
clay layer

400

Cal. cu=27.78
Cal. cu=16.92 range of calculated values
Cal. cu=7.64

200
0

0
100
200
300
400
500
Upward displacement of the anchor,w (mm)Fig. 16 Calculated p in clay vs. measured p.
Therefore, when performing a T-bar penetration
test on site, the p values from the T-bar test can beused to directly estimate the approximate pressure on
the flip anchor as Eq. (5).

where q0 is the net ultimate loads per unit area, cu isthe undrained shear strength of soil, Fc and Nc areanchor breakout factors,  is effective unit weight ofsoil, H is the embedment depth of the anchor, qu is theaverage ultimate pressure, Qu is the pull-out force;and A is the anchor area. The value of Fc or Nc is afunction of embedment ratios, H/B or H/D (B = widthof a plate anchor and D = diameter of a circularanchor), and overburden pressure [11, 17]. The valuesof Fc and Nc are functions of soil profile, anchor shape,

𝐹 = 𝑝𝐴(5)

Currently, the T-bar penetration test is rarely used
for site investigations in Japan. In practice, the cu
values are empirically estimated from the SPT-N
values, VST, pressuremeter test, or unconfinedcompression test. Therefore, if the cu value isobtained, the pressure on the anchor can be estimatedusing Eq. (4).
350

GEOMATE – Melbourne, Australia, 11-13 November 2020[10] Niroumand H., Kassim K.A. and Nazir R.,
Anchor plates in two-layered cohesion less soils,
American Journal of Applied Sciences, Vol. 7,
Issue 10, 2010, pp. 1396-1399.
[11] Das B.M., Model tests for uplift capacity offoundations in clay, Soils and Foundations, Vol.
18, Issue 2, 1978, 17-24.
[12] Das B.M., A procedure for estimation of ultimate
uplift capacity of foundations in clay, Soils andFoundations, Vol. 20, Issue 1, 1980, pp.77-82.
[13] Das B.M., Moreno R. and Dallo K.F., Ultimate
pullout capacity of shallow vertical anchors inclay, Soils and Foundations, Vol. 25, Issue 2,
1985, pp.148-152.
[14] Das B.M. and Puri V.K., Holding capacity of
inclined square plate anchors in clay, Soils andFoundations, Vol. 29, Issue 3, 1989, pp. 138-144.
[15] Han C., Wang D., Gaudin C., O’Loughlin C.D.
and Cassidy M.J., Behaviour of vertically loadedplateanchorsundersustained
uplift,Géotechnique, Vol. 66, Issue 8, 2016, pp. 681-693.
[16] Khatri V.N. and Kumar J., Vertical uplift
resistance of circular plate anchors in clays under
undrained condition, Computers and Geotechnics,
Vol. 36, Issue 8, 2009, pp. 1352-1359.
[17] Merifield R.S., Lyamin A.V., Sloan S.W. and Yu
H.S., Three-dimensional lower bound solutions
for stability of plate anchors in clay, Journal of
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[18] Singh V., Maitra S. and Chatterjee S.,
Generalized design approach for inclined strip
anchors in clay, International journal of
Geomechanics, Vol. 17, Issue 6, 2017.
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installed in dry sand ground, Geotechnics forsustainable infrastructure development - Geotec
Hanoi, 2019, pp. 1245-1252.
[21] Yoshida S., Matsumoto T., Komura K. andYoshida T., Field pull-out experiments of fliptype ground anchors installed in dry sand ground,
Geomate proceedings, 2019, pp. 569-574.
[22] Almeida M.S.S., Oliveira J.R.M.S., Rammah K.I.
and Trejo P.C., Investigation of bearing capacity
factor of T-bar penetrometer at shallow depths inclayey soils, Journal of Geo-Engineering Sciences,Vol. 1, 2013, pp. 1-12.
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M.A. and Randolph M. F., Estimation of intact
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penetration tests in soft clays, Géotechnique, Vol.
60, No. 11, 2010, pp. 843.-859.

CONCLUSION
The pull-out experiments of the flip-type anchors
were conducted in ground where a top sand layer
covered a clay layer to investigate the pull-out
mechanisms of flip anchors in clay. It was clearlyshown that the pull-out mechanism of the anchor inclay was quite different from that in sand. As for theanchors being pulled out in clay, the pull-out force
was not affected by the overburden pressure. It was
proportional to the anchor size. This indicates that thepressure acting on the anchor head was constantregardless of the size of the anchor.
To predict the pull-out force of flip anchors in clay,
the interpretation method of the T-bar penetration test
was used. The predicted p range estimated from thecu values of the VSTs and bearing factor of T-bar Nbof 10.5 agreed well with the measured p range.
Therefore, this interpretation method can bepromising for the prediction of the pull-out capacityof flip anchors in clay.
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351

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

FINITE DIFFERENCE APPROXIMATION FOR SOLVING
TRANSIENT HEAT CONDUCCTION EQUATIONOF THE CAST
IRON
1Dalal Adnan Maturi 1
Departement of Mathematics, Faculty of Science, King Abdulaziz University, P.O.Box 42664,Jeddah 21551,
Saudi Arabia

ABSTRACTIn this paper, finite differences for solving Transient heat conduction equation of the Cast iron by using thefinite difference method using the MATLAB algorithm for the direct problem of calculating the value oftemperature in different time quantities for a separate point required as well as checking the temperature fieldaccording to the time quantity as shown by Examples and Figures, the proposed method is still very accurate andsince the cast iron is an important material and metal in mineral installations in various engineering fields
through many bridges, tunnels, buildings, towers, sports facilities and industrial facilities and others.
We note that the design standards that relate to structural stability, the length of endurance, fire resistance, andcontrol of environmental pollution and cost have become more acute and decisive. These standards, in addition
to the continuous decline in national resources, have resulted in heavy pressure on engineers to try to reduce thequantities of construction materials required for the construction process as they in turn require induction
Technical methods and knowledge of the quality of minerals used for construction, it is evident from theexamples and forms that we obtain, which shows the effectiveness of the method used to detect the quality ofcast iron metal and the possibility of using it in making tools that are not subjected to shocks such as: water pipes
or gas pipes or In a mother in the field of construction, it was limited to some simple structural elements to
support the old stone and wooden buildings and help in the implementation of some of the roofs with a wider
metaphor due to the property that it bears the extreme pressure and its low tolerance to tension.

Keywords: Finite Difference Method, Transient Heat Conduction Equation, Cast Iron, Matlab.
Benjamin Huntsman in England in 1740 managed
to melt iron and prepare cast iron in crucible fashion.
In 1811, Friedrich Kropp founded the first cast ironpreparation plant in Essen. This gave a major
breakthrough in iron production for the nineteenth
century, and the emergence of several inventions that
helped to increase its manufacture: steam machinesprovided to the steel industry strong labor, and coalmines provided the coal needed for its manufacture,railways and development, as well as steam ships, allof which use products Steel which is a popularmarket.
The steel industry in all countries was of great
importance from an economic point of view and has
political significance, as it was considered one of the
pride of the state among nations. The importance ofsteel at the time appears in the construction of theEiffel Tower, as it opened on the occasion of theInternational Exhibition in 1889 as one of the pillarsof technological progress.

INTRODUCTION
Finite difference method is one of several
techniques for obtaining numerical solution of partial
differential equation. Finite difference method
describes functions as discrete values across a gridand approximate their derivatives as differences
between points on the grid. Knowing a little about
how difference methods are formulated and in what
regimes they are stable can help save a lot of time,
both in the design of finite differencing algorithms,
and in the time that they take to run, the finite
difference approximations for derivatives are one of
the simplest and of the oldest methods to solve
differential equations.
We need a fast, realistic and reliable method tosolve the heat conduction equation of the Cast Iron.
The exploitation and manufacture of iron began
during the thousand years BC in Syria and in the
country Hitter, and the history of the simplest typesof iron dates back to the early BC.
It is the twelfth century AD developed charcoal kilns
in Europe and was able to provide the heat necessary
to melt iron. The iron produced in this way was not
amenable to road because of the high carbon content
in it, so it was necessary to purify it, that is, to reducethe carbon content in it by burning it.

FINITE DIFFRENCE METHOD FOR HEATCONDUCTION EQUATION
The linear second order partial differential
equation352

GEOMATE – Melbourne, Australia, 11-13 November 2020

𝐴𝐴𝑢𝑢𝑥𝑥𝑥𝑥 + 2𝐵𝐵𝑢𝑢𝑥𝑥𝑥𝑥 + 𝐶𝐶𝑢𝑢𝑦𝑦𝑦𝑦 + 𝐷𝐷𝑢𝑢𝑥𝑥 + 𝐸𝐸𝑢𝑢𝑦𝑦 + 𝐹𝐹𝐹𝐹 + 𝐺𝐺
=0
as a parabolic equation if 𝐵𝐵2 − 𝐴𝐴𝐴𝐴 = 0. A
parabolic equation holds in an open domain or in asemi-open domain.
The partial differential equation governing theflow of heat in the rod is given by the parabolic
equation𝒕𝒕 > 𝟎𝟎.
𝒖𝒖𝒕𝒕 = 𝒄𝒄𝟐𝟐 𝒖𝒖𝒙𝒙𝒙𝒙 , 𝟎𝟎 ≤ 𝒙𝒙 ≤ 𝒍𝒍,
Where 𝒄𝒄𝟐𝟐 is a constant and depends on the material
properties of the rod.
The heat conduction equation of the Cast Iron
𝒖𝒖𝒕𝒕 = (𝟎𝟎. 𝟏𝟏𝟐𝟐)𝒖𝒖𝒙𝒙𝒙𝒙
𝒖𝒖(𝟎𝟎, 𝒕𝒕) = 𝒖𝒖(𝟏𝟏, 𝒕𝒕) = 𝟎𝟎, ∀𝒕𝒕 ∈ (𝟎𝟎, 𝒕𝒕𝑭𝑭 )𝒖𝒖(𝒙𝒙, 𝟎𝟎) = 𝒖𝒖𝟎𝟎 (𝒙𝒙), ∀𝒙𝒙 ∈ [𝟎𝟎, 𝟏𝟏],

Where 𝒕𝒕𝑭𝑭 Denotes the terminal time for the model.
Here without loss of generality, we assume that the
spatial domain is [𝟎𝟎, 𝟏𝟏].
At first divide the physical domain (𝟎𝟎, 𝒕𝒕𝑭𝑭 ) × (𝟎𝟎, 𝟏𝟏)
by 𝑵𝑵 × 𝑱𝑱 uniform grid points
𝒕𝒕𝑭𝑭, 𝒏𝒏 = 𝟎𝟎, 𝟏𝟏, ⋯ , 𝑵𝑵,𝑵𝑵
𝟏𝟏𝒙𝒙𝒋𝒋 = 𝒋𝒋𝚫𝚫𝒙𝒙, 𝚫𝚫𝒙𝒙 = , 𝒋𝒋 = 𝟎𝟎, 𝟏𝟏, ⋯ , 𝑱𝑱.
𝑱𝑱

𝒕𝒕𝒏𝒏 = 𝒏𝒏𝚫𝚫𝒕𝒕, 𝚫𝚫𝒕𝒕 =Fig.1 Temperature distributions at several times forthe heat conduction of the Cast Iron for example 1.

Then, we denote the approximate solution 𝒖𝒖𝒏𝒏𝒋𝒋 ≈𝒖𝒖(𝒙𝒙𝒋𝒋 , 𝒕𝒕𝒏𝒏 ) .At an arbitrary point (𝒙𝒙𝒋𝒋 , 𝒕𝒕𝒏𝒏 ) . To obtainafinite difference scheme, we need to approximate
the derivatives in (1) by some finite differences.
(Explicit scheme)
Substituting(ii) Example2. Find the solution of the heat
conduction equation of the Cast Iron
𝒕𝒕 > 𝟎𝟎;
𝒖𝒖𝒕𝒕 = (𝟎𝟎. 𝟏𝟏𝟏𝟏)𝒖𝒖𝒙𝒙𝒙𝒙 𝟎𝟎 < 𝒙𝒙 < 𝟏𝟏,𝒖𝒖(𝟎𝟎, 𝒕𝒕) = 𝒖𝒖(𝟏𝟏, 𝒕𝒕) = 𝟎𝟎, 𝒕𝒕 > 𝟎𝟎;
𝒖𝒖(𝒙𝒙, 𝟎𝟎) = 𝐬𝐬𝐬𝐬𝐬𝐬 𝝅𝝅𝝅𝝅 , 𝟎𝟎 ≤ 𝒙𝒙 ≤ 𝟏𝟏.

𝒖𝒖𝒕𝒕 �𝒙𝒙𝒋𝒋 , 𝒕𝒕𝒏𝒏 � ≈ (𝒖𝒖𝒏𝒏𝒋𝒋 − 𝒖𝒖𝒏𝒏−𝟏𝟏)/∆𝒕𝒕,
𝒋𝒋
𝒖𝒖𝒙𝒙𝒙𝒙 �𝒙𝒙𝒋𝒋 , 𝒕𝒕𝒏𝒏 � ≈ (𝒖𝒖𝒏𝒏𝒋𝒋+𝟏𝟏 − 𝟐𝟐𝒖𝒖𝒏𝒏𝒋𝒋 + 𝒖𝒖𝒏𝒏𝒋𝒋−𝟏𝟏 )/(∆𝒙𝒙)𝟐𝟐 ,Into (1), another difference scheme for (1) can beconstructed as:
𝒖𝒖𝒏𝒏𝒋𝒋 − 𝒖𝒖𝒏𝒏−𝟏𝟏
𝒖𝒖𝒏𝒏𝒋𝒋+𝟏𝟏 − 𝟐𝟐𝒖𝒖𝒏𝒏𝒋𝒋 + 𝒖𝒖𝒏𝒏𝒋𝒋−𝟏𝟏
𝒋𝒋=, 𝟏𝟏 ≤ 𝒋𝒋(∆𝒙𝒙)𝟐𝟐∆𝒕𝒕≤ 𝑱𝑱 − 𝟏𝟏, 𝟏𝟏 ≤ 𝒏𝒏 ≤ 𝑵𝑵.

Applying the finite difference method using Matlab,
then the result show as follows.

SEVERALS EXAMPLES(i) Example1. Find the solution of the heat
conduction equation of the Cast Iron
𝒕𝒕 > 𝟎𝟎;
𝒖𝒖𝒕𝒕 = (𝟎𝟎. 𝟏𝟏𝟏𝟏)𝒖𝒖𝒙𝒙𝒙𝒙 𝟎𝟎 < 𝒙𝒙 < 𝟏𝟏,𝒖𝒖(𝟎𝟎, 𝒕𝒕) = 𝒖𝒖(𝟏𝟏, 𝒕𝒕) = 𝟎𝟎, 𝒕𝒕 > 𝟎𝟎;
𝒖𝒖(𝒙𝒙, 𝟎𝟎) = 𝟐𝟐 𝐬𝐬𝐬𝐬𝐬𝐬(𝝅𝝅𝝅𝝅/𝟐𝟐) − 𝐬𝐬𝐬𝐬𝐬𝐬 𝝅𝝅𝝅𝝅 + 𝟒𝟒 𝐬𝐬𝐬𝐬𝐬𝐬 𝟐𝟐𝟐𝟐𝟐𝟐 ,𝟎𝟎 ≤ 𝒙𝒙 ≤ 𝟏𝟏.

Applying the finite difference method using Matlab,
then the result show as follows.

353

GEOMATE – Melbourne, Australia, 11-13 November 2020

CONCLUSION
In this paper, calculations are performed by
the MATLAB Programming Program andEvolutionary Laws for a one-dimensional thermal
conductivity equation using the finite difference
method in solving the thermal conductivity equation
for cast iron. All examples demonstrate that the finite
difference method is a powerful mathematical tool forsolving the cast iron thermal conductivity formula.
The graphic shapes we provided to define a higher
resolution and simplicity for the proposed method. In
addition, it should be noted that the described method
can easily be generalized to build more heat fordifferent materials. This work focuses on cast ironbecause Gray cast iron is an alloy of cast iron with agraphite microscopic structure. It is called graybecause it is the color that appears inside it when
breaking, which indicates the presence of graphite.
Gray cast iron is the most popular and widely usedcast iron alloy. Gray cast iron is used in applications
that are not progressively perceived, such as theinternal combustion engine, pump bodies, funds,
electrical boxes and cosmetic castings. Gray cast ironalso has high thermal capacity and thermal
conductivity, often used in some cookware.

Fig.2 Temperature distributions at several times forthe heat conduction of the Cast Iron for example 2.
(iii) Example3. Find the solution of the heat
conduction equation of the Cast Iron
𝒕𝒕 > 𝟎𝟎;
𝒖𝒖𝒕𝒕 = (𝟎𝟎. 𝟏𝟏𝟏𝟏)𝒖𝒖𝒙𝒙𝒙𝒙 𝟎𝟎 < 𝒙𝒙 < 𝟏𝟏,𝒖𝒖(𝟎𝟎, 𝒕𝒕) = 𝒖𝒖(𝟏𝟏, 𝒕𝒕) = 𝟎𝟎, 𝒕𝒕 > 𝟎𝟎;
𝒖𝒖(𝒙𝒙, 𝟎𝟎) = 𝐬𝐬𝐬𝐬𝐬𝐬 𝟐𝟐𝟐𝟐𝟐𝟐 − 𝐬𝐬𝐬𝐬𝐬𝐬 𝟓𝟓𝟓𝟓𝟓𝟓 , 𝟎𝟎 ≤ 𝒙𝒙 ≤ 𝟏𝟏.

Applying the finite difference method using Matlab,
then the result show as follows.

ACKNOWLEDGMENTS
This paper was supported by the Deanship ofScientific Research (DSR), King Abdulaziz
University, Jeddah.
REFERENCES[1] William F.A., Numerical Methods for Partial
Differential Equations. Academic Press, New
York, NY, 3rd edition, 1992.
[2] Jim D., and H.H.R., On the numerical solution ofheat conduction problems in two and three spacevariables. Trans. Amer. Math.Soc., 82: 1956,
pp.421-439.
[3] Mitchell A.R., and Griffiths D.F., The Finite
Difference Method in Partial Differential
Equations. John Wiley & Sons, Chichester, 1980.
[4] Mitchell A.R., and Griffiths D.F., The Finite
Difference Method in Partial Differential
Equations. John Wiley & Sons, Chichester, 1980.
[5] Dalal A.M, Amani Z.B., Badreeh M.G.,
Numerical Solution of Volterra Integral Equationof Second Kind Using Implicit Trapezoidal,
Journal of Advances In Mathematics, Vol8, No.2,
2014, pp.1540-1553.
[6] Dalal A.M., Adomian Decomposition Method ofFredholm Integral Equation of the Second Kind
Using Maple, Journal of Advances In
Mathematics, Vol9, No.1, 2014, pp.1868-1875.
[7] Dalal A.M., Application of Adomian
Decomposition Method for Solving of Fredholm

Fig.3 Temperature distributions at several times forthe heat conduction of the Cast Iron for example 3.
354

GEOMATE – Melbourne, Australia, 11-13 November 2020

Integral Equation of the Second Kind, European
Journal of Science and Engineering, Vol9, No.2,
2014, pp.1-9.
[8] Dalal A.M., Adomian Decomposition Method
for Solving of Fredholm Integral Equation of theSecond Kind Using Matlab, International Journalof GEOMATE, Dec. 2016, Vol. 11, Issue 28,
pp.2830-2833. Special Issue on Science,
Engineering and Environment, ISSN: 21862990, Japan.
[9] Dalal A.M., and Honida M.M., Numerical
Solution of System of Three Nonlinear Volterra
Integral Equation Using Implicit Trapezoidal,
Journal of Mathematics Research, Vol.10, No. 1,
February 2018, ISSN 1916-9795 E-ISSN 19169809.
[10] Dalal A.M., Finite Difference Method for
Solving Heat Conduction Equation of The

Granite, International Journal of GEOMATE,
Sept. 2019, Vol. 17, Issue 61, pp.135-140.
Special Issue on Science, Engineering andEnvironment,
ISSN:
2186-2982(P),21862990(O), Japan.
[11] Dalal A.M., FINITE DIFFERENCE METHODFOR SOLVING HEAT CONDUCTIONEQUATION OF THE BRICK, International
Journal of GEOMATE, April, 2020 Vol.18, Issue
68, pp.114-119.
[12] Dalal A.M., Eman A., Simbawa M., THE
MODIFIED DECOMPOSITION METHODFOR SOLVING VOLTERRA FREDHOLM
INTEGRO-DIFFERENTIAL
EQUATION
USING MAPLE, International Journal of
GEOMATE, March, 2020 Vol.18, Issue 67,
pp.84-89.

355

10th Int. Conf.
on Geotechnique,
Construction
GEOMATE – Melbourne,
Australia,
11-13 November
2020 Materials and Environment,

Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DEVELOPMENT OF ARTIFICIAL BEACHROCK USINGWATERMELON SEEDS AS A SOURCE OF UREASE
Md. Al Imran1, Kazunori Nakashima2, Niki Evelpidou3 and Satoru Kawasaki4
1Graduate School of Engineering, Hokkaido University, Sapporo, Japan
2,4
Faculty of Engineering, Hokkaido University, Sapporo, Japan
3
Faculty of Geology and Geoenvironment, National and Kapodistrian University of Athens, Athens, Greece

ABSTRACTIn recent years, the formation of artificial beachrock and bio-cementation method has gained considerable
attention as a sustainable alternative tool in the area of geotechnical and geo-environmental engineering field
for soil improvement and construction materials. In general, earlier methods of soil improvement were mostly
concentrated on microbes (Bacteria, Fungi, etc.) as a source of urease enzyme widely known as MICP method(Microbial Induced Carbonate Precipitation). To address some of the key limitations of MICP method thisstudy focused on using crude enzyme (low cost, eco-friendly). Crude enzyme was extracted from watermelon
seeds (Citrullus lanatus) considered as “food waste material” and the carbonate formation process known as
EICP “Enzyme Induced Carbonate Precipitation.” Crushed and blended watermelon seeds (both dry andgerminated) used as a source of urease enzyme. Subsequently, their urease activity was also investigated withvarious environmental parameters (Temperature, pH, etc.) and investigated the carbonate precipitation trendusing calcium chloride (CaCl 2 ) and urea [(CO(NH 2 ) 2 ]. The form of carbonate (calcite, aragonite, vaterite, etc.)
was also confirmed by XRD and SEM-EDX analysis. Finally, syringe (d = 2.3 cm, h = 7.1 cm) sandsolidification test was conducted using commercially available “Mikawa sand” (mean diameter, D 50 = 870mm) and successfully achieved unconfined compressive strength (UCS) of about 1.2 MPa at neutral pH (~7)
and temperature condition (30 °C) considering various curing days (14 days) and conditions. This study could
be useful as an eco-friendly and sustainable method for numerous bio-geotechnical applications (for instance,
ground improvement, liquefaction mitigation, artificial beach rock formations, coastal erosion protection, etc.)
and the extracted crude urease from watermelon seeds could play as an alternative to replace commerciallyavailable urease for carbonate precipitation.
Keywords: EICP, Watermelon seeds, Bio-cementation, Urease enzyme, Crude extract, Artificial beachrock

penetration, microorganism strength, etc. marked
this approach a complicated process. It challenged
the use of MICP method in real field applications.
Likewise, researchers around the world have tried
to overcome some of these leading limitations, butyet, some unresolved issues related to MICP
persisted. As an alternative technology, EICP
(Enzyme Induced Carbonate Precipitation) has
drawn considerable interest to the researcher forinvestigating the feasibility of using this methodfrom laboratory scale to real field application.
The demand for using the EICP process may be
fulfilled with commercially available urease
enzyme, which is very costly because it has been
manufactured for use in some specific applications
in small amounts at high purity levels [5],[6],[7].
Therefore, it was essential to find an alternative
source of urease enzyme to optimize the benefits
through bio-inspired engineering applications andmaking artificial beachrock considering cost,environmental safety, and sustainability. This study
focused on using crude urease extracted fromwatermelon (Citrullus lanatus) seeds as the sourceof urease enzyme considering waste utilization,

INTRODUCTION
Recently bio-inspired techniques have been
widely used for different geotechnical applications.
The maintenance, management of all of thesemethods were expensive and not eco-friendly [1, 2,
3]. Therefore, a new alternative countermeasure
was needed. One of the emerging alternative
approach known as “Microbial Induced Carbonate
Precipitation-MICP method,” learned from “natural
beachrock.” A beach rock consisted of marine
sediments that have been cemented in the intertidal
layer mostly by CaCO 3 , and its deposition time wasmuch shorter than that of other sedimentary rocks.
The cemented portion of beachrock primarily madeup of both calcium carbonate and silica.
Several researchers have studied the beachrock
formation mechanisms [3, 4]. However, some of the
difficulties related to the current MICP method,
have created requirements for alternative demand ofsolutions considering economic and ecologicalsustainability. For instance, the screening, isolationtransportation, filtering, physical non-homogeneity,
oxygen requirement, urease activity, viscosity, deep356

GEOMATE – Melbourne, Australia, 11-13 November 2020

cost-effectiveness, availability of the material, andenvironmental safety.
During the CaCO 3 precipitation, the following
reaction occurred in the presence of urea, urease
enzyme, and calcium chloride, Eq. (1-5).
𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈𝑈 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸

corresponds to time curve. Finally, to validate thedata, the urease activity was compared to available
commercial urease enzymes (jack bean, Wako purechemical industries, Ltd., Japan).

𝐶𝐶𝐶𝐶(𝑁𝑁𝑁𝑁2 )2 + 𝐻𝐻2 𝑂𝑂 �⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯� 𝑁𝑁𝑁𝑁2 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 + 𝑁𝑁𝑁𝑁3 (1)𝑁𝑁𝑁𝑁2 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 + 𝐻𝐻2 𝑂𝑂 → 𝑁𝑁𝑁𝑁3 + 𝐻𝐻2 𝐶𝐶𝐶𝐶3(2)

𝐻𝐻𝐻𝐻𝐻𝐻3− + 𝐻𝐻 + + 2𝑁𝑁𝑁𝑁4+ + 2𝑂𝑂𝑂𝑂− ↔

(4)

𝐻𝐻2 𝐶𝐶𝐶𝐶3 ↔

𝐻𝐻𝐻𝐻𝐻𝐻3− + 𝐻𝐻 +𝐶𝐶𝐶𝐶32− + 2𝑁𝑁𝑁𝑁4+ + 2𝐻𝐻2 𝑂𝑂

𝐶𝐶𝐶𝐶2+ + 𝐶𝐶𝐶𝐶32− ↔ 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶3 ↓(3)Watermelon seed (rawmaterial)

Strayingwith DW

Grinding

(5)

Urea

The primary objectives of this study were to findout investigate an effective extraction procedure of
crude urease enzyme from watermelon seeds and todevelop cemented samples towards making
artificial beachrock. In this study series of test-tube
experiments were conducted to identify thevariations of precipitated crystals and theirmorphology considering their reaction rate withtime. The precipitated crystals were analyzed with
SEM (Scanning electron microscope) and XRD (XRay Diffraction) to confirm their morphology.
Finally, the extracted crude enzyme was analyzed
and compared with a pure commercial enzyme(Urease from Jack bean: Wako pure chemicalindustries, Ltd., Japan) for measuring urease
activity.
Besides, syringe sand solidification tests wereconducted using the extracted crude enzyme andsuccessfully obtained the cemented sand specimen
in a laboratory scale up to several MPa, which could
be a mimic of natural beachrock. The findings ofthese studies could play a significant role in thedevelopment of bio-cemented sand (natural
beachrock imitation) using low-cost plant-derived
urease enzyme. Moreover, it could also be possible
to recycle waste food waste materials likewatermelon seeds.

Cemented
specimenSandsolidification test

Filtration

Centrifuge

CaCl

2

SEM and
XRD
Analysis

CaCO precipitate3

Crudeextract

Re-filtration

Fig. 1 Extraction procedure of crude enzyme for making
artificial beachrock

CaCO 3 precipitation test
A series of experiments were conducted toconfirm the morphology of CaCO 3 precipitationand its precipitation trend, by various combinationsof CaCl 2 -urea and urease. The set of experiments
conducted in transparent polypropylene tubes. 10
mL of equimolar solutions of CaCl 2 -urea wasformulated as 0.3 mol/L, 0.5 M, and 0.7 M andadjusted the concentrated distinctly by adding crude
extract solution from watermelon seed. The
adjusted solution was then added to the test tube andkept in a shaker at 30 °C for 48 h. The obtained
precipitation (CaCO 3 ) was separated from thesolution using a filter paper (Whatman filter paper,
grade 4) and kept in an oven drier for about 24 h at
100 °C. The crystal precipitation ratio (%) was
calculated by the mass of the precipitated materials
obtained from the test divided by the theoreticalmass of CaCO 3 (g) crystals. The sand solidification
testing conditions were shown in table 1.

METHODS
Extraction of crude urease
Finely crushed and blended watermelon seeds(0.5 g) using 10 mL of distilled water (concentration
estimated 50 g / L) stirred at 600 rpm for around 1
h. The crude extract was collected after
centrifugation and filtration. After centrifugation,
the collected transparent supernatant and pellets
were used to investigate the urease activity
considering different environmental parameters(temperature, pH, etc.) followed by the indophenol
method [8], [9]. The extraction procedure presented
in Fig. 1. The urease activity of the enzyme (crude
extracts) solution was calculated by dividing theslope of the initial linear part of the ammonium

Table 1: Testing conditions
Urea-CaCl 2(M)
EnzymeSolution
Initial pH
Final pH

Control
0.3/0.5/0.7
M
No

0.3

0.5

0.7

Yes

Yes

Yes

7.5/7.6/7.6
7.5/7.6/7.7

7.6
7.9

7.7
8.1

7.6
7.9

The hydrolysis rate was measured immediately
after mixing of solutions with time, and a standard

357

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 3 Status of sand solidification test using extracted
crude enzyme

curve was established using the obtained results
after complete hydrolysis of several concentrationsof urea. Finally, precipitated CaCO 3 weighed usinga scale to calculate the total carbonate precipitate
amount. Scanning Electron Microscope (SEM) and
X-ray diffraction (XRD) analysis were also
conducted to investigate the morphology of the
precipitated carbonate.

Table 2: Testing conditions
Cases Crudeurease123Sand solidification (syringe) test
Commercially available dried “Mikawa” sand(Fig. 2) placed at 110 °C for 24 h and were thentransferred (85 g) into a 50 mL syringe (diameter,
φ= 2.3 cm, height h=7.1 cm) and compacted gently.

Crude
Urea- Temp Curing
urease CaCl 2 (M) (˚C) days
(addedInjectioninterval everyday)
No
Control sample
Added
48 h
0.5
30
14
Added
48 h
0.5
30
21

RESULTS AND DISCUSSION
Results of urease extraction

100

A summary of urease activity (U/mL) of theextracted crude enzyme from watermelon seeds
presented in Fig. 4. The urease activity was also
compared with the commercially available urease
enzyme made from jack bean (Wako pure chemicalindustries, Ltd., Japan). According to the results, theurease activity was highest for non-treated crude
extract (around10 U/mL) and gradually decreased
with time (up to 07 days). For the treated sample,
the urease activity was somewhat lower than the
non-treated sample (8 U/mL) but almost constantwith time (up to 07 days).

Percentage passing（%）

Mikawa Sand
80
60
40
20
0
0.1

1
Particle Diameter（mm）

10

Fig. 2 Particle size distribution of Mikawa sand

16.0

The prepared samples were then placed in an
incubator (30 °C). Subsequently, 26 mL of preparedurea-CaCl 2 -urease solution (crude extract) was
added to the syringe and maintained the final levelof the solution above the top surface of the sandsample. After 24 h, a new cementation solution(urea-CaCl 2 ) was injected into the samples with thesame volume and concentration and drained thepreviously injected solution gradually (Fig. 3). The
crude urease injection interval was 48 h. Different
curing days were chosen for investigating the effect,
variations, and distribution of precipitated calcium
carbonate within the sand particles. The conditionof the syringe solidification test outlined in Table 2.

Urease activity (U/mL)

14.0

Wako pure enzymes
Non-treatedtreated

12.0
10.0
8.0
6.0
4.0
2.0
0.0
0

1

2

3

4

5

6

7

8

Days

Fig. 4 Urease activity of the extracted solution

For the commercial jack bean urease, the urease
activity was observed around 15 U/mL. The
purification level of the extracted crude enzyme wasaround 60-70% (Table 3). The fluctuation of thesepurification level and urease activity of the crude
enzyme to commercial enzyme was influenced bycertain metabolic factors [10]-[11] of seeds,
including some biological reactions and presence ofspecific organic compounds. The maximum urease
activity was obtained from jack bean seeds, asreported previously [9]-[11]. However, from thefindings of this study, the urease enzyme could be
extracted up to a purity level of 60 percent, which
could be sufficient to make bio-cemented for
different geotechnical approaches.
358

GEOMATE – Melbourne, Australia, 11-13 November 2020

It was revealed that, the efficiency of biocemented samples depends on the type and structureof the precipitated CaCO 3 polymorphs (vaterite or
calcite). When the crystals formed bridges between
the existing sand/soil grains, they prevented the
movement of the grains, which identified as a
primary reason for improving the strength andstiffness of the material [16]. Fig. 6 indicated thetrend of CaCO 3 precipitation at different CaCl 2 urea concentration. Results showed that, higher
concentration of CaCl 2 -urea leads to precipitate
higher amount of CaCO 3 crystal.
In addition, from the SEM images it showed thatthat the precipitated crystals morphology (CaCO 3 )is highly inconsistent in shape and size (Fig. 7). But
the value for crystals precipitation depends on theconcentration of urease enzyme and concentrationof reactant solutions, which was also reported inprevious studies [17]-[18]. The results of this study
showed that, precipitated calcium carbonate
crystals from crude extract enzyme may appear as
highly disorder and amorphous due to presence orinheritance of different organic compounds
confined to the raw materials (crude extract solution
from watermelon seeds).
Furthermore, the results also indicated that thereaction rate of EICP process were very fast whichleads to precipitated different crystalline phases of
CaCO 3 crystals. However, the observation of thisstudy could be considered as significant outcomesfor various bio-geotechnical applications.

Volume of extraction (mL)

Total Units (U)Specific activity (U/mL)

Purification fold

Loss of enzyme (%)

Wako
pure
enzymes
NontreatedTreatedUrease activity (U/mL)

Product

Table 3: Crude urease extraction results with their
purification percentage

15

100

150

10

0.67

0

10

100

100

10

1

33

8

100

80

10

1.25

47

* Loss of enzyme = [Sum of crude extract (U) – Total
Units after treatment) / Sum of crude extract (U)]CaCO 3 precipitation test
It is reported that, CaCO 3 acts as the mainbinding materials in between the sand particles tostabilize the soil for producing artificial beach rockand bio-cemented sand [12-15]. In EICP process,the crystal precipitation amount closely related with
CaCl 2 -urea concentration, and the CaCO 3 crystals
started to form immediately because of theaccessibility of free urease enzyme (Fig. 5), which
was also reported by previous studies [16]. Fig. 5
showed the evidence of forming and enlargement of
CaCO 3 crystals.
CaCO3 crystals
started to form

Free urease
enzymeVariations of precipitated crystals [in-termsof size (small or large) and types (e.g:vaterite, calcite, aragonite)

Enlargement of CaCO3 crystals

Precipitated CaCO3 crystals
Growing CaCO3 crystals

Fig. 5 Growing of CaCO3 crystals

0.7
Watermelon Seeds
Control

Precipitate CaCO3 (g)

0.6
0.5

Fig. 7 SEM images of precipitated crystals

0.4
0.3

Sand solidification test (Small scale)

Precipitated crystals

0.2

Mikawa sand was used for sand solidification
using the extracted crude solutions fromwatermelon seeds. From the results, it showed thatthe higher UCS was obtained for the longest curing
sample (case 2) compared with case 1. The
variations of the UCS strength was also observed in

0.1
0Control

0.3 M
0.5 M
solution solution
CaCl2-Urea concentration

0.7 M
solutionFig. 6 Trend of CaCO 3 precipitation

359

GEOMATE – Melbourne, Australia, 11-13 November 2020

further investigation is needed to improve thismethodology, which could be appropriate for field
application.

top, middle and bottom portions of the both
specimens (case 1 and case 2). From the results, it
was observed that, the bottom portion showed thehigher UCS value compared to top and middle
portions of the both treated samples (Fig. 8). The
possible reason was the fast reaction rate of the ureahydrolysis and distribution variations ofprecipitated CaCO 3 within the specimens (case 1
and case 2). Because of the reaction rate, most of
the precipitated CaCO 3 tends to deposit at the
middle and bottom portion and increased thebonding capacity in between the sand particles atthe bottom and middle portion and increased thestrength (UCS) (for both case 1 and case 2). But it
was also observed that, with increasing the curing
time (21 days), the distribution of CaCO 3
precipitation was also fluctuate at the top, middle
and bottom portion (case 2) and strength (UCS) was
also increased. Further investigation is needed for
obtaining homogenous strength of the sample byensuring uniform distribution of CaCO 3
precipitation.
In addition, another reason could be higher
solubility, lower viscosity, fast penetration anddeposition at the bottom portion of the sand, which
also supported by previous studies [19]-[20]. A
higher UCS was also observed obtained (around 1.2
MPa) after continuous curing (21 days) at 2.5% of
CaCO 3 for case 2. In case of 1, maximum UCS was
obtained (below 1 MPa) with less than 2% of
CaCO 3 content and the bottom portion bearing thehighest strength Fig. 9. And Fig. 10 also showed theresults of the precipitated crystal behaviour inbetween the cemented sand particles.

3.5
3% CaC03

2.5
2
1.5
1
0.5
0
Controll

Case 1Case 2

No. of Cases

Fig. 9 Percentage (%) of CaCO3 content of thecemented sand specimen

Fig. 10 Status of solidified sand (left images) and SEM
images of solidified sand showing the CaCO 3 bonding in
between the sand particles (right images)

CONCLUSIONSThe prospect of using the enzyme induced
carbonate precipitation (EICP) method as aneffective bio-inspired technique has been evaluatedin this study using natural source of urease enzyme(watermelon seeds). The development of alternative
sources for urease enzymes which are economicaland easy to regulate in the natural world was a major
problem in the feasibility of calcite Precipitations
Strategies. Through extracting the enzyme fromwatermelon seeds (upto 60% purity level) in asustainable, cost-effective, and eco-friendly way,
this research successfully addressed thesechallenges. Furthermore, this study successfully
obtained solidified sand sample upto 1.2 MPa using
watermelon seeds as a source of urease enzyme andthe results from this study clearly indicated that,plant derived urease enzymes could play a vital role
for making artificial beachrock. However, stillsome major challenges for field implementations of
this EICP method remained in this novel area.
Nevertheless, the findings of these studies could
play a substantial role for soil/sand stabilization,
coastal erosion protection, ground improvement indeeper portion of the soil, improvement of piles andslope stability, to support foundation and

Fig. 8 Estimated UCS of the solidified sand with % of
CaCO 3 content

Form the results, it showed that, CaCO 3 is theprimary binding materials for improving thestrength of sand particles which is an agreement ofprevious studies [21]. Another important
observation was viscosity of the cementation
solution could affect the crystal precipitation rate toform CaCO 3 and improving the sand strength to amost stable phase of calcium carbonate. However,360GEOMATE – Melbourne, Australia, 11-13 November 2020

embankment, improvement soils liquefactions
under the existing structures and other biogeotechnical applications.

[10]

[11]
ACKNOWLEDGEMENTS
This work was partly supported by JSPS
KAKENHI, grant number JP19H02229.
[12]
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I.A.; Shivhare, U.S. Characterisation andfunctional properties of watermelon(Citrullus lanatus) seed proteins. J. Sci.
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Carbonate Crystals Formation by Ureolytic
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STUDY ON STRUCTURAL EFFECTIVENESS OF A BUILDINGSSTRUCTURE BY USING A CFRTP
Yasuhide Mochida1, Kouhei Kasahara2
Professor of Department of Architecture and Urban Design, Graduate School of Science and Engineering,
Ritsumeikan University, Japan; 2Master Degree Student of Department of Architecture and Urban Design,
Graduate School of Science and Engineering, Ritsumeikan University, Japan

1

ABSTRACTAt present, carbon-fiber reinforced plastics (CFRP) are not permitted to be used as structural members in thearchi-tectural field. Therefore, in order to popularize CFRP in the architectural field, this study aims to show theusefulness of CFRP (CFRTP) using thermoplastic resin, which is inexpensive to manufacture and excellent inrecyclability among CFRP, and evaluates the effectiveness when CFRTP strand rods are used for bracing
material. As a result of comparing by eigenvalue analysis using round steel, PC steel stranded wire, and CFRTP
for braces, the long period was able to be confirmed by the use of the frame of CFRTP brace. Then, this paper
carries out comparisons by time history response analysis between a seismic resistant model using a threedimensional frame model and a seismic isolation brace model using a CFRTP brace and an oil damper in thelowest layer. As the result, it was proven that the isolation brace showed the reduction performance equivalent tothe conventional isolation device for the input seismic motion.
Keywords: carbon fiber reinforced thermo plastics, strand rod, brace of isolation, seismic isolation layerrods, and to contribute to the popularization of
CFRTP in the architectural field.
The structural analysis software midas iGen wasused for the analysis of this study.

INTRODUCTION
Carbon-fiber reinforced plastics (Carbon Fiber
Reinforced Plastics: CFRP) are materials utilized invarious fields because of their excellent properties
such as light weight, high strength, corrosion
resistance, and non-magnetism. Among them, a
CFRP using a thermoplastic resin (Carbon Fiber
Reinforced Thermo Plastics: CFRTP) as a base resin
can be produced at a lower cost than a CFRP using athermosetting resin which is generally popular, iseasy to carry, and is also excellent in recyclability
because it can be separated by heating a basematerial and a carbon fiber. Therefore, it is amaterial which can also be expected to be
popularized in the architectural field in the future.
However, CFRP is currently used in the civilengineering field as a substitute for reinforcing bars
and carbon-fiber sheet wrapping reinforcement
method, etc., but its use for structural members is
not permitted because it is not designated as adesignated building material under Article 37 of the
Building Standards Construction Law in thearchitectural field, and it has not generally become
popular.
This study aims to evaluate the effectiveness of
CFRTP strand rods for buildings when they are usedas brace materials by referring to the use method of
PC (Prestressed Concrete) steel materials, and to
examine their application to the seismic isolation
device "seismic isolation brace" (Fig. 1) of brace
frames as an application method suitable for strand

Fig. 1 Seismic isolation
STUDY ON TWISTING PERFORMANCE OF
CFRTP
In the bonding method of CFRTP using the innerthread cutting steel pipe by previous studies, theadhesion force between the adhesive resin is
increased by using a member obtained by adding
twisting, it has been confirmed that the advantagethat the tensile stiffness of the joint is increased canbe obtained. Therefore, in this chapter, we derive theequation for evaluating the twist angle of CFRTP
strand rods and the tensile stiffness and tensile
strength of members, and clarify the relation
between them. (1a), (1b) The equation is the 2) ofthe equation for evaluating the twist angle of thestrand rope of the steel material.
362

GEOMATE – Melbourne, Australia, 11-13 November 2020

strength.
Further, the value of the twist angle 14.7 °obtained from the evaluation equation is a result
which is not large difference even compared withthe value of the experimental value measured fromthe test piece of the same twist angle, it was
confirmed that the evaluation formula can be usedroughly.

𝐸𝐸𝑤𝑤 = 𝐸𝐸 (𝑐𝑐𝑐𝑐𝑐𝑐 3 𝛼𝛼 − 𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠2 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 )(𝑐𝑐𝑐𝑐𝑐𝑐 3 𝛽𝛽 −𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠2 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽 )(1a)𝑃𝑃𝑤𝑤 = 𝑚𝑚 ∙ 𝑛𝑛 ∙ 𝐸𝐸𝐸𝐸(𝑐𝑐𝑐𝑐𝑐𝑐 3 𝛼𝛼 − 𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠2 𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼𝛼 )(𝑐𝑐𝑐𝑐𝑐𝑐 3 𝛽𝛽 −𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠2 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽 )𝜀𝜀𝑧𝑧(1b)

𝐸𝐸𝑤𝑤 : Young's modulus of member
𝑃𝑃𝑤𝑤 : Axial force of child wire (steel)𝛼𝛼 : Strand angle of strand
𝛽𝛽 : Twist angle of child wire
E : Young's modulus of the child line𝜈𝜈 : Poisson's ratio of the child line
m, n: m × n strand ropes

COMPARATIVE STUDY OF PRESTRESSINGSTEEL BY NUMERICAL ANALYSIS
Outline of the Analysis Model
The effect of prestressing steel and the change in
deformation performance and natural period due toprestressing force are verified when CFRTP strand
rods are used with braced members in Table-2 andwhen prestressing steel stranded wires or round steel
are used for the steel frame validation model shownin Fig. 3 with a span of 8000mm and a height of
3500mm. Incidentally, since CFRTP is a material
having a property of brittle fracture without plastic
zone, this analysis is to consider only the elastic
range, the braced section was set to stay in theelastic zone.

When this equation is adapted to CFRTP strandrods, the equation for evaluating tensile stiffness andtensile strength is as follows.
𝐸𝐸𝑐𝑐 = 𝐸𝐸 (𝑐𝑐𝑐𝑐𝑐𝑐 3 𝛽𝛽 − 𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠2 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽 )𝜎𝜎

𝑠𝑠 𝑚𝑚𝑚𝑚𝑚𝑚
𝜎𝜎𝑐𝑐 𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑐𝑐𝑐𝑐𝑐𝑐 3𝛽𝛽−𝜈𝜈𝑠𝑠𝑠𝑠𝑠𝑠
2 𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽𝛽

(1c)
(1d)

: Young's modulus of CFRTP strand rod
𝐸𝐸𝑐𝑐
𝜎𝜎𝑐𝑐 𝑚𝑚𝑚𝑚𝑚𝑚 : Breaking strength of CFRTP strand rods
𝜎𝜎𝑠𝑠 𝑚𝑚𝑚𝑚𝑚𝑚 : Breaking strength of the strands(1c), (1d) From the equation, the relationshipbetween the twist angle and Young's modulus using
the physical properties in Table-1 and the breaking
strength is shown in the graph as shown in Figure-2.

Table 2 Comparative brace member
Types ofBraces(naming)

Table 1 Physical Properties of Strand Rod
Young's modulus of the childwire(kN/mm²)

94

Breaking Strength of Wire(N/mm²)

1644

Poisson's ratio

0.06

Young'sStrength Diam Tension
coefficient of yield eter force(kN/mm²) point(mm) (kN)(N/mm²)

Round
steel
(SS400)PC steel
wire(SWPR7B)
CFRTP(24
K3P×7)

Fig. 2 Relationship between twist angle and physicalproperties

205

235

27

0

200

1583

10

0
61

85

1629

10

0
61

Fig. 3 Analysis model

Young's modulus viewed the property ofdecreasing as the twist angle increases, the resultthat the strength increases as the twist angle
increases conversely with respect to the breaking

Static Analysis
In the static analysis, the horizontal displacement
was confirmed by applying a horizontal load of363

GEOMATE – Melbourne, Australia, 11-13 November 2020

100kN to the model upper beam. Figure 4 shows theresults.

CFRTP(tension)

1.37

EXAMINATION OF SEISMIC ISOLATIONBRACES ALONE
Outline of Analysis
In this analysis, time calendar response analyses
were carried out by changing three types ofparameters such as span length (2000-6000m inevery 1000mm), brace cross section (9, 10, 16mm indiameter), and twist angle of brace (14.7°, 30°) of
the framing model as shown in Fig. 3 to clarify the
relationship between the critical horizontal
displacement, brace proof stress usage rate, natural
period, maximum acceleration of the upper beam,and horizontal stiffness, and to examine the shape ofthe brace frame suitable for seismic isolation braces.
The time calendar response load used Tohoku1978
NS seismic waves as the maximal acceleration of
500gal.

Fig. 4 Horizontal displacement of the model
As shown in Fig. 5, CFRTP with the smallest
axial stiffness had the greatest displacement, and in
the same member, the displacement became smaller
because both the tension side and the compression
side braces resisted the horizontal force byintroducing the prestressing force.

Analysis Results
Span Length (Fig. 6, 7)
The critical horizontal displacement becamecomparatively large in the models of 2000mm spanand 6000mm span. This increases the aspect ratio of
the frame, especially in the case of short spans, andthe horizontal displacement increased because thelarge effect of the brace deformation on the column.
Also, if the span is long, the axial stiffness decreases
due to the length of the brace and the amount of
deformation increases. (Fig. 6)
For the bearing capacity utilization factor (=sectional force/breaking load) of the brace member,it was kept relatively small at 2000 and 6000mm
spans, but no large difference between spans wasobserved. (Fig. 6)
Although the natural period is influenced by the
aspect ratio of the frame and the axial stiffness of thebrace, the natural period tends to be longer as thespan becomes longer as a whole model comparison.
Since the maximum acceleration of the upper beam
is affected by the natural period, the longer the span
and the longer the natural period, the smaller the
maximum acceleration can be suppressed. The
horizontal stiffness of the model became larger forthe longer span, because the angle of the brace faced
horizontally as the span became larger. (Fig. 7)
From the above, it is said that a frame with a
long
span
is
suitable,consideringtheappropriateness of the seismic isolation brace from(𝑃𝑃 + ∆𝑃𝑃𝑖𝑖 )𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 − (𝑃𝑃 − ∆𝑃𝑃𝑖𝑖 )𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 2∆𝑃𝑃𝑖𝑖 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

Fig. 5 Concept of Horizontal Resistance
Eigenvalue Analysis

As shown in Table-3, the natural period is longer
in the order of CFRTP, PC-steel stranded wire, andround steel. In the case of the tensioned steel and thenon-tensioned steel, the horizontal stiffness of theframe increases due to the introduction of tensioned
force. Therefore, the natural period of the nontensioned steel model is longer, and the natural
period of CFRTP is 1.93s at the highest. From this, it
was clarified that the long period of the frame could
be attempted by the utilization as a bracing member
in CFRTP of non-tension.
Table 3 Natural Period
Bracing member

Fixed cycle (s)Round steel(non-tension)

0.46

PC steel wire(non-tension)

1.26

PC steel wire(tension)

0.89

CFRTP(non-tension)

1.93
364

GEOMATE – Melbourne, Australia, 11-13 November 2020the viewpoint of the span length.

brace member tends to increase slightly as the crosssection increases, there was no big difference. The
phenomenon in which the use rate is kept to thesame level in each section is thin.
It is considered that the brace has a softer
structure as the whole frame, and the axial force
borne by the brace material is small.
Therefore, the smaller the cross-section of the brace,
the better its performance as a seismic isolation
device.
Brace Twist Angle (Fig. 9)

Fig. 6 Comparison result by span ①

Fig. 9 Comparison Results by Twist Angle
As shown in Chapter 2, the strength of themember is increased by increasing the twist angle. It
has been found that there are physical propertychanges of degree increase and rigidity decrease.
Therefore, the critical horizontal displacement of thebracing material was higher at a twist angle of 30°
with a lower Young's modulus and higher fracture
strength, and the intrinsic period was longer,
resulting in a lower strength utilization rate of thebracing material. And, the maximum acceleration
was reduced by the longer natural period, and thehorizontal stiffness lowered.
It can be said that using a strand rod with a large
twist angle becomes a long period and is suitable forseismic isolation.

Fig. 7 Comparison result by span ②
Brace Section (Fig. 8)

Fig. 8 Comparison results by cross section of brace

EXAMINATION IN STEREO FRAME MODEL

The larger the cross section, the higher the axial
stiffness and the horizontal stiffness of the frame,
and the shorter natural period was shown. On theother hand, the smaller the axial stiffness, the longer
the natural period and the smaller the horizontal
stiffness.
The maximum acceleration of the upper beam
becomes smaller with the model of small section
with longer natural period.
Though the proof stress utilization factor of theOutline of Analysis
In the five-story stereoscopic frame model, thispaper carries out time calendar response analysis
using a seismic resistant structural model of S purerigid frame and a model incorporating a seismic
isolation brace in the same model, and evaluates theperformance as a seismic isolation device of seismic
isolation brace from each response value.

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GEOMATE – Melbourne, Australia, 11-13 November 2020

A model of 3500,4500mm was set for the firstfloor height. Referring to the results of Chapter 4,
which showed high seismic isolation performance,
the span was set to 6000mm, and two CFRTP strand
rods with a twist angle of 30° were used for thebraces at 9mm and 10mm diameters. In addition, an
oil damper is incorporated in the seismic isolation
brace model, and the performance is shown in Tab.6.
There are a total of six types of analytical models
according to the height of the first floor, thestructure, and the diameter of the strand rod, as
shown in Table-5.
Five types of El Centro1940 NS, Taft1952 EW,
Hachinohe1968 NS, Tohoku1978 NS, JMKobe1995 NS seismic waves (Fig. 10) were adopted
as time calendar response loads, and time history
response analyses were carried out for each seismic
wave. The seismic waves were used in the X‐ andY‐axis directions as the maximum acceleration of
500gal.

Fig. 10 Waveform of input seismic wave
Analysis Results

Table 4 Oil damper performanceType

Oil damper

Company

Kayaba System
Machinery, Inc

Product

BDS model 500kN
stroke500-1400

Model number

BDS901400-L-4

Initial attenuation
coefficient

0.25kN＊s/mm

Spring rigidity

155kN/mm

The natural period was lengthened to 3.7-4.6 s
for the seismic isolation model, compared to 0.8-1.0
s for the seismic model (Fig.11).The maximum
acceleration is also amplified in the upper floors of
the seismic isolation model, whereas the maximum
acceleration in the upper part of the seismic isolation
bracing model is In terms of structure, it is possible
to reduce the earthquake motion by 50-60% of the
input motion, which is about the same as that ofordinary seismic isolation devices such as laminatedrubber(Figs.12 and 13). The interlaminar
deformation angle is more than 1/200 for the seismic
structure model, while the seismic isolation bracemodel is more than 1/200. For each model, for the 25 floors above the seismic isolation layer, about
1/750 in the X-axis, 1/600 in the Y-axis It was
reduced to the extent (Figs. 14 and 15).

Table 5 List of Analysis Models
ModelsymbolFormatBrace
section

Dam
per

3.5-

earthquakeresistant━━

3.5-9dseismic
isolation braces

9 mm

〇

3.5-10d

seismic
isolation braces

10 mm

〇

3.5-

earthquakeresistant━━

4.5-9dseismic
isolation braces

9 mm

〇

4.5-10d

seismic
isolation braces

10 mm

〇

Fig.11 Eigenfrequency of each model

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GEOMATE – Melbourne, Australia, 11-13 November 2020

SUMMARY AND CONCLUSIONSIn order to show the usefulness of CFRTP, thisstudy examined the performance of CFRETP and
evaluated the performance when CFRTP was usedas bracing material.
The equation for evaluating Young's modulus
and twist angle of CFRTP was derived, and it wasproven that the Young's modulus decreased and therupture strength increased as the twist angle
increased for CFRTP strand rod.

Fig.12 X-axis maximum acceleration

When CFRTP was used for the brace frame, it
was more rich in deformation performance than thesteel material, and the natural period becamecomparatively long, and it came to the application asa seismic isolation brace.
By the examination in the seismic isolation brace
single body model, the result was that the shape ofthe seismic isolation brace has the aspect ratio of 1or less, the brace member is long and thin, and theseismic isolation performance is improved as thetwist angle is bigger.
When a seismic isolation brace was incorporated
into a three-dimensional model of a five-story S
structure, it was possible to make a four-second
seismic isolation in which the maximum
acceleration was reduced to 50-60% of the vibration
at the time of input, but the point that the
deformation of the first layer, which is a seismic
isolation layer, becomes larger than the seismic
structure is a problem. And, it was proven that
security of the floor height of the base isolation layerand small section of the member were effective inorder to suppress the proof stress use rate of thebrace material.

Fig.13 Y-axis maximum acceleration

Fig.14 X-axis interlayer deformation angle

REFERENCES[1] Toyo Maeda, Kozo Kimura: Cutting-edge p213216 of carbon fiber, 2007.1.
[2] Shizuo Shimada: Deformation and Stress of
Elastic Wires, Society of Civil Engineers, 1961,
72Appendix1, p. 1-16, 1961.
[3] Naoaki Hamada: Construction of External
Seismic Reinforcement Work for School
Facilities Using Oblique PC Steel Materials,
p.377-378, 2010.10.
[4] Kunio Saito, Akira Okada: Challenges andDevelopment of Cable Structure in Architecture,
p1-4, September 1994
[5] Hiroshi Sakata and Mikio Kodama: 8. Case
Study (4): PC with Special Materials, p43-44

Fig.15 Y-axis interlayer deformation angle

367

GEOMATE – Melbourne, Australia, 11-13 November 2020

[6] Naaki Hamada: Construction of external seismicrein-forcement of school facilities using slanted
PC steel, p.377-378, 2010.10[7] Tensile Test Results: Better Living, 2013.5,
General Foundation[8] Architectural Society of Japan: Cable Structural
Design Guidelines and Explanations, p85-87,
June 1994.

[9] Shizuo Shimada: Deformation and Stress of
Elastic Wires, Society of Civil Engineers, 1961,
72Appendix1 p.1-16,1961[10] Shimadzu Test CSC News No. 270, 2007,
Applied Tech-nology Department, Analytical
Measurement Division, Shimadzu Works.

368

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

UTILIZATION GGBFS AS CEMENT SUBSTITUTION TO REDUCEPRODUCTION COST OF CONSTRUCTION PROJECT
Ranti Hidayawanti1, Yusnita Rahayu2, Soetjipto Soewono3, Fahdun Ibnu Wachid4 and Rony Panca Adi
Widodo5
1,2Faculty of Territorial Science Engineering, Institut Teknologi PLN, Indonesia; 3Faculty of Electricity and
Renewable Energy, Institute Technology PLN, Indonesia; 4PT PJB, Indonesia; 5Adhimix RMC, Indonesia
ABSTRACTThe development of construction is currently advancing rapidly, the positive and negative impacts of theconstruction of this construction can begin to be felt. One negative impact is the reduction in natural resources thatcan not be renewed as the main material for development, for example limestone as the material for cement. It is
well known that concrete is a building material and as an effort to meet high quality concrete, a good compiler isalso needed. Various kinds of research and experiments have been carried out to improve the quality of concrete.
Starting from the addition of additives, concrete treatments, improving the quality of concrete materials and theutilization of industrial waste as a concrete reinforcement mixture. This study aims to utilize waste from burning
iron, namely Grand Granulated Blast Furnace Slag (GGBFS) or cement slag. GGBFS can be used for concrete
mixes because it has almost the same properties as cement, C3S2 which can increase the elasticity and reduce theheat of hydration in concrete. GGBFS is used to replace some of the fine aggregate in concrete that aims todetermine the extent of its effect on compressive strength of the high quality of concrete. The composition ofsubstitution of fine aggregate with GGBFS is 0%, 25%, 50%, 75% of the volume cement. The method be used isinitial setting time, compressive strength, linear regression and slag activity index. Based on research result initial
setting time increases with a variation of 75%, for the optimum compressive strength test GGBFS in this mixtureof 75%, linear regression reaches a value of 0.9781 with a category of strong relationship and applicable as well
as slag index activity values enter grades 100 and 120 approaching OPC 100. Beside that GGBFS is very good to
be used as a substitute for some cement in concrete and can also reduce production cost 3% and environmentallyfriendly.
Keywords: GGBFS, Cement, Concrete, Compressive Strength, Production Cost
concrete slag waste. Utilization of industrial waste in theconstruction sector is used to improve the quality andcompressive strength of concrete because it contributes
to reducing the use of natural resources. 70% ofconstruction materials are concrete [3]. With theutilization of concrete slag can reduce the potential loss
or save money of construction companies [4]. The
purpose of this research is to measure the utilization of
GGBFS with variations of 0%, 25%, 50% and 75% in
the quality of K200 - K700. From this variation, will bechosen the optimum one for Tokyo Apartment
construction. From the laboratory test results, it isrecommended to utilize GGBFS with a variation of 25%
with the quality of the K400.

INTRODUCTION
In the development world of construction, mostbuilding compilers are concrete. For examples roadinfrastructure, buildings and many more and encourage
us to be able to maintain the quality of the concrete.
Concrete is very popular because the basic materials areeasily obtained from natural resources and easy to use.
Along with the development of concrete material needs,
the need for cement which is becoming scarce andexpensive is needed for innovation to develop
technology including utilizing industrial waste. For
examples GGBFS (ground granulated blast-furnaceslag) or cement slag. GGBFS according to ACI 233R95 [1], is a slag furnace of metal or iron with atemperature of about 1500o C. GGBFS is used to makestrong and durable concrete structures that can be
combined with ordinary Portland or other pozzolan
materials. When cement is blended with slag, it modifies
mineralogical distribution and surface electricalproperties, increases tortuosity, and reduces porosity
and threshold pore size to show high chloride resistanceand better durability performance[2]. In developed
countries the use of Concrete Slag has been used, but inIndonesia not all constructions have implemented

Fig.1 Tokyo Apartment Project using GGBFS

369

GEOMATE – Melbourne, Australia, 11-13 November 2020

Figure 3 shows some of the tools that used in theexperiments of mortar and concrete specimens.

Figure 1 shows The Tokyo Tower Apartment
construction project is owned by PT Agung Sedayu
Group (ASG) and is carried out by PT Multikon, PT
Rekaguna Teknik and PT Pulau Intan, with an
implementation time of 356 (three hundred and fifty six)calendar days starting on 1 August 2019. The Tokyo
Tower Apartment construction project was carried out
by 6 towers in advance with 2 towers from eachcontractor, of which 5 towers will be carried out after the
3rd quarter of 2020. The land area of 35.000 m2 and thevolume of concrete for 6 towers 300.000 m3, foundation
work 56.000 m3, has a contract value of 1.5 trillion
rupiah. To minimize the need for cement which has ahigher price, it is necessary to innovate to substitutecement with other materials without reducing quality.
In designing concrete used for high-rise buildings,
good quality is needed to meet the specifications
including the quality of the K200 - K700 concrete. This
compressive strength calculation is supported by otherformulations such as initial setting time (w/c), slag index
activity and linear regression.

METHODOLOGY
Experiments carried out at the laboratory scale
include mortar cube experiments and concrete
experiments using a small mixer with a capacity of 100
liters. Variations in mortar experiments are with amixture of GGBFS 0%, 25%, 50% and 75%. While theconcrete experiment was carried out looking for thecorrelation w/c (cement water factor) to the percentage
of GGBFS use is w/c K200 – K700.
To find out the quality of GBFS in addition to direct
testing of the slag material both physical and chemical
testing, a comparison method is also performed. By
conducting laboratory scale experiments on cube
compressive strength of 5x5x5 cm, which refers to
ASTM-C 109 Standard Test Method for Compressive
Strength of Hydraulic Cement Mortars [5]. As well as
the compressive strength of concrete cylinders 15 xheight 30 cm which refers to ASTM-C39 [6] Standard
Test Method for Compressive Strength of Cylindrical
Concrete Specimens. Furthermore, applying Slag index
activity and linear regression.

TOOLS AND MATERIALS
To support the fulfillment of concrete needs, thematerial used are as follows :a. GGBFS Slag : ex. PT. KSI
b. Natural Sand : Ex Belitungc. Coarse aggregaten10-25mm : ex. Purwakartad. Admixture Type F : Standard Adhimix
e. Cement : Type 1 (OPC) and water : Concrete Standard

Fig. 2 Materials ResearchTools need that are used as follows :a. Mortar Mixer : 1 Unit
b. Mortar Cube Mold 5x5x5 cm : 12 set @ 3 ea
c. Mortar Flow Test : 1 Set
d. Cylindrical Mold D15 X h30 cm : 40 ea
e. Compressive Strength equipment
Fig. 4 Research Methodology
RESULT
Mortar Experiment
The results of mortar experiments conducted 3(three) times obtained the following average data as
follows :Fig. 3 Required Tools

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GEOMATE – Melbourne, Australia, 11-13 November 2020

a = water requirement (ltr/m3)
b = w/chours : minutey = 0.0192x + 0.141
R² = 0.8606
6:00
4:48
3:36
2:24
1:12
0:00

4:00

4:13

4:28

5:27

determination coefficient of the correlation relationshipis formulated:
r2=

0%

25%

50%

(∑ )

(

)

(∑

)

( )

( )

(2)

coefficient value of relations
0 = no relation
1 = perfect relation

75%

Variation of GGBFS

Concrete ExperimentFig. 5 Initial setting Time of Mortar

The results of concrete experiments using
variations of GGBFS compared to 100% OPC are as
follows:
Compressive strength =(3)
f’c = Concrete compressive strength (Mpa)
P = maximum load (N)A = press area of test object (mm2)

From figure 5 shows the addition of GGBFS has theeffect of increasing the initial setting time, as the greateruse of GGBFS the longer the initial setting time.

Fig. 6 Mortar Strength Mortar Comparison
Figure 6 shows, the greater the use of GGBFS, thecompressive strength at the age of 3 and 7 days are
resulting lower, but at the age of 28 days there was asignificant increase can even exceed 100% OPC so thatwill influence the value of linear regression. At the ageof 28 days and 56 days the need for water is greater sousing slag is not recommended.

Fig. 8 Initial setting time of Concrete
Figure 8 shows, The use of GGBFS has a tendency
that the initial concrete setting time will increase by anaverage of 30 minutes. Although the data does not show
a clear pattern and some data shows the opposite,namely in w/c sample K200 for GGBFS 25% and w/csample K600 GGBFS 75%. The greater the use of
GGBS the smaller air needed to get the same working /slump ability. It can be seen that the greater quality ofconcrete the smaller initial setting time since theachievement of GGBFS is greater and the requisite forcement is smaller so that the binding time of concrete islower.
From Fig. 8 for water per cement (W/C) is not too
significant because the addition of water to cement foreach constant quality is between 10-20 liters.
Before making a testing sample or printing a test
sample on a cylindrical mold, a slump test is needed sothat the researcher can find out how much water isneeded in a concrete mixture. The effects ofcombinations of mineral admixtures in concrete on theperformance-related properties under different aspectratio are compared to conventional concrete[7]. The
following are the results of water needs and slump tests:

Fig. 7 Development of Mortar Strength
Figure 7 shows, The development of compressive
strength using GGBFS at the age of 3 and 7 days is very
slow compared to 100% OPC. With the development of
a 50% variation the regression value reached 0.8577,
meaning that the strong relationship between mortar age
and compressive strength can be used. The linear
regression formulation is as follows:
Y = a+bx(1)

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Figure 11 shows, the percentage achievement of 28days of age varies 0% -75% in accordance with therequirements for compressive strength but for 75%
variation the use of slag is not recommended because thecompressive strength does not meet the specifics eventhough the percentage is 100%.

Fig. 9 Water requirements perm3, slump 12cm, size
25mm
Figure 9 shows, the greater the use of GGBFS theless water needs to get the same workability / slump.
With the utilization of GGBFS 50%, the greater thewater/cement used, the less water used and the results of
the linear regression show the value of 0.9896 meansthat it has a strong relationship and can be used[8].

Fig.12 Quality of Compressive Strength (K300)
Based on the results of the compressive strength testof K-300 concrete quality, it can be seen that in each test
3 days, 7 days, 28 days and 56 days the compressive
strength of concrete has decreased every additionalvariation in the percentage of GGBFS substitution for
cement. At the age of 28 days, the quality of K300 using
GGBFS can use a mixture of Slag with a variation of
0%, 25% and 50%, the variation of 75% does not meetthe compressive strength and does not reach the requiredcompressive strength. This can be seen at the age of 28days, the compressive strength value of 0% variation is359.79 kg / cm2, GGBFS variation 25% = 345.88 kg /cm2 and 50% GGBFS variation 314.60 kg / cm2, while
the 75% variation in compressive strength value
decreases 225.95 kg/cm2 and not included in the
category required.

Fig.10 Quality of Compressive Strength (K200)
The greater the use of GGBFS, the compressive strength
produced is lower, especially in the early age. But thedevelopment of compressive strength increases every
time, this can be seen from the graph above. For K200
quality, the utilization of GGBFS can use a Slag mixture
with a variation of 0%, 25% and 50%, a variation of 75%
does not meet the compressive strength and does notreach the required compressive strength.

Fig.13 Development of Quality Concrete (K-300)
Figure 13 shows the development of compressive
strength increases every time, this can be seen from thegraph above. The achievement of the percentage of 28days of variation 0% -50% meets the requirements forcompressive strength but for variation of 75% is notrecommended because the compressive strength does
not meet specifications even though the percentage is100%.

Fig. 11 Development of Quality Concrete (K-200)372

GEOMATE – Melbourne, Australia, 11-13 November 2020

GGBFS is not required because from the point of viewof compressive strength still does not meet therequirements.

Fig. 14 Quality of Compressive Strength (K400)
Figure 14 shows, the results of the compressive
strength test of K-400 concrete quality, at the age of 28days the incoming variation is only 0% with acompressive strength of 420.62kg / cm2 and 25% with acompressive strength of 415.41kg/cm2.

Fig.17 Development of Quality Concrete (K500)
Figure 17 shows, compressive strength increases
every time, this can be seen from the graph above. The
percentage of K 500 quality development increases
every time, for example the age of 3 days with avariation of 72.85% at the age of 28 days to 100%.
Linear Regression Experiment
From the results of the compressive strength of theK200 – K400 quality taken samples for linear regression
calculations, where the result is the higher the age of theconcrete produced the higher the compressive strength.
From the results of linear regression analysis it can be R
= 0.9781 on variation of 25% with quality of K400
which means it approaches the value of 1 and theconcrete on the construction project can be applied [8].

Fig.15 Development of Quality Concrete (K400)
Figure 15 shows compressive strength increases
every time, this can be seen from the graph above. The
achievement of the percentage of 28 days of variation
0%-25% meets the requirements for the compressive
strength but for variations of 50% and 75% are notrecommended because the compressive strength does
not meet specifications even though the percentage is100%.

Fig.18 Quality of Compressive Strength (K600)
Figure 18 shows, the results of the compressive
strength test of K-600 concrete quality, it can be seenthat in each test 3 days, 7 days, 28 days and 56 days thecompressive strength of concrete has decreased witheach variation of the GGBFS substitution percentage
variation for cement. For K600 quality is not required
because the compressive strength for variation 0% =577.05 kg / cm2, 25% = 535.34 kg / cm2, 50% =451.91kg / cm2 and 75% = 359.79 kg / cm2, the result ofall variations are below the standard even though thepercentage values reaches 100% and the linear
regression approaches 1.

Fig.16 Quality of Compressive Strength (K500)
Figure 16 shows the compressive strength test of theK-500 concrete quality, it can be seen that the use of373

GEOMATE – Melbourne, Australia, 11-13 November 2020

Figure 21 shows, the percentage of K 700 quality
development increases every time, while the age of 28days the quality development increases to 100%.

The higher the quality of the concrete, the morecement is needed so that the use of GGBFS to achieve
the compressive strength is not optimal since it isinfluenced by the quality of materials other than cement.

Slag Activity Index Experiment𝑺𝑷
Slag Activity Index, % = x100%(4)𝑷
SP = average compressive strength of the slagreference cement cubes
P = average compressive strength of the reference
cement cubes
In this test, the compressive strength values of 7 daysand 28 days [9]were compared by comparing samples
with a 25%, 50%, 75% GBFS mixture divided by OPC
cement with a sample that had a 100% OPC cement
mixture. Following are the results of slag activity index
testing:Fig.19 Development of Quality Concrete (K600)

Table 1. Slag Index Activity

Figure 19 shows, the percentage of K 600 quality
development increases every time, for example the ageof 7 days with a variation of 50% results 41.28% while
the age of 28 days the quality of development rises to100%. The use of the K600 is not recommended because
it does not comply with the required standards.

Variationof
GGBFS

Compressive
Strength(kg/cm2)0%384.0928
days
475.8625%

387.49533.650%384.09475.8650%
0%343.30384.09605.03475.8675%295.72458.87

7 daysSlag Index Activity
28
days

7 days100.89Grade120

112.14

Grade
100

89.38

Grade
100

127.14

Grade
120

76.99

Grade
100

96.43

Grade
100

Production Cost
Production costs are all costs associated with the goods
produced, in which there are elements of raw material
costs, direct labor costs, and overhead costs. Based on[10] As production costs increase, it is necessary to findalternative solutions for the mix of raw materials forconcrete production. Prices for concrete for K200 K700 concrete for 2020 are as follows:

Fig.20 Quality of Compressive Strength (K700)
Figure 20 shows, the results of compressive
strength testing of K-700 concrete quality, it can be seenthat in each test at the age of 28 days does not meet the
specifications because the compressive strength does
not meet the required standards.

Table 2. Price of Concrete Quality 2020
Price per m³ (IDR)
Concrete Quality
GGBFS
Conventional
K200
K300
K400
K500
K600
K700
Fig.21 Quality of Compressive Strength (K700)

Concrete

Concrete

700.000
750.000
800.000
900.000
1.000.000
1.200.000

720.000
775.000
830.000
930.000
1.030.000
1.230.000

In implementing the Tokyo Apartment construction
project used the K 400 concrete quality, with theutilization of slag, the resulting difference is:374

GEOMATE – Melbourne, Australia, 11-13 November 2020

Cost = Volume x Selling Price(4)
So, the calculation is as follows:
1. Utilization of Slag 300.000m3 x 900k = 270
billion
2. With Conventional Concrete 300.000m3 x
930k = 279 billion
From the difference generated for the sale price with theuse of slag is 9 billion. The construction value of theconstruction can reduce the efficiency of materials or
cost of production by 3%. The utilization of this slag has
an impact to improve a friendly environment without
reducing quality in the use of high quality of concrete.

0094.SK/2/A0/2020.
REFERENCES[1] ACI(American Concrete Institute), ACI 233R-03.
Slag Cement in Concrete and Mortar. in ; American
Concrete Institute, 2003, pp. 1–19.
[2] Yogarajah E, Nawa T, Igarashi T, Physical,
chemical, and mineralogical characteristics of blast
furnace slag on durability of concrete. MATEC Web
Conf., vol. 147, p. 01007, 2018.
[3] Hidayawanti R, Legino S, Harjanto D. Optimizing
the Utilization Cement Slag and Fly Ash ofConcrete Quality. in The IIER International
Conference, 2018, March, pp. 14–18.
[4] Hidayawanti R, Legino S, Sangadji I, Panca Adi
Widodo R. The Efficiency of Fly Ash and Cement
Slag to Development Building. Geomate, vol. 16,
no. 57, pp. 95–100, 2019.
[5] ASTM Committee. ASTM C109/C109M-02.
Standard Test Method for Compressive Strength of
Hydraulic Cement Mortars. in Annual Book of
ASTM Standards. vol. 04, 2002, pp. 1–6.
[6] ASTM C-39International, Standard Test Method
for Compressive Strength of Cylindrical Concrete
Specimens. no. C. 2010. p 1-7[7] Cleetus A, Shibu R, Paul VK, Jacob. B. Analysis
and Study of the Effect of Ggbfs on Concrete
Structures. Irjet, vol. 5, no. 3, pp. 3033–3037, 2018.
[8] Hidayawanti R, Purnama DD, Iduwin T, Legino S,
Wachid FI. The Impact Aggregate Quality
Material as a Linear Regression. GEOMATE, vol.
18, no. 70, pp. 23–29, 2020.
[9] ASTM C989. Standard Specification for Slag
Cement for Use in Concrete and Mortars. in : ASTM
Standards, vol. 44, no. 0, 2013, pp. 1–8.
[10] Pourmahmoud N, Sadeghifar H, Torkavannejad
A. International Journal of Heat and Mass Transfer
A novel , state-of-the-art tubular architecture forpolymer electrolyte membrane fuel cells :Performance enhancement , size and cost reduction.
Int. J. Heat Mass Transf., vol. 108, pp. 577–584,
2017.

CONCLUSIONSIn mortar experiments, the more levels of GGBFS
content, the longer the initial time or the initial bindingof concrete for 30 minutes.
Concrete compressive strength and thedevelopment of K200-K700 concrete quality at the ageof concrete 3 days, 7 days, 28 days and 56 days on eachvariation will decrease in value. For examples variationsof 0%, 25%, 50% and 75% in the age of concrete results
will decrease. However, the compressive strength valueof each variation will increase, for example 0%
variation, 3,7,14 days and 28 days the value will
increase. The use of Slag for over K500 quality is notrecommended because it does not meet specifications
even though the percentage is 100% and the linear
regression is close to 1.
Slag index activity is categorized in grades 100 and
120 according to ASTM C989, Linear Regression
generated from K500 concrete quality with a correlationof R = 0.913 can be used for high level quality ofconcrete.
The utilization of GGBFS can reduce the cost of
production by 9 billion or 3% and it can also improve ahealthy environment without reducing the quality ofhigh quality concrete.
ACKNOWLEDGMENTS
The author would like to thank the Rector and theInstitute for Community Service Research in internal
grants for the Superior Research of the Civil Departmentof the PLN Institute of Technology No. SK:375

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

POSSIBLE RECYCLING OF WASTE GLASS IN SUSTAINABLE FIRED
CLAY BRICKS: A REVIEW
Yuecheng Xin, Abbas Mohajerani, Halenur Kurmus, John V. Smith
School of Engineering, RMIT University, Melbourne, Australia
ABSTRACTThe production of waste glass (WG) in Australia from 2016 to 2017 was about 1.1 million tonnes, andapproximately 612 kilotonnes were recycled, while around 467 kilotonnes went to landfill. The current recycling
techniques practiced for crushed waste glass (CWG) are limited, resulting in a growing environmental challenge.
Therefore, there is an interest in the development of novel technologies to recycle WG. This paper provides acomprehensive review of the research conducted on recycling CWG in fired-clay bricks. The relationship between
the firing temperature and the resulting physical/mechanical properties of bricks incorporating CWG is particularly
examined. It was found that recycling CWG in bricks facilitates in reducing the global carbon footprint andimproves the physical/mechanical properties of bricks. Therefore, incorporating CWG in fired clay bricks is apractical and feasible method to develop an environmentally friendly construction material and solve a growing
waste issue. However, further research on the application of CWG in fired clay bricks is recommended.
Keywords: Waste glass, Recycling, Fired clay bricks, Physical and mechanical properties, Sustainability,
Geomaterials.

INTRODUCTION
Bricks are a common construction material usedwidely around the world [1]. The production of fired
clay bricks relies on extensive mining of natural
resources and the use of non-renewable raw materials.
Therefore, it is becoming vital to develop
environmentally friendly and energy-efficientconstruction material by the addition of wastes. A
number of studies have been conducted on recycling
various wastes in fired clay bricks. Such wastes
include cigarette butts [2], biosolids [1], microplastics[3], olive mill wastewater (OMW) [4], rice husk [5],foundry by-products [6] and marble powder [7].
Figure 1 illustrates the overall trend of documents
published by year on recycling wastes and waste glass(WG) in fired clay bricks. An upward trend in thenumber of papers published on recycling WG in fired
clay bricks can be seen to increase dramaticallyfollowing the year 2015.
30

General wastes
Waste glass

20
15
10
5
0
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

Documents

25

Year

Fig. 1 The number of documents publishedbetween 1996 to 2020 on recycling waste and WG in
fired clay bricks.
WG is a readily available domestic material, andaround 11 million tonnes of glass containers are
manufactured each year. Typically, WG is depositedin landfills. However, many countries are limited withspace, and therefore landfills are facing a growing
issue with the space available for WG [8]. For
instance, the amount of waste generated per capita inAustralia has gradually increased to an unprecedented
level in the recent period. According to the Australia
National Waste Report [9], a total of 1.1 million
tonnes of WG was generated in the 2016 to 2017
period alone. 612 kilotonnes of WG were recycled,
and landfills received the rest of 467 kilotonnes.
Besides, the disposal of solid waste has an immense
impact on the environment, economy, and health,which is a significant management issue that many
countries are facing. Based on the statements from theWorld Wildlife Fund (WWF), recycling WG is afeasible method to reduce the space consumed inlandfills. Likewise, because using recycled glass to
produce glass products decreases the use of energy
and raw materials, related air pollution and waterpollution will be reduced by 20% and 50%
respectively [10]. Evidently, using CWG as anadditive in the making of fired clay bricks is
becoming popular.
In Victoria, according to the fact sheet from Net
Balance, each year, around 257,000 tonnes of glass
waste is produced, and the majority was from glass
packings such as beverage bottles, jars, beads, and376

GEOMATE – Melbourne, Australia, 11-13 November 2020

bowls. The remaining glass waste was from postconsumer glasses, flat glass (windows), and othersources. While 195,000 tonnes are recovered, only
124,000 tonnes are crushed into glass cullet for glass
manufacturing. Victoria's recycled glass inventory is
estimated to exceed 300,000 tonnes, mainlycontaminated by ceramics, stoneware, Pyrex glass,and plastic. Besides, up to 62,000 tonnes of glass is
expected to be sent to landfills each year [11].
Most glass packaging is collected fromhouseholds or commercial sites (such as bars, clubs,
sports venues, hotels, and restaurants) in mechanized,
bin-based, fully commingled systems. Some glass iscollected separately from other materials. On-site
glass crushers are often used to reduce volume. The
mixed materials are sorted from paper grads, plastics
and metals by using the recycling facilities in thecapital and regional centers. The glass is then sent to
one of the beneficiation plants, including Visy, SKM,
and Polytrade [12]. However, an issue has been
indicated by many stakeholders that although thecollection efficiency has been improved, mixedmaterials and compaction can cause crosscontamination and reduce the quality of the collectedmaterials.
Figure 2 indicates the glass cullet and glass fines
recovery process in Victoria.

Note: MSW: Municipal solid waste C&I: Commercial &IndustrialC&D: Collection & Disposal

applications. The advantage is that it canaccommodate large quantities of material [12]. As
time progressed, the development of novel
technology increased, including the addition of CWG
in construction materials. According to a study [8],CWG is being added into concrete as a fine aggregate,
in asphalt concrete, and in ultra-lightweight fibre
reinforced concrete.
The objective of this paper is to collect and review
major research studies on the recycling of CWG in
fired clay bricks. Six CWG recycling proposals andpractices are studied, including; type 1: 30% black
dust and 70% superfine WG, type 2: recycled PC andTV WG, type 3: soda-lime glass powder WG, type 4:
shop glazing window WG, type 5: thin-film transition
liquid crystal display (TFT-LCD) WG powder andtype 6: the combination of sludge and WG.
RESEARCH ON RECYCLING OF WASTE
GLASSType 1: 30% Black Dust and 70% Superfine WG
Federico [13] studied the effects of CWG on theproperties of fired clay bricks. Eleven sets wereprepared with A (0% glass), B (5% glass), C (5%glass), D (10% glass), E (10% glass), F (15% glass),
G (15% glass), L (0% glass), X (0% glass), FL (0%glass), and FX (0% glass). Each set contained 30
samples. Set A was categorized as the control group,and the remaining were test groups. B, D, and F
contained different percentages of WG with the same
glass mesh size. C, E, and G contained different
percentages of WG with the same glass mesh size. As
a result, the influence of the percentage of WG
addition by mass, as well as the influence of WG
particle size on the properties and durability of thebricks can be investigated. In addition, samples L, X,
FL, and FX were prepared to investigate the influenceof the methodology used and the firing temperatureson the bricks. Lignosulfonate (20 ml) was added to
the water during the mixing process in order toincrease the plasticity of the clay. The test results
show that the percentage of WG has a significant
effect on compressive strength. The maximum
compressive strength was found to be 133.4 MPa for
G (15% glass). Additionally, the particle size of the
WG had a great effect on water absorption properties.
A microstructural analysis was conducted, it was
found the increase in WG reduced the porosity of thebrick samples.

Fig. 2 The generic process for glass cullet and glassfines recovery in Victoria [11].

Type 2: Recycled PC and TV WG

In addition to reusing glass packaging waste for
packaging production, many other secondary markets
can be used. The glass can be used for water quality
projects as a filter medium. Similarly, CWG can also
be used in various road base and sub-baseAccording to a study, the disposal of PC monitors
and TV sets is becoming a growing issue,
approximately 25,000 m3 of PC monitors and TV sets
are being produced in European countries per year[14]. Moreover, a high concentration of lead, barium,
377

GEOMATE – Melbourne, Australia, 11-13 November 2020and strontium exists in the funnel and panel of thecathode ray tube display, which results in thetechnical problem of re-using PC and TV glass to
manufacture new glass. Therefore, the focus of thisstudy is to add PC and TV glass into fired clay bricks.
Three different mixture ratios of clay and glass areprepared: control sample without waste (C0), 2% and
5% by weight (wt.) funnel glass (CT2 and CT5), and
2% and 5% by wt. panel glass (CS2 and CS5). The
samples were fired at three different temperatures
900°C, 950°C, and 1000°C. The firing shrinkage,
bending strength, water absorption, open porosity,and bulk density were investigated. The results
revealed bricks incorporating CWG result in higher
bulk density, bending strength, and firing shrinkage
and lower water absorption. The maximum bending
strength (22.8 MPa) was found for bricks
incorporating 5% screen glass, with a firing
temperature of 950 °C.

resulting in higher density and lower water absorption
and drying shrinkage.
In 2017, a study proved the firing temperature cansuccessfully be decreased from 1050°C to 650°C for
fired clay bricks with the addition of WG powder(borosilicate glass, colored glass, and soda-lime
glass) [17]. The ratio of glass content used in the
study included 0%, 20%, 35%, and 50% by wt. andthe specimens were fired at 650°C, 850°C, and1050°C. Besides, the results indicated that the bricks(35% soda-lime glass) have better compressive
strength than others, as shown in Table 2.
Table 2 Compressive strength test results [17].
Waste glasscontent
0%35% sodalime glass

Type 3: Soda-lime Glass Powder WG
Demir [15] investigated the effect of milled WG
on the properties of fired clay bricks. The waste wasadded at a ratio of 0%, 2.5%, 5%, and 10% by wt. andthe samples were fired at four different temperaturesof 850°C, 950°C, and 1050°C. The results showed alinear relationship between shrinkage and the firing
temperature. Shrinkage increased with an increase intemperature, while the increase in firing temperature
resulted in a decrease in porosity and water
absorption. The results additionally revealed higher
firing temperature increases the mechanical strength,
as shown in Table 1.
Table 1 Compressive strength test results [15].
Waste glasscontent
0%
2.5%
5%
10%

Compressive strength MPa
850 °C
950 °C
1050 °C
16.45
18.75
20.15
20.62

19.50
22.65
25.13
27.56

20.37
24.50
27.15
29.35

Another study discussed the effects on thephysical-mechanical properties of fired clay bricks
when incorporated with WG (soda-lime glass) [16].
The study aims to reduce the firing temperature of thebrick in order to save energy. The study has been
performed by utilizing different amounts of glass,including 0%, 5%, and 10% by wt., with a firing
temperature within the range from 900°C to 1000°C.
The glass additive contains sodium oxide (Na2O) andhas a Non-crystalline structure, which helps with
reducing the required firing temperature, increasing
the glassy phase in the brick to enhance the durability,
and reducing the costs of manufacturing. In addition,
the Na2O assists in the vitrification of the bricks

35%
borosilicate
glass
35% coloured
glass

Compressive strength MPa
650 °C
850 °C
1050 °C
21.88
22.22
25.62
28.88
31.79
35.14
24.18

27.44

31.78

26.84

27.04

30.08

Furthermore, a study was performed on the
addition of various sizes of glass cullet in red clay
bricks with a focus on the subsequent physical andmechanical properties [18]. All specimens were airdried at room temperature for 72 hr and later fired at
1000°C for 12 hr. The bricks with glass cullet
incorporated demonstrated a lower water absorption
compared to the controlled bricks. The compressive
strength increased with the decrease in the particle
size of the glass cullet brick samples. The results
show that increasing the glass content and reducing
the particle size of the glass can significantly improve
the properties of the fired clay bricks for water
absorption and compressive strength.
Type 4: Shop Glazing Window WG
Abdeen [19] studied the performance of fired clay
bricks containing waste glazing window glass. The
selected contents of waste glazing window glass
included 0%, 10%, 20%, 30%, and 40% by wt. The
firing temperatures for the fired clay bricks
incorporated with glazing window glass were 900°C,
1000°C, and 1100°C. The result revealed thecompressive strength of the fired clay bricks
increased with the increase in glazing window glasscontent and firing temperature, as shown in Table 3.
Table 3 Compressive strength test results [19]378GEOMATE – Melbourne, Australia, 11-13 November 2020

Waste
glass
content _%
0
10
20
30
40

Compressive strength MPa
900°C1000 °C
1100 °C
18.61
22.91
20.41
21.91
17.88

21.33
30.67
32.34
42.75
43.17

28.82
47.45
77.75
96.37
55.98

Type 5: Thin-ﬁlm Transition Liquid Crystal
Display WG Powder
In 2018, Chao-Wei [20] investigated the addition
of TFT-LCD WG in fired clay bricks. The ratio of
TFT-LCD glass content used in this study were 0%,10%, 20% and 30% by wt. The specimens were fired
at 900°C, 950°C, 1000°C, and 1050°C. Several tests
were conducted to evaluate the effects of adding TFTLCD WG powder in fired clay bricks. The results
displayed a decrease in water absorption and anincrease in compressive strength with the increase in
TFT-LCD WG content in the fired clay brick samples.
The maximum compressive strength (48.2 MPa) was
reached by adding 20% glass cullet into bricks andfiring at 1050°C.
Type 6: The combination of Sludge and WG
Rahman et al. investigated the effects of adding
WG in textile sludge-based bricks [21]. The bricks
contained textile sludge, which increased the porosity,and as a result, reducing the mechanical properties ofthe fired clay bricks. However, the compressive
strength and water absorption are known to increase
with the addition of WG. Therefore, textile sludge
and WG additions were combined and incorporated
in fired clay bricks for the purpose of thisinvestigation. Various percentages of sludge content
were added in the production of bricks between 0%
to 40% by wt., in addition to 5% and 10% by wt. WG.
All specimens were fired at 1150°C. The results
confirm the addition of WG to the textile sludgebased bricks improved the compressive strength,
shrinkage, and water absorption properties. In
addition, the bricks incorporating sludge and WG
were tested for heavy metal leaching. The results
revealed the leachate concentrations for heavy metals
for bricks incorporating WG and sludge were below
the standard regulatory limits. Therefore, recycling
fabric sludge and WG in the production of fired clay
bricks is an environmentally friendly solution to a
growing pollution issue.
A group of researchers investigated the use of WG
in addition to galvanized sludge in brick production
to boost the immobilization of heavy metals [22].
Various contents of WG were studied, including 0%,10%, 15%, 20%, 25%, and 30% by wt., while thegalvanized sludge content studied was 10%. The
firing temperatures were 850°C, 950°C and 1050°C

respectively. The experimental results revealed whenmore than 15% WG content was added, theconcentration of heavy metals leached from bricks
incorporating 10% galvanized sludge was found to be
below the regulatory limits. Therefore, WG can beused as an enhancer in conjunction with hazardous
wastes (galvanized sludge) in the production of fired
clay bricks for the stabilization and solidification ofheavy metals.
CONCLUSION
In conclusion, this study provides a review of theresearch conducted in utilizing various WG types in
the production of fired clay bricks. The six CWG
recycling proposals and practices studied were 30%
black dust and 70% superfine WG, recycled PC andTV WG, soda-lime glass powder WG, shop glazing
window WG, thin-film transition liquid crystal
display (TFT-LCD), WG powder and the
combination of sludge and WG. The effects of the
WG particle size, WG content, firing temperature,and brick preparation process on the physical andmechanical properties of fired clay bricks wereanalyzed. The distinct procedures and sample types
followed in recycling WG into fired clay bricks are
summarized in Table 4.
It was found whether WG is added exclusively orwith the addition of another waste, the addition of
WG in fired clay bricks demonstrated encouraging
physical and mechanical properties, particularly with
the improvement in compressive strength. Moreover,
the addition of WG assists in reducing the maximum
desired firing temperature significantly and,
consequently, the firing time. Therefore, it
contributes to minimizing the world’s carbon
footprint. Besides, the reviewed research studies
reveal and confirm the addition of WG in fired clay
bricks is a feasible and effective approach to reduce
the growing amount of WG in landfills worldwide.
Furthermore, the partial replacement of clay with WG
in brick production will assist in decreasing theconsumption of natural materials.
RECOMMENDATIONS
Based on the review, the following research anddevelopments are recommended for widespreadindustry adoption:• Further study is recommended to determine theoptimal ratio of WG content in fired clay bricks,
in addition to the optimal firing temperature.
• A comprehensive study on the combination of
WG with other waste additives is recommended
with an emphasis on environmental implications.
• A life cycle assessment is recommended toevaluate the negative and positive environmental
impacts of incorporating WG in fired clay bricks.

379

GEOMATE – Melbourne, Australia, 11-13 November 2020•

A comprehensive and feasible manufacturingmethod is recommended to be developed for theincorporation of CWG in fired clay bricks.

Table 4 Summary of some of the research conducted in recycling various WG types in fired clay bricks
Glass sample
typeBlack dust andsuperfines

Recycled PC
and TV waste
glass.
Crushed waste
glass powder(bottle)Particle size
150 -300µm
75-150 µm
45-75 µm
Passing 0.25
mm sieve

Waste glassby wt.
0%, 5%,
10% and15%
2% and 5%

Drying process

Firing process

Ref.

Air dry for 24 hr
Oven dry for 12 hr at
110 °C

Fired between
999.6°C and
1106 °C[13]

Air dry for 48 hr
Oven dry at 100°C
overnight
Air dry for 24 hr
Oven dry at 110 °C

Fired at 900°C,
950°C and 1000°C
with 4 hr soak time
Fired at 850°C,
950°C, and 1050°C
with a heating rateof 3°C/min and heldfor 2 hrFired at 900°C,
950°C and 1000°C
and held for 1 hr

[14]

Fired at 650°C,
850°C and 1050°C
for 3 hr with aheating rate of 5°C/min and soaking
period of 15 min
Fired at 1000 °C for
12h

[17]

Fired at 900°C,
1000°C and 1100°C
with a heating rateof 2.5°C/min until
600°C reached, andthen 5°C/min until
900°C reached,
1000°C and 1100°C
Fired at 900°C,
950°C, 1000°C, and1050 °C[19]

Fired at 1150°C[21]

Fired at 850°C,
950°C, and 1050°C
with a heating rateof 300°C/h for 3 hr
until target
temperature reached

[22]

21 µm

0%, 2%,
2.5%, 5%and 10%

Soda-lime
glass

Ball millcrushed (1hr)

0%, 5% and
10%

Air dry for 24 hr
Oven dry for 24 hr at
110 °C ± 5°C

Sodalime
glass, coloured
glass, andborosilicate
glass

0.85 mm

0%, 20%,35%, and50%

Air-dry overnight
Oven dry at 110°C for
24 hr

Crushed Glass
Cullet<500, <300,
and <212𝜇𝜇m
150 𝜇𝜇m and<600 𝜇𝜇m

0%, 20%,25%, and30%
0%, 10%,20%, 30%
and 40%Air dry for 72hr

Thin-ﬁlm
transition
liquid crystal
display waste
glass powder

Ball millcrushed0%, 10%,20%, and30%

Combinationof sludge andwaste glass(bottle)<250𝜇𝜇m

5% and 10%<74μm

0%, 5%,
10%, 15%,20%, 25%
and 30%.

Shop Glazing
windows

Air dry for 24 hr
Oven dry at 45°C for 6
hr and then at 110°C
for 24 h

Set one: air dry for twodays
Set two: air dry one
day
Set three: Oven dry at
100°C for one day
Set four: oven dry at
50°C for one day
Air dry for one day andoven-dry overnight
Oven dry for 24 hr at
110°C.

380

[15]

[16]

[18]

[20]

GEOMATE – Melbourne, Australia, 11-13 November 2020

ACKNOWLEDGMENT
This work is part of an ongoing study on recycling
waste in construction materials. The authors would
like to thank Visy industries Australia Pty Ltd and
RMIT University for their financial and in-kind
support.
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Swaney, M., Ahmed, M., Rodwell, G., Bartolo,
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for Recycling the World’s Unused Stockpiles ofTreated Wastewater Sludge (Biosolids) in FiredClay Bricks, 2019, 9(1), 14.
[2] Kurmus, H. and Mohajerani, A.J.M. Recyclingof Cigarette Butts in Fired Clay Bricks: A New
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[3] Mohajerani, A. and Karabatak, B.J.W.M.
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381

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DYNAMIC PROPERTIES OF RIGID POLYURETHANE FOAM INCYCLIC TRIAXIAL TESTSAtsushi Koyama1, Daisuke Suetsugu1 and Yoshinori Fukubayashi1
1Faculty of Engineering, University of Miyazaki, Japan
ABSTRACTThe aim of this study is to clarify the dynamic properties of rigid polyurethane foam. A method for renovatinga deteriorated bridge as a lightweight embankment was proposed. The space underneath a bridge is filled withpolyurethane to support the upper structure. In this case, the upper structure of the bridge is considerably heavier
than the polyurethane. Therefore, it is important to examine the seismic behavior of this new lightweight
embankment. However, the dynamic deformation characteristics of polyurethane have not been clarified
previously. In this study, stress-controlled cyclic triaxial tests based on JGS0542-2009 are used to evaluate theeffect of the confining stress and the presence of a rigid layer on the dynamic properties of rigid polyurethane
foam. As a result, the shear modulus of rigid polyurethane foam increases with increasing confining stressregardless of the absence of the rigid layer. The shear modulus of polyurethane with the rigid layer is lower thanthat of polyurethane without the rigid layer. The value of shear modulus of rigid polyurethane foam wasmeasured in the range of approximately 1.6~3.2MPa. Moreover, the stiffness degradation and of rigid
polyurethane foam are in good agreement with the Hardin-Drnevich model.
Keywords: Cyclic triaxial tests, Dynamic properties, Lightweight embankment, Rigid polyurethane① Deteriorated bridge

INTRODUCTION
There are approximately 720,000 bridges in Japan
will be more than 50 years old within 10 years.
Along with a decline in the number of civilengineers engaged in bridge maintenance work,
local governments may not be able to adequately
cover inspection costs. Therefore, measures against
bridge deterioration must be undertaken to improve
maintenance efficiency [1]. Solution achieve this,the aim of this study is to establish a method forrenovating a deteriorated bridge as a lightweight
embankment by filling the bridge’s substructure
space with polyurethane foam, which is alightweight embankment material. This lightweight
embankment method takes advantage of the low
weight (36 kg/m3), adhesiveness, and foamability ofpolyurethane.
In this method, two types of rigid polyurethane
foam stock solution are mixed and foamed at site.
The volume increases by approximately 30 times
because of the foaming, and a lightweight
embankment of any shape can be formed to suit the
topography and shape of the structure. Because thepolyurethane is formed of closed cells, it isimpermeable to water and air. In this study, thistechnology was applied to renovate deteriorated
bridges as lightweight embankments by directly
spraying/filling the substructure space and allowing
the polyurethane to take the vertical load.
Figure 1 shows a schematic diagram of themethod of renovation of a simple girder bridge to alightweight embankment. Both dead and live loads

Superstructure

Substructure
Superstructure桁② Rigid polyurethane and bearing
support superstructure

RPUR
foam

Left bearing
Lower space is filled with rigid
polyurethane foam

Fig. 1
Renovation of the deteriorated bridge tolightweight embankment.

are supported by spraying/filling the substructure
space of the bridge with polyurethane. Owing to theadhesiveness of polyurethane, water/air is prevented
from penetrating the bridge member in positions
where the polyurethane adheres to the member, andthis is expected to inhibit member deterioration. The
design should be such that even if, after a long
period of time, the bearing capacity of the support
covered in polyurethane is reduced or lost becauseof deterioration and the condition cannot be
confirmed visually, the dead and live loads arewithin a range that can be supported by thepolyurethane.
In a situation where the vertical load is supported
382

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Properties of polyurethane
Name

Densityρ (kg/m3)

Yield strengthσy (kPa)

Allowable stress
σa (kPa)

Poisson’s ratio
ν

Cell type

Rigidpolyurethane

36

120

60

0.05

ClosedGolpazir et al.

31.3

68

―

0.02

Open

by the polyurethane, the embankment would beheavier at the top, and its seismic stability could
decrease. However, the dynamic properties ofpolyurethane required to evaluate the seismic
stability of an embankment have not been
investigated [2]. The mechanical properties of
expanded polystyrene (EPS), which is a lightweight
polymeric material with properties similar to thoseof polyurethane, have been evaluated in static anddynamic shear tests. For example, the higher is thedensity of EPS, the greater is its compressive
strength. Moreover, the shear modulus G also
increases. The density is reported to have little
influence on the damping ratio h [3]–[6]. Gatto et al.
[7] investigated the dynamic properties ofpolyurethane using resonance tests. They reported
that, in the micro-strain range, G and h were notaffected by the changes in the confining pressure
when the confining pressure was in the range 50–300
kPa. Golpazir et al. [8] used stress-controlled cyclic
triaxial tests to show the influence of initial shear
stress on the dynamic properties of one-liquid typepolyurethane. Their results showed that, as the initialshear stress increases, G decreases. They also
reported that, unlike typical soil materials, h ofpolyurethane decreases with increasing shear strain,
and the viscoelasticity of polyurethane is considered
to be the cause. A decrease in h with increasing
shear strain was also reported for EPS [6].
Currently, there is limited research on the
dynamic properties of polyurethane foam using
cyclic triaxial tests, and data needs to beaccumulated. The rigid polyurethane foam used inthis study is foamed approximately every 10 cm, andpolyurethane layers form inside the embankment. A
rigid surface, called a “skin,” exists at theboundaries between layers (see Fig. 3). The
influence of this skin on the dynamic properties has
not been examined previously.
Therefore, the objective of this study was to
examine the influence of the presence or absence ofa skin and the differences in confining pressure on
the dynamic properties of polyurethane using stresscontrolled cyclic triaxial tests.

Fig. 2 Test specimens of polyurethane (left: twolayer sample; right: one- layer sample,  =50mm).

Gap

Fig. 3 Gap due to the rigid layer (skin) of two-layer
specimen.

Fig. 4 Cyclic triaxial apparatus.
383

GEOMATE – Melbourne, Australia, 11-13 November 2020

MATERIALS AND METHOD

12

1 layer, sc=20kPa,

9

Sample preparation

q=7.7kPa(ea)SA= 0.16%

6
3

q (kPa)The rigid polyurethane foam used in this study isa two-liquid (polyol and isocyanate) mixed-type thatis used in lightweight embankment construction
methods. Table 1 shows the values of the physicalproperties of the rigid polyurethane foam. For
comparison, Table 1 also shows the physicalproperty values of the one-liquid type polyurethane
used by Golpazir et al. [8] The density of the rigid
polyurethane foam used in this study is higher thanthat used by Golpazir et al. [8] and the yield strengthis approximately double. Furthermore, the rigid
polyurethane foam used in this study is a closed-cell
type, whereas Golpazir et al. [8] used an open-cell
type.
The time required since the spraying until thecompletion of foaming is approximately 1 min.
When the foam hardens, more than 95% of theprescribed strength develops within 24 h. In thisstudy, a sample in which polyurethane foam is
foamed continuously to a height of 100 mm is called
a “one-layer sample,” and a sample in which
polyurethane foam is foamed to a height of 50 mm
and a second layer is sprayed/foamed on top suchthat it has two layers and a skin is called a “twolayer sample” (Fig. 2). As shown in Fig. 3, an area
where the density of the polyurethane is low occurs
at the boundary in the samples with a skin.

0
q=2.8kPa(ea)SA= 0.052%

-3
-6
-9
-12
-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

Axial strain ea(%)

Fig. 5Stress-strain hysteresis loops for rigid
polyurethane samples with different deviator
stress.
-0.5
Vertical displacement (mm)

0.0
0.5
1.0
1.5
1 layer (sc=20kPa)

2.0

1 layer (sc=40kPa)
2 layer (sc=20kPa)

2.5

2 layer (sc=40kPa)

3.0
1

10

100

1000

7000

Time (s) (logarithm)

Test procedure for cyclic triaxial shear tests
Fig. 6 Vertical displacement of polyurethane
during isotropic compression.

In this study, a stress-controlled cyclic triaxial test
was performed to evaluate the effect of the confining
stress and the presence or absence of a rigid layer on
the dynamic properties, namely, the shear modulus
and damping ratio of polyurethane.
Figure 4 shows the cyclic triaxial test apparatus.
The cyclic triaxial test was performed in accordancewith the Japanese Geotechnical Society standard
0542-2009 [9]. The sample was covered with amembrane and placed in the tester and an isotropic
stress was applied until the prescribed confiningpressure σc was reached. The sample was left tostand for 1 h, and then cyclic loading was started
immediately. At each cyclic loading stage, asinusoidal cyclic load at a frequency f = 0.1 Hz wasapplied for 11 cycles, under a prescribed deviator
stress q. The deviator stress was increased in 15–17
stages. The load and axial displacement were
measured during the cyclic loading. The axial
displacement was measured using a gap sensor.
In the polyurethane lightweight embankment
method, the allowable compressive stress of thepolyurethane is established as 60 kPa. To minimize
the creep deformation of the polyurethane when abridge is renovated as a lightweight embankment,

the dead load acting on the polyurethane should bedesigned to be approximately 20 kPa. Therefore, σc
was set at 20 kPa and 40 kPa in the cyclic triaxial
test. These values of σc are within the elastic regionof the rigid polyurethane foam. The one-layer andtwo-layer samples were tested with the two
aforementioned values of σc.
The equivalent Young’s modulus Eeq was
obtained from the hysteresis loop using Eq. (1) andthis was converted to the equivalent shear modulus
Geq using Eq. (2). Here, v = 0.05.
Eeq =

σd
1
×
(MPa)
(εa )SA 10

(1)

where
σd: single amplitude cyclic deviator stress (kPa)(εa)SA: single amplitude axial strain (%)
Geq =

384

Eeq
×(1+v) (MPa)2(2)

GEOMATE – Melbourne, Australia, 11-13 November 2020

The hysteresis damping ratio h was determinedusing Eq. (3).

In this study, Geq and h were calculated using datafrom the 10th cycle in each loading stage. Figure 5 isan example of hysteresis loops that show the
relationship between q and (εa)SA for one-layer
samples at σc = 20 kPa. It is evident from the figurethat polyurethane deforms to the same extent incompression and extension. However, when (εa)SAexceeded approximately 0.4%, the extension
deformation of the polyurethane did not follow theload on the extension side and pulled the membrane.
Therefore, in this study, the test was stopped at
approximately (εa)SA = 0.4%.

However, Athanasopoulos et al. [4] reported that, inthe case of EPS, the loading frequency has little
effect on the dynamic properties. Therefore, it isassumed that the differences in the structure of thepolyurethane, such as open and closed cells, andstrength influence the test results, not the different
test methods. Therefore, data must be collected fromtests performed using several types of polyurethanewith different physical properties.
Figure 8 shows the relationship Geq–γSA and h–γ
comparing the presence and absence of a skin.
Regardless of the size of σc, Geq was larger and h
was smaller in the one-layer samples compared withthe two-layer samples. Geq was small in the twolayer samples because there was cyclic loading at
the gap caused by the skin. The effect of thepresence or absence of a skin on Geq and h was
smaller when σc = 40 kPa than when σc = 20 kPa.
This is considered to be because, as shown in Fig. 6,
the greater the value of σc, the more the gap causedby the skin was filled, and therefore the differencesin Geq and h between the one-layer and two-layer
samples were smaller.

RESULTS AND DISCUSSION

Degradation curves of rigid polyurethane foam

h=

1 ∆W
∙
×100 (%)
2π W

(3)

where
ΔW: area of the hysteresis curve (N･cm)
W: equivalent elastic energy in that loading
cycle

Figure 9 shows the relationship between G/G0,
which is Geq normalized by the initial shear modulus
G0, and γSA. Figure 9 also shows the G/G0–γSArelationship approximated by the Hardin-Drnevichmodel (H-D model) [10], which is a nonlinear soilmodel represented by Eq. (4). The H-D model is anonlinear formula that easily determines the related
model parameters. Table 2 shows the parameters
used in the H-D model.

Dynamic properties of rigid polyurethane foam
Figure 6 shows the relationship between thevertical displacement and time for one-layer andtwo-layer samples under each σc. As shown in the
figure, the vertical displacement of each sample
increased with an increase in σc. The amount ofcompression was greater in the two-layer samples atboth the confining pressures. This is because the gap
caused by the skin (Fig. 2) was filled when thesample was isotropically compressed. Two-layer
samples have a gap between the bottom and top
layer. It is probable that the density of polyurethane
is low in this gap. In all the samples, the verticaldisplacement became constant after approximately
120 s from the initiation of loading. Therefore,
isotropic compression was stopped at 1 h.
Figure 7 shows the relationship Geq–γSA and h–γSA
for the one-layer and two-layer samples, respectively.
γSA is the single amplitude shear strain.
As shown in Fig. 7, the value of Geq increases andthe value of h decreases with increasing σc,regardless of the presence or absence of a skin. Thisis assumed to be because the higher the confiningpressure, the harder the polyurethane becomes.
At either value of σc, h increased with increasing γ.
This trend is opposite to that shown in the test
results by Golpazir et al. [8]. Additionally, the valueof h in this study was approximately one tenth of thevalue in their test results. The loading frequency ofthe cyclic load in tests performed by Golpazir et al.
[8] was 0.02 Hz, whereas in this study it was 0.1 Hz.

G1= γ
G0 1+ ⁄γ

(4)
r

where
G0: shear modulus at minimum shear strain
γr：shear strain in G/G0=0.5
In the cyclic triaxial test results, the relationshipbetween G/G0 and γSA can be expressed using σc,regardless of the presence or absence of a skin. It isevident from Fig. 9 that there is good agreement
between the experimental values and the H-D model.
Figure 10 shows the relationship between h andγSA from the H-D model, calculated using Eq. (5),and the experimental values from the cyclic triaxial
test.
G
h=hmax ×(1- )
(5)
G0
where
hmax: maximum value of damping ratio385

8
1 layer

Geq (sc=40kPa)

3

6
Geq (sc=20kPa)

22
h (sc=40kPa)

0

0.001

0.01

0.1

1

Geq (sc=40kPa)

6

2

1

2
h (sc=40kPa)

0.001

0.01

Equivalent shear moduls Geq (Mpa)

Geq (1 layer)

Geq (1 layer)
Geq (2 layer)h (1 layer)h (2 layer)

6

4

Geq (2 layer)h (2 layer)

12

Damping ratio h (%)

Equivalent shear moduls Geq (Mpa)

(b)

8

3

h (1 layer)

0

0.001

0.01

0.1

1

4

0

σc = two-layer

sc=40kPa

Geq (1 layer)

Geq (1 layer)
Geq (2 layer)h (1 layer)h (2 layer)

8

3

6
Geq (2 layer)24h (2 layer)12
h (1 layer)

0

0

0.001

0.01

0.1

1

0

Single amplitude shear strain γSA (%)

Single amplitude shear strain γSA (%)(a) σc = 20 kPa
Fig. 81

Relationship Geq–γSA and h–γSA of rigid polyurethane foam with varying confining stress.

sc=20kPa2

0.1

Single amplitude shear strain γSA (%)(a) σc = one-layer

44

Geq (sc=20kPa)
h (sc=20kPa)

Single amplitude shear strain γSA (%)

Fig. 78300Geq (σc=20kPa)
Geq (σc=40kPa)
h (σc=20kPa)
h (σc=40kPa)

2 layer

Damping ratio h (%)

14

h (sc=20kPa)

Geq (σc=20kPa)
Geq (σc=40kPa)
h (σc=20kPa)
h (σc=40kPa)

4Damping ratio h (%)

Equivalent shear moduls Geq (Mpa)

4Damping ratio h (%)

Equivalent shear moduls Geq (Mpa)

GEOMATE – Melbourne, Australia, 11-13 November 2020(b)σc = 40 kPa

Relationship Geq–γSA and h–γSA of rigid polyurethane foam with different type of specimens.

Figure 10 shows that there is a difference in the h–γSA relationship between the experimental values andthe H-D model. Unlike the G/G0–γSA relationship,
the h–γSA relationship differs depending on thepresence or absence of a skin under both values of σc,particularly when γSA = 0.02% or more. Although notillustrated here, even when a different nonlinear
model (the modified Ramberg-Osgood model [11])was used, it was not possible to accurately
approximate the h–γSA relationship. This means thatthe h–γSA relationship obtained using Masing’s rule,
as in the H-D model and the modified R-O model,
might not be appropriate for the rigid polyurethane
foam.
Figures 9 and 10 show the test results for marine
clay (void ratio e=1.391) at σc = 50 kPa. In marine
clay, G/G0 decreases and h increases with increasing
γSA. In clay, G/G0 decreases significantly after γSA =0.003%, whereas a striking decrease in G/G0 is notobserved in rigid polyurethane foam until γSA = 0.1%.
Furthermore, in clay, G0 = 15.6 MPa, which isapproximately three times greater than that in rigid
polyurethane foam. Additionally, h of rigid
polyurethane foam is substantially small comparedwith that of clay. Consequently, rigid polyurethane
foam can be considered as a material with lower
stiffness than soil materials, but one whose stiffness
does not readily decrease.
CONCLUSIONS(1) For the rigid polyurethane foam used in thisstudy, the value of Geq increases and the valueof h decreases with increasing confiningpressure. This trend was not affected by thepresence or absence of a skin.
(2) In samples with a skin, there was cyclicloading at the gap caused by the skin; therefore,
Geq was smaller in two-layer samples than in
one-layer samples. When σc increased, the gap
caused by the skin was filled, and thus, thedifference in the Geq–γSA relationship between
386

GEOMATE – Melbourne, Australia, 11-13 November 2020

the one-layer and two-layer samples became
smaller.
(3) When the H-D model representing thenonlinearity of soil was applied to the rigid
polyurethane foam, there was good agreementwith the experimental values for the G/G0–γSArelationship, but not for the h–γSA relationship.
Therefore, the applicability of other nonlinear
models and prediction formulas must be
examined.

Table 2 Parameters of H-D model
σc
G0
γr
hmax
(kPa)
(MPa)
(%)
(%)
20
3.1
0.65
4.5
40
3.4
1.8
3.5
1.4

sc=20kPa
1 layer
2 layer
H-D model

1.2

sc=40kPa
1 layer
2 layer
H-D model

1.0
Geq/G0ACKNOWLEDGMENTS
This study was financially supported by the geoscience center foundation and RBH Researchtechnical committee. We wish to offer our sincere
gratitude.

0.8
0.6
Marine clay
sc=50kPa, G0=15.6MPa

0.4
0.2
0.0

REFERENCES

0.001

0.01

0.1

1

Single amplitude shear strain γSA (%)[1] MLIT web page, https: //www.mlit. go.jp/en /index.html (accessed 2020-07-02)[2] Nakamura H., Enomoto K., Aoki T. and Mitabe
H., Ful1−scale shaking
table
tests
on
poured−in−placerigid
polyurethane
embankments, Tsuti to Kiso, Vol.51, No.4, 2003,
pp.14-16 (in Japanese).
[3] Chun S. B., Lim S. H., Sagong. M. and Kim K.,
Development of a hyperbolic constitutive modelfor expanded polystyrene (EPS) geofoam under
triaxial compression tests, Geotextiles andGeomembranes, Vol.22, 2004, pp.223-237.
[4] Athanasopoulos A., Pelekis P.C. and Xenaki V.C,
Dynamic properties of EPS geofoam : anexperimental
investigation,
Geosynthetics
International, Vol.6, No.3, 1999, pp.171-194.
[5] Ossa A. and Romo M.P., Dynamic
characterization of EPS geofoam, Geotextiles
and Geomembranes, Vol.29, 2011, pp.40-50.
[6] Trandafir A. C., Bartlett S. F. and Lingwall B. N.,
Behavior of EPS geofoam in stress-controlled
cyclicuniaxial
tests,
Geotextiles
andGeomembranes, Vol.28, 2010, pp.514-524.
[7] Gatto M. P. A., Montrasio L., Tsinaris A.,
Pitilakis D. and Anastasiadis A., The dynamic
behaviour of polyurethane foams in geotechnicalconditions, in Proc. 7th International Conferenceon Earthquake Geotechnical Engineering, 2019,
pp.2566-2573.
[8] Golpazir I., Ghalandarzadeh A., Jafari K. M. and
MahdaviM.,
Dynamic
properties
ofpolyurethane foam-sand mixtures using cyclic
triaxial tests, Construction and Building
Materials., Vol.118, 2016, pp.104-115.
[9] JGS0542-2009, Method for cyclic triaxial tests
to determine deformation properties ofgeomaterials, 2009, pp.751-767.
[10] Hardin B. O. and Drnevich, V. P., Shear

Fig. 9 Relationship between normalized shear modulus
and shear strain of rigid polyurethane foam with H-Dmodel.

Damping ratio h (%)

86

sc=20kPa
1 layer
2 layer
H-D model

sc=40kPa
1 layer
2 layer
H-D model

Marine clay
sc=50kPa

4

2

0
0.001

0.01

0.1

1

Single amplitude shear strain γSA (%)

Fig. 10 Relationship between damping ratio and shear
strain of rigid polyurethane foam with H-D model.
modulus and damping in soils: design equations
and curves, in Proc. of the American Society ofcivil engineers, Vol.98, No.SM7, 1972, pp.667692.
[11] Tatsuoka F. and Fukushima S, Stress-strain
relation of sand for irregular cyclic excitation(1), Seisan Kenkyu, Vol.30, No.9, 1978, pp.2629.

387

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EVALUATION OF FROST DAMAGE RESISTANCE OF LPC-FA
COMBINED CONCRETE CURED FOR A LONG TIME
Shogo KAWAMORITA1, Kazuhito NIWASE2
1,2

National Institute of Technology (KOSEN), Hachinohe College, JAPANABSTRACTIn cold regions in the world, the deterioration of a concrete surface such as scaling and pop-out is caused byfreezing and thawing action, which has a major influence on the durability and the appearance of the concrete
structure. Deterioration resistance generally improves with densification. In this study, I evaluated the frost damage
resistance of concrete using Low Heat Portland cement and fly ash (LPC-FA concrete). In long-term material,
various tests were carried out using specimens that had been cured in water and exposure, focusing on the void
structure and strength properties. In the strength properties, all mix proportions are around 50 N/mm2, it was
confirmed that it had a sufficient strength regardless of the W/B and Air. On the other hand, the exposed specimens
are expected to have supplied less water. Therefore, it is considered that the densification of the structure by the
pozzolanic reaction is insufficient. About the scaling test, the mix proportion of W/B 45% - Air 2.5% was 0.70
kg/m2 when cured in water for 1 year, so resistance equal to or higher than that of general concrete was confirmed.
However, in the exposed specimens, gravel peeling was observed in small proportions of Air. In this regard,
considering the results of the air void spacing factor and the pore size distribution, it was suggested that a dense
structure with a lot of fine voids less than 60nm that is difficult to freeze has high deterioration resistance.
Keywords: Air voids structure, Compressive strength, Fly ash, Frost damage, Low heat portland cement

INTRODUCTION

pressure and osmotic pressure [4].
Therefore, in this study, I fabricated a specimen
whose properties change significantly with age. At
various ages, various tests such as scaling tests were
performed, and the deterioration resistance at longterm age was examined from the viewpoint ofstrength characteristics and void structure.

Concrete structure and concrete products arestrictly controlled to use materials, mix proportions,curing, construction and the like so as to havepredetermined quality and performance. However, in
cold regions where the temperature is below freezing,
surface deterioration such as scaling and pop-out issometimes observed due to freezing and thawing
action. Furthermore, the compounding with salt
damage, carbonation and the like has a major
influence on the durability and the appearance of theconcrete structure [1].
Since these deteriorations progress from thesurface, the degree of deterioration tends to be small
if the strength property and the mass transfer
resistance of the surface layer are favorable. The
concrete surface layer has a role as a protective layer
to prevent external deterioration factors entering theinside, and is a very important part for enhancing
deterioration resistance.
In order to improve the resistance to deteriorationof concrete, it is necessary to densify the surface layer.
It is known that these concretes become very highresistance to deterioration by curing long term [2]. In
particular, concrete using fly ash degrades in responseto changes in void structure due to material age, suchas the transition zone that becomes coarse voids witha diameter of 50nm several years resistance alsochanges [3]. Deterioration resistance generally
improves with densification. However, the resistanceof frost damage may be reduced in terms of expansion

EXPERIMENT OUTLINE
Materials used and mix proportion
Design policy
In this study, concrete using low heat portland
cement and fly ash (LPC-FA concrete) was taken asa cement matrix with large changes in strength
property and void structure on material age. The usedmaterials and the mix proportions were designed inconsideration of strength development, physical andchemical performance, suppression of cracks, andworkability so that effects other than void structurechange are minimized. The design principal of thematerials used and the ingredients are shown in Table
1.
Materials used
As the binder, low heat portland cement and fly ash
were added with super plasticizer and limestone fine388

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table. 1

Design principal

Characteristic
Strength expression

Table. 2 Outline of the materials
Material, Name

PolicyIncrease compressive strength
Reduce permeability and diffusivity

Physical andchemical
performance

Low heat portland cement

LPC

Fly ashFA

Limestone fine powder

LS

Sand : Lime crushed sandS

Suppression of drying shrinkage
Suppression of self-contraction

Gravel : Lime crushed stoneG

Increase flowability andmaterial separation resistance

Admixture : Air entraining and highrange water reducing admixture,
Air amount regulatorSP

Set appropriate congelation time
Use chemically stable materials
Reduce the large air voids and densify
Suppression of heat of hydration

Crack control

Workability

Note

Code

AS

Density=3.24g/cm³,
Specific surface area=3730cm²/g
Density=2.17g/cm³, JIS typeⅡ,
Specific surface area=3610cm²/g
Density=2.70g/cm³,
Specific surface area=5250cm²/g
Density=2.66g/cm³, FM=2.79
Hachinohe Matsudate Production
Density=2.69g/cm³, FM=6.63
Hachinohe Matsudate Production
Carboxyl group-containing
polyether compound
Polyalkylene glycol derivative

Table. 3 Mix proportion

W/B
(%)

Gmax
(mm)

s/a
(%)

Slump
flow
(cm)

Air
(%)

65±5.0

2.5
5.0
7.5
2.5
5.0
7.5
2.5
5.0
7.5

45

60

20

75
※

53.4

W

Unit amount (kg/m3)
Powder : P
Binder : B
S
LS
LPC
FA

160

249

107

178

883

156

186

80

265

886

155

148

64

318

887

G

780

SP

0.95

AS

0.060
0.150
※
0.005
0.060
0.030
0.015
0.090

Defoamerpowder in consideration of self-filling property sothat there was no difference in the quality of thespecimens due to the factors at the concrete placing.
Also, in order to eliminate the influence caused by theaggregate as much as possible, the materials otherthan the binder were unified with the materials basedon chemically stable limestone. An outline of thematerials used is shown in Table 2.

Experimental method
Specimens
The concrete was mixed in a mixing amount of
about 20 to 50 liters per batch and the mixing time for120 seconds by a twin screw forced mixer. Each mix
proportion was placing concrete in two batches of
100*100*400 mm, 100*500*400 mm, φ100*200mm.
The content of air was adjusted to beapproximately 2.5%, 5.0%, 7.5% depending on thecontent of admixture added, and the slump flow wasconfirmed to be 65±5.0cm. The prepared specimens
were demolded at 1 day of material age, and after
curing water for 28 days, 91 days, 365 days inconstant temperature curing tank (20℃) respectively,
they were subjected to various tests. Figure 1 showsthe exposed specimens.
The test sample for bubble measurement was
100*100*50 mm, and the test sample for scaling test
was cut to 150*150*50 mm for the test. For thecompressive strength test and the electrophoresis test,
a specimen of φ100*200 mm was used, and theMix proportion
The mix proportion was based on LPC-FA
concrete with the water binder ratio of 45%, which
has execution of workability test in previous studies[5] [6]. The water binder ratio was set to 60% and
75% to make a clear difference in pore structure. In
addition, the unit amounts of water, powder andaggregate were adjusted so that the fresh property
would not cause harmful materials separation, and theslump flow would have the same ratio within therange satisfying the standard. The mix proportion of
the specimens is shown in Table 3.

389

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 2 Before and after the scaling test
Fig. 1 Exposed specimens
crushed specimen after the compressive strength test
was used for the mercury injection method. As for
exposed specimens, specimens of φ50*100 mm andφ100*200 mm were sampled by coring. The φ50*100
mm specimen was subjected to the compressive
strength test and the mercury injection method, andthe φ100*200 mm specimen was subjected to thescaling test.
Experimental item

Fig. 3In the experiment, the Compressive strength wasmeasured to obtain a general indicator, and a Scaling
test and Electrophoretic diffusion test based ondeterioration in a chloride environment was
performed. Moreover, in order to obtain the index of
the air voids structure, manual linear traverse method
was carried out. They based on various standards. The
test items and methods are as follows.
➢ Scaling amount : Scaling test (RILEM-CDF
method)➢ Compressive strength : Compressive strength
test (JIS R 5201)➢ Air content and Air void spacing factor after
curing : Manual linear traverse method (ASTM
C 457)➢ Pore size distribution : Mercury injection
method➢ Effective diffusion coefficient : Electrophoresis
test (JSCE-G571)

Scaling amountlarger W/B, the lower the resistance regardless of the
air content. However, at this point, even with aproportion of W/B 60% and air content 7.5%, 2.625
kg/m2 was obtained, which is equivalent to that ofgeneral concrete. Furthermore, at 365 days of age, all
the formulations were less than 2.5 kg/m2, confirminga significant improvement in resistance with material
age. In particular, it was 0.70 kg/m2 at W/B 45% andair content 2.5%, and the result was that the scaling
amount was extremely small even when the aircontent was small.
Then, I will compare the specimens that were aged
365 days in water and the specimens that were
exposed for 3 years. In the case of a proportion witha small amount of air, the exposed specimen tends togreatly exceed the scaling amount. On the other hand,with the formulations with air content 7.5% and theformulation with W/B 45% air content 5.0%, theexposed specimen had a smaller scaling amount.
Especially for W/B 45%, when the air content was
5.0% and 7.5%, the specimens cured in water were0.789 kg/m2 and 1.213 kg/m2 respectively, but the
exposed specimens were 0.237 kg/m2 and 0.416
kg/m2, which was about one-third the value of the
exposed specimens for both formulations comparedto the specimens cured in water. This is equivalent toor better than the commonly used concrete structures.

TEST RESULTS AND DISCUSSION
Scaling test
The scaling amounts up to 50 cycles was measured
under each mix proportions and material age. In the
exposed specimens, when the air content was small,the coarse aggregate peeled and the surface layer wasdestroyed. Figure 2 shows the exposed specimen
before and after the scaling test. The test results areshown in Figure 3.
At 28 days of age, it was confirmed that the larger
the air content, the higher the resistance, and theCompressive strength test
Figure 4 shows the compressive strength test
results of the specimens that were aged in water for 1

390

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 4Compressive strength (water curing)

Fig. 6

Fig. 5 Compressive strength (exposed specimens)

Air content of hardened concreteFig. 7

year and 3 years, and Figure 5 shows the compressive
strength test results of the exposed specimens thatwere aged in air for 3 years. Regarding the exposed
specimens, it was 3.8% and 7.5% due to theadjustment of the air volume. Moreover, cracks could
not be confirmed visually.
As a result, the strength after 1 years underwater
curing and 3 years in-air curing were close to eachother, and there was not much difference due to thecomposition. At W/B 75%, the strength tends to be alittle low, but still 40 to 50 N/mm2 was secured. From
Figure 5, the strength of W/B 45% and 60% are about
60 N/mm2, which is about the same, and nocorrelation depending on the air content was seen.
Then, when the cumulative temperature at 1 years
old age of the specimen cured in water is calculated,
it becomes 10950℃. From the Japan Meteorological
Agency data, the cumulative temperature of the
exposed specimen was approximately 12580℃, andno clear difference was observed. And, the number ofcycles that received the freeze-thaw action was about
330 times. In a previous study, in freeze-thaw tests,
specimens that had been aged in water for 1 year
tended to break at 150 cycles at a W/B 75% and an aircontent 2.5%. However, the exposed specimens had
sufficient strength for all mix proportions [7]. In
addition, it is estimated that about 50 m3 of water was
supplied in three years from the rainfall. It can be seenthat this is extremely small compared with the supply
amount in underwater curing.

Air void spacing factor

From these, it is considered that the difference dueto the curing method is not due to the cumulative
temperature or the freeze-thaw action, and is because
the water supply necessary for the pozzolanic action
is not sufficient. It is estimated that strength willdevelop with the supply of water due to precipitationin the future, so observation will continue until thematerial age of 5 years.
Air content after curing and air voids spacing
factorAir volume after curing
The content of air of each mix proportions
measured using the linear traverse method is shownin Figure 6. The air content was about 2/3 of the aircontent of air at the time of freshness. In addition, thevalues were almost the same for each mix proportions
regardless of the curing period. This is considered tobe due to the fact that the influence of densification is
not reflected, since the measurement of the content of
air by the linear traverse method cannot measure thepore diameter of about 10μm or less.
Air voids spacing factor
Figure 7 shows the air voids spacing factor for eachcompounding condition measured using the linear
traverse method.
391

GEOMATE – Melbourne, Australia, 11-13 November 2020

The air voids spacing factor tended to double in thespecimens with a relatively small content of air at 365
days of age. This is considered to be due to the factthat during the curing period of 365 days, the voids
with minute bubble diameters became less than the
bubble diameters that could be measured by the
progress of the pozzolanic reaction, and the air voids
spacing factor increased significantly. There was nochange in the specimen with an air content of about
7.5% when fresh.
Here, based on the results of the scaling test, in the
exposed specimens, the air voids spacing factor isabout 800 to 1200 µm in all of the formulations inwhich the scaling amount was remarkably large. On
the contrary, it can be confirmed that the mix
proportions with good results is about 400 µm or less.
Therefore, if the air voids spacing factor exceeds acertain value, it is considered that there is a highpossibility that deterioration such as peeling of coarse
aggregate will occur along with a significant decreasein scaling resistance.

Fig. 8 Pore size distribution (water curing, 365 days)

Pore size distribution
Figure 8 shows the results of 365 days of the poresize distribution of each specimen obtained by the
mercury injection method, and Figure 9 shows theresults of the exposed specimen.
From Figure 8, it can be confirmed that in the 365
days old specimens, an extremely dense cement
matrix was formed in all the mix proportions. It is
known that the voids that affect freeze-thaw action
are 100 to 1000 nm, and voids of 6 to 20 nm do notfreeze up to -43℃ [8]. Furthermore, the capillary
voids (3 to 2000 nm) occupy about 2/3 of the voids inconcrete. Based on this, it can be confirmed that the
specimens cured in water for 365 days have a verysmall amounts of voids of 100 to 1000 nm, and mostof them are fine voids of 30 to 40 nm or less that are
hard to freeze. From this, it is considered that since
the water in the voids does not freeze, a dense
structure with high resistance that can relieve theexpansion pressure and the osmotic pressure isformed.
On the other hand, as shown in Figure 9, theexposure specimens varied depending on thecomposition, and the correlation with the scaling test
could not be confirmed. It is considered that this isbecause the densification of the tissue due to thehydration reaction and the pozzolan reaction was
insufficient because it was not cured in water. In
particular, in the case of W/B 60% air content 7.5%
and W/B 75% proportions, there is a peak at about
100 nm, so there is concern about the effect of freezethaw action.
From these results, it is considered important todensify the voids, which are detrimental to the freezethaw action, in water for a certain period of time sothat the voids do not freeze until they become dense.

Fig. 9 Pore size distribution (exposed for 3 years)

Fig. 10 Effective diffusion coefficient
As a result, deterioration resistance was able to be
improved, so it is expected that the specimens at 365
days of age had good scaling resistance, although the
bubble spacing coefficient was significantly large.
Effective diffusion coefficient
Figure 10 shows the effective diffusion coefficientof each mix proportions measured by theelectrophoresis test.
Until 28 days of age, the effective diffusion
coefficient was in the order of 10-12 m2/s in all cases.
At 91 and 365 days of age, most of the mix
proportions had small values on the order of 10-13392

GEOMATE – Melbourne, Australia, 11-13 November 2020

m2/s. The reason why the effective diffusion
coefficient is large in any of the mix proportions is
considered to be that the number of samples is one.
However, very good results have been obtained incomparison with general concrete. From these results,
the mass transfer resistance, which is important forcomplex deterioration such as salt damage andneutralization, was confirmed that is sufficiently
small regardless of the mix proportions if good curingconditions are satisfied using LPC-FA combined
concrete.

ACKNOWLEDGMENT
Part of this research was conducted in response to a
grant fund for research or activities related to themaintenance and maintenance of social capital in
FY2019.
REFERENCES[1] M. Shoya and Y. Tsukinaga, “Frost Damage ofConcrete Structure in Tohoku District,”
Concrete Journal, Vol.42, No.12, pp.3-8, 2004
(in Japanese)[2] K. Niwase, M. Hironaga and Y. Tsuji, “Designof the Concrete Used for Sub-surface LLW
Disposal Facility,” Concrete Journal, Vol. 44,
No. 2, pp.3-8, 2006 (in Japanese)[3] H. Uchikawa, S. Hanehara and D. Sawaki,“Estimation of the Thickness of Transition Zone
in Hardened Mortar and Concrete, andInvestigation of the Relationship between their
Thickness and Strength Development,”
Concrete Research and Technology, Vol. 4, No.
2, 1993 (in Japanese)[4] Powers,T.C, “Mechanisms of shrinkage andreversible creep of hardened cement paste,”
International Conference on the Structure ofConcrete, Cement and Concrete Association,
London, pp.319-344, 1968
[5] K. Niwase, N. Sugihashi and Y. Tsuji,“Materials and Proportion’s Design of Selfcompacting Mortar Used for Low Diffusion
Layer in Sub-surface Radioactive Waste
Disposal Facility in Japan,” Concrete Research
and Technology, Vol. 21, No. 3, pp.43-51, 2010
(in Japanese)[6] K. Niwase, N. Sugihashi and Y. Tsuji,“Required Initial Performance of Low Diffusion
Layer in Sub-surface Radioactive Waste
Disposal Facilities After Carry in out a Full-size
Model Test, Concrete Research andTechnology,” Vol. 21, No. 3, pp.53-62, 2010 (inJapanese)[7] H. Mawatari, K. Niwase, T. Sugawara and Y.
Edamatsu, “Changes of Frost Damage
Resistance and Air Voids Structure of LPC-FA
Concrete According to Curing Periods,” Our
World in Concrete & Structures, August 2017
(in Japanese)[8] S. Hanehara, D. Sawaki, “Void structure andphysical properties of hardened concrete,”
Gypsum & Lime, No.240, pp.32-34, 1992 (inJapanese)

CONCLUSIONSIn this study, the deterioration resistance of LPCFA concrete was evaluated from the viewpoint ofstrength characteristics and void structure.
In compressive strength, it was suggested that thewater supply was low and the pozzolanic reaction did
not proceed sufficiently in the exposed specimen.
However, no difference due to W/B was observed,
and it was confirmed that even W/B75% had
sufficient strength, so it is considered that the strengthcharacteristics are sufficient.
From the viewpoint of the void structure, it has
been confirmed that the scaling deterioration
resistance is significantly reduced when the air voids
spacing factor exceeds a certain value. On the other
hand, it was suggested from the results of the poresize distribution that it is important to perform
underwater curing for an appropriate period to form adense structure with many fine voids that are difficult
to freeze. In this case, it is considered that thedeterioration resistance may not decrease even if the
air voids spacing factor is large.
From the result of the effective diffusion
coefficient, the invasion of deterioration factors fromthe outside was found to be sufficiently small
regardless of the mix proportions at the material ageof 365 days.
From these, it is important to densify the structure
to reduce the deterioration of frost damage underchloride environment, and it is necessary to
sufficiently advance the pozzolanic reaction after anappropriate curing period. By satisfying theserequirements, LPC-FA concrete is considered to havesufficient practicability.
In the future, I plan to conduct an electrophoresis
test and voids measurement of exposed specimens at
the material age of 3 years. Furthermore, I willcontinue to observe for 5 years of material age, andwill carry out various tests such as compressive
strength test, scaling test, mercury injection method,etc. to evaluate and examine changes in strengthcharacteristics and voids structure.

393

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13, 2020, ISBN: 978-4-909106049 C3051

IMPROVEMENT OF INITIAL STRENGTH OF
LPC-FA CONCRETE BY USING C-S-H-TYPE ACCELERATOR
Takuya KUDO* and Kazuhito NIWASE*
National Institute of Technology, Hachinohe College (Tamonoki Hachinohe 039-1192, Japan)

1

ABSTRACT
Most social infrastructures in Japan have been aging rapidly because there were constructed during the highgrowth period. Moreover, the number of skilled workers with a lot of experience are decreasing. As a result, there
is a concern about degradation of structure quality. That is why it is necessary to develop new materials that haveexcellent long -tern durability and easy workability.
Owing to previous studies, it is known that concrete using low heat portland cement and fly ash (hereinafter,
this is called "LPC-FA concrete") has excellent long - term durability, but there is a problem with low initialstrength. Therefore, in this study, basic research was carried out to enhance the initial strength of LPC-FA concrete.
The initial strength of LPC-FA concrete was tried to improve by using a combination of accelerating admixture(MXS) to accelerate the hydration reaction with C-S-H nanocrystals, air entraining and high-range water reducing
admixture (ME). Two materials have been confirmed to be good combination by previous research.
In this study, the W/B were at 30% evaluated the initial strength and fluidity. In addition, the effective diffusion
coefficient of chloride ions was measured as the mass transfer resistance of the deterioration factor from thesurface.
From the test results, it was confirmed that the initial strength of LPC-FA concrete could be increased by
combining ME and MXS, and an extremely dense cement matrix was formed.

the penetration resistance of the deterioration factor
from the surface.

INTRODUCTION
Most social infrastructures in Japan have been
aging rapidly because there were constructed during
the high growth period. Moreover, the number ofskilled workers with a lot of experience aredecreasing. As a result, there is a concern about
degradation of structure quality. That is why it isnecessary to develop new materials that haveexcellent long -tern durability and easy workability.
In this study, we carried out basic research aimed
at improving the initial strength of fly ash concrete(LPC-FA concrete) using low heat Portland cement.
Owing to previous studies, it is known that concrete
using low heat Portland cement and fly ash(hereinafter, this is called "LPC-FA concrete") has
excellent long - term durability, but there is a problem
with low initial strength. On the other hand, thedevelopment of chemical admixture technology has
enabled us to obtain products with unprecedented
results. Therefore, in this study, we tried to improve
the initial strength of LPC-FA concrete by using air
entraining and high-range water reducing admixture
and accelerating admixture together. Also, by
reducing W / B, voids can be reduced, and high initialstrength and improved mass transfer resistance can be
expected.
In the experiment, the initial strength and fluidityof LPC-FA concrete were confirmed by preliminary
experiments using mortar. In addition, the effective
diffusion coefficient of chloride ions was measured as

EXAMINAION SUMMARY
Fluidity Evaluation
A slump flow test was performed to evaluate thefluidity. Fluidity evaluation was performed according
to THE SLUMP FLOW TEST METHOD FOR
CONCRETE (JIS A 1150). Assuming on-site
construction for the construction time, after kneadingfor 0, 30, 60, 90 and 120 minutes, tests wereconducted using mortar.
STRENGTH EVALUATIONThe strength evaluation was performed according
to THE COMPRESSIVE STRENGTH TESTMETHOD FOR CONCRETE (JIS A 1108). The
specimens were cured by underwater curing. Material
age was 3, 7, 28 and 91 days.
MASS TRANSFER RESISTANCEEVALUTION
Evaluation of mass transfer resistance wasconducted in accordance with the Society of Civil
Engineers' Procedure “TEST METHOD FOR
394

GEOMATE - Melbourne, Australia, 11-13, 2020

EFFECTIVE DIFFUSION COEFFICIENT OFCHLORIDE ION CONCRETE BY MIGRATION”(JSCE-G 571-2003)1). As the specimen, a column
specimen cut into a size of φ 100 × h 50 mm was used.
The diffusion cell used in this test is shown in Fig.1.
This diffusion cell is taken into consideration so thatthe side of the specimen is made into the state shownin Fig.2 with the epoxy resin and the heat shrinkablerubber, and the cells on the NaCl solution side and the
NaOH solution side are connected. As a result,chloride ion moves only in the specimen, andmovement from the joint surface at the side of thespecimen is blocked. Even when solution leakage
occurs from a fine gap, there is no contact of thesolution outside the specimen, so it has an advantagethat is not reflected in the data.

confirmed at the edge. It did not separate at 20% andbecame high flow. At 30%, the fluidity decreased anddid not flow uniformly on the flow table. From thisresult, the substitution rate was determined to be 20%.
There was no change in the LS substitution rate when
W / B = 50%. It is considered that this is because theunit cement amount and fly ash amount increased dueto the change of W / B, and the chemical admixture
amount also increased.

MIX PROPORTIONS DESIGN

LS10

178 483 207 145 1304 14.5

14.5

LS20

178 483 207 290 1160 14.5

14.5

LS30

178 484 207 436 1016 14.5

14.5

MIX POPORTIONS DURING VARIOUS WITH
MORTARMix proportions of mortar were determined in table2.
table.3 shows the materials used. Mix proportions are
Table1. Viscosity study mix proportionsType

Mix Proportions of Viscosity Study by Adjusting
the Substitution Rate of Limestone Fine PowerThe basic mix proportions were created based on
the W / B = 50% mortar composition 2) of the previous
study. We believe that fluidity will change
significantly with changes in W / B, so we decided toadjust with substitution rate of limestone fine powder(LS)4) . It was set in three mix proportions of 10%,20% and 30%. Below, LS10 indicates that the ratio oflimestone fine powder to fine aggregate is 10%. The
common condition is W / B = 30%. Fly ash (FA)W

Unit volume weight （kg/㎥）
Ｃ Ｆ LSS
ME MXS

Table2 mix proportions during various with mortarTypeaddition rate is 30%, and the addition amounts of

W/B

W

Unit volume weight （kg/㎥）
Ｃ Ｆ LSS
ME MXS

OPC-ME-MXS

30 178 483 207 290 1161 14.5 14.5

OPC-ME-MXS

50 211 322 138 322 1288

OPC-ME
LPC-ME-MXS

9.7

9.7

30 193 483 207 290 1161 14.5

ー

30 179 483 208 291 1164 14.5 14.5

OPC：Ordinary Portland Cement，LPC：Low Heat Portland
Cement，F：fly ash，LS : Limestone fine powder，ME：Air
entraining and high-range water reducing admixture，MXS：
Accelerating admixture

Table3. Using materials
Fig.1 Diffusion cell

Material

Abbreviation

Overview
Ordinary Portland Cement

OPC

Density=3.160g/cm3
Specific surface

CementLow Heat Portland Cement
LPC

Density=3.220g/cm3
Specific surface
JIS type Ⅱ

Fly ashFA

Density=2.190g/cm3
Specific surface

Limestone fine
powder

Fig.2 Specimen for electrophoretic methodLS

Air entraining
and high-range

ME and MXS are both C × 3%. Trial mixing waswater reducing

carried out with mix proportions shown in table.1. In
LS10, the fluidity is high, but separation wasME

Density=2.700g/cm3

PAE、Amount to use=C×0.5∼
3.0%

admixture
Accelerating
admixture

395

MXS

Amount to use = C×0.5 6.0%

GEOMATE - Melbourne, Australia, 11-13, 2020

OPC-ME which added only air entraining and highrange water reducing admixture to OPC and OPCME-MXS which added air entraining and high-range
water reducing admixture and accelerating admixture
to OPC, LPC-ME-MXS which added air entraining
and high-range water reducing admixture andaccelerating admixture to LPC, In three types. As ageneral condition, W / B = 30%, FA replacement rate
30%. C and 3% were added to ME and MXS, and LS
was 20% to fine aggregate.
FLUIDITY EVALUATION
Slump Flow Test Results
Fig.3 shows the decrease in viscosity with the three
formulations (W/B=30%). Fig.4 shows the W / B =50% viscosity reduction obtained in the previous
study 2) and the W / B = 30% viscosity reduction
obtained in this test. Fig.5 to 7 show the state of slump
flow immediately after kneading the three types ofmix proportions (W/B =30%). LPC-ME-MXS (30%)
showed very high fluidity. As a result, it could not bemeasured because it did not fit in the 80 x 80 cm flowtable. In addition, the measurement was performed
from 2 hours after kneading, but no decrease in theflow value was observed. Both OPC-ME and OPCME-MXS showed very high fluidity immediately
after kneading. Comparing the flow values of the twomix proportions, the flow value immediately after

Slump flow value(cm)

90.0
70.0
50.0

80

80

73.0
60.9

64.0
57.8

OPC-ME
OPC-ME-MXS
LPC-ME-MXS

30.0

0

80
61.1
55.3

80

80

57.9

55.3

Fig.5.OPC-ME

54.4 52.2
Fig.6 OPC-ME-MXS

30
60
90
standing time(min)120

Slump flow value（cm）

Fig.3 Slump flow test results
90.0
80.0
70.0

80.0
73.0

76.3
64.0

72.0
61.1

60.0

72.5
57.9

55.3

50%
30%

50.0
40.0

71.5

0

30

60

90

standing time(min)120
Fig.7 LPC-ME-MXS

Fig.4 OPC-ME-MXS slump flow test results
396

GEOMATE - Melbourne, Australia, 11-13, 2020

kneading was 60.9 cm for OPC-ME and 73.0 cm for
OPCME-MXS, a difference of 12.1 cm. After that,the flow values of the two mix proportions decreased
with time, and after 2 hours, OPC-ME was 52.2 cm
and OPC-ME-MXS was 55.3 cm. The decrease in theflow value after the lapse of 2 hours was 8.7 cm for
OPC-ME and 17.7 cm for OPC-MEMXS. The OPCME-MXS has a larger decrease in the flow value overtime.
The flow values immediately after kneading of
OPC-ME-MXS and LPC-ME-MXS were muchlarger than those of OPC-ME without MXS added.
However, after 2 hours, the LPC-ME-MXS kept ahigh flow, but the flow value of OPC-ME-MXS
decreased to the same level as OPC-ME. Comparing
OPC-ME-MXS with W / B = 50% and W / B = 30%,
50% had a decrease in flow value of 8.5 cm after 2hours, which was about half of 30%. By setting theW/B to 30%, the decrease in the flow value with thepassage of time became large. Therefore, it isnecessary to consider the deterioration of fluidity withthe passage of time when constructing with a low
water cement ratio.

STRENGTH EVALUATION
Compressive Strength Test Results
Fig.8 shows the results of the compressive strength
test. As a result, the 3-days strength was 20 N/mm2 or
more in all mix proportions, and sufficient strength
could be obtained. Among them, OPC-ME-MXS was
61.1 N/mm2, which was about 1.7 times stronger than
OPC-ME. The lowest strength mix proportion was
LPC-ME-MXS at 26.3 N / mm2. However, theincrease from 3 to 7 days intensity was the largest andincreased by about 22N / mm2. Therefore, it became
the same level as OPC-ME.
Next, pay attention to the presence or absence of
MXS and compare OPC-ME-MXS and OPC-ME. At
3-days strength, the difference in strength between
the two mix proportions were 25.1 N/mm2. After that,the strength of OPC-ME-MXS was higher than thatof OPC-ME. From 28-days strength to 91-days
strength, OPC-ME increased by about 12 N/mm2,
while OPC-ME-MXS increased by about 25 N/mm2,
which was about twice the increase of OPC-ME. It
was confirmed that the effect of adding MXS was
remarkable by the age of 3 days. It was found that thelong-term strength increase was higher in the mix
proportion containing MXS. It can be considered thatMXS influenced the pozzolan reaction by FA.
The strength of the two OPC mix proportions
continued to increase up to the 91-days age, while
LPC decreased by 11.6 N/mm2 from the 28-days
strength to the 91-days strength. Fig.9 shows a 28days strength comparison of LPCME-MXS with W/B= 30% and W/B = 50% obtained in a previous study2).
By lowering W / B, W/B = 30% was about 15 N /mm2 higher than W / B = 50% at 3 days strength.
Even in the strength after 3 days, the mix proportionof W / B = 30% was high. The increase from 3-days
intensity to 28-days intensity was about 40 N / mm2for W / B = 50% and about 70 N / mm2 for W / B =30%. Therefore, a large increase in strength could be
confirmed in the long term.

SLUMP FLOW TEST CONSIDERATIONS
The result of the slump flow test of LPC was
extremely large compared to the result of the OPC test.
Also, there was a big difference in the tendency ofviscosity decrease. This is probably because LPC hasless interstitial phase that adsorbs the admixture moreeasily than OPC5) . Especially at low water cement
ratios such as high strength concrete, low porosity andadmixtures such as fly ash tend to be compatible.
Therefore, it is considered that such a difference inthe flow value appears. The results of the slump flow
test of OPC-ME and OPC-ME-MXS show that theaddition of MXS has a great influence on the fluidity.
It is generally believed that addition of MXS
accelerates curing and reduces the flow value.
However, with this mix proportions, the flow value of
OPC-ME-MXS and LPC-ME-MXS with MXS
increased. From this, it is considered that chemicalfactors that have not been confirmed so far haveoccurred, such as MXS inhibiting the adsorption of
ME. In the future, it is necessary to examine thechemical factors. Regarding the properties related to
pumpability, all three mix proportions showed
thixotropy, in which the viscosity decreased when theshear stress was continued and increased when it was
stationary. Therefore, when applying it, it isnecessary to confirm the pumpability in considerationof its workability. Then, in some cases, it isconsidered that the driving with the bucket may besuitable.

COMPRESSIVE STRENGTH TESTCONSIDERATIONS
According to the compression test results of thismix proportions, a compression strength of over 20
N/mm2 was obtained for all the mix proportions at
the time of 3-days strength. This is considered to bebecause the W / B of this mix proportion was 30%,
and the voids were reduced due to the very low
water-cement ratio. In addition, the water-powder
ratio was also extremely small at 20%, so it isconsidered that the specimen was a dense sample.
OPC-ME-MXS has much higher compressive
strength than OPC-ME. Among them, there was aremarkable difference in the amount of increase instrength up to 3 days. This is considered to be due to397

GEOMATE - Melbourne, Australia, 11-13, 2020

the mechanism of hardening promotion by MXS
shown in Fig. 10. MXS is accelerating admixture
whose main component is calcium silicate hydrate(C-S-H) nanocrystals. This introduced C-S-H seed
crystals into the liquid phase and accelerated
hardening without waiting for C-S-H to form fromthe cement. It is considered that this improves theinitial strength. Furthermore, the increase in OPCME-MXS from 28-days intensity to 91-days
intensity was about double that of OPC-ME. It isconsidered that this is because the C-S-H structure
was more compacted by the pozzolan reaction withFA.
LPC-ME-MXS was the smallest at 3-days
intensity but the largest at 28-days intensity. This isconsidered to be due to the characteristic that thestrength of LPC increases over a long period of time.
The cause of the 91-days intensity decline has notbeen clarified at present. Fig.11 shows the conditionof LPC-ME-MXS specimens after the 91-days-old
compression. It is considered that the peelingobserved on the side surface of the test piece after
the compression test is one of the causes of thedecrease in strength. It is known that there is a close

Compressive Strength[N/mm2]

72.9
61.1

47.6
50.0
36.0

0.0

0

MASS TRANSFER RESISTANCEEVALUTION
Mass Transfer Resistance Results
Fig.12 shows the electrophoresis test results. All
were below the order of 1.0 × 10⁻12 m2 / s in this mix
proportions. OPC-ME-MXS has a smaller value than
OPC-ME, and LPC-ME-MXS has an even smaller
value. The average value of LPC-ME-MXS was 1.0
× 10⁻14m2 / s. There were also specimens of the orderof 1.0 × 10⁻14m2 / s in OPC- ME-MXS. The averagevalue of OPC-ME-MXS was less than half theaverage value of OPC-ME.

111.5

98.5

100.0

increases and the total length of cracks that occur
until the destruction. That is, it is said that thesmaller the size of the test piece, the shorter theelapsed time from the occurrence of local
microcracks to the destruction of the entire system,so that the strength decreases. In the future, we will
investigate the cause of the decrease in 91-days
strength from the viewpoint of fracture mode andspecimen size.

MASS TRANSFER RESISTANCECONSIDERATION

86.9

86.2
65.0

The value of the effective diffusion coefficientshows that an extremely dense cement matrix wasformed. This time, we were able to obtain very smallvalues for all mix proportions. It is considered that
because W / B was very low at 30%, the voids werereduced and the mortar became dense. The
water/powder ratio was about 20%, and the amount

77.1

48.0
26.3

OPC-ME(30%)
OPC-ME-MXS(30%)
LPC-ME-MXS(30%)

20

40

60

80

Curing period (Day)100

Compressive Strength [N/mm2]

Fig.8 Compression test result
120.0

98.5

100.0

Fig.10 the mechanism of hardening promotion by MXS

80.0
60.0

48.0

40.0

26.3
19.7
11.4

20.0
0.0

0

10

50.7
LPC-ME-MXS(50％)
LPC-ME-MXS(30%)
20

Curing period (Day)30

Fig.9 LPC-ME-MXS compression test results
relationship between the phenomenon that thestrength increases as the size of the specimen

Fig.11 LPC-ME-MXS specimens after the 91days-old compression test.
398

GEOMATE - Melbourne, Australia, 11-13, 2020

coefficien(×10⁻¹³m2/s)

Effective diffusion

8.00
6.00
4.00
2.00

6.45

OPC-ME
OPC-ME-MXS
LPC-ME-MXS

reduced by the pozzolanic reaction due to theaddition of fly ash.
FUTURE TASKS

2.88

1)

0.92

0.00

2)

Fig.12. Mass transfer resistance results

3)

of powder was very large. This is also considered to
be one of the factors that reduced the effective
diffusion coefficient.
OPC-ME-MXS was able to get a smaller value
than OPC-ME. It is considered that this is due to theseed crystal of the above nanoparticles contained in
MXS. From this, it is considered that the addition of
MXS is effective in increasing the mass transfer
resistance.
This mix proportions have extremely high masstransfer resistance even at 28 days of age, and it was
found that the effect of MXS was much larger than
expected. In the future, it is expected that the effective
diffusion coefficient will be further reduced by the
pozzolanic reaction due to the addition of fly ash.

4)

5)

Examine the chemical basis for the factors ofhigh workability, strength, and mass transfer
resistance obtained by adding MXS.
Examine the factors that reduced the long-term
strength of LPC-ME-MXS.
We will study the fluidity and initial strength
when W / B is further reduced.
Considering economic efficiency, we will study
the fluidity and initial strength when the addition
rate of the chemical admixture is reduced.
The concrete mix proportion is decided based on
the data of the preliminary experiment withmortar, and each test is conducted.

REFERENCE
1)

2)

CONCLUSION
The following findings have become clear fromthis study.
1) By using MXS and ME together, a large flow
value could be obtained for mortar. Moreover,when MXS and ME are used together, LPC has
a larger flow value than OPC.
2) By combining MXS and ME, it was confirmedthat a high initial strength could be obtained in
the LPC-FA mortar, and a very dense cement
matrix was formed.
3) In mortar, LPC-ME-MXS had the highest
compressive strength of 28-days-old specimens.
However, the 91-days intensity was decreased
only in LPC-ME-MXS.
4) By setting the W / B to 30%, the strength of all
three mix proportions was higher than that of theprevious study 2) where the W / B was 50%.
5) At the 3-days intensity, OPC-ME-MXS showeda marked increase. However, the increase in
LPC-ME-MXS also increased at 7-days intensity.
6) An extremely small effective diffusion
coefficient was obtained. This is probably
because the W / B was 30% and the voids werereduced. Moreover, it is expected that the large
amount of powder contributed to thecompactness of the specimen. The effective
diffusion coefficient was reduced by adding
MXS, and the mass transfer resistance was
improved. In the future, it is expected that theeffective diffusion coefficient will be further

3)

4)

5)

6)

7)

399

TEST
METHODFOR
EFFECTIVE
DIFFUSION COEFFICIENT OF CHLORIDEION CONCRETE BY MIGRATION (JSCE-G
571-2003) (in Japan)
Sora SUTO, Kazuhito NIWASE ：Study on
Improvement of Early Strength of Highly
Durable Concrete by Combined Use ofChemical Admixtures, The 3rd International
Conferenceon
Structural
and
Civil
Engineering(ICSCE2019),2019
Sora SUTO, Kazuhito NIWASE：Evaluation ofeffect on strength development by combined useof Air entraining and high-range water reducing
admixture and Accelerating admixture, JSCE
Hokkaido Branch Papers,Vol 75,Jan. 2019 (inJapan)
K.NIWASE,N,SUGIHASHI,Y,TSUJI:Mortar
mix design of low diffusion layer for subsurface radioactive waste disposal facilities injapan, The 7th International Symposium on
Cement & Concrete (ISCC2010), The 11th
International Conference on Advance inConcrete
Technology
andSustainable
Development,2010
Toyoharu NAWA: Current Status of Belite Rich
Cement, Concrete Journal, Vol.34, No.12,
pp16~25, Dec.1996
Taisei SAKAI，Kazuhito NIWASE：Basic
Study of Cement Solidification Technology for
Solidification of Cesium Adsorbed Zeolite
Evaluation of Microscopic Structure of Cement
Solidification ， 8th Int. Conf. on Geotechnique,
Construction Materials and Environment,
pp.537-542,2018
Yasuo TANIGAWA, Kazuo YAMADA, On thesize effect of compressive strength of concrete,
Proceedings of Architectural Institute of Japan
No. 262, December 1977 (in Japan)

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

POSSIBLE RECYCLING OPTIONS FOR WASTE MATERIALS IN
CERAMIC TILES
Qibin Yuan, Abbas Mohajerani, Halenur Kurmus, John V. Smith
School of Engineering, RMIT University, Australia
ABSTRACT
Throughout the world, the generation of wastes is increasing in quantity and diversity at a rapid rate. To slow
down the deterioration of our ecosystem, effective and efficient methods must be developed to dispose of andrecycle the wastes produced. Besides, there is a need to reduce the resources utilised and lower the impact on ourenvironment. In 2017, over 13.5 million square meters of tiles were produced globally, and the demand for ceramictiles has escalated along with social development. There has been an increasing incentive to recycle waste materials
in ceramic tiles due to the high heterogeneity of the raw materials in the composition of ceramics and the matrix
for creating ceramic tiles being flexibly interchangeable. The objective of this paper is to review and discuss thevarious recycling options of waste materials in the production of ceramic tiles. Past experimentation includes the
addition of glass, coal ash, municipal solid waste ash, and sewage sludge in the production of ceramic tiles. They
were used as an additive or substitute of the key ingredient to the ceramic matrix. The results show that there are
generally enhancements on the physical and mechanical properties with minor adverse effects of the tiles.
Furthermore, the heavy metals found in toxic wastes were found to be immobilised during the firing process oftiles. Hence, the addition of wastes in tiles offers a sustainable solution in easing the necessity of managing wastes
and decreasing the consumption of natural resources. Further research developments are essential.
Keywords: Waste Materials; Ceramic Tiles; Recycling; Sustainability; Geomaterials

tiles due to the various formulations and wide
selection of raw materials [4].
Research confirmed that modern ceramic
products exhibit high tolerance in its compositional
structure, which shows the potential to recycle wastematerials without compensating the overall quality[5]. Furthermore, research reported that there are
13.552 million m2 of ceramic tiles produced in 2017,with a compound annual growth rate of 5.2% [6].
Therefore, the ceramic tile industry has the capability
to recycle a large amount of waste material while
reducing the usage of natural raw materials. Utilisingwaste materials in the manufacturing process ofceramic tile can be a sustainable solution to ease thepressure on waste management.

INTRODUCTION
From the beginning of the industrialisation andthe continued growth in the population of worldwide,
how to deal with wastes has become a significant
problem that can't be ignored [1]. In 2016, the annual
global municipal solid waste generation reached 2.01
billion tonnes, and it will rise to 3.40 billion tonnes
by 2050 if all conditions remain the same. The same
report from the World Bank also indicates more than
33% of waste is mismanaged, and only 13.5% ofwaste has been recycled globally [2]. Moreover, thecommon uncontrolled disposal methods indeveloping and transition countries can cause severe
heavy metal pollution to the ecosystem and havenegative impacts on human society, such as economic
sustainability and social inclusion [3]. As natural
resources are depleting and climate change by gas
emission and environmental contaminations are
increasing, many researchers are focusing on reusing
waste materials, and the outcomes indicate aprominent future for recycling.
Ceramic tiles are considered to be a feasible
product for waste materials recycling. Technologicalinnovations in the manufacturing cycle of the ceramic
tile industry cause extreme changes in the range ofraw materials and the development of novel ceramic
products like porcelain tile. These novel ceramic
products have significant advancement in technicalperformance and aesthetic appearance, but also havebrought difficulties to the classification of ceramic

USE OF WASTE MATERIALS
Waste glass, coal fly ash, coal bottom ash,municipal solid waste incineration (MSWI) fly ash,
MSWI bottom ash, sewage sludge and sewage sludge
incineration ash will be reviewed and discussed inthis article.
Glass Waste
Glass is one of the most common materials in ourlife. Despite the superior properties of glass products,
the popularity is because of its high recyclability,
where recycled glass products have no apparent loss
in purity and quality [7]. Due to the different chemical

400

GEOMATE – Melbourne, Australia, 11-13 November 2020

compositions of different types of glass, it is essential
to categorise glass wastes before the recycling
process. Most glass wastes are soda-lime-silicate(SLS) glass, which can be found in containers and flatglasses of windows and doors. Other common glass
types that will be reviewed include alkali-barium
silicate from cathode ray tubes, sodium borosilicate
from liquid crystal display and Pyrex, lead glass fromcrystal glassware and cathode-ray tubes, andalumino-borosilicate from glass fibres [8]. Due to theglass types, other than SLS, commonly being used forelectronics and other purposes, it is necessary to sort
before recycling as they might contain hazardous
chemicals and heavy metals. However, recent studies
show special treatment like washing with acid canincrease the recovery rate [8, 9].
Incorporating high amounts (>60 wt.%) of end oflife fluorescent lamp glass (FLG) and waste packing
glass (PWG) into ceramic tiles have received
promising experimental results. The final products
achieve comparable mechanical properties to
traditional ceramic tiles. Furthermore, FLG (<400µm) tile passes the ISO requirement of BIA group ofwater absorption (<0.5%) and bending strength(>35MPa), which is equivalent to high sintered floor
tiles. It is to be noted that these two types of glasses
are close to typical SLS glass but contain different
impurities and at different ratios of chemicalcomponents. The differences in the composition ofthese two glass wastes cause different characteristic
temperatures, which further affect the properties ofthe final tiles (shown in table 1). Unfortunately, thisexperiment didn't carry out control sample tile and
PLG (<100 µm) tile, so we can only assume that the
finer particle size of PGW results in better
performance, and the tiles with different waste
glasses have noticeable performance gap [10].

enhance the properties. Water absorption will
decrease with the increase of glass regardless of thetypes of glass waste, and bending strength will
generally improve with a high amount of glass waste
and higher temperatures [8]. It is worth mentioningthat multiple studies have found that firing
temperature is critical to the performance of the tiles.
Firing shrinkage is found to increases with increasing
firing temperature [11], but it will decrease if thefiring temperature is beyond a certain range [12].
Firing temperature also has a similar effect on
flexural strength. Flexural strength improves at higher
temperature [13], but another study found flexuralstrength decreases if firing temperature rises beyond
the range of 1120℃ to 1140℃ [14]. Besides, waste
glass can increase the hardness of tile, but there issome controversy about the effects on abrasion
resistance [11, 15].
Significantly, studies found tiles incorporating
waste glass contain a high content of heavy metals;
however, the leachate of heavy metals decreases after
the firing process [16, 17]. Therefore, it can beconsidered safe to recycle hazardous glass waste into
the ceramic tile as the firing process can destruct toxic
organic compounds and encapsulated hazardous
contaminants through immobilisation. Recycling
waste glass into ceramic tiles can reduce natural
resource usage, lower the mixing requirement, reduceenergy usage on firing, and offers a possible recycling
option for hazardous glass waste [8, 10].
Coal Ash
Many power plants generate electricity from coal
combustion. The unburned solid residues are known
as ash, and they can be classified as fly ash or bottom
ash according to the location where it was captured[18]. Due to the enormous amount of coal ashes andpotential impact on the ecosystem, such as leaching
toxic or chemical substances and accumulation
throughout the food chain [19], researchers put lots ofeffort into utilising coal ash [20].

Table 1 Properties of ceramic tiles with waste glass[10].
Sampletype

PGW(<400μm)
PGW(<100μm)
FLG(<400μm)

Linearshrinkage(LS %)

Water
absorption(WA %)

Bending
strength
(MPa)

4.87

1.83

23.37

5.46

0.29

30.6

8.79

0.02

40.93

Fly ashZimmer and Bergmann incorporated 20 – 80 wt.% ofcoal fly ash in their experiments on tile production.
They have found 60% of fly ash is the maximum
amount that satisfied the minimum flexural strength(green) requirement of handling under industrial
conditions. The optimum sample tile, with 60% fly
ash, passed the technical properties requirement witha flexural strength of 30 MPa, water absorption of
12%, and shrinkage of 7.5%. Moreover, with additionof limestone and feldspar into fly ash tiles can result
in reducing total linear shrinkage and reduce porositywith flexural strength increase achieving therequirements for more versatile applications [21].
Another researcher achieved promising results on

Note: Results are under the same manufacturing processes.
Sintering 60 min at 1000 °C, and formed by pressing (40 MPa).

Another comprehensive review paper comparedmultiple research studies on recycling different typesof glass into ceramic tiles. Overall, waste glass has
the potential to be recycled into ceramic tiles and401

GEOMATE – Melbourne, Australia, 11-13 November 2020

recycling coal fly ash with borax solid waste to
replace part of the potassium feldspar in the formulaof ceramic wall tiles. The 10% fly ash tile showed anincrease in flexural strength with other comparable
properties to the control wall tiles, and the 10% fly
ash and 5% borax waste tile have a significant
improvement on its flexural and water absorption but
slightly increase its firing shrinkage [22]. In thenorthwest of China, researchers incorporated 70 wt.%high alumina coal fly ash into ceramic tile to achieve
67 MPa flexural strength and apparent porosity of
0.13% [23], which have a massive potential to theceramic industry and to reduce the quantity of wastes.
However, coal fly ash usually contains toxic heavy
metal, such as Pb, Cd and Cr, and even radioactive
elements (238U, 232Th, 226Ra, 40K, etc.). Therefore, it is
crucial to measure the level of leachability andradioactivity, and then apply proper mitigations on
coal fly ash before reusing it [24].

in high compressive strength of 18.6 MPa and low
water absorption of 7.4% after being sintered at
960°C. Also, the firing temperature above 1000℃
can stabilise heavy metals. However, Cd, Hg, Pb, andZn are still exceeding the limit when the leachability
analysis was conduct on an unglazed tile. In contrast,
the glazed tile can effectively encapsulate heavy
metals in ceramic tiles to a lower amount of leaching,and even below the detection limit for the instruments
used [27]. Another research also discovered that glaze
could effectively stabilise fly ash, and only when used
in a strongly acidic environment will cause the heavy
metal leach out [28].
Bottom ash
Italian researchers also attempted to makeporcelainised tiles with untreated MSWI bottom ash
and vitrified bottom ash (50% MSWI bottom ash +50% glass cullet). Results show all tiles with up to
15% of untreated or vitrified bottom ash can meet therequirements for floor tiles with the features of water
absorption below 0.1%, bending strength around 45
to 53 MPa, and some improvement on linear
shrinkage compare to the control tile. Besides, thevitrified bottom ash tile has more visible
enhancement of properties and would be moresuitable for ceramic tile making. However, bottom
ash can cause noticeable deformation on the planarity[29]. Therefore, consideration should be taken on theamount of bottom ash used or maybe reformulate therecipe for better performance.
In another study, researchers used 2 – 5 wt.%
untreated bottom ash and 5 – 10 wt.% vitrified bottomash (with glass) to replace part of Na-feldspar sand totest the possibilities of making porcelain tiles andmonoporsa tile. The result shows MSWI bottom ash
is technically feasible for ceramic tile making. For
porous single firing tile, with the addition of 5%,
untreated bottom ash does not have many impacts on
its water absorption and linear shrinkage. Vitrified
bottom ash is not tested due to its not cost-effective to
put in production. For porcelain stone tile, vitrified
bottom ash tiles exhibit clear improvement on its
water absorption and linear shrinkage compared withthe control tile at a temperature above 1160℃.
Additionally, MSWI bottom ash is classified as notdangerous waste, but it does release heavy metals andsolubilisation of alkaline metals. Therefore, aleachability test was conducted. The result of theleaching test shows all fired tiles only release a small
amount of leachate, and they are below the limit value
for inert waste [30]. Similarly, another research result
also shows that post-treated bottom ash does not havesignificant ecotoxicity impacts on the environment
due to the sintering and vitrification process which
can immobilise the heavy metals, alkali and anions[31]. Thus, there is an opportunity for recycling
MSWI bottom ash into ceramic tiles.

Bottom ash
Coal bottom ash also can be used as a raw material
for ceramic tiles. Namkane et al. [25] incorporated 20- 80 wt.% of bottom coal ash into the clay body, andthey have found all samples can be classified as BIbgroup according to the ISO rules for water absorption
under 1175℃ except for a sample that contained 80%
bottom ash. The optimal bottom ash tile is the tile
with 40% of bottom ash and 60% of clay as they havethe lowest linear shrinkage with the smallest
deformation, and the flexural strength can reach 24.8
MPa, which is feasible for ceramic tile but lower thancommercial products. Consequently, Namakane andhis team continued their research with the addition ofsand and leonardite, which are common wastes fromcoal mining, to enhance the plasticity of tiles, asbottom ash is a non-plastic material. The highest
amount of waste content that achieved the properties
required for ceramic tiles can reach up to 42.8 wt.%
(21.4 wt.% of bottom coal ash and leonardite).
Additionally, this tile can be used as a floor tile, as it
satisfies the BIIa group of flexural strength (22-30
MPa) and UA group for chemical aggression
resistance [26], and further proved the possibility torecycle bottom coal ash in ceramic tiles.
Municipal Solid Waste Incineration Ash
Fly ashMunicipal solid waste incineration (MSWI) ash isa type of hazardous waste, and significant quantities
are generated daily all over the world. Researchers in
China studied the recycling of untreated MSWI fly
ash into ceramic tiles. They found that incorporating
20 wt.% of MSWI fly ash would be the ideal ratio forrecycling the maximal amount of waste and creatingtiles in line with the Chinese standards, which shows
402

GEOMATE – Melbourne, Australia, 11-13 November 2020

reviewed and summarised the main findings ofvarious research studies. In general, recycling sewage
sludge ash into glazed and unglazed tiles is a possible
solution with no significant production issues. The
water absorption of unglazed ceramic tiles often
exceeds the required limit; however, it only has minor
impacts on low ash content tiles and application ofglaze can effectively reduce water absorption.
Bending strength and abrasion limit fulfils thestandards, but they will decrease with the increase of
sewage sludge ash. Moreover, there is no impact on
chemical resistance, no significant effects on firing
shrinkage, and there is a reduction of loss on ignition
due to organic matter having been burned off prior to
the tile firing process [34]. Subsequently, researchshows the overall performance can be improved byadding nano-SiO2 additive into the tile recipe [35].
Thereupon, more in-depth studies on the
manufacturing formula and processes need to beundertaken in order to recycle sewage sludge ash withhigh efficiency and high quality.

Sewage Sludge
Sewage sludge is another high volume solid waste
generated by human activities from wastewater
treatment plants. Zhou et al. [32] used 50 - 65 wt.%
untreated sewage sludge to replace the kaolin in the
raw materials of tiles. The XRD pattern indicates thecomposition of sewage sludge is like natural clayeymaterial, mainly composed of quartz, kaolinite, and asmall amount of illite and albite. Hence, it would be a
viable alternative to traditional raw materials.
Furthermore, tiles with up to 60% of sewage sludge
can achieve the tile requirements. Tiles with 60% of
sewage sludge have water absorption of 1.14%,
bending strength of 25.5 MPa, and linear sintering
shrinkage around 12% under sintering temperature at
1210℃. To sum up the key findings, tiles with a highcontent (more than 60 wt.%) of sewage sludge andunder high firing temperature (above 1210℃) easily
have severe deformation defects. Tiles containing lessthan or equal to 60 wt.% sewage sludge satisfied therequirements of split tiles (bending strength > 23MPa& water absorption < 3%). The leachability of sewage
sludge tiles are reduced significantly once they are
fired, and the leaching is far below the regulatory
limit [32]. However, in another research, the results
are almost entirely different. Jordán et al. [33] tested
the effects of adding 0 - 10 wt.% of three sources ofsewage sludges into two types of ceramic clays intheir experiments. The overall results show theamount of sewage sludge has no impacts to linear
shrinkage (before and after firing), porosity and water
absorption will increase with the increases in sewage
sludge content, and bending strength will decrease
while sewage sludge increases [33]. Differences inresults might be caused by a different process of
preparation, diverse raw materials selection, different
firing temperatures etc. Besides, the methods of
Jordán et al. [33] are not revealed in their report, andthe processes and results of Zhou et al. [32] are shownin Fig.1.

DISCUSSION AND CONCLUSIONAs shown above, recycling of different wastes inceramic tiles shows the ability to enhance someproperties of ceramic tiles. Still, at the same time, theother properties might be compensated due to theaddition of wastes. In addition, most studies did not
apply glazing or use some fluxing agents like
feldspar, which have shown the ability to improve theperformance further, and they are commonly used in
the industry. Moreover, many journal articles did notreveal the process of manufacture or did not set acontrol ceramic tile to compare with experimental
tiles. This brought some difficulty compared with theproperties of commercial tiles directly as the formula
may not be able to produce qualified tiles.
Many research studies have discovered thatsome factors can influence the properties critically.
One is the firing temperature; many studies show thatwaste samples appear to have better performance thanthe control samples with increasing the firing
temperature. However, if the temperature is over or
below the optimal temperature range, inferior
performance will occur. A suitable range of firing
temperature needs to be determined, such as using
XRD to determine the composition of waste material.
The second factor is the differences in materials.
Waste materials or raw materials of ceramic tile may
have
different
chemicalormineralogicalcompositions if the acquired location is different.
Therefore, it is necessary to determine thecomposition of materials and test them locally before
they are included in the manufacturing process. The
third one is the variations in manufacturing
techniques. Since there are many commercial
methods for making ceramic tiles, the experimental
methods may not be suitable, and the results may be

Fig.1 Flowchart of preparation process of municipal
sewage sludge (MSS) [32].
Incinerated ash
Sewage sludge is also often incinerated, as thisprocess can significantly reduce the mass, volume
and hazardous components [34]. In a comprehensive
review paper written by Lynn et al. [34], they403

GEOMATE – Melbourne, Australia, 11-13 November 2020

different. Hence, a suitable method in productionneeds to be selected and tested in trials in production
conditions.
Ceramic tiles have proven their strong tolerance
for recycling waste materials. The high heterogeneityand versatile uses create the possibility to use various
wastes to make the most widely used products. Plus,tiles used for different purposes have different
product standards, which further expand thefeasibility of recycling. For future development, thetiles with waste need to achieve a balanced
performance by finding the optimal formula ratio andproduction method. As most of the studies did not
apply glazing, use fluxing agents, and suchtechniques, more in-depth experiments can test theireffect on performance and properties. In the future,researchers can investigate recycling more different
kinds of waste material into ceramic tiles.

7.

8.

9.

10.

ACKNOWLEDGMENTS
This work is part of an ongoing study on recycling
waste in construction materials. The authors would
like to thank Visy Industries Australia Pty Ltd, RMIT
University and the Australian Government ResearchTraining Program (RTP) scholarship for theirfinancial and in-kind support.

11.

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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EFFECTS OF SIZE AND GRADING, TYPE AND QUALITY OF
AGGREGATES ON MECHANICAL PROPERTIES OF UNBOUNDROADBED MATERIALS: A LITERATURE REVIEW
Hong Nam Thai1, Akira Kato1, Hoang Giang Nguyen2, Tien Dung Nguyen2, Ton Kien Tong2,
Van Tuan Nguyen2, Taro Uchimura1, Takeshi Maki1, Ken Kawamoto1,2
1
Graduate School of Science and Engineering, Saitama University, Saitama, Japan; 2 National University of
Civil Engineering, Hanoi, Vietnam.

ABSTRACT
Unbound graded aggregates are used to construct the base and subbase of the road pavement structure, and it
is known that the mechanical properties of the unbound graded aggregates depend significantly on aggregate sizeand grading, especially on the maximum particle size and fines content (typically, particle size < 0.075 mm). In
recent years, not only natural aggregates but also recycled aggregates from construction and demolition waste(CDW) have been used actively in the construction of road base and subbase. This paper investigated previous
studies and reviewed results about effects of aggregate size and grading, type and quality of aggregates on themechanical properties such as compaction, bearing capacity, resilient modulus, and shear strength for recycled
aggregates from CDW and others including natural aggregates, and discuss how these properties affect theirperformance as unbound roadbed layers. The results collected from the literature indicated that the mechanicalproperties of most recycled materials and mixtures were comparable to natural aggregates and could be used toconstruct road base and subbase, except for reclaimed asphalt pavement and fine recycled glass.
Keywords: Road Base and Subbase, Aggregate Size and Grading, Construction and Demolition Waste,
Mechanical Properties

the size distribution of aggregate, particularly the
proportion of fines was the most important. Xiao et al.
[4] investigated the effect of particle size distribution
on the mechanical properties, permeability, frost
susceptibility, and susceptibility to erosion. Therefore,
the size of the aggregate particles, grading, especiallyfines content (F c , kg/kg in %) need to be controlled
to meet the technical requirements, ensuring theability to work properly during construction andimproving the performance and longevity of thepavement structure.
This paper concentrated on reviewing previous
studies to examine the effects of size and grading,type and quality of aggregates on the mechanicalproperties of unbound road base and subbase layers.
The review working was done by comparing kinds ofmaterials used, F c , maximum particle size (D max ,mm) mixing proportion, test results of references
selected and suggestion the future research.

INTRODUCTION
Nowadays, apart from materials crushed fromnatural stones, there are many kinds of materials used
for unbound road base layers, in which the CDW are
the most widely used materials. In several developed
countries, the use of CDW instead of natural
aggregate (NA) in road base construction has become
popular. Specifically, in 2017 Spain had thepercentage of CDW recycled was highest by 98%,
followed by Japan, France, Germany at 96%, 93%,85% respectively [1]-[2]. The use of CDW in road
construction is one of the solutions to reduce thedumping of CDW, as well as the consumption of NA;
however, there are some difficulties to recycle CDW
because the types and quality of recycled aggregates
affect significantly the mechanical properties of
unbound roadbed materials.
Base and subbase layers are the important layers
in pavement structure, they are required to have highrigidity and strength to bear the vertical loadtransmitted from surface layers. Besides, due to the
low impact of horizontal load, the base and subbase
layers of the pavement structure are usually made ofunbound aggregates. For example, Yoder andWitczak [3] presented that the stability of unboundaggregate depends on some factors such as particlesize distribution, particle shape, relative density,internal friction, and cohesion. Among of the factors,

METHODOLOGY
Based on the keywords such as unboundaggregate, road base and subbase, aggregate size,
fines content, grading, and CDW materials, journal
papers, books, and reports published in English after
1970’s were searched in this study [e.g., Web of
ScienceTM (Clarivate Analytics)]. The classificationand detailed search were conducted to find the406

GEOMATE – Melbourne, Australia, 11-13 November 2020

conferences related to the effects of size and grading,type and quality of aggregates on mechanicalproperties such as compaction, bearing capacity,
stiffness and shear strength of unbound aggregate forroad base and subbase. A total of 33 references was
selected to compare and summarize the data in thisstudy.
A variety of materials were used in selectedreferences, including NA, recycled aggregates (RA)such as recycled concrete (RC), recycled clay brick(RCB), reclaimed asphalt pavement (RAP), finerecycled glass (FRG), waste rock (WR) and mixed
aggregate between them. These RA were classified
with different properties as followed:+ RC: has a minimum of 90%, by mass, ofPortland cement-based fragments and NA [5].
+ RCB: is mainly contained brick rubble and also
a high amount of adhered mortar and other impurities
such as tile [6].
+ RAP: is more than 90% of composition
composed of asphalt-based materials [5].
+ FRG: is the by-product of crushing mixed color
bottles and other glass products collected from both
municipal and industrial waste streams [7].
+ WR: is excavated during site preparation, theywould have been disposed as waste [7].
There were two groups of materials classified
according to D max of aggregates; D max ≤ 25mm for theroad base and D max > 25mm for the road subbase.

a) Dmax ≤ 25 mm

2.4

MDD (g/cm3)

2.2
2

1.8
1.6
1.4
1.2

0

5

10

15

20

25

30

Fines content, Fc (%)

35

40

2.4

b) Dmax > 25 mm

MDD (g/cm3)

2.2
2
1.8
1.6
1.4
1.2

0

5

10

15

20

25

30

35

40

Fines content, Fc (%)
Fig.1 The effects of fines content and type of
aggregates on MDD. (a) Samples with D max ≤ 25 mm,
and (b) samples with D max > 25 mm
2.4

RESULTS AND DISCUSSIONa) Dmax ≤ 25 mm

MDD (g/cm3)

2.2

Effects of Size and Grading, Type and Quality ofAggregates on the Compaction Characteristic

2

1.8
1.6

The maximum dry density (MDD) measured by
the compaction test is one of the factors to evaluate
the effects of aggregate size and grading, quality ofaggregate on the compaction characteristic. Figure 1
shows the relationship between F c and MDD. It canbe noticed that most of NA had higher MDD than RA
regardless of D max and F c since NA has less porousin particle than RA [6]. In comparison among RAs,
MDD tends to increase with increasing F c without
regard for the type of aggregate, while it seems thatthe opposite tendency was shown when D max > 25mm from a comprehensive point of view. This isbecause fines fill the void of the mass of aggregate.
However, it could be assumed that fines fully
occupy the void of the mass at lower F c , which isaround 3-10%, and assume a role as a framework ofsoil structure if F c exceeds a certain value. As theoccupation of fines in the mass increase, dry density
therefore decreases. In addition, it might be assumed
that MDD depends on intrinsic particle strength ofNA, RC, and RCB [6], that is the reason why thelarger particle and the breakable material wascompacted, the lower MDDs of RC and RCB wereobtained.

1.4

0

10 20 30 40 50 60 70 80 90 100

RC or NA content (%)

MDD (g/cm3)

2.4b) Dmax > 25 mm

2.2
2
1.8
1.6
1.4

0

10 20 30 40 50 60 70 80 90 100

RC content (%)

Fig. 2 The effects of mixing proportion on MDD. (a)Samples with D max ≤ 25 mm, and (b) samples withD max > 25 mm
Figure 2 shows the relationship between mixing
proportion of RC or NA to RCB or RAP. It seems that
MDD gradually increases or is almost constant withincreasing the mixing proportion of NA or RC
407

GEOMATE – Melbourne, Australia, 11-13 November 2020

regardless of D max . Focusing on results of [12] and[23], differences between specific gravities of RCB
and NA or RC might cause the tendency given thatsingle-particle strength becomes high in proportion to
the specific gravity, whereas specific gravities of RC
and RCB in [23] are not much difference (Fig. 2a).
An increase in coarse-grained fraction causes a
reduction of MDD since the larger particle is more
breakable during compaction (Fig. 2b).

CBR (%)

Fig. 3 The effects of fines content and type of
aggregates on CBR. (a) Samples with D max ≤ 25 mm,
and (b) samples with D max > 25 mm

Effects of Size and Grading, Type and Quality ofAggregates On Bearing Capacity

220
200
180
160
140
120
100
80
60
40
20
0

a) Dmax ≤ 25 mm

0 10 20 30 40 50 60 70 80 90 100

RC or NA content (%)

CBR (%)

220
200
180
160
140
120
100
80
60
40
20
0

CBR (%)

Figure 3 illustrates the effects of F c and type,
quality of aggregates on CBR. It can be mentionedthat CBR of recycled materials is much higher thanthat of natural materials except for FRG and RAP
regardless of D max . Fines contribute to increase in
MDD for NA, while it cannot be clearly seen atendency due to F c for RA. As is the case with thecompaction test results, the peak of CBR also
appeared around 3-10% of F c for all materials, and
CBR gradually decreases with increasing F c sincefines bear the load as a main frame of soil structure.
With regard to the effect of the mixing proportionof RC or NA to RCB, CBR values exceed 100 %under all mixing conditions although a tendency due
to mixing proportion did not appear, as shown in Fig.
4a. Whereas, CBR of RAP was improved mixing RC.
On the other hand, the dependence of mixing
proportion of RC to CBR is clearly shown increasingcoarse-grains fraction. One possible reason might bethat the amount of fine-grain fraction by breakage
increases by breakage for RCB.

5

10

15

20

25

30

35

40

RC content (%)
Fig. 4 The effects of the mixing proportion on CBR.
(a) Samples with D max ≤ 25 mm, and (b) samples withD max > 25 mm
Effects of Size and Grading, Type and Quality ofAggregates on Stiffness Characteristic

35

40

Resilient modulus (M r ) is a parameter
representing the stiffness of the material and was usedin most selected conferences. M r is determined by therepeated load compression tests on test specimens.
There were three trends of M r with NA samples; M r
increased when F c increased [26], M r decreased
when F c increased [27], and M r reached the highest
value at a F c value [16], [28] as shown in Table 1.
Several researchers presented that M r of RC washigher than M r of NA, as in [29], [31], and satisfied
to be greater than the minimum requirement of 300
MPa [31] (Australia standard). This can be explainedthat there was an amount of existing cement particles
inside RC, when RC was mixed to water, theseparticles would be hydrated and contributed toincreasing the strength of RC samples [5]. In thecomparison among M r of kinds of RA, RC had thehighest M r , followed by RCB and WR (Fig. 5).
Turning to the mixtures, Arulrajah et al. [23] reported
that CB-RC blends had the higher M r values than CBNA blends and most of the blends would perform
satisfactorily as a subbase material. Arisha et al. [20]showed that M r of RCB-RC blends reached highestwith sample using 45% RCB, while Cameron et al.

CBR (%)

Fines content, Fc (%)
220
200
180
160
140
120
100
80
60
40
20
0

b) Dmax > 25 mm

0

5

10

15

20

25

30

b) Dmax > 25 mm

0 10 20 30 40 50 60 70 80 90 100a) Dmax ≤ 25 mm

0

220
200
180
160
140
120
100
80
60
40
20
0

Fines content, Fc (%)

408

GEOMATE – Melbourne, Australia, 11-13 November 2020

mixtures increased with the increase of RAP content[22] indicated that M r of RCB-RC blends decreased
when RCB content increased. Mr of NA-RAP(Fig. 6).
Table 1 The effects of size and grading, type and quality of aggregate on stiffness characteristic
Ref.

Test conditions
Materials

Mixing proportionNA

RC, RCB
RC, CB,
NA
NA
NA
NA
RC
RC, CB,WR
RC, NA
RAP, NA

Range of D max(mm)
Fc

Effects on M r

M r reached highest with 5% fines<0.075 0 - 20% 31.75 content and lowest with 10% fines [16]content*

RC, RCB

Fines
size
(mm)

RC-RCB: 100-0, 90-10,
M reached highest with sample using
80-20, 70-30, 55-45, 40- <0.075*
37.5 r[20]
45% RCB60, 20-80, and 0-100
RC-RCB: 90-10, 80-20,
A decrease in M r as the RCB content
*
*
20
[22]
70-30
increased
CB-RC/NA: 0-100, 15-85,
The CB-RC blends had higher M r values
20-80, 25-75, 30-70, 40- <0.075
*
20
[23]
than CB-NA blends.
60, and 50-50*<0.075 7 - 12%* M r increased with the increase of F c[26]*<0.075 0 -17.7% 25 M r decreased when F c increased[27]*<0.074 2 - 14% 38.1 M r reached highest with F c = 6-10%[28]*<0.075*
20 RC had higher M r than NA[29]
RC had the highest M r value and WR had*<0.075*20[30]
the lowest M r value
RC had higher M r than NA and was*<0.075*20 greater than the minimum requirement of [31]300 MPa
RAP-NA: 0-100, 25-75,
50-50, 75-25<0.075*20

M r increased as the RAP content[32]
increased

Note: * data not available

Mr (MPa)

Fig.6 The effects of the mixing proportion on M r
800
700
600
500
400
300
200
100
0

RCA

150

CB

WR

Effects of Size and Grading, Type and Quality ofAggregate on Shear Strength Characteristic
The shear strength properties are assessed via thecohesion (c), friction angle (φ) of materials
determined by drawing Mohr circles for the confiningpressures of the triaxial tests. Figure 7 shows theeffects of F c on c and φ of NA samples in [16], [33].
It can be seen that φ reached the highest value whenF c = 5%, while c decreased when F c from 0 to 5%
and increased when F c from 5% to 20%. Shearstrength peaked highest with F c =10% and reducedwith higher fines [16]. This can be explained in [3],[33]. Specifically, when samples contain little or nofines (Fig. 8a), shear strength is formed based on
grain to grain contact. When samples containsufficient fines (Fig. 8b), the fines fill the voids
between the grains and bind them together. This
combines grain to grain contact to increase the shear
strength of samples. However, with samples containhigh fines content (Fig. 8c), the grains are surroundedby fines, grain to grain contact will be reduced
significantly, the shear strength decrease and dependon the shear strength of fines. However, Osouli et al.

250
350
Deviator stress (kPa)

Mr (MPa)

Fig.5 M r of RA samples [30]
800
700
600
500
400
300
200
100
0
0 10 20 30 40 50 60 70 80 90 100
RCB or RAP content (%)

409

GEOMATE – Melbourne, Australia, 11-13 November 2020

quality of fines, c = 70 kPa and φ = 41 degrees forboth samples.

80
70
60
50
40
30
20
10
0

0

5

10

c [16]
φ [16]
φ [33]
15
20

80
70
60
50
40
30
20
10
0

the effects of F c and D max on mechanical propertiesof recycled aggregates which are becoming popular
as substitutes for natural aggregates. Therefore, it isnecessary to study the effects of F c and D max on themechanical properties of recycled aggregates in the
future.
+ Samples with D max ≤ 25 mm often have thehigher MDD and CBR than that with D max > 25 mm.
+ RC, RCB, WR and RC-RCB mixtures had
lower MDD than NA but their CBR are comparable
to or higher than NA and satisfied to be greater than
the requirement for road base and subbase. CBR of
RAP, FRG was smallest and did not meet thetechnical requirement for road base and subbase.
+ Due to the hydration of the remaining
unhydrated cement particles in RC, M r values of RC,
RC-RCB blends were higher than that of NA, NARCB blends respectively, and met the technicalrequirement for road base and subbase.
+ Most RA samples had lower cohesion andequivalent friction angle in comparison with NA
samples.

φ (degree)c (kPa)[9] studied crushed stone aggregates of D max = 25 mm
using two values of F c (5 and 12 %), and presented
that shear strength properties were not affected by
Figure 9 shows that RAP had the highest c by 53
kPa, followed by WR, RC, RCB (from 41 to 46 kPa)
and FRG had the lowest c by 0 kPa. WR, RC, RCB
had φ ranging from 49 to 51 degrees, whereas RAP
and FRG had φ = 37 degrees. Arulrajah et al. [23]showed that RCB–RC blends had c ranging from 41
to 80 kPa and φ ranging from 43 to 50 degrees, and
RCB-NA blends had lower c by 41 to 62 kPa andhigher φ by 46 to 51 degrees.

Fines content, Fc (%)
Fig.7 The effects of fines content on c and φ of NA
samplesa)b)

ACKNOWLEDGMENTS
This research was supported by JST-JICA
Science and Technology Research Partnership for
Sustainable Development (SATREPS) project (No.
JPMJSA1701).

c)

REFERENCES

80
70
60
50
40
30
20
10
0

φ

c
49

48

51
37 37

44

41

46

53

RC

RCB

WR

RAP

0
FRG

80
70
60
50
40
30
20
10
0

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Effects of Fines Content on Hydraulic andMechanical Performance of Unbound Granular
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[30] A. Arulrajah, J. Piratheepan, M. M. Disfani, andM. W. Bo, Resilient moduli response of recycledconstruction and demolition materials inpavement subbase applications, J. Mater. Civ.
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asphalt pavement, Transp. Res. Rec., no. 2005,
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pp. 16–25.

412

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

RECYCLING OF CIGARETTE BUTTS IN STONE MASTIC ASPHALT
Md Tareq Rahman, Abbas Mohajerani and Filippo Giustozzi
School of Engineering, RMIT University, Melbourne, Australia
ABSTRACT
Trillions of cigarettes are produced every year around the globe, resulting in a large amount of mephitic waste
cigarette butts (CBs) being dumped into the environment. Waste CBs take years to decompose with mostcontaining a cellulose-acetate based filter, tar, nicotine, tobacco, and many highly toxic chemicals that leach into
the soil and waterbodies thereby resulting in contamination and toxicity. In the area of asset management, the roadand highway sector are the largest in the world. This research work has investigated the possible recycling of CBs
in stone mastic asphalt. Waste CBs were processed and encapsulated with bitumen before the preparation of themix. Modified stone mastic asphalt samples were prepared by replacing up to 2% of coarse aggregate with bitumen
encapsulated CBs in accordance with the existing AASHTOO and Austroads guidelines. Laboratory tests wereconducted on the CB modified asphalt samples and the results were compared with those of the standard control
samples prepared without CBs. The preliminary results found are promising and show that recycling encapsulated
CBs in asphalt concrete could contribute a solution to CB pollution around the world. This paper presents theexperimental procedures and discusses some of the significant outcomes of the study.
Keywords: Cigarette butts; Stone mastic asphalt; Recycling; Waste Management; Flexible pavementwith bitumen and paraffin wax, consecutively [12],[13]. The results revealed the possibility of using
encapsulated CBs in asphalt for light, medium, andheavy traffic conditions. The development ofadvanced materials incorporating waste cigarettebutts will bring a new dimension to the construction
materials.
Cigarette butt pollution exists all over the world.
However, a sustainable technique to recycle waste
CBs in asphalt concrete and bitumen can solve thisproblem. Previous studies have proved the prospectof recycling different waste materials in asphalt [14][16]. This study can contribute to the solution of CB
pollution problem around the world and bringsustainability to the environment by introducing a
new procedure to recycle CBs in asphalt concrete forflexible pavements. This research adds knowledge tothe sector of waste management and the roads andhighway industry.

INTRODUCTION
Cigarette butt pollution
Around the world, approximately one billion
people are cigarette smokers [1]. As reported byEuromonitor, during the year 2013, the total
production of cigarettes was 5.7 trillion, 99% of
which have a cellulose acetate (plastic based) filter.
This production is expected to increase to 9 trillion by
the year 2025 [2]-[4]. Hence, the ongoing concern
about the recycling of waste cigarette butts. The‘Cigarette Butt Pollution Project’ in the USA
estimated that approximately one-third of the total
production is littered in the environment [2], [5].
Cigarette butts contain toxic materials that spread into
the environment slowly as cellulose acetate is
photodegradable but not biodegradable [6]-[8]. In
order to save the environment from the pollution
caused by waste cigarette butts, efficient recycling
methods are needed.
Cigarette butt waste clean-up is very costly, witha San Francisco litter audit study finding the cost to
be more than $7 million annually [5]. Furthermore,
the incineration of waste cigarette butts createshazardous fumes that are severely detrimental to
public health and the environment [9], [10]. However,waste cigarette butts can be collected and processed
for recycling in construction materials. Recent
research at RMIT University has proved that waste
cigarette butts can be used in the preparation of clay
bricks [2], [11]. Mohajerani et al. (2017) developed a
CB encapsulation method to incorporate CBs in dense
asphalt for the first time [12]. Dense asphalt was
prepared with bitumen C170 and C320 encapsulated

Asphalt
Hot mix asphalt (HMA) can be dense or open
graded. As the name suggests, dense-graded HMA
has a lower void ratio compared to open grade HMA.
Dense-graded HMA contains a large variety ofparticle sizes to spread through the asphalt concrete
mix effectively. Furthermore, dense grade HMA suits
all traffic condition types and is considered the mostcommonly used type of asphalt concrete around theworld [17]. Open-graded HMA is typically used as adrainage layer due to the higher void ratio, whichallows the mix to be more permeable [17], [18].
Stone mastic asphalt (SMA) is a gap gradedasphalt that is commonly used throughout Europe413

GEOMATE – Melbourne, Australia, 11-13 November 2020[18]. The aggregates used in SMA mixes are often of
higher quality compared to the aggregates used forstandard HMA mixes due to the superior physical andmechanical properties that are required for the stoneto-stone contact structure. The high content of coarse
aggregates in SMA creates high rutting resistance andimproves the longevity of the structure [19].
The comparative differences between HMA andSMA are illustrated in Table 1. Although thegradation may vary among the different types ofasphalt, the basic ingredients are mostly the same. In
the case of HMA, open-graded aggregates andbitumen are used, while gap-graded aggregate, fibers,
and bitumen are used in SMA.

engineering properties in terms of the stability,
resilient modulus, and flow of asphalt concrete.
.
RESEARCH DESIGNThis research was designed by following theindustry standards. The materials were selectedaccording to the Austroads and VicRoads guidelines.
Stone mastic asphalt was selected for this part of theinvestigation. The research design diagram is shownin Fig. 1. In this research, asphalt samples wereprepared with CBs by replacing coarse aggregates up
to 2% by weight. Volumetric analysis andpreliminary mechanical performance assessment
were carried out for all samples, and the results werereviewed and compared with the control samples.

Table 1. Advantages and disadvantages of HMA andSMA [14]-[16].
Type ofAsphalt

Hot Mix
Asphalt(HMA)

Select type ofAsphalt mix

Advantages

Disadvantages

Low cost

Lower rutting
resistance

Effective in all
trafficconditions

Select materials (e.g.
aggregates, binder,
filler, fibers, CBs)

Shorter service
life

Prepare the materials
for asphalt mixture(e.g. weight, oven dry

Encapsulate CBs with
bitumenPrepare and condition samples mix with selected materials
including encapsulated CBs in the following percentages
0% CBs (control sample), 1% CBs, 2% CBs and 3% CBs

Poorer quality
aggregates used

Gyratory compaction

Long service life
High resistanceto deformation
Stone
MasticAsphalt(SMA)

Increased
fatigue testinglife
Noise-reductive
properties
Decreased water
spray whenraining

Determine air voids,
VMA, VFB

Low skid
resistanceHigh cost

Marshall Stability andflow

Increased risk offlat spots
occurring due to
SMA design
procedureCompare withstandard control
sampleFig. 1. Research design.
RESEARCH DESIGN

Use of waste materials in asphalt

Pre-processing and encapsulation of CBs

According to the statistics, around 95% of theworld’s highways are flexible pavements that are
constructed using asphalt concrete [15], [20]. The useof additives and modifiers can improve the properties
and performance of asphalt concrete. Several researchworks have been carried out to investigate the use ofwaste materials in asphalt concrete, such as rice husk,
fly ash, palm oil fruit ash, coffee grounds, waste
cooking oil, waste rubber ground, steel slag, coppermine tailing, waste plastic, and crushed glass, etc.
[21]-[26]. Waste cigarette butts can be a potential
modifier in asphalt concrete and can introduce
advanced asphalt concrete that will exhibit better

Background of CB encapsulation
Bitumen encapsulated CBs were first developedand incorporated in dense asphalt by Mohajerani et al.
(2017) [12]. Encapsulation was done by curing ovendried CBs with hot bitumen. Fig. 2. exhibits thebitumen encapsulated CBs and some of the dense
asphalt samples prepared in this breakthroughresearch. The physical and mechanical performanceof the samples were propitious and this work has
widened the scope of research for recycling cigarettebutts (CBs) in asphalt concrete [12], [27]. Asphalt414

GEOMATE – Melbourne, Australia, 11-13 November 2020

samples were prepared incorporating CBs 10 kg/m3,
15 kg/m3,
and 25 kg/m3, and without CBs (control samples).
The impacts of different quantities of CBs
encapsulated with different classes of bitumen interms of the Marshall Stability and Flow of dense
asphalt samples are shown in Fig. 3.

Cigarette butts were pre-processed byencapsulation with bitumen. Encapsulation wasachieved by curing CBs in hot liquid bitumen. Fig. 5.
and Fig. 6. show the condition of the CBs before andafter encapsulation, consecutively.

Fig. 5. CBs before encapsulation. Waste CBs (left)
and oven dried and cleaned CBs (right).

Fig. 2. Encapsulated CBs and a few dense asphalt
samples used in the research conducted by
Mohajerani et al. in 2017 [12].

Fig. 3. Marshall Stability and Flow of asphalt
prepared with different amounts of CBs,
encapsulated with C170, C320, and C600
[12].

Fig. 6. Encapsulated CBs.
A few arbitrary encapsulated CBs were pickedand cut into pieces to observe the sections. It was
found that the CBs were encapsulated properly, andthat all the pores of the CBs were filled with bitumen.
The sections of the encapsulated CBs are shown in
Fig. 7.

Mohajerani et al. (2017) assessed the resilient
modulus of asphalt concrete prepared with CBs andfound that all the samples met the standard range
2500-4000 MPa for bitumen class C170 [12]. The
results are shown in Fig. 4.

Fig. 7. Sections of Bitumen encapsulated CBs.
Asphalt sample preparation

Fig. 4. Resilient Modulus of Asphalt Concrete
prepared with different amounts of bitumenclass C170 encapsulated CBs [12]The samples were prepared in the laboratory
following the Austroads and VicRoads standards. The
type of aggregate was granite, the gradation of which
was adapted from VicRoads section 404; illustrated

Preparation of bitumen encapsulated CBs for stone
mastic asphalt415

GEOMATE – Melbourne, Australia, 11-13 November 2020

in Table 2 [28]. The amount of materials used in the
preparation of the sample is given in Table 3.

Fig. 8. Preparation of stone mastic asphalt sample.
Mix without addition of bitumen (top left),
Mixing process with bitumen (top right),
compaction done with gyratory compactor(bottom left), and compacted stone mastic
asphalt samples (bottom right)

Table 2. Adapted gradation for the preparation ofstone mastic asphalt.
Sieve
retained
(mm)
9.5
6.7
4.7
2.36
<2.36

% Passing(Adapted
for this
research)
95.0
35.0
32.0
22.0
0.0

% Passing range
according to
VicRoads Standard

LABORATORY TESTING

90-100
25-45
18-32
15-30
13-24

Preliminary laboratory investigation involved
volumetric assessment and the Marshall Stability andFlow Test following AS/NZS 2891.5:2015 guideline.
The stone mastic asphalt (SMA) samples wereprepared following 4.8% - 5.2% air void according to
VicRoads [28][29].

The stone mastic asphalt mix was prepared using
selected aggregates as per the gradation, 6% ofmineral fillers, 0.3% of cellulose fibers, and 6.5% ofbitumen PMB class A10E. The asphalt mix was
prepared at 170℃. Compaction was done with a
Gyratory Compactor. Pictures of some of the
prepared stone mastic asphalt samples are shown in
Fig. 8.

Results and discussion
After preparation of the control sample, the CB
modified samples were prepared with bitumen
encapsulated CBs by 1% and 2%. The aggregatesretained in the 9.5 mm sieve were replaced withencapsulated CBs by weight. Volumetric analysis on
the control sample and the Marshall stability and flow
test were conducted for all the samples as apreliminary investigation. The results are shown in
Table 4.

Table 3. Amount of materials in stone mastic asphalt
sampleAggregate Size
(Retained)
9.5 mm
6.7 mm
4.7 mm2.36 mm
Fines
Filler(limestone)
Fiber
Bitumen% Measured forasphalt mix (byweight)
4.36
52.32
2.62
8.72
19.18
6
0.3
6.5

Table 4. Preliminary test results.
Type ofSampleControl
Sample416

Type oftest
Air void

Test
Results
5%

VMA

19.1%

VFA

73.8%

Marshall
Stability

13.6 KN

Standard checkwith the range
VicRoads andAustroads(4.8%-5.2%)
VicRoads andAustroads(13%-20%)
VicRoads andAustroads(65%-80%)
VicRoads andAustroads

GEOMATE – Melbourne, Australia, 11-13 November 2020

Sampleswith 2%CBs

10.8 mmMarshall
Stability

13.9 KN

Flow

9.2 mmMarshall
Stability

15.1 KN

Flow

10 mm

results as shown in Fig. 11. It was found that thecontrol sample with 0% CBs had a higher sustaining
period, which is 13 seconds, however, this sample
failed at a lower loading condition. Although thesamples with 1% CBs and 2 % CBs failed quickly
compared to the control sample, they can sustain
higher loading conditions. The addition of 2% CB
stone mastic asphalt presented a better sustaining
period than the sample with 1% CBs under higher
loading conditions.
Time at peak load

14
13Time (s)Samplewith 1%CBs

Flow(minimum
5.5KN)
Asphalt Institute(8 mm-16 mm)
VicRoads andAustroads(minimum
5.5KN)
Asphalt Institute(8 mm-16 mm)
VicRoads andAustroads(minimum
5.5KN)
Asphalt Institute(8 mm-16 mm)

The results show an increase in the stability with
the increase of CBs in the mix. Fig. 9. shows thechange in stability of the samples before and after
modification with encapsulated CBs. This shows that
CB modified stone mastic asphalt can sustain underhigher loading conditions. It has been observed that
encapsulated CBs in stone mastic asphalt improves
the strength. The flow value is within the range of 816 mm. Comparative analysis shows a better
perspective of the results in Fig. 10.

12
11
10
0% CB

1% CB

2% CB

Fig. 11. Sustaining period of samples.

CONCLUSION
Marshall Stability (N)16000

The aim of this study was to recycle waste CBs inasphalt. Stone mastic asphalt samples were preparedwith bitumen encapsulated CBs in this research andthe Marshall stability and flow test were performed toassess performance. The results from the laboratory
investigation herald a new area to explore in the
sector of advanced materials for flexible pavements.
This paper has presented and discussed some of theresults from a comprehensive study on recycling
cigarette butts in asphalt. The results are promising,as incorporation of CBs enhanced the performance ofasphalt. The outcomes were analyzed and comparedwith the control sample. This investigation reveals apossible sustainable method to recycle CBs in stone
mastic asphalt. The results of this research provide
evidence of the mechanical properties of thisadvanced material. This confirms the prospect ofbitumen encapsulated CBs in asphalt concrete. Thismethod can recycle up to 15kg CBs in each m3 ofstone mastic asphalt. The improved method of
encapsulation and mixing process was found suitablefor heavy traffic conditions. This work contributes tothe roads and highways sector in terms of adopting asustainable approach in construction.

15000
14000
13000
12000
0% CB

1% CB

2% CB

N

Fig. 9. Stability of the samples.

16000
14000
12000
10000
8000
6000
4000
2000
0

0% CB
1% CB
2% CB

0

10
mm

20

ACKNOWLEDGEMENTS

Fig. 10. Comparative analysis of Marshall stability
and flow results.

This work is part of an ongoing postgraduate
study on recycling cigarette butts in asphalt concrete.
The authors would like to thank Butt-Out Australia

The sustaining period of the samples wasanalyzed based on the Marshall stability and flow
417

GEOMATE – Melbourne, Australia, 11-13 November 2020

Pty Ltd, RMIT University and the AustralianGovernment Research Training Program (RTP)
scholarship for their financial and in-kind support.

[14] Rahman, M.T., M.R. Hainin, and W.A.W.A.
Bakar, Use of waste cooking oil, tire rubber
powder and palm oil fuel ash in partial
replacement of bitumen. Construction and
Building Materials, 2017. 150: p. 95-104.
[15] Aziz, M.M.A., M.T. Rahman, M.R. Hainin, andW.A.W.A. Bakar, An overview on alternative
binders for flexible pavement. Construction and
Building Materials, 2015. 84: p. 315-319.
[16] Putman,
B.J.,
S.N.J.R.
Amirkhanian,conservation, and recycling, Utilization of waste
fibers in stone matrix asphalt mixtures. 2004.
42(3): p. 265-274.
[17] Garcia, J. and K. Hansen, HMA pavement mix
type selection guide. 2002: National Asphalt
Pavement Association.
[18] Pavement Interactive, HMA Pavement. 2012
[cited2018
25/06];
Availablefrom:
http://www.pavementinteractive.org/hmapavement/.
[19] Blazejowski, K., Stone matrix asphalt: Theory
and practice. 2016: CRC Press.
[20] Shafabakhsh, G. and Y. Sajed, Investigation of
dynamic behavior of hot mix asphalt containing
waste materials; case study: Glass cullet. Case
Studies in Construction Materials, 2014. 1: p. 96103.

REFERENCES[1] World Health Organization, World healthorganization Tobacco fact sheet. 2014.
[2] Mohajerani, A., A.A. Kadir, and L. Larobina, A
practical proposal for solving the world’s
cigarette butt problem: Recycling in fired clay
bricks. Waste management, 2016. 52: p. 228-244.
[3] Novotny, T.E., K. Lum, E. Smith, V. Wang, and
R. Barnes, Filtered cigarettes and the case for anenvironmental policy on cigarette waste. Int. J.
Environ. Res. Public Health, 2009. 6: p. 1-14.
[4] Euromonitor International, Global Tobacco: Key
Findings Part 1 – Tobacco Overview, Cigarettes
and the Future. 2014 [cited 2018 29 November];
Availablefrom:https://www.euromonitor.com/global-tobaccokey-findings-part-1-tobacco-overviewcigarettes-and-the-future/report.
[5] Cigarette Butt Pollution, The Environmental
Impact of Cigarette Butt Waste: Just the Facts.
2013. Available from:[6] https://www.reefrelief.org/2013/02/theenvironmental-impact-of-cigarette-butt-wastejust-the-facts/[7] Kurmus, H., Mohajerani, A, The toxicity andvalorization options of cigarette butts. Waste
Management. 2020 Mar 1; 104:104-18.
[8] Hon, N.S., Photodegradation of cellulose acetate
fibers. Journal of Polymer Science: Polymer
Chemistry Edition, 1977. 15(3): p. 725-744.
[9] Rahman, M.T., Mohajerani, A., Giustozzi, F,
Possible Recycling of Cigarette Butts as Fiber
Modifier in Bitumen for Asphalt Concrete.
Materials. 2020 Jan;13(3):734.
[10] Knox, A., An overview of incineration and EFW
technology as applied to the management ofmunicipal solid waste (MSW). ONEIA Energy
Subcommitte, 2005.
[11] Mohajerani, A., A.A. Kadir, and L.J.W.m.
Larobina, A practical proposal for solving theworld’s cigarette butt problem: Recycling in fired
clay bricks. 2016. 52: p. 228-244.
[12] Mohajerani, A., Y. Tanriverdi, B.T. Nguyen, K.K.
Wong, H.N. Dissanayake, L. Johnson, D.
Whitfield, G. Thomson, E. Alqattan, and A.
Rezaei, Physico-mechanical properties of asphalt
concrete incorporated with encapsulated cigarettebutts. Construction and Building Materials, 2017.
153: p. 69-80.
[13] Rahman MT, Mohajerani A, Giustozzi F.
Recycling of Waste Materials for Asphalt
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2020 Jan;13(7):1495.

[21] National Research Council, Highway Capacity Manual.
2000, Transportation Research Board (TRB):
Washington, DC.
[22] Waller, H.F. Use of waste materials in hot-mix asphalt.
1993. ASTM.
[23] Rokade, S. Use of waste plastic and waste rubber tyres
in flexible highway pavements. in International
conference on future environment and energy, IPCBEE.
2012.
[24] Fini, E.H., I.L. Al-Qadi, Z. You, B. Zada, and J. MillsBeale, Partial replacement of asphalt binder with biobinder: characterisation and modification. International
Journal of Pavement Engineering, 2012. 13(6): p. 515522.
[25] Huang, S.-C., D. Salomon, and J.E. Haddock,
Alternative Binders for Sustainable Asphalt
Pavements: Papers from a Workshop. Workshop
Introduction. Transportation Research E-Circular,
2012(E-C165).
[26] Rusbintardjo, G., M.R. Hainin, M.A. Mubaraki, and
N.I.M. Yusoff, The performance characteristics ofstone mastic asphalt mixtures using oil palm fruit ashmodified bitumen. International Journal of Pavement
Research and Technology, 2014. 7(4): p. 227-236.
[27] Oluwasola, E.A., M.R. Hainin, and M.M.A. Aziz,
Evaluation of asphalt mixtures incorporating electricarc furnace steel slag and copper mine tailings for road
construction. Transportation Geotechnics, 2015. 2: p.
47-55.
[28] VicRoads, SECTION 404 - STONE MASTICASPHALT. 2012: Australia.
[29] Rebbechi J, YEO R. Pavement surfacings trial: longterm performance review. In Research into Practice:
22nd ARRB Conference ARRB 2006.

418

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13, 2020, ISBN: 978-4-909106049 C3051

A STUDY ON COMPRESSIVE STRENGTH AND EFFECTIVE
DIFFUSION COEFFICIENT OF SOLIDIFIED BODY WITH HIGHZEOLITE MIXING RATE
Ryo Takano1 and Kazuhito Niwase1
National Institute of Technology, Hachinohe College (Tamonoki Hachinohe 039-1192, JAPAN)
ABSTRACTThe accident at the Fukushima Daiichi Nuclear Power Plant caused a large amount of high concentrationof radioactive contaminated water due to groundwater flowing into the reactor building and core cooling. The
radioactive contaminated water has been removed by adsorbing cesium, a major nuclide, on zeolite. Cement
solidification technology is considered as one of the options as a solidification method for stabilizing cesiumadsorbed zeolite based on economics, manufacturability, and experience with low-level radioactive waste.
In previous studies on the development of cement solidification technology, we have studied zeolite mixing
rates that can be solidified into good conditions from the viewpoint of fresh properties, physical stability, andnuclide containment performance. At present, it is possible to confirm the feasibility of zeolite mixing rate up
to about 50% of the solidified body volume. In this study, we focused on further improvement of the volume
reduction rate and prepared a specimen with a solidified body volume of 75 % and conducted the same study.
As a result, it was confirmed that production was possible and excellent strength development.
In addition, as for the physical properties after curing, the hydration reaction and the pozzolanic reaction were
promoted because the curing condition was sealed at 80 °C and the compressive strength and the effective
diffusion coefficient showed a peak after 28 days of curing. When the curing period was 91 days, changes in
the pore structure and the occurrence of microcracks were confirmed.
Keywords: Cesium adsorbed zeolite, Cement solidification, Disposal of low level radioactive waste, Effective
diffusion coefficientsatisfy the requirements described above, it is oneof the options. In the past study,[1] the relationshipbetween thermal influence, mechanical stability,and nuclide confinement was evaluated for the
purpose of feasibility study of cement solidification
technology. As a result, in the formulation
conditions of 50% of the solidified body volume
zeolite, cement solidification technique of cesium
adsorption zeolite could be determined to befeasible.
Furthermore, the main component of zeolite issilica and alumina, causing a reaction similar to the
pozzolanic reaction, it was suggested that thezeolite itself is dense.
The purpose of this study is to study the
maximum mixing rate of zeolite by focusing on
improving the reduction rate in waste bodies fromthe viewpoint of fresh properties, mechanicalstability, and nuclide confinement performance.

INTRODUCTIONOn March 11, 2011, an accident at the
Fukushima Daiichi Nuclear Power Plant during the
Tohoku Pacific Ocean Earthquake caused a large
amount of high concentrations of radioactive
contaminated water caused by groundwater andcore cooling that flowed into the reactor building.
Currently, the radioactive contaminated water, byzeolite having a high cation exchange function, hasbeen removed by adsorbing cesium is a major
nuclide.
Zeolite adsorbed the cesium is assumed to beintermediate management until the radiation effectand heat generation is reduced, the current situationis stored as a secondary waste in the box culvert,
continues to increase.
Therefore, it is urgent to establish anintermediate management method based on the
final disposal. As required matters, as well as tosuppress the leakage of radioactivity, and the
production of the waste body in a high radiation
environment, economic sand due to the reduction of
the waste body based on the amount of occurrenceis large it is required.
Cement solidification technology, from theresults of the buried disposal of low-level
radioactive waste, as a processing method that can

PRELIMINARY EXPERIMENTIn this study, A preliminary experiment was
conducted on the formulation used in the test. The
mixing ratio is shown in Table-1.
Blended with three types of zeolite mixing rate
(75%, 80%,85%), water cement ratio (hereinafter,
419

GEOMATE- Melbourne, Australia, 11-13,2020

Table 1 Mix proportion
3

ZV

W/C

0.75
0.75

Unit contents(kg/m )SP(ME)

W

OPC

Z

WZ

0.8

179

224

545

294

13.4

0.9

185

206

545

294

12.3

C×6%

0.75

1.0

190

190

545

294

11.4

0.80

0.8

143

179

582

313

10.7

0.80

0.9

148

164

582

313

9.9

0.80

1.0

152

152

582

313

9.1

0.85

0.8

107

134

618

333

8.1

0.85

0.9

111

123

618

333

7.4

0.85

1.0

114

114

618

333

6.8

W/C) three (0.8,0.9,1.0), high-performance AE
reduction Water agent (hereinafter, SP) normal
Portland cement (density: 3.16g / cm 3, specificsurface area: 3340cm 2 / g) (hereinafter, OPC) was
adjusted to 6% of, a total of nine formulations were
examined. Particle size of the natural zeolite used(moldenite type) is 0.5mm or less, and mixed in adry state. Zeolite is porous, the water absorption
from the cement paste, since the decrease inworkability is concerned, apart from the unit water
volume, was added water absorption content in theouter crack. [2] Kneading mixing was carried out ina Mortar mixer. The procedure is shown below.
1) Using a kneading machine, the kneading bowl
and paddle were fixed in the mixing position.
Then, the prescribed amount of water andafterward the cement were added.
2) Immediately start the kneading machine at alow speed.
3) After starting the kneading machine for 30
seconds, put the specified amount of zeolite in
30 seconds.
4) Speed up and continue kneading for 90
seconds.
5) Pause for 90 seconds and scrape off for the
first 15 seconds of the pause.
6) After the pause, start again at high speed andmix for 60 seconds.
The table flow test was measured flow value ofthe formulation examined. The situation is shown in
Fig.1and Fig.2.
By the previous study, if the flow value isobtained 150mm or more, [3]it was set the criteriathat there is sufficient liquidity. The results areshown in Fig. 3. Those flow value satisfies 150 mm,
zeolite mixing rate 75 % water cement ratio 80 %Fig.1

ZV75%-0.8 flow test

Fig.2

ZV75%-0.9 flow test

(hereinafter, ZV75 %-0.8), zeolite mixing rate
75% water cement ratio 90 % (hereinafter, ZV75 %0.9) because it was two types, this formulation was
the subject of study.
SPECIMEN PREPARATION
420

GEOMATE- Melbourne, Australia, 11-13,2020

The kneading was carried out with a biaxial
forced kneading mixer. In the past, we had
considered a method of making cement paste by
self-filling, but we thought that it is impossible to
make it by self-filling because zeolite with a verysmall particle size of 0.5 mm or less is used.
Therefore, the procedure is shown below.
1) Add cement to the twin screw forced kneading
mixer.
2) Add one third of the total amount of water andmix for 30 seconds.
3) Add half the total amount of zeolite and mix
for 90 seconds. At this time, add 1/3 of the total
amount of water while mixing.
4) Add zeolite and water as in 3), and mix for 180
seconds.
5) After kneading, let stand for 5 minutes.
6) Place it in the formwork and let it stand in aconstant temperature room (20°C) for 2 days.
7) After the curing period, cut and polish for each
test.
This time, as the test piece, a cylindrical test
piece was prepared. For the cylindrical specimen, acylindrical plastic mold (φ=100 mm, h=200 mm)
and a steel cylindrical form (φ=50 mm, h=100 mm)
were used. The situation is shown in Fig.4 and Fig.5.

Fig.3 Flow Value

Fig.4 Cylindrical plastic mold (φ=100mm,
h=200mm)

CURING METHODCuring method was sealed curing. Thus, byconstant moisture inside the specimen, to block theeffect of water from the external environment.
Zeolite adsorbed cesium in consideration that it
has a heating property, it was carried out hightemperature curing. As realistic concentration
adjustment is performed through the half-life,
curing temperature was set to 80°. Curing period is
7 days, 28 days, and 91 days. Fig. 6 shows thesealing process.

Fig.5 Steel cylinder formwork (φ=50mm,
h=100mm)COMPRESSIVE STRENGTH TESTMETHOD
Compressive strength test, in accordance withthe compressive strength test method of concrete(JIS A 1108), was carried out.
ELECTROPHPRESIS TEST METHODElectrophoresis test was carried out inaccordance with the “TEST METHOD FOR
EFFECTIVE DIFFUSION COEFFICIENT OF

Fig.6
421

Sealed state

GEOMATE- Melbourne, Australia, 11-13,2020

CHLORIDE ION CONCRETE BY MIGRATION”(JSCE-G 571-2003)[4]Also, I show a photograph ofthe test in Fig. 7 This is a photograph of the
diffusion cell originally developed in thisexperiment.
Unlike the conventional experimental system,
cells of NaCl solution side and NaOH solution side
are considered to be divided. Thus, chloride ionsmove only the specimen, movement from the test
body side junction surface is blocked. By any
chance from the fine gap, even if the solution
leakage has occurred, since there is no contact of thesolution outside the specimen, it has the advantagethat it is not reflected in the data.

Fig.7 Diffusion cell

MERCURY POROSIMETRY METHOD
In relation to the effective diffusion coefficient,measured the pore size distribution. The cut
cylindrical specimen was crushed to a particle sizeof 2.5-5.0 mm, subjected to acetone immersion andfurnace drying treatment of 24 hours, and those that
stopped hydration as a sample. The test was carriedout using a mercury-press-fitting porosimeter,
focusing on the peak of the pore size, to evaluate thedenseness of the specimen.
In the mercury injection method, when mercury
enters the void,
The pore size was calculated from the injection
pressure and the amount of mercury injected, andthe pore structure was calculated.
The relationship between the pressure at thetime of mercury injection and the void diameter d isexpressed by the Washburm equation (1) from themeasured pressure p, the surface tension γ of
mercury, and the contact angle θ between the
mercury and the wall of the void. A schematic
diagram is shown in Fig.8.[5]d= -(4γ/p) cosθFig.8 Schematic diagram of pore measurement(1)

COMPRESSIVE STRENGTH TEST RESULT

Fig.9 Compressive strength test result

Fig.9 shows the compressive strength test
results. In the formulation of ZV75%-0.8, 7 days
strength was 9.54 N/mm2, 28 days strength was 8.00
N/mm2, and 91 days strength was 3.72 N/mm2 .
Strength was expected to improve over time, in thisformulation, decrease trend was confirmed.
Possible causes, by sealing during curing was notcomplete, drying shrinkage occurs, and a decreasein strength due to microcracks generated. In thisexperiment using a drying furnace that imparts aconstant temperature of 80 degrees, by sealing thehumidity, it was prevented a scatter of water.
However, since the sealing was not complete, water
will be released from the specimen, it is believed tohave led to a change in pore structure from drying
shrinkage. In addition, ZV75%-0.8 is poor fillingFig.10 ZV75%-0.8 specimen422

GEOMATE- Melbourne, Australia, 11-13,2020

property, it is also considered to be the cause of thestrength reduction that was an individual with a lotof voids. The situation is shown in Fig.10. With
respect to the decrease in strength, it shows adecrease in proportion to the passage of time.
Therefore, at the stage of 91 days strength, but was
greater than 1.5 N/mm2 is the reference value of thewaste body, since the possibility of showing a
subsequent downward trend remains sufficiently, it
is considered that it is necessary to review.
On the other hand, in the formulation of
ZV75%-0.9, 7 days strength was 9.08 N/mm2, 28
days strength was 9.34 N/mm2, it can be seen thatthe strength is increased. Therefore, ZV75%-0.9,
compared to ZV75%-0.8, more cement volume
mixed, strength is considered to have improved
even 28 days strength. 91 days strength was 6.26
N/mm2. As with ZV75%-0.8, it can be confirmedthat it has decreased significantly compared to 28
days strength. This is also the same as ZV75%-0.8,
it is believed that the change in pore structure due
to drying shrinkage has led to a decrease in strength.
Both formulations in 91 days, significant
strength reduction was observed. However, since it
exceeded 1.5N/mm2 is a technical reference valueof the waste body, mechanical stability at the 91
days strength time was determined to have been
evaluated.[6] However, since there is a possibility
that it will continue to show a declining trend after
this, it is necessary to consider.

coefficient in 91-day curing was confirmed as well
as ZV75%-0.8.
In both specimens, at the time of 28-day curing,
the promotion of hydration reaction from 7 days
curing to 28 days curing, densification of theinternal structure occurs, over 91 days curing, bypromoting the pozzolanic reaction by zeolite, it was
expected to be further densified. However, theeffective diffusion coefficient increased in the 91
days curing. Possible causes from this is a change
in the pore structure due to long-term thermal
history. Concrete as the heating temperature is high,chloride permeability and permeability is
increased.[7] The long-term heat application, thetemperature inside the concrete is increased, since
the permeability of the chloride ions is increased, it
is considered that the effective diffusion coefficientis increased. Further, the high temperature heat
imparting, hydration reaction is promoted, at thetime of 7-day curing is toward the end, in the 28day strength, pozzolanic reaction by zeolite isconsidered to have been toward the end.
MERCURY POROSIMETRY RESULT
Fig.12 shows the pore size distribution of
ZV75 %-0.8. Both specimens can be seen that there
is a gap from 10 nm to 100 nm. This is believed tobe those that the gap of the zeolite is reflected. In 7
days curing, but the first peak is at 30 nm, in the 28
days curing, the first peak is 20 nm. Similarly, the

ELECTROPHPRESIS TEST RESULT
Fig.11 shows the effective diffusion coefficient
calculated by electrophoresis test. The vertical axisof the graph shows the value of the effective
diffusion coefficient and is organized based on 1.0
×10-12 m2/s. The horizontal axis shows the curing
period, 7 days curing from the left, 28 days curing,and shows the 91 days curing. Focusing on ZV75%0.8, the execution diffusion coefficient of 3.07×1012
m2/s was shown in 7 days curing. In the 28-day
curing, it shows the effective diffusion coefficientof 1.88×10-12 m2/s, it can be seen that it isdecreasing. This is believed to be due todensification of the specimen inside. However, inthe 91 days curing, the effective diffusion
coefficient of 3.68×10-12 m2/s was shown, and anincrease in the effective diffusion coefficient was
confirmed.
On the other hand, the 7-day curing of ZV75%0.9 showed the execution diffusion coefficient of
3.33 ×10-12 m2/s / s. In the 28-day curing, it showsthe effective diffusion coefficient of 2.38 ×10-12
m2/s / s, it can be seen that it is decreasing. This isbelieved to be due to densification of the specimen
inside. However, in the 91-day curing, the effective
diffusion coefficient of 3.20 ×10-12 m2/s / s wasshown, and the increase of the effective diffusion

Fig.11 Effective diffusion coefficient423

Fig.12

Pore size distribution ZV75 %-0.8

GEOMATE- Melbourne, Australia, 11-13,2020second peak that was present in the vicinity of 1000
nm of 7 days curing has a second peak in thevicinity of 100 nm of 28 days curing, the void of
100000 nm from 10000 nm of 7 days curing, it canbe confirmed that it has become a third peak that ispresent in the vicinity of 1000 nm of 28 days curing.
Therefore, ZV75%-0.8 from 7 days curing to 28
days curing, it was confirmed that it is densified.
This pozzolanic reaction of hydration reaction andnatural zeolite by cement is affected, it is considered
to be those densified.
Fig.13 shows the pore size distribution of
ZV75%-0.9. And Fig. 14 shows the pore size
distribution of zeolite. ZV75%-0.8, it can be seenthat there is a gap between 10 nm and 100 nm in
both specimens. This is believed to be those that the
gap of the zeolite is reflected. In 7days curing, thefirst peak has 80 nm from 10 nm, the second peakcan be seen to be present in the vicinity of 1000 nm.
In 28 days curing, it can be seen that the first peakis present in 40 nm from 10 nm. Further, the second
peak is present in the vicinity of 100 nm. When
comparing the 7 days curing and 28 days curing,almost unchanged, pore size distribution isconsistent. Further, it can be confirmed that the dataof the void structure from 10 nm to 50 nm is
unstable.

(1) In this study, a solidified body with a highmixing ratio, in which 75 % of the whole specimen
was zeolite, could be prepared by the blending
examination.[6](2) The compressive strength of all the specimens
reached 1.5 N/mm², which is considered to be thetechnical standard for waste, and the mechanicalstability could be evaluated. However, it isnecessary to improve the fresh property, because
the individual difference greatly appeared due to thevoids caused by the poor filling property, especiallyin ZV75 %-0.8.
(3) The hydration reaction and the pozzolanic
reaction were accelerated by the application of heat
history, and the compressive strength and theeffective diffusion coefficient showed peaks after
28 days of curing. However, structural changes andfine cracks will begin to occur on the 91 days, so
there is room for further improvement inconsideration of the amount of calcium hydroxide
produced.
REFERENCES[1] K.Niwase,T.Sakai,H.mawatari: Basic Study on
Design of Solidified Mixture of Zeolite for
Cement Solidification Technology of Cesium
Adsorbed Zeolite, Proceedings of Annual
Meeting of Atomic Energy Society of Japan (CDROM),Mar.2018[2] H. Mawatari, K. Niwase and M.Satou,
Evaluation of Physical Properties of Fast Ash
Cement and Fly Ash Combination Type Filler in
Cement Solidification Technique of Cesium
Adsorbing Zeolite, Cement and Concrete
Collected Papers, Vol.71, Mar. 2018 (inJapanese)[3]
K.Niwase,A.Imabuchi:COMPRESSIVE
STRENGTH OF CEMENT SOLID FORM
MIXED
WITH
ZEOLITEFORSOLIDIFICATION OF CESIUM ADSORBEDZEOLITE,43rd Conference on Our World inConcrete & Structures,30-31,August 2018.
[4] Test method for effective diffusion coefficientof chloride ion in concrete by JSCE standard
electrophoresis (draft)（JSCEG 571-2003）[5] Washburn E.V. : Dynamics of Capillary Flow,
Physical Review, Vol. 17, pp.273-283 (1921).
[6] Nuclear Regulatory Agency: Operational
procedure for waste confirmation, EstablishedFebruary 26, 2014 Original pipe dismantling No.
1402262[7] Y.sugino, R.kamata, T.tanibe, M.Ozawa ：Effect of surface impregnant on pore structure
and resistance to substance intrusion of heatedconcrete, Taiheiyo Cement Research Report No.
174 (2008)

CONCLUSION

Fig.13

Pore size distribution ZV75%-0.9

Fig.14

Zeolite pore size distribution424

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INVESTIGATING COMPRESSIVE, PULLOUT AND TENSILESTRENGTH OF CONCRETE CONTAINING POLYPROPYLENE
Md Jahidul Islam1, Niaz Md Ashraful haque2 and Anika Tahsin3
1, 2, 3

Department of Civil Engineering, Military Institute of Science and Technology, Bangladesh;
ABSTRACT

Every year a large number of plastic wastes are generated throughout the world which is a major threat to theenvironment due to its non-biodegradable nature where polypropylene consists of more than 35 percent of the total.
These plastics need to be recycled and reused in an environment friendly way. On the other hand, for construction
purposes an alternative of natural stones, whose reserve is depleting, must be sought. Therefore, aim of this study
is to investigate the compressive, tensile and pull-out strength of concrete partially mixed with recycled waste
polypropylene (PP) as a coarse aggregate. In order to evaluate the strength of concrete, twelve different mixes are
considered where stone chips are replaced by PP at 10% and 20% (by volume) with four different water-cement
ratios (0.35 and 0.4 with admixture; 0.45 and 0.5 without admixture) and compared with concrete with no PP
aggregate. At 90 days, for concrete with 10% PP replacement compressive strength reduction was between 4%
and 26%; whereas, tensile strength decreased from 7% to 11%. The results of pull out strength reveals that in mostcases concrete with 10% PP replacement have higher pull out stress compare to concrete without PP. Finally, theuse of 10% PP in concrete is safe and can generate strong structural elements.
Keywords: Polypropylene, Composite concrete, Compressive strength, Pull out strength, Split tensile strengthrisk, as the earthquake forces are linearly dependent
on the dead weight. Application of recycled PP as apartial substitute of coarse aggregate in concrete
offers certain advantages, i.e. reduction in the usageof natural resources, low cost alternative toconventional aggregates, recycling of polymer waste,
avoidance of contamination and energy savings.
Polypropylene (PP) is an affordable and plentiful
thermoplastic used in a wide variety of applications
including food packaging, textiles, laboratory
equipment, vehicle parts, and banknotes. It is slightly
harder, more heat resistant, mechanically ruggedmaterial; and has a high chemical resistance [6]. It is
the second-most widely produced commodity plastic
and can be used in making composite concrete for its
excellent material property [7]. The melting point ofpolypropylene is exceptionally high compared toother plastics at 320°F (160°C).
Several studies have been carried out in recent
years on specific types of composites such as concretewith PP fibers and PET aggregates, but not much withcrushed PP aggregates and particularly with regard topull out strength. Therefore, in the present study
emphasize is given on to finding out compressive
strength, pull-out strength and tensile strength of
concrete with polypropylene as partial replacement ofcoarse aggregates at various proportion.

INTRODUCTION
Civilization has made the waste disposal andwaste management a major natural concern
worldwide. Now a day, the plastic products havebecome one of the most essential material of ourcivilized lifestyle; and thus the global plastic
production has increased immensely during the past
50 years [1]. Among various waste materials, plasticbased waste materials are worst as they don't
effectively debase or decayed [2]. Plastic is accessible
nearly all over and its utilization around the world
expanded from 5 million tons to almost 335 million
tons amidst the year 1950–2016 [3]. Only around 7%of these produced plastics are recycled, and the rest
are went to the landfill and polluting the earth. Hence,plastic wastes are considered to be a serious
environmental problem universally. According tostatistical report, around 34% of these huge plastic
wastes are polypropylene found in municipal solid
waste [4].
Currently, the global concrete consumption isestimated around 25 billion tons per year and annual
coarse aggregate (stone) usage is more than 3.9billion tones [5]. The use of waste product likeplastics in concrete not only makes it economical but
also helps in reducing the plastic waste disposal
problems.
The use of polypropylene (PP), due to its low unit
weight, reduces the unit weight of concrete which
results in a reduction in the dead load of a structuralconcrete member of a building. Reducing a building's
self-weight would help reduce the building's seismic

MATERIALS
For the experimental work, stone aggregates andrecycled waste PP were used as two different types ofcoarse aggregates. Specification of aggregate was425

GEOMATE – Melbourne, Australia, 11-13 November 2020

selected based on the ASTM C33 [8]. Ordinary
Portland Cement (OPC) has been used as binding
material for this research work. River (Lalakhal,Sylhet, Bangladesh) sand, as demonstrated in Fig.
1(a), has been collected and used as fine aggregates(FA). Coarse aggregate (CA) as crushed stones werepurchased from local market according to required
quantities and displayed in Fig. 1(b). Shredded PP
aggregate, as shown in Fig. 2, was used as partial
replacement of coarse aggregate. Polypropylene (PP)aggregate was prepared using a collection, washing,and scraping procedure. It had been melted andcooled into a certain shape after that. Such plastic
cooled molds were then shredded into different sizes.
This process made the surface of the concrete roughand little porous that could result in better bonding
behavior in concrete.

Table 1 Properties of coarse and fine aggregates
Description
Maximum Size(mm)
Fineness
ModulusSpecific
Gravity
Water
Absorption(%)
Compacted
Unit Weight(kg/m3)AIV
ACV

CrushedStone

Polypropylene
(PP)

Sand

19

12

-

7.12

6.77

2.46

2.61

0.85

2.7

0.36

0.3

2.8

1560

622

1604

20.3
21.85

0.47
3.6

-

METHODOLOGY
Using plastic waste as aggregate in construction isan economical and green concept of modern civilengineering [16]. To achieve the objectives of thisstudy, a number of experiments have been conductedin the laboratories. The concrete samples wereprepared with 0%, 10% and 20% replacement of PP
as coarse aggregate with four different w/c ratios(0.35, 0.40, 0.45 and 0.50). Concrete cylinder
samples, 100 mm x 200 mm, were prepared as per
requirement of the test methods following ASTM C
192 [17]. The compressive strength was determined
after 7 days, 28 days and 90 days. The splitting tensile
strength was determined after 28 and 90 days.
Moreover, a total of 36 concrete cylinders insertedwith steel bars, as shown in Fig. 3, were casted forpull out strength determination at 28 days. All the
specimen was cast in steel molds and compactedusing an internal vibrator. After casting, thespecimens were stored in a humid room for 24 hours
then demolded. After demolding the specimens were
cured in a water tank at controlled temperature (23 ±2°C) condition for 28 days except for samples thatwere tested at 7 days.

(b)

(a)
Fig. 1Aggregate: (a) coarse, and (b) fine.

Fig. 2

Processed polypropylene (PP) aggregates

The properties of materials were determined byconducting laboratory tests, such as aggregate sieve
analysis (ASTM C 136) [9], specific gravity andabsorption capacity of coarse and fine aggregate(ASTM C 127 and ASTM C 128) [10-11], unit weight(ASTM C 29) [12], aggregate impact value (AIV)(BS 812-112) [13], and aggregate crushing value(ACV) (BS 812-110) [14]. The properties of thematerials are summarized in Table 1.
Water-reducing admixture following ASTM C
494 [15], to maintain uniform workability for lower
water-cement ratios (0.35 and 0.40), was used for all
combination of fresh concrete. The specific gravity of
admixture was 1.21.

Fig. 3Specimen for pull out strength test.

Mix Design426

GEOMATE – Melbourne, Australia, 11-13 November 2020(b)(a)
Twelve different concrete mixtures were prepared
for this study. Among them, six were prepared with
admixtures and the other six were without admixtures.
Mix design for the concrete specimens was proposedconsidering stone chips with three different partial
replacement of PP (0%, 10% and 20% by volume)along with four different water cement ratios (0.35,
0.40, 0.45 and 0.50). Each mix design was designatedwith a unique name for ease in referencing within the
text. Table 2 shows the mix proportion for 1 m3 of
concrete.
Table 2 Mix design for 1m3 of concrete
(c)
Desig
nation

Water
(kg)

Ce
m
(kg)
589

CA
(kg)

PP
(kg)

FA
(kg)

Ad
m.
(l)
1.85

WC3
206
977
487
5P0
WC4
206
514 975
552 1.23
0P0
WC4
205
456 972
605
5P0
WC5
205
410 972
643
0P0
WC3
206
589 87939
474 1.85
5P10
WC4
206
514 87739
539 1.23
0P10
WC4
205
456 875
39
592
5P10
WC5
205
410 875
39
630
0P10
WC3
206
589 78178
461 1.85
5P20
WC4
206
514 78078
526 1.23
0P20
WC4
205
456 778
77
579
5P20
WC5
205
410 77877
617
0P20
Note: WCXPY, “X” stands for the water cement ratioand “Y” stands for percentage of PP replacement.

Fig. 4

Test setup (a) compressive strength test, (b)splitting tensile strength test, (c) Pull out
strength test of concrete.

RESULTS AND DISCUSSIONSThe effect of using Polypropylene chips as apartial replacement of coarse aggregates oncompressive strength, splitting tensile strength andpull out strength are presented in this section
Effect on Compressive strength
The compressive strengths at 7, 28 and 90 days
after casting were measured for all type of concretes.
Figure 5 represents some of the failed concrete
cylinders under compressive force. In most cases,
failure was limited to cone and shear failure. An
illustrative comparison of compressive strength
among various mixes are also shown in Fig. 6 and Fig.
7.
Overall, it is seen that the compressive strengths
are increased almost 32% and 48% at 28 days and 90
days compared to 7 days. Based on the test data, it canbe seen that, concrete with PP produces lower
strength compared to regular concrete with no PP
content. This is expected as synthetic PP does notbond well with the binding material used in concrete.
However, with increase in concrete age interfacialtransition zone between the aggregate and cement
matrix gain strength. It is evident from the test results
as variation in concrete compressive strength

Testing Procedure
The compressive strength test, as shown in Fig.
4(a) was performed according to ASTM C 39 [18]and the loading rate was kept at 0.25 ± 0.05 MPa/sec.
The splitting tensile strength was performed
according to ASTM C 496 [19]. Pull out strength
determination was conducted according to ASTM C
900 [20]. The test setup for different tests are shownin Fig. 4.
427

GEOMATE – Melbourne, Australia, 11-13 November 2020

becomes less at 90 days (4 to 26%) compare to thecompressive strength variation at 28 days (14 to 27%).
For concrete with w/c ratio of 0.4 and admixture,compressive strength at 90 days was 44.3 MPa
compare to the control concrete with compressive
strength of 46.2 MPa. However, with 20% PP
concrete reduction was higher with 37.2 MPacompressive strength.

concrete samples with admixture and w/cratio of 0.35 and 0.40

Compressive Strength (MPa)50

Adding admixtures at lower w/c ratios reduces thecompressive strength variation for concrete with PP,
especially for 10% replacement. As observed fromthe Fig. 6 and 7, at 90 days compressive strengthvariations are only 13% and 4% for concrete with
admixture and w/c ratios of 0.35 and 0.40,
respectively. On the other hand, compressive strengthvariations at 90 days are 16% and 14% for concreteno admixture and w/c ratios of 0.45 and 0.5,
respectively. Replacing stone aggregate with PP
aggregate reduces the compressive strength of
concrete. However, the minimum strength is 27.2
MPa for concrete with 20% PP and w/c ratio of 0.50
which is still high enough to be used in structuralconcrete.

Fig. 530
20
10
0

7 days28 days

90 daysFig. 7

Compressive strength comparison of
concrete samples without admixture andw/c ratio of 0.35 and 0.40
Effect on Tensile Strength
The tensile strengths at 28 days and 90 days after
casting were measured according to ASTM C496 forall type of concretes. Specimen after the tensile test is
shown in Fig. 8. Comparison of tensile strength
among various mixes are illustrated in Fig. 9 and Fig.
10. Based on the test data, it can be said that, concretewith PP produces little lower tensile strengthcompared to regular concrete with no PP content. The
tensile strengths are decreased with increasing w/c
ratio. Among all the w/c ration at 28 days, for 10%
PP replacement the relative reduction of strength was
between 3% and 15% and the minimum strength was
3.2 MPa at the w/c ratio of 0.50. For 20% PP
replacement the relative reduction was between 20%
and 24% and minimum strength was 2.7 MPa. Similar
trend is also seen for test results at 90 days.

Failure of concrete cylinder undercompressive force.

60
Compressive Strength (MPa)

40

50
40
30
20
10
0

7 daysFig. 628 days

90 daysFig. 8Compressive strength comparison of428

Specimen after split tensile test.

GEOMATE – Melbourne, Australia, 11-13 November 2020

Splitting Tensile Strength
(MPa)

5

28 days

90 Days4

Designation

Equation

Proposed Eqn.
Babu et. al [21]𝒇𝒇𝒕𝒕 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝒇𝒇´𝟎𝟎.𝟕𝟕𝟕𝟕
𝒄𝒄𝒇𝒇𝒕𝒕 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝒇𝒇´𝟎𝟎.𝟕𝟕𝟕𝟕
𝒄𝒄𝒇𝒇𝒕𝒕 = 𝟎𝟎. 𝟓𝟓𝟓𝟓𝟓𝟓𝒇𝒇´𝟎𝟎.𝟓𝟓
𝒄𝒄

ACI 318 [22]3Aggregate
type
PP+NA
EPS
NA

Here: f´c = compressive strength at 28 days; ft = splitting tensile
strength at 28 days.

2
0% PP
20% PP
Babu et al [21]1

10% PP
Proposed Eqn
ACI 318 [22]6Fig. 9Tensile strength comparison of concrete
samples with admixture and w/c ratio of0.35 and 0.40.
5Splitting Tensile Strength(MPa)

Tensile Stress, MPa

028 days

90 Days

5
4
3
2
1
0

4

0

10

3

20

30

40

50

Compressive Stress, MPa

2
1Fig. 11 Relationship between compressive andtensile strength0

Effect on Pull out Strength
The pull out strength test was conductedaccording to ASTM C 900. In this test method, thestrength was determined by pulling a cast in place
insert rebar by means of a jack reacting against abearing ring. The pull out strength was determined bymeasuring the maximum force to pull the inserted
rebar from concrete mass. All the measured forces
and corresponding pull out strengths for various
combinations are demonstrated in Table 4.

Fig. 10. Tensile strength comparison of concrete
samples without admixture and w/c ratio of0.35 and 0.40.
Relationship between Compressive and Tensile
Strength
The relationship between the compressive andtensile strength for 0, 10 and 20% PP replaced
concrete, as shown in Fig. 11, indicates that tensile
strength increases with increasing compressive
strength. Based on the test results an equation is
proposed for tensile strength with moderatecorrelation (R2 = 0.775). Babu et al. [21] used
expanded polystyrene (EPS) in concrete andproposed an expression for splitting tensile strength.
ACI 318 [22] also provides a guideline for calculating
splitting tensile strength from the compressive
strength. Both these equations along with theproposed equation is illustrated in Table 3 and Figure
11. As observed from Figure 11, equation proposedby Babu et al. overestimates the tensile strength. ACI
318 equation gives better prediction for compressive
strength from 27 to 38 MPa only.

Table 4 Pull out strength of concrete specimens
Designation
WC35P0WC35P10
WC35P20
WC40P0
WC40P10
WC40P20
WC45P0WC45P10
WC45P20
WC50P0
WC50P10
WC50P20

Table 3 Equations for split tensile strength

429

Pull out
Force, (kN)
42.3
37.8
26.3
38.1
43.7
22.9
31.1
36.2
22.5
24.8
28.8
22.5

Pull Out Stress
(MPa)
5.95
5.31
3.70
5.34
6.14
3.22
4.36
5.08
3.16
3.48
4.04
3.14

GEOMATE – Melbourne, Australia, 11-13 November 2020

REFERENCES[1] Gu, Lei & Ozbakkaloglu, Togay. (2016). Use of
Recycled Plastics in Concrete: A Critical Review.
Waste
Management.
51.
10.1016/j.wasman.2016.03.005.
[2] Frigione, M. 2010. Recycling of PET bottles asfine aggregate in concrete. Waste Management,
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[3] Statista, 2016. Global production of plastic since1950. [online]. Available at:https://www.statista.com/statistics/282732/global-production-of-plastics-since-1950. [Accessed 12
October 2019].
[4] Islam, Md, Sarwar, Navid and Al shafian, Sultan,
Conference
proceedinginInternational
Conference on Recent Innovation in Civil
Engineering for Sustainable Development(IICSD-2015), An Investigation of Concrete
Properties with Polypropylene (PP) as Partial
Replacement of Coarse Aggregate. pp. 1.
[5] USGS Mineral Commodity Summaries, 2017.
[online], https://mineralsmakelife.org/wpcontent/uploads/2017/04/Mineral_Commodity_S
ummaries_2017.pdf [Accessed 12 June 2019][6] Whiteley, K. S., Heggs,T. G., Koch, H., Mawer,
R. L., & Immel, W. 2005. Polyolefins in
Ullmann's Encyclopedia of Industrial Chemistry.
Weinheim: Wiley-VCH Verlag GmbH & Co.
[7] Bagherzadeh R., Pakravan, H. R., Sadeghi, A. H.,
Latifi, M., & Merati, A. A. 2012. An Investigation
on Adding Polypropylene Fibers to Reinforce
Lightweight Cement Composites (LWC). Journalof Engineered Fabrics & Fibers (JEFF). 7 (4): 1321.
[8] ASTM C33 / C33M-18, Standard Specification
for Concrete Aggregates, ASTM International,West Conshohocken, PA, 2018, www.astm.org[9] ASTM C136/C136M-14, Standard Test Method
for Sieve Analysis of Fine and Coarse
Aggregates,
ASTM International,
West
Conshohocken, PA, 2014, www.astm.org.
[10] ASTM C127-15, Standard Test Method for
Relative Density (Specific Gravity), andAbsorption of Coarse Aggregate, ASTM
International, West Conshohocken, PA, 2015,
www.astm.org.
[11] ASTM C128-15, Standard Test Method for
Relative Density (Specific Gravity), andAbsorption of Fine Aggregate, ASTM
International, West Conshohocken, PA, 2015,
www.astm.org.
[12] ASTM C29/C29M-09, Standard Test Method for
Bulk Density (“Unit Weight”) and Voids inAggregate,
ASTM
International,
West
Conshohocken, PA, 2009, www.astm.org.
[13] BS 812-112, Testing aggregates — Part 112:
Methods for determination of aggregate impact

Fig. 12 Concrete conic frustum of cast in placerebar in pull out test.
From the pull out force data it can be observed that,for all four types of water cement ratios, with theincrement of PP percentage the bonding strength
between the concrete and rebar decreases. However,except for concrete with w/c ratio of 0.35, the 10% PP
replaced concrete showed 14 – 17% higher bonding
strength than the control sample with no PP concrete.
Higher pull out strength indicates good bonding
behavior between the rebar and concrete with 10% PP
replacement.
CONCLUSION
Compared to regular concrete, PP concrete has thelower compressive strength as well as the lower
tensile strength. It is to be expected as the synthetic
nature of the PP provides lesser bonding with thecement mortar compare to the natural stone aggregate.
Based on the experimental results following
observations can be made:i. With increase in age interfacial transition zone of
PP concrete improved and gave lower variation.
For concrete with admixtures (w/c ratios of 0.35
and 0.4) and 10% PP replacement, variations in
concrete strength at 28 days were 20% and 13%,respectively; whereas, variations at 90 days were 18%
and 4%, respectively.
ii. Compressive strength is reduced with PP percentage
increase. However, concrete w/c ratios of 0.45 and
0.50, and 20% PP replacement still produced
minimum of 33 MPa at 90 days.
iii. Tensile strength of PP concrete showed better
performances. Concrete with 10% PP replacement
showed reduction up to 11% at 90 days for all w/c
ratios.
iv. Except for w/c ratio of 0.35, concrete with 10% PP
replacement showed higher pull out strength compare
to the concrete without PP.
v. Therefore, it can be concluded that the recycled PP with
an optimum percentage can be adopted as partial
replacement of coarse aggregate in concrete used forstructural purposes.

430

GEOMATE – Melbourne, Australia, 11-13 November 2020

value (AIV), 1990, BSI.
[14] BS 812-110, Testing aggregates — Part 110:
Methods for determination of aggregate crushingvalue (ACV), 1990, BSI.
[15] ASTM
C494/
C494M-19,
Standard
Specification for Chemical Admixtures forConcrete,
ASTM
International,
West
Conshohocken, PA, 2019, www.astm.org[16] Islam, M. J., Meherier, M. S., Islam, A. K., M. R.
2016. Effects of waste PET as coarse aggregate on
the fresh and harden properties of concrete.
Construction and Building Materials, 125,946951.
[17] ASTM C192 / C192M-18, Standard Practice for
Making and Curing Concrete Test Specimens in
the Laboratory, ASTM International, WestConshohocken, PA, 2018, www.astm.org[18] ASTM C39 / C39M-18, Standard Test Method
for Compressive Strength of Cylindrical Concrete
Specimens,
ASTM
International,
West

Conshohocken, PA, 2018, www.astm.org[19] ASTM C496 / C496M-17, Standard Test Method
for Splitting Tensile Strength of Cylindrical
Concrete Specimens, ASTM International, WestConshohocken, PA, 2017, www.astm.org[20] ASTM C900-19, Standard Test Method for
Pullout Strength of Hardened Concrete, ASTM
International, West Conshohocken, PA, 2019,www.astm.org[21] Babu, D. S., Ganesh Babu, K., & Tiong-Huan, W.
2006. Effect of polystyrene aggregate size on
strength and moisture migration characteristics
of lightweight concrete. Cement and Concrete
Composites, 28(6): 520-527.
[22] ACI
Committee 318. Building
CodeRequirements for Structural Concrete: (ACI 31895); and Commentary (ACI 318R-95).
Farmington Hills, MI: American Concrete
Institute, 1995.

431

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SOLAR PANEL FOUNDATIONS ON SLOPING LAND AND
OFFSHORE ~ A REVIEWAlex Otieno Owino1, Jamie Ure Ovia1, Jim Shiau2, Zakaria Hossain1
12

Dept. of Environmental Science Graduate School of Bioresources, Mie University, Japan
School of Civil Engineering and Surveying, University of Southern Queensland, Queensland, Australia
ABSTRACT

Renewable energy is rapidly becoming an alternative to fossil fuel-based energy generation. With new
innovative advances in renewable energy technology more so solar energy, and the markets improving, solar panel
prices have become more affordable. Currently, more and more countries are opting for solar power energy. Owing
to urbanization and population increase on flat terrains, finding flat lands to accommodate this technology is
becoming more difficult. This review hence provides an expert view on how to suit the emerging energy sourceby venturing ideas into solar panel installation techniques on sloping land and offshores to offer an alternative
solution to this flat terrain land scarcity problem. Only a handful of companies handle the latest design andinstallation techniques of solar panels on slopes and offshores. These companies make use of engineering
structures such as piles, cables, and anchors to achieve the best foundation capacities in these potential areas. This
paper, therefore, aims to provide a review of a couple of designs that have been used to best suit the two suitableinstallation locations that act as the best alternatives to the flat land terrains. Furthermore, the potential effects ofsolar panel installation, such as slope instability, offshore wave fluctuations, photovoltaic shading, the factor ofsafety, ultimate foundation capacity and several foundation design considerations such as the seismic response,
foundation geometry, lateral and pull-out capacities act as the primary considerations for the future researchprospects.
Keywords: Solar energy, Sloping land, Offshore, Piles, Cables, Anchors.
INTRODUCTION

regions affected contributes to the gap in accessbetween the urban areas and the rural areas. As aresult, there has been a hike in the installation of offgrid electricity supply systems, and the major
contributor to the off-grid systems is electricity fromrenewable sources, more so, solar energy throughSolar Home Systems, SHSs. [4]Recently, the demand for renewable energy has
started to increase globally. A report published by theWorld Bank, within the last decade, shows there has
been an increase in the levels of electrification. This
electrification has seen an increase from 83% world
population in 2010 to 89% world population in 2017
in most of the areas with abridged grid connectivity
rates more so in the developing countries. With a2030 target for 92% electrification globally andaccess to clean energy, the exertions have led to anincrease in the number of researches on this field withmore emphasis on the production of green energy
concerning the goals towards low carbon emission.
[1][2]
With the increase in electrification on the rise insome parts of the World, accessibility to electricity
has had a tremendous sharp decline due to rising
populations and economic factors. Some of thecommon challenges facing the energy sector include
low electrification levels, high grid, and related
system losses, persistent power interruptions andpower shortages, high electricity prices, unstable
power prices caused by volatile oil prices, theinstitutional governance and related operational
challenges including lack of synergy and duplication.
The global population without access to the electricity
grid is, as shown in figure 1 below. [3]The lack of access to the main electricity grids in the

Total

Urban

8%

11%

Rural
8%
32%

1990

28%

46%

27%

47%

40%

18%

18%
27%
12%

21%

2017

5%
68%

Central Asia

Sub. Africa

24%

4%

5%

80%

66%

East&SouthAsia

Rest

Fig. 1. Regional shares of the global energy accessdeficit, in total and along the urban/rural divide,
1990 and 2017, (Source: World Bank) [3]
The shifting to solar energy preference for the offgrid connectivity is on the boom since 2017, where todate, about 120 million of the global population had
access to the basic electrification systems like solar
lights below 11 watts. This off-grid connectivity isevident in countries like Kenya, Benin, Rwanda,
Burkina Faso, Tanzania, Papua New Guinea, Fiji,
Jordan, Samoa, and Vanuatu. Based on data from the432

GEOMATE – Melbourne, Australia, 11-13 November 2020

exploration of the next available land terrains thenbecomes handy. This review paper focuses on two
relatively new topographical conditions that have thepotential to expand solar energy generation in many
countries; slopes and offshore. The main areas underconsideration are sloping lands with higher slope
degrees, and offshore areas of islands, freshwaterlakes, abandoned quarries, lagoons, water pans,
irrigation canals, and salty water in oceans and seas.
It is paramount importance to conduct this study as
there is still a limited number of researches conductedon these locations, which could be new data assets for
future research.

Population connected to
off-grid solar supply

International Renewable Energy Agency (IRENA) on
off-grid solar home systems installations, access tomini-grids increased by 16% between 2015 and 2017
in several countries more so in sub-Saharan Africa,
Asia, and the Pacific regions as shown in Figure 2 [5].
With these rising trends and an indication of theadvancement and maturity of off-grid and or minigrids technology, there is potential to fully exploit all
the ventures in engineering design and research forbetter suitable and economic systems to support the
dream of solar energy provision in the developing
countries. [6]SLOPES
Engineering construction works such as theconstruction of photovoltaic systems have, for long,neglected sloping lands. However, with the depletionof the low terrain lands in most parts of the World, it
leaves us with one option that is to explore the unused
slopes. Another factor contributing to the utilizationof the sloping land is the high prices associated with
low terrain land, which in most countries are either
allocated for agriculture or city expansion projects.
With the increasing demands of energy by theenlarging cities, we, therefore, propose the utilizationof the sloping lands for generating green energy
through the installation of solar panels.
For the last decade, piles stabilized slopes andbecame one of the most commonly used slope
reinforcement techniques in the construction industry,
more so in the ground mount photovoltaic systems.
Due to the unique stabilization setups of piles,
analysis of the interaction of the piles with thesurrounding soils have employed different methods,
both numerically and analytically [8-12]. Some ofthese analyses include El Sawwaf (2006) and (2008)
who use a series of shallow rigid piles to examine theinfluences generated in sandy sloping soils [13,14]
while Chen & Martin (2001), conducted an analysis
based on the finite element method to determine theeffect generated by the installation of piles near the
crest of slopes [15]. Besides, another study by
Georgiadis & Georgiadis (2010), examined theeffects of piles on sloping land under the undrainedlateral loading conditions using FEM and concluded
that the soil adhesion has a significant role to play on
the pile-soil lateral capacity [16].

Fig. 2 Top 20 countries with the highest share ofsolar lighting systems 2017 (Source: IRENA,
International Renewable Energy Agency). [5].
Solar energy tops the list when it comes toavailability, cleanliness, and the abundance in mostparts of the World. As for developing countries withlow economies, solar energy is a viable solution toenergy distribution because most of these countries
have more than nine hours of sunshine per day. Figure
3 below is a burst of sunshine tracking record along
the equator, northern hemisphere, and the southernhemisphere regions that experience at least nine hours
of sunshine throughout the year [7].
Sun path

Earth’s sunlight zonesa) Along theequatorb) Northernhemispherec) Southernhemisphere

Fig. 3 World - Sunrise, sunset, dawn and dusk times,(Source of Sunrise, sunset, dawn and dusk times
around the World, www.gaisma.com, Accessed2020.03.25.) [7]

Concept of slope mounted photovoltaic systems

With the abundance of solar panels in the
production line, comes another challenge of where to
install them. Flat terrains are a huge attraction whenit comes to the development of engineering projects.
A question, therefore, arises that, what next, when theflat terrain landscape becomes scarce?
An

Developing any engineering structure on slopes is
not a commonly practiced phenomenon due to thechallenges involved and the complexities of the
project design. Nevertheless, such locations can bemade available by conducting thorough analysis and433

GEOMATE – Melbourne, Australia, 11-13 November 2020

were majorly targeting lands with low degrees ofslope angle. Some of the support structures in use
today include the driven piles, screw piles, concrete
piles, and the ballasted piles. Their principal design
considerations focused on the ability to support thestructures above ground with minimum consideration
to the ground response when it comes to extreme
conditions such as those experienced in sloping lands.
The flaws associated with such design led to thedevelopment of different types of foundations to be
used in a single project, as witnessed in most solar
farms. These assemblies of different support structurearrays have a higher risk of inducing an imbalance inthe ground in-situ parameters hence failure more so ifused in sloping land.

understanding of the required slope parameters to
support any structure imposed on them, including inthis case the ground mount solar photovoltaic systems.
Figure 4 represents a ground-mounted photovoltaic
system on slopes.
This engineering concept involves the use of
single piles to locate the solar panels on sloping landhence making use of somewhat an idle land. Since theslope is already at an angle to the sun, the amount ofsolar irradiance to the solar cells is improved, andhigher energy tapping efficiencies achieved. This
technology tackles two engineering problems, i.e., .to
use the installed piles to act as the ground stabilizers
to curb ground slip and to provide green energy to theconsumers nearby.

Photovoltaic mounting systems
Solar Panel frame

The mounting systems component is very crucial
for any photovoltaic installation. In most cases, theuse of aluminum frames has been practiced due tolightweight and quality while ensuring strength. A
quality mounting frame should attach easily to thefoundation structure and should be able to hold a goodnumber of the photovoltaic modules over the area
specified. [22-27]

Slip
surfaceAnchor depth

Slope baseFoundation

Slopping land

Solar photovoltaic modules

Fig. 4 Slope mounted photovoltaic support structures

These are the main energy tapping components ofthe solar farm. The module consisting of photovoltaic
cells use sunlight as the energy source to produce
direct current. To this time, most of the photovoltaic
modules consist of wafer-based crystalline siliconcells or thin-film cells. With the hiking progress on
photovoltaic
system developments, detailed
researches on various kinds of modules depending on
the prevailing environmental conditions such as
shading, dust accumulation, heat dissipation,
performance
characterization,
temperaturedependence, to improve on efficiency have been
carried out [28-33]. Non the less more evaluations
have also been done considering the cost andperformance criterion [34].

Slope mount design components
Foundations
In this field of engineering construction, a few
companies have been able to come up with solar
farms on sloping lands due to numerous factors thatarise into play when it comes to the viability andstrength of the foundation. Hence a comprehensive
evaluation based on the slope stability, ease of
installation, and ultimate pile bearing capacity need
to be done. All the previously stated researches, bringabout an important parameter that needs more
attention called lateral and axial capacity. The lateraland axial capacity of piles, both shallow and deep,
requires an in-depth understanding of the interactionof the pile and the surrounding soils to be able toevaluate the ultimate bearing capacity [17-19]. To
further understand the interaction of the piles and thesurrounding soil medium, numerical solutions havealso been used in past researches to illustrate how theground mount photovoltaic support structures
responded during both lateral and axial loading. A
seamless example was the use of civil engineering
software FLAC2D incorporating the Finite
Difference Method to simulate the foundation
response and the ultimate foundation bearing capacity[20,21].
Also, in this specific field of support structures inthe solar industry, most of the foundations involved

Cables, Batteries and Inverters
Once the solar photovoltaic modules are setup,cables need to be then connected to transfer the power
to inverters and batteries. While the inverter housing
can be built further away on the well-leveled ground,the cables from different photovoltaic arrays must beconnected and aligned along the slope length to theinverter housing. Batteries, on the other hand, areused to store the direct energy generated for use in theabsence of the sun.
Slope mount design aspects

434

GEOMATE – Melbourne, Australia, 11-13 November 2020

Site topography

In addition to the earth forces, most engineering
structures exposed to outward forces that may
experience detrimental effects on the structures if notadequately incorporated into the design loading
calculations. These loadings include incidences suchas strong winds (Typhoons), cyclones, and seismic
forces from earthquakes and landslides. For thisreason, slopes are susceptible areas, and a detailed
analysis is essential to examine the static and dynamic
response upon photovoltaic structure inclusion.

Before commencing the development of solar
panel ground mount structures, survey procedures arenecessary to establish the critical ground features forthe design to be viable. Some of the features checkedin this case include the slope angle, the slope
orientation to the sun, and if the geometry of the
proposed project area can accommodate thephotovoltaic modules according to the projected
power output.

Existing solar farm on sloping land
Foundation depth and Pile length
As can be seen previously, constructing solar
farms on sloping land is no easy feat. Nevertheless,there have been successful construction projects asinvestigated next. The first large-scale solar power
plant, known as the Dream Solar Higuchi Nagasaki isin Togitsucho, Nishisonogi-gun, Nagasaki Prefecture,and constructions were completed on September
2015 by the Daiwa Lease Co Ltd (Fig.5)

To further understand the phenomenon of the pileand soil interaction, understanding of the pile element
properties and the forces that are induced to the pile
by soil movements is essential. The performance of apile in a slope will be much dependent on thefoundation depth and pile length. The interaction
between these two parameters produces the soil
induced driving forces, the earth pressures, and thereaction forces applied to the unstable sloping terrain.
Hence a thorough design and analysis should be
conducted to investigate their responses on sloping
terrains.
Slope slip surface
The slips surface is the failure plane upon which
the sloping bank is liable to fail when there is an
imbalance on the forces imposed on it. In most
instances, for extreme high slopes, the use of anchors
is practiced to hold the different layers to the failure
plane. Nevertheless, considering the soil strengths insome given projects, pile have been used to performthe same purpose when deeply rooted into the ground.
Deep piles ensure the stability of the ground at higher
slope angles without failure hence increased factor ofsafety of the sloping land as will be discussed in someof the existing structures later in this paper

Fig. 5 Dream Solar Higuchi Nagasaki foundation
designs (Source: Daiwa Lease/Daiwa House
Industry, Nikkei B.P., Photos of Type B Daiwa
Lease/Daiwa House Industry)
This solar power plant has an output of about
15MW and located on the southern slopes of a former
hilly golf course. The initial planning of the solar
power plant took into consideration the types offoundations needed for the solar panels. Since themountain terrain was previously a golf course, theDaiwa Group optimized on the situation and did not
do significant landscaping of the slope. Instead, thecompany only cut down a few trees on-site to arraysolar panels more evenly. Due to the undulating slope
terrains, the company used four kinds of foundations
to strengthen the solar array which includes, use of
the precast concrete ballast blocks for the flat areas,
precast blocks with ground anchors for areas withhigher slope degrees, rock anchors for areas where theslope was even more than the later and piles.
Ultimately a technique was used to hammer in thepiles while crushing the bedrock to ensuring excellent
grip at the soil pile interface. Subsequently, the panel

Ground drainage patterns
Groundwater discharge capability is anotherimperative factor worth considering when
undertaking construction on slopes to ensure proper
water flow and liquidity. An increase in the waterpressure in the soil is the number one contributor toslope failure. Besides, solar panel shadowing effects
may induce the differences in the water retaining
abilities of the soil as compared to the in-situ state dueto the influence on zones for water infiltration.
Therefore, in the design of solar panel farms,
shadowing effects on-site drainage should beconsidered.
Loading patterns

435

GEOMATE – Melbourne, Australia, 11-13 November 2020

assemblies were attached to the foundations while
maintaining the lowest possible eccentricity todampen all the moments that may arise when acted
upon by outward forces [35,36].
Next, taking a look into the Exeo Matsue Solar
Farm, which was constructed by Kyowa Exeo Corp,
provides us with an understanding of pile foundations
construction. This solar farm has a capacity of 2.3
MW and is located in Matsue City, Shimane
Prefecture, as shown in figure 6.

The H-shaped prestressed concrete piles were set up
at equal intervals bellow the proposed array area thendriven into the ground. Next, the lateral piles, set up
to run parallel to each other, supported the mounting
structures. For this foundation and superstructure set
up, the tolerance was more robust because the lateral
H-shaped piles were attached inside the ground by thelongitudinal piles hence transmitting all the loads to
the ground. The solar power plant location aerial view
and a representation of the foundation set up are asshown in figure 7 [38].

Fig. 6 Exeo Matsue Solar Farm H Pile foundations
assembly (source: Kyowa Exeo)

Fig. 7 Ashigara Oi Solar Way H-shaped P.C. laterals
and longitudinal piles (source: Japan Asia Group)

The support structure consisted of a reinforced
concrete pile inserted at a depth below the ground andused as a post above ground. The type of reinforced
concrete pile used was called the H Pile, a Shinei
Tech Co Limited concrete secondary pile product.
Since the H pile could adapt quickly to the sloping
terrain, the strength further improved by the inclusionof the mounting systems that allowed for ease of
attachment of the solar panels above by anchorage
into the concrete column. This installation was anefficient setup since the slope was facing the southern
direction hence direct nominal irradiance andconsisted of 1,900 concrete pile driven into the slope
covering a rented area of about 25,000m2 [37].
Also, Kanagawa prefectures largest solar plant,
Ashigara Oi Solar Way is a perfect example of a solar
plant located in a hilly environment. This plant covers
a former testing ground site from Komatsu Limited.
With an output of 13MW, the solar plant is a perfect
example of how idle lands can be converted and madeuseful by setting up renewable energy power
generation plants. The joint effort between Toko
Electrical Construction, Kumagaigumi Co Limited,
and Aizawa Concrete Corp (Tomakomai City,
Hokkaido) came up with a strong foundation thatwould withstand the weight of the solar panel arrays
and also taking care of the sloping ground challenges
involved at such locations uphill, i.e., slope stability.
As a result of the joint venture, a multipurpose highintensity concrete H-shaped prestressed concrete
piles were used as the foundation structure and as thelaterals on which the mounting structures attached.

Fig. 8 Sakura-no-Sato Mega Power Plant (source:
Toda Corp)
Besides, Figure 8 is a mega solar power plant that wasconstructed at the hills north of Nagasaki Fishing Port
on a steep south-facing roadside slope by
Sakuranosato Mega Power. This solar power planthas 2.141MW output and is known as the
Sakuranosato Mega Power Plant, which started
operations in October 2015. The main challenge
when constructing this solar power system was theangle of the slope, which was 30⁰, a very steep slope.
As a result of this steep slope, all construction works
operated almost no heavy machinery. In the middle ofthe solar power plant, there was an access road thatran east-west, used by heavy trucks and cranevehicles needed to load or offload heavy materials.
Furthermore, a monorail was set up from bottom to
top to transfer construction materials. Due to the
topography factor, a new type of mortar application
method involved casting the mortar into a cubic frame
436

GEOMATE – Melbourne, Australia, 11-13 November 2020

molded by the four corners of the driven steel frames.
The array mounting structures were then bolted on
top of the concrete cube foundations as shown infigure 9need evaporation control measures since they may besupplying water for other purposes, for example,
agriculture and household use. Locating the solar
panels over the water bodies have also proven to
improve the panel efficiencies due to the cooling
effect in the immediate surrounding [40-42]. Also,
another motivating factor is on the matter of landownership and how it can be solved to avoid the usageof private land for the development of solar energy
farms. Since land ownership in most parts of theWorld is considered personal property, thedevelopment of projects that need vast parcels of landin the populated zones always tends to halt due to theunreadiness of the landowners to give out their lands.
Consequently, more focus away from the land area isneeded, and preference developed for the installationof the power plants further offshore.
The vast areas offshore, with minimum
shadowing, have also contributed to more focus on
the methods essential to make the offshore farms
more productive due to the increased sunshine hours.
A study to analyze the effects of temperature on theperformance of solar panels was first done by the
National Institute of Advanced Science andTechnology in Japan [43] and funded by the Japanese
Ministry for the Environment [44]. As a relatively
new field of research, further research should be
conducted to ensure proper design, and show thatthese photovoltaic farms are economical andenvironmentally friendly.

Fig 9 Sakura-no-Sato MPP, Casted mortar
foundations with steel mounting frame attachments(source: Toda Corp, Nikkei B.P.)
With such high slope angles, came an additional
challenge when it comes to maintenance. To curb thischallenge, the company opted to use a woven weed
barrier to discourage weed growth on the site hence
reduced weed management [39].
Ultimately, for all the power plants constructed on
the slopes, water flow was given a significant priority
to prevent the imbalance in the soil moisture content
due to the added ground cover. To achieve natural
water flow along the slope, drainage channels
constructed downslope met with the area drainage
network for efficient disposal. The next section of thisreview will focus on the installations of solar
photovoltaic arrays offshore.

Concept of offshore photovoltaic systems
Offshore installations of photovoltaic systems
being a new venture in the solar energy industry,
several installation methods are available to ensure
the viability of the projects. One of the major
contributing factors in offshore farm economics is theability to keep the arrays further offshore without any
potential damage incurred for the longest timepossible. This durability concern, therefore, brings in
the key challenges, i.e., superstructure foundation
design, mounting structure design, and locating of theinverter housing. The new concept makes use of thebuoyancy conditions created by the floats to keep thepanels afloat and, in some cases, track the movementsof the sun for efficient exposure of the panel surfaces.
Another incentive of the offshore photovoltaic
installations is the proper use of the vast areas
available for power generation hence high energy
yield capabilities.

OFFSHORE
Most countries worldwide enjoy a vastavailability of waterfronts through waterways,
abandoned quarries, lagoons, lakes, rivers, and a longcoastline with seas and oceans. Therefore,
considering the communities living along thesewaterfronts, and the distances from the national
electricity grid, the development of the offshore solar
farms can be beneficial in tackling the energy
challenges faced by the citizens by introducing minigrids from the solar energy generated in such
localizes areas. Another factor that makes thewaterbodies highly reliable for the solar farminstallation is the presence of vast areas without
shadowing effects from nearby built environments
hence the high direct nominal irradiance (DNI). Since
these areas also experience high rates of solar
radiation throughout the year, the shadowing effectcreated by the vast areas of the solar panels can also
lead to water conservations on the water bodies by
inhibiting the extra exposure to the direct heat fromthe sun hence reduced water loss throughevaporation. i.e., concerning the water bodies that

Components of offshore photovoltaic systems
The figures 10a and 10b show the crucialcomponents of the offshore photovoltaic systems
which are;
The foundation - Also known as the mooring system
437

GEOMATE – Melbourne, Australia, 11-13 November 2020

upon which the panels permanently attach to achieve
the desired strength. The main aim of the foundation
structure is to keep the structure afloat in a fixed
position, never to be swayed away by the waves in thewaterbody under consideration [45].
Buoyancy floats - These are the structures on which
the photovoltaic arrays attach to keep them floatingon the water surface, as the name depicts. They
constitute high-density poly-ethylene blocks, andtheir arrangement on the water surface is mostly
dependent on the array geometry and the power
output required for a given project.
P.V. arrays and cables and inverter housing These are the central energy tapping systems andpower transmission equipment, respectively, as
explained in the slope section above.
Water
level

Wire
pane

Floater
Solar
PanelsService
pane

Bolts

Cable
Plate
Anchorearthquakes and water waves. This new concept ofsolar arrays attachment is most suitable for all water
depths. The mitigations factors put in place include
the investigation of the effects of corrosion, durabilityof the floats, design ultimate capacity, projected
electricity output, and the ease of installation. Since
the offshore farms are developing rapidly, mostdeveloping bottom anchorage installations areevident in countries like Japan, Fig 11 [46], France
Fig 12 [47], South Korea [48], United Kingdom [49],
United States [50], with more recent construction
works in the United Arab Emirates [51].

Cable
Sea
Bed

Fig 11 The Yamakura Dam plant (Source Kyocera
Corp)
Fig. 10a A completely offshore solar farmcomponents.
Offshorepile
Cable
floatsFloater

Solar
Panels Service
pane

Bolts

Cable

Sea bedEnd ballast

Fig. 12 Piolenc in Southern France. (Source AFP)
Land-fixed offshore solar farm foundation

Fig. 10b Land-fixed offshore solar farm foundation
components

This foundation concept involves the combinationof a pile foundation onshore and a cable attachment
to transfer the assembled solar panel arrays further
offshore. Panels are assembled on land and moved
offshore, depending on the required capacity. The
cable system dominates this construction assembly
due to its robust strength factors, and it allows formuch less material usage in the foundation structure.
Also, this combination employs the use of anti-tideballasted floats for superstructure stability. The
ballasted floats reduce the wave action on the panel
area during the fluctuation in the wave strength andwave direction. An excellent example of such afoundation is in the construction of the 145 MW
floating photovoltaic plant built on a 225ha section of
the Cirata Reservoir in West Java. Indonesia. Thismethod of land support utilizes the vast archipelago

Existing offshore foundation Design
Some of the existing offshore photovoltaic arrayfoundations design include the following;
Bottom anchored foundations
In the construction field, a good number of the
fixed bottom foundation structures are into practice.
In this scenario, the review paper draws the focus on
the use of the bottom anchoring system for theattachment of the solar panel arrays offshore. This
foundation type consists of the assembly of a bottom
anchor placed at a design depth below the seabed. The
anchor dampens all the lateral forces subjected to thesolar panel array at the water surface, more so
438

GEOMATE – Melbourne, Australia, 11-13 November 2020

present in the region. Thus, supporting foundation
structures, as shown in figure 13 [52].

of cross-cutting techniques that have implemented totap into solar energy at all locations, both on slopes
and offshore. Furthermore, more research on design
charts for photovoltaics installation on slopes isneeded as well as to fully understand the effects thatthe solar panels have on overall stability to the slope.
As for the offshore structures, the review paper showsthat this new installation method is the right way ofsaving vast areas of land to be used for other purposes
while also improving on the photovoltaic arrayefficiency due to the cooling effect from thesurrounding waterfronts. For this reason, further
research is called for when it comes to design if theoverall efficiency in installation and electricity
production needs to be realized, not forgetting thestrength and durability of the structure. Since thetechnology readiness of solar energy is high andattractive now, scaling up its potential isindispensable

Fig. 13 145 MW of floating P.V. in Indonesia.
(Source Hanwha Q Cells)
POWER TRANSMISSION AND STORAGECONSIDERATIONSWith efforts to increase the area covered by thesolar panels to tap the abundant energy source, themain challenge is energy storage in the night. The
solar energy research field has led to the innovationof new ways to store energy in lithium batteries andthrough direct feed into the grid straight from thesolar farm. A comprehensive assessment is necessary
to determine which method of transmission suits best
the end-user, more so if the energy generated is to bereadily available to those parts that are isolated fromthe main grids. In the case energy produced exceeds
the capacity of a given area, then a link to the primarygrid should be included to supply the excess energy.
As researchers in this field of renewable energy, thisreview is a solution-based projection analysis of thepossible ways to curb energy demands. Figure 14
below illustrates an energy storage and distribution
network that best suits the isolated grids to ensureround the clock energy provision to the target
customers.

FURTHER RESEARCH GOALSThis review depicts a state that allows forknowledge expansion and for improving existing
technologies when it comes to foundation design andthe developments related to support structures ofgreen energy sources. With the prices of solar panels
reducing over time, the need to facilitate theirinstallation is scaling in size, and alternative locations
to develop the farms are necessary.
This paper brings to light that solar panels on
slopes are a great way to use sloping land that would
otherwise be left idle effectively. In addition to this,the offshore foundation design is necessary to makeuse of the vast areas with maximum direct nominal
irradiance (DNI) as a result of zero shadowings andobstruction. After reviewing the current industry
practices, several pieces of research questions wereraised for an answer in the future regarding the effectof pile and plate anchor installation on the overall
stability of the slope and offshore power projects,
respectively. Also, the reduction of pile and plateanchor capacity to the lateral loading and pull-out
force is the critical research items in the next stage ofthis research. This paper has also highlighted the needto bring industry partners and government bodies tojoin the research program to develop new measureswhen it comes to venturing into renewable energies
without boundaries or any hindrance whatsoever. The
research questions that this review paper aims ataddressing and expounding on in the next stage of thisresearch are;✓ What are the best foundation types for solar
panels on slopes?
✓ What are the best foundation types for solar
panels on nearshore and further offshore?
✓ What is the maximum or optimum slope angle
for solar panels on slopes?

DC Power
StorageTransformer

Inverter
House

Household
usage

To
Main
Grid

Fig. 14 The power transmission and storage network
CONCLUSION
From this review, it is evident that we have a variety
439

GEOMATE – Melbourne, Australia, 11-13 November 2020

✓
✓
✓
✓

✓
✓

Rural Communities.’” August 1, 2018.
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What is the optimum depth of anchors and cable
assemblies required for efficient foundation
stability?
What are the influences of lateral capacity andpull-out capacity of the pile in slope and anchorsin offshore scenarios, respectively?
What is the factor of safety before and after the
installation of solar panels on slope and plateanchors in offshore scenarios?
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as well as that of the plate anchor that would notbring in adverse reduction to the factor of safety,
FoS?
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panels installed? Is there a difference?
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economically viable when it comes to theconstruction costs and ease of installation?

Therefore, fundamental research is mandatory onthese emerging support structures to evaluate theirperformance, efficiency, and design cost, as will beconceptualized and put into the design charts in thenext task of this research process.
ACKNOWLEDGMENTS
The first author and second author would like toexpress their gratitude to the Japanese Government
through MEXT and Japan International Cooperation
Agency (JICA), respectively, for providing
scholarships to study and research at Mie University,
Japan. Much gratitude to the School of Civil
Engineering and Surveying, University of Southern
Queensland, Queensland, Australia, for the continued
support in matters of research in the civil engineering
field.
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442

Environment443

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE DYNAMICS OF LAND COVER CHANGE AND CAUSAL
FACTORS IN THE KURANJI WATERSHED
Teguh Haria Aditia Putra1, Bambang Istijono2, Aprisal3, Bujang Rusman4 and Taufika Ophiyandri5
1
Doctoral Program in Agricultural Science, Andalas University, Indonesia
2,5
Department of Civil Engineering, Andalas University, Indonesia
3,4
Department of Soil Science, Andalas University, Indonesia
ABSTRACTLand cover changes (LCC) are one indicator of an increase in population in a watershed area that demands
the provision of housing locations, road infrastructure, and productive land to meet the needs of life. LCC canhave a positive impact in the form of increased land use values and incomes, while also negative impacts in theform of erosion, landslides and floods. The watershed area should have a balance in its development. This
research is important as a first step in overcoming changes in land cover that can threaten the sustainability andsustainability of the Kuranji watershed. This study details LCC in the Kuranji watershed from 1985 to 2018 andthe factors causing these changes. The methodology used is descriptive qualitative with 60 respondents. Data
were obtained by interviewing respondents. Respondents are those having agricultural land and working in the
agricultural sector and this research was carried out in 2019. The results showed an increase in the type of land
cover of settlements and mixed gardens, while forests, rice fields and shrubs have decreased. The main factor
influencing changes in land cover is economic (59%), followed by social factors (25%) and politics (16%). It
was also found that local wisdom of the Minangkabau culture was able to reduce LCC. The local wisdom of the
Minangkabau culture explains that land owned by the community, which is a hereditary inheritance, should not
be traded without very important reasons.
Keywords: LCC, ArcGIS, Descriptive qualitativearea.
The change in the Kuranji watershed is very
dynamic because it is in an urban area. LCC in the
Kuranji watershed is also triggered by the growth oftertiary education areas. The development of thiseducation area has triggered the community toprovide infrastructure to support educational
activities such as student boarding houses, foodstalls, photocopy stalls and others. This makes thearea around the tertiary education a satellite city.
LCC in the Kuranji watershed have increased
quite rapidly in the past 10 years. This change is dueto the occurrence of a large earthquake in Padang
City on 30 September, 2009, having the potential fora tsunami. Since the earthquake, some people who
lived on the coast have moved to the central Kuranji
watershed, which is a tsunami safe zone. The moveof coastal communities in the central part of theKuranji watershed was also encouraged by theprediction of the Mentawai Megathrust earthquake
accompanied by a tsunami.
LCC in the Kuranji watershed are very
dangerous, especially in the upper and middle
watersheds. This is because the Kuranji watershed
has a steep topography of 54.66% of the watershedarea. Judging from the shape of the watershed, theKuranji watershed has a shape that extends near themoon in the upper reaches. This form explains that
the Kuranji watershed has a great potential forflooding if vegetation cover is reduced and does not

INTRODUCTION
Land cover in the watershed is always changing.
This change is a major issue in controlling thesustainability of watershed areas. This change
becomes very dangerous if it occurs in the upstream
watershed, which is a protected area andconservation area. Changes in land cover have an
influence on the carrying capacity of watershedareas if done on a large scale. Changes in land covercan causelandslides,
floods and otherhydrogeological disasters.
Land cover changes (LCC) with increasing
population. The higher the population, the landneeds are also increasing. The increasing need forland must be accompanied by soil and water
conservation techniques so that the impact caused bychanges in land cover can be minimized.
One of the watersheds undergoing land coverchange is the Kuranji watershed. This watershed has
an area of 22,469.55 hectare located in Padang City.
Statistical data show there has been an increase inpopulation in the Kuranji watershed, namely in theDistricts of Kuranji, Pauh and Koto Tangah in thepast five years. The increase was 60,441 people,
with a population growth rate of 2.28 - 2.83% [1].
The high rate of population growth triggers land
conversion that aims to meet the needs of life.
Examples of these changes are forest area into
agriculture, and agricultural area into a developed444

GEOMATE – Melbourne, Australia, 11-13 November 2020

function as a soil protector. More details about theslope of the Kuranji watershed can be seen in Fig. 1.

respondents.
In technical data collection, the researcher asks
questions to the respondent related to the purpose of
the study and the respondent answers them. Dataneeded from respondents are divided into threefactors. 1). Economic factors, including community
income, land value, development of education
centers. 2). Social factors, including population
growth, land ownership, environmental knowledge,
agricultural land needs. 3). Political factors,
including the high cost of living and weak law
enforcement.
After obtaining research data, tabulation of thedata is based on economic, social and politicalfactors to obtain a percentage value. Then, aqualitative descriptive analysis is performed.

36,94 %

9,93 %
32 %

17,72 %
2,74 %

Fig. 1
Slope
Watershedconditions

in

the

Kuranji

RESULT AND DISCUSSION

The purpose of the study was to analyze LCC
Kuranji watershed in the last 33 years as wellknowing the factors causing changes in land cover.

Land Cover in 1985
Land cover in the Kuranji watershed in 1985 was
obtained by conducting spatial analysis using a
topographic map in 1985. Land cover is grouped
into five types. The land cover is classified into
forests with an area of 13,242.54 hectare,settlements of 1,507.54 hectare, paddy fields
4,131.67 hectare, shrubs 2,064.41 hectare and mixed
gardens 1,523.39 hectare. The complete land coverof the Kuranji watershed in 1985 can be seen in Fig.
2.

MATERIALS AND METHOD
Materials
This research was conducted at the Kuranji
watershed in Padang City, West Sumatra, Indonesia.
It has a maximum height of 1860 meters above sea
level with annual rainfall of more than 5,054 mm.
Based on geographical position, the Kuranji
watershed is located between 100021'18,84" E 100033'52,87" E and 0047'23,36" S - 0056'13,71" S.
Determination of LCC was by using a 1985
topographic map with a scale of 1: 50,000 and SAS
Planet (Arc-GIS Imagery) satellite imagery in 2018with a spatial resolution of 20x20 meters. The tools
used are GPS and ArcGIS 10.6. Information
gathering factors that cause changes in land cover
was by open interview techniques.
MethodThe methodology used in determining land coverchange is to perform interpretation and delineation
analysis on each map year using ArcGIS 10.6. Then,an area value analysis is performed on the attributetable, so that, every year, the map will contain thevalue of each land cover and the factors that supportchanges in land cover determined using descriptive
qualitative analysis methods.
This research was conducted from July 2019 toNovember 2019. Data were collected throughinterviews with respondents with a purposive
sampling technique. Requirements to be a
respondent are people who have agricultural landand people who work in the agricultural sector. The
respondent sampling locations are people who livein the middle and upper Kuranji watersheds with 60

Fig. 2 Type of Land Cover 1985 in Kuranji
Watershed
Land Cover in 2018
Based on the results of the interpretation ofsatellite imagery in 2018, land cover in the Kuranji
watershed is grouped into five types. The land coveris a forest with an area of 12,114.12 hectare,settlement 4,084.62 hectare, rice fields 2,935.98
hectare, shrubs 747.61 hectare and mixed gardens
2,587.22 hectare. The land cover of the Kuranji
watershed in 2018 can be seen in Fig. 3.
LCC in the Kuranji watershed that occurred in445

GEOMATE – Melbourne, Australia, 11-13 November 2020

impact on the increase in flood inundation areas.
In addition to reducing land cover, several typesof land cover in the Kuranji watershed have also
increased. The increase was experienced by mixed
garden land cover of 1,063.83 hectare andsettlements of 2,577.08 hectare. The high change inland cover is due to the high rate of population
growth. Changes in land cover in the Kuranji
watershed have increased since 1985 and in 2009
after the earthquake that hit Padang City. Moredetails on differences in LCC rom 1985 - 2018 canbe seen in Fig. 4.

1985 to 2018 were dominated by the reduction offorest area by 1,128.42 hectare. Forest areas turned
into mixed gardens and shrubs. Paddy fields also
experienced a reduction of 1,195.69 hectare. Most of
the changes in rice fields were into settlements.
Shrubs also experienced a reduction of 1,316.8
hectare, turning into mixed gardens, rice fields andsettlements. Reducing forest areas or vegetation
areas to other areas will have an impact on theenvironment, including erosion, flooding andreduced groundwater supplies.

Fig. 3 Type of Land Cover 2018 in Kuranji
Watershed

Fig. 4

LCC 1985-2018 in Kuranji Watershed

LCC to land that does or does not lack vegetation
has an impact on the amount of erosion, surface
runoff and flooding. [9] mentioned that changes inforest cover result in critical land and have an impact
on landslides, especially on steep slopes and exceed
40%. [10] One such impact is caused by the loss ofvegetation (plant roots) in protecting the soil fromthe danger of soil erosion. It was also added by [11]that changes in land use into built up areas would
have an impact on changes in flow characteristics,
flooding in the rainy season, decreasing riverdischarge and drought in the dry season.
The topography of the Kuranji watershed in the
middle and upstream is quite steep. This area haschanged forest cover through illegal logging. This
type of forest land covers turns into shrubs, mixed
gardens and agricultural crops. This area is a
protected forest and conservation area and theprotection of this area aims to maintain the quality of
the watershed, such as physical land, water
management and biodiversity. For more details onchanges in types of forest land cover due to illegal
logging in protected and conservation areas (Fig. 5).
Fig. 5 explains that land cover conversion
converts forests into shrubs and agricultural crops.
Protected forests have changed by 118.17 hectare(4.1%) and conservation areas by 16.74 hectare(0.18%). In other areas of use, there was a change inland cover of mixed estate gardens to become shrubs
and agricultural crops by 63.1 hectare (0.6%).

The impact caused by the reduction of forest areaor vegetation area has also been conveyed by several
experts. According to [2], the decrease in vegetation
cover in the watershed area by 91% caused erosionof 7.2 to 7.7 tons / hectare / year. According to [3],an increase in forest area can increase the quality
and quantity of water, while [4] added that forest
areas were better able to increase water fill fromground level to aquifers compared to agriculturalland.
Not only LCC, high and intensive agriculturalactivities in land management in the Kuranji
watershed can reduce environmental quality. Thiswas stated by [5] that intensively cultivatedagricultural and horticultural land resulted in soil
degradation. Based on the opinion of the experts
above, it can be concluded that maintaining forest
cover and vegetation cover can reduce erosion,
surface runoff and flooding.
The fact that has occurred in the Kuranji
watershed is that forest areas that are in protected
and conservation areas have begun to decrease andsettlement areas are increasing. Reduction of forest
area results in low infiltration and high surface
runoff resulting in flooding. [6] added that theKuranji watershed often results in flooding due to
low infiltration while [7] reported that changes inland cover from natural land use to anthropogenic
have increased surface water. This has a big impact
when accompanied by very high rainfall intensity.
[8] stated that the reduction in vegetation had an
446

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 6. The dominant factor causing changes in land
cover in the Kuranji watershed is economic with avalue of 57%. These economic factors explain thatthe level of income and the number of family
dependents encourage the community to be evenmore active in meeting their daily needs. The
method adopted by the community is to change theforest land cover into mixed gardens and paddy
fields to settlements.

Fig. 5 Illegal Logging in Protected Forests andConservation Areas
LCC have a major impact on soil erosion,
surface runoff and flooding. This change becomesvery dangerous if it occurs in steep sloped areas suchas the Kuranji watershed. The steeper a slope, thefaster the surface flow and the greater potential towash away soil particles. It was also stated by [12]that the slope factor has a great influence on the highsurface runoff and causes flooding.
Previous researchers also conveyed the impact
caused by changes in land cover. [13] stated thatchanges in forest land cover to build up land andagriculture can increase flood discharge.

Fig. 6 Causative Factor LCC in Kuranji Watershed
The high value of land around the tertiary
education area encourages people to sell land andincrease land use value. So what was once planted
agricultural land is converted into built land such asboarding houses, food stalls and shops. All thesechanges are intended for students and the local
community in supporting education and otheractivities. The same thing was said by [15], [16],[17] that changes in land cover will be faster if anarea changes from an agrarian area to an area thatdevelops an economy in the service sector.
Previous researchers have also expressed theiropinions about the factors that cause changes in land
cover. As stated by [18], changes in land cover are
strongly influenced by socioeconomic factors. [19]also stressed that the LCC was driven by economic
development and the development of educational
areas. [20] and [21] added that changes in land coverand increased built up land were influenced byeconomic factors, as apparent in the Kuranji
watershed. This can be explained as that the Kuranji
watershed’s strategic location in the economic field,means it can increase the built area quickly.
The next factor affecting changes in land cover isthe community social factor of 22%. The low levelof income of the community as farmers who live inthe middle part of the Kuranji watershed has led thecommunity to begin to utilize the upstream
watershed area. Small land tenure makes peoplechange forest land cover in the upstream watershedwith the goal is to increase the area of agriculturalland and increase income.
Part of the forest cover is converted into mixed
gardens with durian (durio zibethinus), mangosteen(garcinia mangostana), rambutan (nephelium

The Cause of LCC in the Kuranji Watershed
The condition of the Kuranji watershed area does
have its own attraction in the occurrence of changes
in land cover. This is because the Kuranji watershedis the largest in Padang City, where parts of theregion have economic centers such as the area ofhigher education. The growth of economic centerswill lead to the conversion of land into developed
areas. On a large scale, it will disrupt thesustainability of the watershed. It was stated by [14]that the change of land cover into a residential area
made the Kuranji watershed vulnerable to flooding.
This is caused by damage to the physical propertiesof the soil and has an impact on reducing infiltration.
Based on the analysis of satellite imagery in
2018, changes in the type of land cover from forest
vegetation to mixed plantations and shrubs continue
to occur and threaten the sustainability of the
upstream watershed. The change in land cover is in
the middle Kuranji watershed and toward the
upstream watershed. Generally located in protectedareas and conservation areas.
The LCC explains that there is human activity inthe area that must be protected and the ecosystems
conserved. Thus, it is important to know what
factors are driving the community to make changes
in land cover. More clearly the cause of changes inland cover in the Kuranji watershed can be seen in447

GEOMATE – Melbourne, Australia, 11-13 November 2020

lappaceum), jengkol (archidendron pauciflorum),petai (parkia speciosa), cacao (theobroma cacao),pohon sirih (areca catechu), and pohon jati (Tectona
grandis). In some places that are easily accessible,
people plant serai (cymbopogon citratus), and lime
musk (lat. citrus amblycarpa).
In the Kuranji watershed, the middle part of the
paddy field is converted into a settlement. Shrubs
become mixed settlements and gardens. Changes inland cover are also caused by the high rate of
population growth in the Kuranji watershed, whichranges from 2.28% to 2.83%. In line with theopinion above, [22] added that the increase inagricultural land and population growth mostdetermine the change in land cover. This explainsthat there is a high dependence on forest products inincreasing community income.
The Kuranji watershed community is aware thatchanging forest cover can cause catastrophic floods
and landslides. However, LCC are still being madewith the aim that economic needs continue to be met.
As long as the economic needs have not been met,
the change in forest land cover will continue tooccur. It was also stated by [23] that the change inland cover is strongly influenced by socioeconomic
aspects.
High population growth in the Kuranji watershedalso supports accelerating land cover changes. Thechange is related to the needs of the community for aplace to live and to conduct economic activities. [24]states that an increase in population also has an
impact on increasing changes in land cover.
An interesting factor of community attitudes
toward changes in land cover is also influenced by
Minangkabau culture. This culture explains thatpeople who own customary land can only use it andit cannot be traded. Utilization is for agriculturalland, community plantations, mixed gardens and asmall portion for shelter. The strong culture of
Minangkabau adopted by the community meanschanges in land cover are not so great. [25]explained that local culture can reduce access toforest areas. This has an impact on the reduced
harvesting of forest wood, which causes changes inland cover.
Political factors also affect changes in land coverof the Kuranji watershed with a value of 15%. The
high cost of living resulting from government
policies drives people to try to increase income. One
method is the use of forest areas for agriculture. [26]explained that government policies, population
growth and socioeconomic impact had a large
influence on changes in land cover. [27] also statedthat national and international policies have thepotential for changes in land cover in agriculture.
Weak law enforcement also makes people
uncontrollable in making changes to land cover.

CONCLUSIONSLCC in the Kuranji watershed from 1985-2018
are very high. The biggest change was in residential
land cover with an increase of 2,577.08 hectare(63.1%). Mixed gardens also increased by 1,063.83
hectare (41.12%). Reduction of forest land cover
was by 1,128.42 hectare (8.52%), rice fields by
1,195.69 hectare (28.94%) and shrubs by 1,316.8
hectare (63.78%). LCC have occurred in protected
and conservation areas. This area is a forest area thatshould not be disturbed. Disruption to this area isdue to an increase in population and the necessitiesof people’s lives. The factors causing LCC are
influenced by economic (59%), social (25%) andpolitical (16%) factors. Economic factors are
influenced by low community income, the numberof family dependents and high land values. Social
factors are influenced by population growth,
agricultural land needs, small land tenure andcommunity knowledge and attitudes towards theenvironment. Political factors are influenced by thehigh cost of living and weak law enforcement. One
important finding in this research is that the local
wisdom of the Minangkabau culture adopted by thecommunity can reduce land cover changes.
ACKNOWLEDGEMENTS
We want to express our sincere gratitude to the
University of Andalas for the 2020 Research Grant.
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449

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

CONVERSION OF WASTE STONE-FINE SLURRY INTOGEOPOLYMER CEMENT WITH ADDITION OF COAL FLY ASH

1Takaaki Wajima1 and Say Buntan1
Department of Urban Environment Systems, Chiba University, Japan

ABSTRACTIn the crushed stone production process, waste stone-fine slurry is generated as wastewater, and most of them
are landfilled as dehydrated cake after some treatments. Recently, it is becoming difficult to secure landfill sites,and effective utilization of by-products, dehydrated cake, is desired. On the other hand, a large amount of coal fly
ash was discharged from coal fired power plant, due to the operation suspension of nuclear power plants since the
Fukushima nuclear accident occurs. In this study, we attempted to prepare geopolymer cement from waste stonefine slurry with addition of coal fly ash. Waste stone-fine slurry used in this study was discharged from one of thequarries in Japan. NaOH solution was added into the slurry, and heated at 60 - 180 oC for 0 - 24 h to obtain thehigh Si solution for preparation of geopolymer cement. After heating, the slurry was cooled to room temperatureby quenching with tap water, and various amounts of coal fly ash were added, mixed and cured at 80 oC for 24 h.
The strength and water purification ability of the product were examined. The solution with high Si content to
prepare the geopolymer can be obtained at 120 oC for 9 h in 4 - 8 M NaOH solution, and the products obtained
from 6 - 8 M NaOH slurry solution with addition of 0.55 - 0.70 weight ratio of coal fly ash have higher strengththan other products and Portland cement. The product indicates NH4+ removal ability for wastewater treatment.
Keywords: Waste stone-fine slurry, Coal fly ash, Geopolymer, NH4+ removal, Alkali reaction

more than 0.2 million ton/year of coal ash must be
deposited in landfill. Furthermore, since the Great
East Japan Earthquake on March 11, 2011, the focusof energy production in Japan has shifted from
nuclear power toward coal-generated power. The fly
ash generated at coal-fired power plants was mostly
wasted before the earthquake, more fly ash is
expected to be generated, and the lack of landfill
space is anticipated in Japan. New recycle
technologies for consumption of a large amount ofcoal ash are desired to develop [3-6].
Geopolymer is three dimensional aluminosilicate
based construction and building material, which issustainable and environmental friendly due to low
emission of carbon during their production andprocessing. Geopolymer cement is generally
produced by mixing alkali activator (sodium silicate,sodium hydroxide, water) and various aluminosilicate
precursors such as fly ash and bottom ash [7-12],
clays [13-15], metakaolin [16,17], natural soil [18],glass [19], slags [20, 21], volcanic ashes and rice husk
ash [22-26]. Ordinary Portland cement is mainly
solidified by the formation of needle-like calcium
silicate hydrate (C-S-H), while geopolymer cement ismainly solidified by the polymerization of silicate
ions sparked by metal ions, such as Al3+, Fe3+ and soon, which is similar to the reaction of zeolite synthesis[27,28], and geopolymer cement has high acidresistance, high fire resistance and ion exchange
ability [29-34]. Therefore, geopolymer cement has
attracted attention, and are associated with numerous

INTRODUCTION
In the crushed stone production process, waste
stone-fine slurry is generated as wastewater in large
quantities from crushing, cutting, polishing andcleaning processes, and most of them are landfilled as
dehydrated cake after some treatments, such as
coagulation, sedimentation and filter press. Currently,
it is becoming difficult to secure landfill sites due tothe decrease of the landfill space, and the amount ofdehydrated cake tends to increase. Therefore, further
development of effective utilization of by-products,
such as dehydrated cakes, is desired by laws in Japan,
and there are some researches to reuse the dehydrated
cake for improved soil, roadbed, greening, porous
sintered compact, space filling and so on [1].
Coal as an energy source has an advantage interms of abundant deposits, compared with the otherenergy sources. In 2012, the percentage of electric
power generation in coal power plants in Japanoccupies about 23.4 % to all amount of electric power[2]. By an increase in coal demand, the discharge
amount of coal ash is estimated to be increased.
According to the law relating recycling in Japan, thecoal ash derived from thermal power plant is
especially designated as specified by-product, and theeffective usage of this coal ash is strongly required.
Recently, the reuse and recycle of coal ash are tried
to proceed aggressively, and the percentage andamount of effective usage in 2014 reach about 98 %and 9.4 million ton/year, respectively. However,450

GEOMATE – Melbourne, Australia, 11-13 November 2020

international patents as well as a number ofcommercial engineering project.
In our previous studies, zeolite can be synthesizedfrom green tuff stone cake by NaOH reaction, withextracting high content of Si from the cake [35].
Furthermore, geopolymer cement can be prepared bymixing alkali fused stone dust, water and coal fly ash
due to the reaction between soluble contents of Si andAl from the fused cake and coal fly ash [36]. There is
the possibility to produce geopolymer cement fromcoal fly ash and the slurry by converting the slurry
into alkali activator using alkali reaction.
In this study, we attempted to prepare geopolymer
cement from the waste fine-stone slurry and coal fly
ash. The alkali activator, sodium silicate solution, wasprepared from the slurry by treating with NaOH, andthe prepared activator was mixed with coal fly ash toform geopolymer cement. The conditions, such as
NaOH concentration, heating temperature, heatingtime, and mixing ratio of coal fly ash, to prepare thegeopolymer product with the hard structure were
investigated and the water purification ability of theobtained geopolymer cement was evaluated.

Na2O
K2O
SO3

●: Quartz (SiO2)▲: Albite (NaAlSi3O8)■: Talc (Mg3Si4O10(OH)2)

Intensity (a.u.)◆: Clinochlore((Mg, Fe, Al)6(Si, Al)4O10(OH)8)

▲
●

◆■◆

0

▼
▼▲
▲
▲
◆ ◆

▲▲
◆◆▲▲
◆◆

●

★: Mullite (Si2Al6O13)●★
★ ● ★ ★★★ ★★
★

(b)

Sample

10
20
30
40
50
2θ [CuKα (degree)]60

XRD patterns of (a) fine stone in slurry and(b) coal fly ash.

Preparation

Waste fine-stone slurry generated from one of thedomestic crushed stone companies in Japan was used
for the experiment. Concentration of fine-stone inslurry is 928 g/L, and the diameter of fine stone isunder 250 μm. Coal fly ash used in this study wascommercially available fly ash type V. The chemicalcompositions and mineralogical compositions of thefine-stone in the slurry and coal fly ash are shown in
Table 1 and Figure 1, respectively. The fine slurry
mainly composed of SiO2 (61.7 %), Al2O3 (19.6 %)and some minor elements in the form of quartz (SiO2),
calcite (CaCO3), talc (Mg3Si4O10(OH)2), albite(NaAlSi3O8), anorthite (CaAl2Si2O8), muscovite(KAl3Si3O10(OH)2), clinochlore ((Mg, Fe, Al)6(Si,Al)4O10(OH)8) and so on. Coal fly ash also mainlycomposed of SiO2 (55.0 %), Al2O3 (20.8 %) and someminor elements in the form of quartz (SiO2) andmullite (Si2 Al6O13) with amorphous phases indicating
the broad hump of 20 - 30 o.

Silica solution
Sodium silicate solution for making geopolymer
cement was prepared from the slurry with addition of
NaOH. NaOH solution (5 mL) was added into theslurry (10 mL) to prepare 15 mL of the slurry with 2- 8 M NaOH concentration in a 50 mL of pressure
vessel, and heated at 60 - 180 oC for 0 - 24 h in electric
furnace. It is noted that rising time to setting
temperature is 30 min. After heating, the vessel was
quenched with tap water, the solution was filtered,and the concentration of Si in the filtrate wasmeasured using atomic absorption spectrometer(AAnalyst 200, Perkin Elmer).
Geopolymer cement
Geopolymer cement was prepared from the slurry
treated with NaOH on various condition. Coal fly ash
was mixed with the slurry obtained on various
condition at the mixing weight ratio of ash / slurry =0.25 -0.75, poured into a mold (φ25 mm × 5 mm), andset in an oven at 80 oC for 24 h. After heating, thehardness of the product was judged with a bamboo
needle to insert into the product. The hardened
products were subjected to strength tests using astrength tester (ZT series ZTA-500N, IMADA). The
sharp point attachment (S-3) was inserted into thesample at a speed of 30 mm/min to measure thestrength when the sample broke. Strength tests for the

Table 1 Chemical compositions of fine stone inslurry and coal fly ash.

SiO2
Al2O3
CaO
MgO
Fe2O3

1.0
2.1
1.1

▼: Anorthite (Ca2Al2Si2O8)

Fig. 1Fine stone inslurry
61.7
19.6
2.1
1.4
4.8

●

(a)

EXPERIMENT

Contents (%)

1.6
3.3
1.3

Coal fly ash
55.0
20.8
5.1
0.2
10.7
451

GEOMATE – Melbourne, Australia, 11-13 November 2020

2 - 8 M NaOH concentration at 60 - 180 oC
for 24 h.
Figure 3 shows the Si concentration of the slurry
treated with 2 - 8 M NaOH concentration at 120 oC
for 0 - 18 h. With increasing the NaOH concentration
and reaction time, Si concentration in the slurry
increases, and the slurry treated with 4 - 8 M NaOH
for 9 h contains 20 - 35 g/L of Si within 20 – 60 g/L
of Si for preparing geopolymer cement [37].

ordinary Portland cement were carried out to compare
strength. Portland cement was prepared at watercement ratio (W/C) = 50 %, formed in a mold andcured at room temperature for 7 days.
NH4+ Removal Test
The water purification ability of the product wasestimated for NH4+ removal test. The NH4+ solution
with 900 mg/L, which simulated piggery wastewater,
was prepared using NH4Cl (Wako). The productblock was prepared with a mold of φ25 mm × 5 mm
to remove the mold, and added into 200 mL of NH4+
solution for 168 h. During the immersion of the
product block, the concentration of NH4+ in thesolution was analyzed using thymol blue method formonitoring the NH4+ removal.

Si concentration (g/L)

100

RESULTS AND DISCUSSION
Silicate Solution from The Slurry
Fig.2 shows the Si concentration of the slurry
treated with 2 - 8 M NaOH concentration at 60 – 180oC for 24 h. It is noted that Si concentration for
preparing geopolymer cement is set to 20 - 60 g/L[37]. With increasing the NaOH concentration andtemperature, Si concentration in the obtained slurry
increases. About 60 g/L of Si contains in the slurry
treated with 6 - 8 M NaOH at 140 oC and 180 oC,
while lower than 20 g/L of Si contains in the slurry
treated at 60 oC and 100 oC regardless of NaOH
concentration. In the case of 120 oC, with increasing
the NaOH concentration, Si content increases from 22
g/L to 49 g/L, which are desired contents for
preparing geopolymer cement [37]. Therefore, thetemperature of slurry treatment is set to 120 oC in thisstudy.

Si concentration (g/L)
Fig. 2

o

100 C
o

120 C
o

140 C
o

180 C

40
20

0

2

0

5

10
15
Reaction time (h)20Concentration of Si in the slurry treated with
2 - 8 M NaOH concentration at 120 oC for 0- 18 hWe attempted to form geopolymer product fromthe slurry treated with 4 - 8 M NaOH at 120 oC for 9
h. Figure 4 shows the condition for preparing
hardened product with addition of coal fly ash to theslurry with various NaOH concentration. The
horizontal axis indicates NaOH concentration in theslurry, the vertical axis indicates the mixing ratio ofcoal fly ash, and the plots indicate the condition to
obtain the hardened product without sticking abamboo needle into the product. The hardened
product can be obtained when the mixing ratio of coal
fly ash are 0.55 and 0.70 regardless of NaOH
concentration, and from the slurry with 4 M NaOH
concentration regardless of mixing ratio.

o

0

20

Geopolymer Formation

60 C

60

40

Therefore, silicate solution for preparing
geopolymer cement can be prepared from the slurry
treated with 4 - 8 M NaOH at 120 oC for 9 h.

100
80

60

0

Fig. 32 M NaOH
4 M NaOH
6 M NaOH
8 M NaOH

80

4
NaOH (M)68Concentration of Si in the slurry treated with452

GEOMATE – Melbourne, Australia, 11-13 November 2020

Water Purification

Mixing ratio

0.70

Three product blocks were used for this removal
test. Product-1 was prepared from the slurry with 6 M
NaOH at the weight ratio of coal fly ash = 0.70,
Product-2 from the slurry with 8 M NaOH at theweight ratio of ash = 0.70, and Product-3 from theslurry with 8 M NaOH at the ratio of ash = 0.55.
Figure 6 shows the XRD patterns of the product. All
products indicate almost same XRD patterns
including quartz, mullite, albite and hydroxysodalite(Na6Al6Si6O24‧8H2O). Hydroxysodalite is formed byalkali treatment of the slurry, while quartz, mullite
and albite are originated from fine-stone and coal fly
ash.

0.55
0.40
0.25
4

6

8

NaOH (M)
NaOH(M)

Fig. 4

Conditions for preparing hardened productwith addition of coal fly ash to the slurrywith various NaOH concentration.

Figure 5 shows the strength of the product fromthe slurry with 4 - 8 M NaOH concentration at various
mixing ratio of coal fly ash. It is noted that thestrength of Portland cement sample is 167 N using
this measurement. The strengths of the product are
higher than that of Portland cement and those of the
product from the slurry with 6 - 8 M NaOH indicates345 - 450 N when the mixing ratios are 0.55 and 0.70,
while those are low (5 - 25 N) when the ratio are 0.25
and 0.40.

(c)

●
◇

◇
◇▲ ★
◇★★
●▲▲ ★▲ ★
▲★ ◇▲★★

●: Quartz (SiO2)▲: Albite (NaAlSi3O8)(b)Intensity (a.u.)

500

●
◇

(a)
●

6 M NaOH

Strength (N)

8 M NaOH★: Mullite (Si2Al6O13)

◇ ▲
◇★★
◇▲
●▲▲ ★ ★
★ ◇
★
★★
▲
▲

4 M NaOH

400

0

10

●

◇: Hydroxysodalite(Na6Si6Al6O24・8H2O)◇★
★▲ ★
◇ ★ ◇●▲▲
◇★◇
★▲ ★
▲
▲
★★

300

●

●

20
30
40
50
2θ [CuKα (degree)]60

200
Portland cement

Fig. 6100
00.2

Fig. 50.3

0.4 0.5 0.6
Mixing ratio

0.7

XRD patterns of the products: (a) Product-1,(b) Product-2, (c) Product-3.

The water purification ability of the obtained
geopolymer product was examined using NH4+
solution. Figure 7 shows the NH4+ concentration in
the solution after immersion of the product block. All
products indicate the NH4+ removal ability. In
comparison with NH4+ removal of product when the
mixing ratios of coal fly ash are same, Product-2
obtained from the slurry with 8 M NaOH indicateshigher NH4+ removal than Product-1 from the slurrywith 6 M NaOH. In comparison with NH4+ removalof the product from the slurry with the same
concentration of NaOH, Product-3 obtained at the
mixing ratio of 0.55 indicates higher NH4+ removal
than Product-2 at the mixing ratio of 0.70. Product-3
can decrease NH4+ concentration from 900 mg/L to
540 mg/L for 7 days.

0.8

The strength of the product from the slurrywith 4 - 8 M NaOH concentration at various
mixing ratio of coal fly ash

From these results, the geopolymer product with
sufficient strength can be prepared from the mixtureof the slurry treated with 4 - 8 M NaOH at 120 oC for
9 h and coal fly ash at the mixing weight ratio of coal
fly ash to the slurry = 0.55 – 0.70.

453

GEOMATE – Melbourne, Australia, 11-13 November 2020

[6] Zacco A., Borgese L., Gianoncelli A., Struis R.
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NH4+ concentration (mg/L)

1000
800
600
400
Product-1
Product-2

200

Product-3

0

0

50

100

150

200

Time (h)Fig. 7

NH4+ concentration in the solution after
immersion of the product block.

CONCLUSIONS
We attempted to prepare geopolymer cementfrom the waste fine slurry and coal fly ash via alklai
reaction. The silica solution for preparing geopolymer
cement can be prepared from the slurry by treatingwith 4 - 8M NaOH at 120 oC for 9 h, and the hardened
geopolymer product can be formed by mixing withcoal fly ash (the mixing weight ratio = 0.55 - 0.70).
The product obtained from the slurry treated with 6 8 M NaOH by mixing with coal fly ash (the mixing
ratio = 0.55 - 0.70) indicates higher strength thanordinary Portland cement, and has the NH4+ removal
ability. These results suggested that waste slurry canbe converted into geopolymer cement with sufficientstrength and water purification ability by alkali
reaction and mixing with coal fly ash.
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455

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SALINITY PREDICTION USING DYNAMIC PARAMETER OF
THAILAND’S BANG KRACHAOUma Seeboonruang1, Pinit Tanachaichoksirikun1 and Uba Sirikaew1
1

Department of Civil Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology
Ladkrabang, Thailand
ABSTRACTInsufficient fresh water is one of the many factors for domestic, agriculture, and environment problems. These
problems are affected by seawater intrusion, increasing trend over the time. In the global scale, seawater intrusion,
the uncertainty of climate, and water tide are the major problem of surface and ground water, especially, zone ofdelta. The natural factor is not the only factor, the human activity is also, for example, processing of hydraulicstructure, land use and forcing of water for living, making salinity distribution. Then, monitoring of water quality
provided and alert this information. Moreover, prediction of salinity can protect and conserve water quality. Here,A multiple regression with dynamic parameter was applied to predict salinity of water in the delta of Thailand.
The calibration and verification provided a root mean square error (RMSE) < 5% (calibration) and < 7%(verification) with the high correlation coefficient (R2). This showed a good model for salinity prediction. The
results revealed a good prediction of salinity in one hour that enough for saline water prevention. So, the area could
be improved salinity of water to protect water problem.
Keywords: Regression analysis, Climate Variable, Salinity Sensor, Salt Water, Seawater Intrusion
Surrounding water of Bang Krachao consists ofseawater, fresh water, and brackish water [7].
Because this area is suitable for agriculture. Manyfruits were cultivated in the area, such as Nam Dok
Mai mango and Sapodilla, which can tolerate very
little salinity (0.12 g/l) [9].
Bang Krachao has a floodgate, the Khlong Lat
Pho floodgate, controlling the flow of water andpreventing salinity intrusion into the area. However,
the openings and closings of the floodgates have nota systematic management due to the fact that one
cannot predict the level of salinity that will occur inthe future. Sometimes during the dry season, thefloodgate remains closed, causing water inside theBang Krachao becoming sewage and having a higher
concentrated salt in the water. When this water istranspired, it direct impact on the island’s ecosystems
and agricultural crops.
A prediction of the salinity is executed by using
the MIKE11 simulation model, such as the salinity
intrusion analysis in Tha Chin River [10], the realtime prediction of salinity research in the MekongRiver Delta [11]. These were commonly used to
predict the salinity in large rivers. However, MIKE11
simulation model still requires many variables suchas river’s cross section, river’s slope, slope of friction,and distribution coefficient. Currently, there is no
through survey throughout the river around Bang
Krachao Island. Therefore, this study investigated thepast and above information and focused on thesalinity data in Bang Krachao area, in order to predict
the salinity that will occur in the future and planning

INTRODUCTION
Fresh water is one of the important factors for
agriculture. Current shortage of fresh water in the
Chao Phraya Basin is a problem that become much
more intense as the seawater has intruded because ofclimate change [1]. According to the study of the Pak
Phanang River in April 1994, the intrusion ofseawater had an impact of 100 kilometers radius [2].
For the Tha Chin River, there was an encroachmentof seawater into the main rivers and canals [3].
During the dry season, surface water level decreases,
therefore, allowing the sea to rise and enter the basin.
Not only the natural factors mentioned cause theseawater encroachment problems, humans are also
another factor in causing the problems. For example,
opening and closing the floodgates, farming, forcing
water for use, irrigation buildings, flood prevention,and changes in land use for shrimp farming or salt
farming can cause salinity distribution problems [1].
The excessive use of fresh water for agriculture andthe growing drainage of seawater into freshwater
streams for shrimp farming also cause the fresh water,presenting in the soil and the soil surface cannot push
the salinity out. [4].
Bang Krachao area is a large green area in thecenter of Bangkok (Latitude 13°N and Longitude
100°E) and is known as the green lung of Bangkok.
[6]. This area has absorbed an average of 6,000 tonsof carbon dioxide per year and has produced up to 6
million tons of oxygen per day [7]. This area is a
protect area, controlled building by the Ministerial
Regulations [8].
456GEOMATE – Melbourne, Australia, 11-13 November 2020

water management and systematic openings andclosings of the floodgates in Bang Krachao.
Predicting the salinity in advance in the Chao
Phraya River can have a difference, as water can be
managed and prevented saline water from entering
Bang Krachao. If the salinity was a low level, thewater gate will be opened to allow the water from the
Chao Phraya River flow into the agricultural area. On
the other hand, if the water in the Chao Phraya River
was a high salinity value, floodgate will be closed toprevent salinity in the Chao Phraya River flowing into
agricultural areas. The variables used in this research
are hourly salinity values in the past. The Multiple
Linear Regression method was analyzed because theanalysis results in a short period of time can suggest
opening or closing the floodgates. In addition, thehourly salinity values in each station for salinity
quality measurement were heavily watered between
each other with the current time from 0-6 hours of
every station with SPSS program to find the mostsuitable variable and coefficient value in the amountof 1 hour in advance salinity prediction. In thisamount of time, it should be enough for the openings
and closings of the floodgates to have a lesser impact
on agricultural crops.

Fig. 1 Location and Monitoring of Bang Krachao
Geology
The Bang Krachao area is a floodplain basin,
making a large wetland, caused by the accumulationof sediment in the river mouth. There is a uniqueecosystem called “Three Water” which consists offresh water, saline water, and brackish water. The richfertility of the area is caused by the accumulation ofriver sediment, resulting in agricultural products with
distinctive flavors, such as Nam Dok Mai mango andNam Hom coconut. Terrain and ecology at the archof the Chao Phraya River in Bang Krachao area helps
slow down saline water and sedimentation fromvarious soil conditions that are caused by sedimentdeposition at the delta of the Chao Phraya River,causing the area of Bang Krachao to have soil
fertility, making it a place for resting water, andpreventing flood from happening in the vicinity of
Bangkok with an average ground level of 0.50-0.60
m above mean sea level. The soil in Bang Krachao
consists of clay from sedimentation. Its salinity waschanged according to the influence of the sea. The
delta is influenced by brackish water during the hightide of the sea and the tide is a distribution from themangrove forest. As for the low plains along thewaterfront or the areas in which water is soaked
during the rainy season and dried during the dry
season, they originally were a freshwater swamp
forest. Now it is changed, becoming a garden that has
been dug to help drain water. As for the high uplandin the middle of the bend was once covered bylowland forest which is now transformed into agarden and residential areas.

STUDY AREA
Bang Krachao area was selected for this study due
to 3 factors: i) Bang Krachao area is the largest green
area of Bangkok ii) Bang Krachao area is thedownstream area of the Chao Phraya River with anarea connected to the Gulf of Thailand and iii) Bang
Krachao area is an ecological diversity area, since it
is a connection area between fresh water, brackish
water, and saline water. Bang Krachao area is,therefore, a suitable place for the study of salinity
forecasting to take place, ecologically, engineeringly,
and economically.
General and Topography
Fig. 1 shows the location of Bang Krachao area.
Bang Krachao locates in Phra Pradaeng, SamutPrakan (the south of Bangkok). It is an island with the
Chao Phraya River, with 15 kilometers, covering an
area of 18.9 square kilometers. The shape of the Chao
Phraya River that flows through the Bang Ka Chao
area resembles a stomach. An isthmus from upstream
to downstream about 580 meters in Bang Krachao
bend, covering an area of 6 sub-districts, namely,
Bang Nam Phueng, Bang Yo, Bang Korbuae, Bang
Kasob, Bang Krachao, and Song Khanong. There areboundaries adjacent to Bangkok in 3 directions, with
the Chao Phraya River in the way, separating Bang
Krachao and Bangkok. The south is connected to theGulf of Thailand, which is the sea area of Thailand,causing the Bang Krachao area to be constantly
influenced by encroachment of sea water [12].

Water Level
The water level in Bang Krachao area from thesurvey showed that the average water level is at themean sea level. The highest water level is +1.50 m.
457

GEOMATE – Melbourne, Australia, 11-13 November 2020

(mean sea level) and the lowest water level is -2.00
m. (mean sea level) (Fig. 2 ). The station with thehighest change in water level is OT01 which is thestation that is the arch of Chao Phraya river that water
has low velocity and most influenced by fluctuations
in sea level.

data to the server via internet. The server then showed
the survey results on the website and application.

Fig. 3 Salinity of Bang Krachao
In Fig. 3 showed that the salinity values from thesurvey in Bang Krachao area. The salinity values
were between 0.7 - 18.7 g/l. The station with thehighest salinity is OT04, which is the last station
before the water in the Chao Phraya River flowing
into the Gulf of Thailand. The average salinity in
Bang Krachao area was 10.65 g/l. This showed thatthe average salinity value in Bang Krachao was very
high when compared to the allowed salinity of Nam
Dok Mai mangoes and other plants in Bang Krachao
area (0.12 g/l). The reasons of these stations have highsalinity because the openings and closings of thefloodgates were not suitable for salinity movement.

Fig. 2 Water Level of Bang Krachao
SalinityIn the survey data of this research, monitors were
installed to measure the water level and salinity, as
shown in Fig. 4, which consist of two main parts: theautomatic long distance surface water quality
measurement system and the automatic surface water
quality database system for measuring water level andwater quality, or salinity. After that, the monitors will
send the data to the computer server. There are fivestations: Khlong Ta Sak Station (OT01), Khlong
Bang Krachao Nok Temple (OT02), Bang Nam
Phueng Canal Floodgate Station (OT03), Khlong Yai
Bai Floodgate Station (OT04), and Khlong Phae
Floodgate Station (OT05), as shown in Fig. 1. The
function principle of the salinity meter is that after thesystem measured salinity and water levels, it will send
the measured data to the Remote Terminal Unit
(RTU), a remote-control system will then transmit

Salinity and Problem
The problem of intrusion of saline water occurredin Bang Krachao area because this area is the last areaof the Chao Phraya River where the floodgates wereclosed to prevent the intrusion of saline water.
Nowadays, the openings and closings of thefloodgates were done all the periods of tides.

Fig. 4 Diagram of data collecting [13]458GEOMATE – Melbourne, Australia, 11-13 November 2020

(a)

(b)

Fig. 5 Salinity data and auto-correlation process (a) Salinity data of two stations at the same time (b) Salinity
data of two stations with auto-correlation
When floodgates were opened and closed only during
periods of tides, there will not be openings andclosings of floodgates during droughts to drive off thesaline water. This results in salinity penetrating into
the soil around the island. Higher concentration of salt
in the soil causes the plants, especially famous plants
like mangoes and Nam Hom coconut, low salt
tolerance plants, and grow slower.

multiplication may be positive at some points andnegative at some points, resulting in the result being
either small or near zero.
The position of the time of the data was adjusted
to overlap (Time Shift or Time Lag) until the two setsof data have the same phase, as in Fig. 5(b), of thesum of the products of the two data sets and ultimately
have a positive value and possibly a high value. But
if the two data sets overlap and the phases are not thesame, the result of the sum of the multiplication of the
products will be either small or near zero.
From the aspects of the overlapped times data
showed the results for each time, as they overlap byconsidering the sum of the products from the samedata set at the first time and the time that they overlap.
This can be called autonomy (Auto-correlation), an
examination of the relationships of data in different
sequences in time series, where data at a distance of kapart with lag = k, the autocorrelation coefficient(Autocorrelation Coefficient) with Lag = k can befound from the equation:

MODELING FRAMEWORKThe dynamics analysis of surface water flow wasselected with Multiple Linear Regression and SPSS
to help in the analyzing process because it is a
program that is popular in statistical analysis. It canalso analyze the relationships of various variables.
Multiple Regression Analysis
A multiple linear regression model was applied to
study every linear relationship between one
dependent variable (Yi) and several of independent
variable (Xi). The multiple regression equation is:
Yi = b0 + b1 X1+ b2 X2+ …. + bn Xn

rk 

(1)

where bi are coefficient parameters.
When using this linear prediction, there is alimitation that both variables must be connected in astraight line or as close to a straight line as possible tobe more accurate. In terms of how to use variables inthis work, one can take the relationship between
salinity values and water levels to create a prediction
equation [14].

iN1 k ( xi  x )( xi  k  x )2
iN1 ( xi  x )(2)When taking the correlation coefficient to plot thegraph, in general, a graph with a higher coefficient oftime is zero and the less of the coefficient becomeszero when the time-lapse Gradually increase.
Dynamic Parameter
This analysis takes into account the shift time
analysis, which is an analysis used to analyze areas,such as the prediction at the OT2 station in the future
1 hour ahead. It will use the dimensional data of thecurrent time to the previous 6 hours and the length
dimension data from OT1 station to OT5 station. It
will then look for the relationship via SPSS programand find the equation to use for predicting salinity inthe future. It has a total of 45,000 data. The variables
in each station has the following values:
YOTij is the predicted salinity value at the OTjstation in the future i hours.
XOTij is the observed salinity value of the OTjstation with i hours remaining.

Autocorrelation
Correlation is the measurement of the similarityof two sets of data or signals, which is based on the
product of multiplying the size of both sets of data at
the same time period, then combining all the results
together. If the data of both sets are identical, theresult of multiplying both data together and adding
them together will result in a positive and high value.
But in the case where the two sets of data havedifferent characteristics, as in Fig. 5 ( a) the result of459

GEOMATE – Melbourne, Australia, 11-13 November 2020

Additionally, the data was separated to verify themodel, receiving a total of 22,860 data.

initial salinity variables used in the prediction. This iscaused by the appropriate overlapping time period
and the appropriate distance in each station. It can beobserved from the model summary obtained from theprogram with the highest R2 and the lowest standard
error each and every model will always have salinity
overlapping of one hour of the station used for theprediction.

Calibration Process
Calibrating a model is the most important step. It
is to analyze the accuracy of the model before using
it to predict the characteristics and behavior ofsalinity in the future. This study will use thecorrelation method between variables or CrossCorrelation to examine. This method considers thesimilarities between two different variable data [15].
The consideration of two variables is the series of x
and y values when i = 0, 1, 2, ..., m, and it will result
as Correlation Coefficient (rm) at Lag Time equals tom from the equation:rm 

 ( xi  x )( y i  m  y ) ( xi  x )  ( y i  m  y )2

2

Results of Salinity Prediction with Multiple
Regression
The regression analysis shown in Fig. 6 showsthat the model is highly correlated. It has an equation
similar to Y=X. The results indicated the regression
models appropriated for predicting the dependent
variables. Regression model of YOT01, YOT02, YOT03,
YOT04 and YOT05 were:(3)

YOT01 = 0.442 +2.081XOT13 -0.974XOT11 -0.169XOT22+ 0.095XOT04 + 0.035XOT61
YOT02 = - 0.7 + 0.693XOT12 + 0.039XOT55 +2.29XOT13- 1.97XOT11
YOT03 = 0.876 + 2.570XOT13 - 1.499XOT11 -0.156XOT22+ 0.048XOT21 - 0.017XOT61
YOT04 = 1.161 + 0.777XOT14 - 1.355XOT11 +1.702XOT13- 0.189XOT22
YOT05 = 0.577 + 0.448XOT15 - 1.541XOT11 +2.155XOT13- 0.094XOT22

Where rm is the Correlation Coefficient at timeoverlapping m
Correlation Coefficient (R) is indicates the two
closely related variables [16]. Correlation Coefficient(R2) obtained from the x and y series analysis in the
direction of downward The distribution of the data isthat if the correlation coefficient is positive means
when x is high and the value of y is also high If thecorrelation coefficient is negative, it means that x is
low and the value of y is also lower. If the correlation
coefficient is 0, then the x and y values are not related
at all.

Fig. 6 shows scattered plot between observed dataand computed data. These plots show correlation
coefficient (R2) of 0.88 (OT01), 0.98 (OT02), 0.91
(OT03), 0.85 (OT04) and 0.94 (OT05). These aregood comparable, and the root mean error were 0.71
(OT01), 0.25 (OT02), 0.57 (OT03), 0.87 (OT04) and
0.43 (OT05). They can be explained that thecomputed data close to the observed data andindicated that this model can predicable.

Table 1 Correlation and relationship levels [17]Correlation Coefficient(R2)
0.00 – 0.54
0.55 – 0.69
0.70 – 1.00

Strength of
Correlation
Low
Moderate
High

Application
The salinity obtained from the predictions has ahigh accuracy when compared to the value obtained
from the actual measurement. Therefore, multiple
regression analysis using dynamic variables can beused to predict the salinity values of at least 1 hour inthe future. Predicting just 1 hour of salinity is shown
to result in giving an accurate salinity value. It canalso be used to create a more systematic openings and

RESULTS AND DISCUSSTION
Results of Calibration
From the calibration procedure was showed inTable 2 that the error values and correlation values.
The values of YOT01, YOT02, YOT03, YOT04, YOT05 were
influenced by each other. Each station has different
Table 2. Salinity Calibration
Station
YOT01
YOT02
YOT03
YOT04
YOT05

Predictors
XOT13, XOT11, XOT22, XOT34, XOT61
XOT12, XOT55, XOT13, XOT11
XOT13, XOT11, XOT22, XOT21, XOT61
XOT14, XOT11, XOT13, XOT22
XOT15, XOT11, XOT13, XOT22
460

R2
0.92
0.98
0.95
0.89
0.97

RMSE
0.37
0.08
0.22
0.54
0.13

GEOMATE – Melbourne, Australia, 11-13 November 2020(a) Sta. OT01(b) Sta. OT02(c) Sta. OT03(d) Sta. OT04(e) Sta. OT05
Fig. 6 Scatter plot between observed data and computed data.

461

GEOMATE – Melbourne, Australia, 11-13 November 2020

closings of floodgates, especially the Khlong Lat Pho
floodgate, so that salinity does not enter the Bang
Krachao area.

[3] Nuttawut I., and Wisuwatt T., A Study on
Salinity Intrusion and Control Measure in theThachin River, Journal of Research andDevelopment, 2014.
[4] Bureau of Water and Hydrology Management ofRoyal Irrigation Department, Summary ofsaltwater encroachment situations and mitigation
measures, 2014. (in Thai)[5] Easey R., The Battle to Save Bangkok’s GreenLung.
[6] Marshall A., Best of Asia: Best Urban Oasis,
Time, 2006.
[7] Sri Nakhon Khuean Khan Garden, pttep (in Thai)[8] Ministerial Regulation, Requiring for Bang
Krachao, Bang Nam Phueng, Bang Nam Phueng,
Bang Yo, Bang Krak and Songkanong in Phra
Pradaeng, Samut Prakarn: Is an Environmental
Protection Area, Government Gazette, pp. 3-9.
[9] Land Research and Development, Salty Tolerant
Plants and Plants Like Salt, [Online]. Available:
http://www.ldd.go.th/Lddwebsite/web_ord/Technical/pdf/P_Technical03001_3.pdf. [Accessed
24 May 2020].
[10] Kornkanok A., Department of Water Resources
Engineering, Faculty of Engineering, Kesetsart
University 10th Thaicid National Symposium.
[11] Lam N. T., Real-Time Prediction of Salinity in
the Mekong River Delta, 10th International
Conference on Asian and Pacific Coasts.
[12] Department
ofIntellectual
Property,
Geographical Indication Registration Nam Dok
Mai Mango Registration Number Sor Chor
57100064, 2014.
[13] Seeboonruang, U., and Chulkaivalsucharit, V.,
Development of Automatic Warning System for
Water Salinity in Bang Krachao, Thailand.
Sensors and Materials, Vol. 32 Issue 20, 2020, pp.
587–597.
[14] Nonglak W., Cross Correlation. Auto
Correlation-2D Pattern Identification, 2010. (inThai)[15] Higgins J., Introduction to Multiple Regression,
The Radical Statistician. Vol.1, 2005, pp. 1-15.
[16] Taweesin, K., Seeboonruang, U., andSaraphirom, P., The Influence of Climate
Variability Effects on Groundwater Time Series
in the Lower Central Plains of Thailand. Water,Vol. 290 Issue 10, 2018.

CONCLUSIONSThis research has investigated the salinity ofBang Krachao, Thailand. To aid decision in saline
water management, the values of salinity data forcalibration were demonstrated, while also showing
the strong correlation of shift time of salinity in eachstation, where simulated and measured values agree.
The calibrated process was showed that thesalinity source variables in each station were related
and verified the model by using the equations fromthe calibrate algorithm to analyze. The model isaccurate and suitable, for predicting the salinity
values for opening and closing floodgates.
In summary, the multiple regression analysis
with dynamic variable can be used for planning tomanage the gate operation in the surrounding canals.
In addition, if you need additional research
information, you can access the Website:
http://www.bangkachao-wateronline.com
ACKNOWLEDGMENTS
We thank the Agricultural Research Development
Agency Public Organization (ARDA) for funding.
Special thanks to the Thai Royal Irrigation
Development and Thai Meteorological Department
for the data.
REFERENCES[1] Wongsa S., and Sunaree S., The Preliminary
Prediction Effects of Salinity Intrusion on
Agriculture: The Case Study Assuming Using
IPCC Data on Increasing of Sea Level Rise in
Gulf of Thailand, Kasem Bundit Engineering
Journal, Vol 7 No 1, 2017.
[2] Wisuwatt T., Innovation in Irrigation
Engineering for Remote Control of Irrigation
Buildings and Water Management in Pak
Phanang Upper Irrigation and Maintenance
Project, In Special Book of Technology andinnovation For Sustainable Education, Faculty of
Science, Kamphaengsaen Kasetsart University,
2016, pp. 80-88.

462

10thInt. Conf. on Geotechnique, Construction Materials andEnvironment,
Melbourne, Australia,11-13 November 2020, ISBN: 978-4-909106049C3051

SOIL PARAMETERS IN DIFFERENT LAND USE TYPES IN CU LAO
DUNG, VIETNAM
Duong Minh Truyen1, Truong Hoang Dan2, Duong Van Ni3 and Ly Van Loi4
1

College of Environment and Natural Resources, Cantho University, Vietnam;2,3,4Cantho University,
Vietnam
ABSTRACT

Cu Lao Dung is a land strip lying in the middle of Hau river, at the end of Cuu Long river, flowing into theEast Sea. It is located in the marine economic region of Soc Trang province, Vietnam. This islet has great
advantages with three ecological regions of freshwater, brackish and brine water. However, there are not many
detailed studies on environmental indicators in the different land use types there, especially those in locations near
the islet center. Therefore, the study of soil parameters across different agricultural land use types was carried out
during the rainy season in 2019 to give an overview of the soil environment in the islet. The results showed thatin the surveyed land use types, the amount of potassium fertilizer in the soil was higher than that of nitrogen andphosphorus. In Mango farm and Shrimp farm, pH and EC values collected outperform those in two other land use
types, but still below 5. The main source of irrigation there was rainy water, so the amount of fertilizer accumulated
in the soil was not washed out often. In general, although Cu Lao Dung is located in a favorable geographicallocation, but the people there do not have reasonable cultivation methods to improve the soil environment and still
rely heavily on chemical fertilizers.
Keywords:Cu Lao Dung, soil, freshwater land use types

relation to crops farming has not received muchattention. Therefore, this survey on farming and soil
characters were conducted to understand thisdependences.

INTRODUCTION
Cu Lao Dung (CLD) is the largest islet of
Hauriver, belonging to SocTrang province (figure 1).
The East borders to Dinh An estuary, the Westborders to Hau river, the South borders to the East
Sea and the North borders to another My Phuoc
island in SocTrang province [13].
Because CLD is located at the mouth of Hau riverand connected to the East sea, its terrain is flat andthe canal system is intertwined, therefore, water
quality is varied from saline to fresh seasonally,
however it is still favorable conditions for economic
and agricultural development in centuries ago [7, 13].
Every year, the saline intrusion usually in late
February and early March. Therefore, the amount offresh water flowing from the upstream is insufficient,
not enough to wash salinity in soil, thus affecting thequality of farmland. The fresh water mainly depends
on rainwater [2, 7].
Depend on the present of saline, brackish andfresh water, the land use types in Cu Lao Dung isvery diverse. The area that received fresh water fromthe Hau River, is suitable for perennial fruit trees
such as Mango, Longan, Pomelo, Coconutfruits andthe rest is dominate with cash crops. At the end of
the islet bordering to the sea, this is saline waterpermanent then shrimp farming combine with
exploitation of resources under forest canopy such as
crested gibbons, sesarmid crabs, snails, etc.
Although there are studies on land uses at CLD,
however the in-depth studies in soil characters in

METHODS
The Participatory Rural Appraisal (PRA)social mapping [8] was applied to collectinformation relate to land use types, crop
management in Cu Lao Dung. At the same time,other information relate to the different of natural
conditions, ecosystems and soil quality in the study
area. Soil sampling was conducted on 6 locations
representing common land use types in the surveyed
area.

Figure1.
Sampling
location(sourcecanthopromotion.vn; ontheworldmap.com).
At each land use type, 5 soil samples werecollected in the top from 15 - 20cm. Soil samples

463

GEOMATE–Melbourne, Australia, 11-13 November 2020

were sent to Can Tho University laboratory for
analysis. The analysed characters were:+ Conductivity (EC) (dSm-1)+ pH+ Nitrogen content (N) (%)+ Potassium content (K) (%)+ Phosphorus content (P) (%)
The soil characters of the study area was assessedby comparing between land use types and crops. The
values of pH, nitrogen (N), phosphorus (P) andpotassium levels in the soil were compared based on
the Forest Sector Handbook chapter of Soil and Soil
Nutrition [11, 12].

Strongly
Only tolerant crops>16
salinecan tolerate
Based on information in the table 1, Mango farm
had an EC value of 4.32 dSm-1; considered asslightly saline soil. The EC values in other land use
types fluctualted from 0 to 2 and be classified as salt
free soil.
pH values
Based on the results of soil samples collected at
4 different land use types, the results were shown in
the following chart:

RESULTS AND DISCUSSION

5.00
4.80

EC values

4.60
4.32
4
2

4.40

Shrimpfarm

0.889

Coconut Longan
farm
farm

Salt free

0–2

Slightlysaline2–4

SlightlysalineModerately
saline

4–8
8 – 16

Coconut Longan
farm
farm

Mango
farm

Table 2 pH scalepH<3
3 – 4.5
4.5 – 5.5
5.5 – 6.5
6.5 – 7.0
7.0
7.0 – 7.5
7.5 – 8.0
8.0 – 9.0
>9.0

Table 1 Classification by salinity and effects on
crops
EC in soil
(dS/m)

4.28

Figure 3. pH values observed at four different land
use types.
From the chart, it illustrated that the highest pH
value obtained in the Mango farm of 4.69. The
lowest was at Coconut farm of 4.17.
pH, which represented for “power of hydrogen”,
considered as a measure of how acidic or alkaline asolution was. Using the standard of pH values (table2), with representing the soil characteristics [9, 11,
12, 14], it showed that the pH values of four different
land use types ranged from 4.4 to 4.69. The results
found that soil at Cu Lao Dung Islet was strongly
acidic.

Mango
farm

Figure 2. EC values obtained at four different land
use types.
Six locations in freshwater zone with 4 different
land use types has EC values varied from 0.889 to
4.32 dSm-1 (figure 2). The Mango farm had thehighest EC value of 4.32 dSm-1, followed by
Coconut farm of 1,624 dSm-1; the lowest was
obtained in the Longan farm at 0.889 dSm-1. The
average value of the EC on all land uses types was
1,956 dSm-1, but EC value obtained in the Mango
farm was different to all others as Shrimp, Coconutand Longan farms significantly.
Using standard measurement of salinity [1], thecrop performances was presented below:

Designation

4.17

4.00

1.624

0
Shrimp
farm

4.69

pH

4.20

EC (dS/m)
0.989

4.52

Crop performances
Salinity effects aremostly negligible
The productivity ofmany plants may belimitedThe productivity ofmany crops is limited
Some plants cantolerate

Classification of soil
Very strongly acid
Very strongly acid
Strongly acid
Medium acid
Slightly acid
NeutralSlightly alkaline
Medium alkaline
Strongly alkaline
Very strongly alkaline

According to the results of EC and pH values in
4 land use types in Cu Lao Dung Islet, it was clearthat Mango could tolerate sightly saline (EC at 4.69
dSm-1) and strongly acidic soil (pH of 4.69), while464

GEOMATE–Melbourne, Australia, 11-13 November 2020

Coconut and Longan only sufferred salt free soil
with EC values at 1.624 dSm-1 and 0.889 dSm-1
respectively, they could not suffer saline soil.
However, these crops could endure very strongly
acidic soil, pH ranging from 4.17 to 4.28.
Shrimp farm had salt free value at 0.989 dSm-1
and pH at 4.52 because farmers added freshwater inthe beginning crops and the samples were taken atthat time.
Many things can affect the conductivity of soil,
including the irrigation [1, 5]. Natural rain will dilutethe salt near the roots. This prevents the plant frombeing "burnt" by excess salts and nutrients, as plantroots are clogged with salt and nutrients. Plants
cannot absorb salt, which can be stunted. If irrigationwater is high in salt, salts can accumulate in thefields, increasing salinity and conductivity. For mostplants, conductivity is considered good for the plant
if it does not exceed 4 dSm-1 [16]. However, thisnumber will vary, depending on the specific crop. In
Cu Lao Dung, the irrigation depended on irregularrainwater, which caused the pH values of soil surface
layer to be low due to less washout.

phosphorus. Coconut farm had the highest values of
total N – P – K fertilizers applied than Shrimp,
Mango and Longan farms.
Using the standard stress level of nitrogen,phosphorus, and potassium [3, 4, 6, 15, 17], theconcentration of total N – P – K in cropland was
performed in the table 3.
Table 3 Definition of nitrogen, phosphorus, andpotassium stress levels.

Level
I
II
III
IV
Level

The total value of N - P - K
Cu Lao Dung district has transferred over 400
hectares of sugarcane to aquaculture, mainly Shrimp
farming since July 2018. During the conversion ofcultivation to Shrimp farming, the use of fertilizers
to create a suitable water environment was an
indispensable element. Fertilizers commonly used in
the study area were nitrogen, phosphorus andpotassium (N-P-K). The results of soil samples
collected at 4 land use types in An Thanh I commune
area were shown in the chart below.
% Nito0.250% Photpho

0.155

0.133

0.188
0.140

0.100
0.049

0.053

0.044

0.049

Shrimpfarm

Coconut
farm

Longan
farm

Mango
farm

0.050

II
III
IV

0.10 – 0.15
0.05 – 0.10
< 0.05
Potassium (K)stress level(%)> 0.250.15 – 0.25
0.10 – 0.15
< 0.10

Nitrogenconcentration incroplandNitrogen sufficiency
No nitrogen stress
Nitrogen stress
Nitrogen scarcity
Phosphorus
concentration incroplandPhosphorus
sufficiency
No phosphorus stress
Phosphorus stress
Phosphorus scarcity
Potassium
concentration incroplandPotassium sufficiency
No Potassium stress
Potassium stress
Potassium scarcity

According to the nitrogen (N) stress levels,
Longan farm and Mango farm were in level III –nitrogen stress while those numbers in 2 other land
use types, namely Shrimp farm and Coconut farm
were in level II – no nitrogen stress. It means that theamount of nitrogen (N) applied was deficient inLongan and Mango farms.
Moving on phosphorus (P) fertilizer, threesurveyed land use types, namely Shrimp, Longan
and Mango farms were in level IV – phosphorus
scarcity, while Coconut farm was in level III –phosphorus stress.
Although potassium (K) fertilizer was applied at
the same time with nitrogen and phosphorus
fertilizer, four different land use types had level II –no potasium stress in the table 3. Unlike nitrogen andphosphorus, potassium ions do not participate inpermanent organic compounds in plants, but
obviously exist in the form of inorganic salts anddissolved organic salts. Potassium content in soil
usually varies from 0.5 to 3.0%. It is usually higher
in arid, young, and black alluvial soil than red soil
and weathered rock located in areas with higher
precipitation [3, 10].

0.168

0.150

> 0.15

I
II
III
IV

% Kali

0.195
0.200

I

Level

0.220

0.212

Nitrogen (N)stress level
(%)
>0.30
0.15 – 0.30
0.09 – 0.15
<0.09
Phosphorus(P) stress level
(%)

0.000

Figure 4. Total percentage of nitrogen, phosphorus,and potassium concentration collected in four land
use types.
Four different land uses types in freshwater zone
had varied values of total nitrogen, phosphorus andpotassium, with figures of potassium supplied muchsignificantly higher than that of nitrogen and465

GEOMATE–Melbourne, Australia, 11-13 November 2020

From the results, Mango farm could tolerateslightly alkaline soil. Therefore, it was needed to put
down phosphorus (P) and potassium (K) fertilizer in
Mango farm. Besides, Coconut and Longan farm
could stand acidic soil and nitrogen (N) was
suggested to add in these farms. On the other hand,
Shrimp farm was significantly different because
fertilizer applied depending on the farming time.
Nevertheless, in Cu Lao Dung Islet, local farmers
chose to put more potassium (K) fertilizer in thefarms because this type of fertilizer could increase
drought and salinity tolerance for crops.

Journal of Experimental Agriculture. Vol 3,
Issue 10,1963, pp 190-197.
[5] Lenntech, B. V. Water conductivity. Water
Treatment, 2016. Retrieved 6 May 2020.
[6] Liu J, You L, Amini M, Obersteiner M, Herrero
M, Zehnder AJ, Yang H. A high-resolution
assessment on global nitrogen flows in cropland.
Proceedings of the National Academy of
Sciences. Vol 107, Issue 17,. 2010, pp 80358040.
[7] Mai NT, Kantoush SA, Sumi TE, Thang TD,
Binh DV, Trung LV. The influences of tidal
regime and morphology change on salinity
intrusion in Hau River. InE-proceedings of the
38th IAHR World Congress, 2019, pp 1-6.
[8] Map S. Participatory Rural Appraisal (PRA),
1993.
[9] Marion GM, Millero FJ, Camões MF, Spitzer P,
Feistel R, Chen CT. pH of seawater. Marine
Chemistry. Vol 126, Issue 1-4, 2011, pp 89-96.
[10] Mc Lean EO, Watson ME. Soil measurementsof plant‐available potassium. Potassium inagriculture, 1985, pp 277-308.
[11] McDonald RC, Isbell RF, Speight JG, Walker J,
Hopkins MS. Australian soil and land survey:
field handbook. CSIRO publishing, 1998.
[12] Rengel Z, editor. Handbook of soil acidity. CRC
Press; 2003.
[13] Seung Kyu LE, Truong An DA. Assessment of
Sugarcane Yield in the Cu Lao Dung Isles, Soc
Trang Province of Vietnam in the Context ofWater Scarcity. Transylvanian Review, Vol 1,
Issue 5, 2019;[14] Spurway CH. Soil reaction (pH) preferences ofplants. Michigan State University Agricultural
Experiment Station. Special Bulletin, 1941, pp
306.
[15] Talibudeen O. The Role of Potassium inAgriculture. VJ Kilmer, SE Younts, NC Brady(Eds.) Madison, Wisconsin: American Societyof Agronomy, etc.,196), pp. 523. Experimental
Agriculture. Vol 5, Issue 4, 1969, pp 344.
[16] Valdez-Aguilar LA, Grieve CM, Poss J. Salinity
and alkaline pH in irrigation water affectmarigold plants: I. Growth and shoot dry weightpartitioning, 2009.
[17] Wang SJ, Fox DG, Cherney DJ, Klausner SD,
Bouldin DR. Impact of dairy farming on well
water nitrate level and soil content ofphosphorus and potassium. Journal of dairy
science, Vol 82, Issue 10, 1999, pp 2164-2169.

CONCLUSIONSIn freshwater zone of Cu Lao Dung, the results infour surveyed land use types, namely Shrimp,
Coconut, Longan and Mango farms weresignificantly different with Mango farm had salinity
tolerance while acidity tolerance belonged to
Coconut and Longan farms. To increase crops
tolerance, potassium fertilizer was applied in farm
much more than nitrogen and phosphorus fertilizer.
ACKNOWLEDGMENTS
The process of conducting research and results
achieved through the ODA project, which funded by
Can Tho University. Sincere thanks to the teachers,
instructors, and local officers who assisted inproviding the information to complete this article.
REFERENCE[1] Akpan UE, Okon AA, Ituen EE. Measurementof salinity and electrical conductivity of some
soil samples of Uruan local government Area of
Akwa Ibom State, Nigeria. Global Journal ofpure and applied sciences. Vol. 8, Issue 3, 2002,
pp 399-404.
[2] An TD, Tsujimura M, Le Phu V, Kawachi A, Ha
DT. Chemical characteristics of surface waterand groundwater in coastal watershed, Mekong
Delta, Vietnam. Procedia Environmental
Sciences. Vol 20, 2014, pp 712-721.
[3] Bidari BI, Hebsur NS. Potassium in relation toyield and quality of selected vegetable crops.
Karnataka Journal of Agricultural Sciences. Vol
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[4] Colwell JD. The estimation of the phosphorus
fertilizer requirements of wheat in southern
New South Wales by soil analysis. Australian466

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

PREPARATION OF FLUORIDE ADSORBENT FROM ZIRCON SAND
USING MECHANOCHEMICAL TREATMENT, AND ITSAPPLICATION FOR FLUORIDE REMOVAL
Nagisa Minami1 and Takaaki Wajima1
1

Department of Urban Environment Systems, Graduate School of Engineering, Chiba University, Japan;
1-33 Yayoi-cho, Inage-ku, Chiba, Japan
ABSTRACT

Removal of low-concentration fluoride in natural water is an important issue all over the world. Japan is a
volcanic country to have many hot springs, and the removal of low-concentration fluoride from hot spring drainage
is desired. In this study, we attempted to prepare a novel adsorbent with high selective removal ability of fluoride
from zircon sand using mechanochemical treatment, and applied it for removing fluoride from hot spring water.
Mechanochemical treatment of zircon sand was carried out using a planetary ball mill, and fluoride removal of the
product was examined using fluoride aqueous solution and hot spring water. The hot spring water is collected fromone of the hot spring in Japan, and a typical sulfic acid hot spring water pH 2 including high contents of SO42(12000 mg ⋅ L-1) and Cl- (5000 mg ⋅ L-1) with 11 - 15 mg ⋅ L-1 of fluoride ion. Regardless of ball sizes, with increasing
the mechanochemical treatment time to 10 min, the removal ability of the product for fluoride increases, and thenbe almost constant. With decreasing pH of the solution to 2, the adsorption amount increases, and then be almost
constant. With increasing the temperature of the solution, the adsorption amount decreases, while the rate ofadsorption increases and the amount increases. The adsorption isotherm at pH 2 follows much better Langmuirmodel than Freundlich model, and the calculated maximum adsorption amount is 0.09 mmol ⋅ g-1. Fluoride can beadsorbed selectively from hot spring water below Japanese effluent standard for fluoride (8.0 mg ⋅ L-1).
Keywords: Zircon sand, Fluoride removal, Mechanochemical treatment, Hot spring waterhigh selective fluoride adsorption ability from zircon
sand by mechanochemical treatment, and applied it toremove fluoride from hot spring water.

INTRODUCTION
Fluorine is a dangerous anionic species, andexcessive ingestion of fluorine causes fluorosis andneurological abnormalities in the human body.
Therefore, removal of low concentrations of fluoride
in natural water is an important issue all over theworld. There are coagulation-sedimentation, reverse
osmosis membrane, and adsorption as methods ofremoving fluoride in wastewater. However,coagulation-sedimentation method has a problem ofsludge treatment, and reverse osmosis membrane
method is a high equipment cost [1].
In Japan, there are many hot spring areas, and hot
spring water contains fluoride coexisting with somecations and anions. Therefore, selective removal for
low concentrations of fluoride in hot spring
wastewater is desired, and simple on-site adsorption
method for fluoride is focused on.
Zirconia (ZrO2) attracts as an adsorbent having
Zr-O-H on its surface, which has a selective
adsorption capacity for fluorine [2]. However, thezirconia adsorbents are expensive because its
manufacturing requires a multi-steps with a lot ofenergy [3]. In previous studies, zircon sand can beconverted into the adsorbent with Zr-O-H using
simple mechanochemical treatment, and phosphorus
in seawater can be removed selectively [4][5].
In this study, we prepares a new adsorbent with

EXPERIMANTAL
SampleIn this study, raw zircon sand (ZrSiO4) (WAKO)
was used, and hot spring water used was collectedfrom one of hot spring water in Japan. The chemicalcomposition of hot spring water is shown in Table 1,
and the pH of hot spring water is about 2. This hot
spring water is sulfic acid water with 12 mg ⋅ g-1 of F-.
Table 1

Chemical composition of hot spring water.
Ion
FClSO42Na2+
K+
Mg2+
Ca2+
Fe2+
Al3+

467

Concentration [mg/L]
12
5101
12720
210
140
103
25
149
122

GEOMATE – Melbourne, Australia, 11-13 November 2020

supernatant was measured by a fluoride ion meter tocalculate the fluoride adsorption amount using theequations (1).
The amount of fluoride adsorption in diluted
seawater was examined for fluoride selective
adsorption of the product and raw sand. 0.1 g of the
product or raw sand was added into 10 mL of diluted
seawater, which diluted 10 – 1000 times, with 0.1
mmol ⋅ L-1 F-, and shaken for 12 h. After shaking, themixture was centrifuged, the pH (equilibrium pH) of
the supernatant was measured with a pH meter, andthe fluoride concentration in the supernatant wasmeasured by a fluoride ion meter to calculate thefluoride adsorption amount using equations (1).
Fluoride adsorption isotherm was examined using
KF solution with pH 2, adjusted by HCl solution. 0.1
g of the product or raw zircon sand was added into
0.05 – 4.0 mmol ⋅ L-1 KF solution, and shaken for 12
h. After shaking, the mixture was centrifuged, the pH(equilibrium pH) of the supernatant was measured
with a pH meter, and the fluoride concentration in the
supernatant was measured by a fluoride ion meter tocalculate the fluoride adsorption amount using
equations (1).
Fluoride adsorption kinetics of the product in KF
solution was examined. 1.0 g of the product wasadded into 200 mL of 0.1 mmol ⋅ L-1 KF solution, andwas stirred for 1.5 h at 20, 30 and 40 oC. During
stirring, a part of the solution (2 mL) was collected ateach time, and was filtered using a membrane filter.
The fluoride concentration in the filtrate wasdetermined by a fluoride ion meter to calculate thefluoride adsorption amount using (1).

Mechanochemical Treatment
A planetary ball mill (P-6, Fritsch) was used formechanochemical treatment. Pots and balls weremade of silicon nitride with high wear resistance.
Treatment was carried out at rotation speed of 400
rpm for 1 to 60 min using three different diameterballs (ϕ 5, 10, 15 mm). The volume filling rate of theball into the pot was about 30 %, and the number ofballs with ϕ 5 mm, ϕ 10 mm and ϕ 15 mm were 180,
18 and 7, respectively.
The
products
obtainedunder
eachmechanochemical treatment condition for fluoride
adsorption was estimated as follows. 0.1 g of the
product after mechanochemical treatment was added
into 10 mL of KF aqueous solution with 0.1 mmol ⋅ L1
in 50 mL of the tube, and shaken with reciprocal
shaker at room temperature for 12 h. After shaking,the tube was centrifuged, and a part of the supernatant
was collected. The pH of the collected solution wasmeasured with a pH meter (F-72, HORIBA), and thefluoride concentration in the collected solution wasmeasured with a fluoride ion meter (TiN-5101,
TOKO). The adsorption amount (mmol ⋅ g-1) andremoval percent (%) for fluoride were calculated
from the measured fluoride concentration using thefollowing equations:𝒒𝒒 = (𝑪𝑪𝟎𝟎 − 𝑪𝑪) ∙ 𝑳𝑳 ∙ 𝑾𝑾−𝟏𝟏

𝑹𝑹 = {(𝑪𝑪𝟎𝟎 − 𝑪𝑪) ∗ 𝟏𝟏𝟏𝟏𝟏𝟏}∙ 𝑪𝑪𝟎𝟎 −𝟏𝟏

(𝟏𝟏)
(𝟐𝟐)

where q is adsorption amount [mmol ⋅ g-1], C0 is initial
fluoride concentration [mmol ⋅ L-1], C is fluoride
concentration after shaking [mmol ⋅ L-1], W is weightof sample [g] , L is volume of solution [L] and R is
the removal percentage of fluoride [%].

RESULTS AND DISCUSSION
Fluoride Adsorption Ability
The amount of fluoride adsorption of the productobtained by mechanochemical treatment is shown in
Fig. 1. With increasing the mechanochemicaltreatment time to 10 minutes, the fluoride adsorptionof the product increased, and then became almost
constant, regardless of the ball diameter. The fluoride
adsorption amount of the product after 10 minutes
mechanochemical treatment is 0.004 mmol ⋅ g-1,
which is almost 2 times as that of raw zircon sand(0.002 mmol ⋅ g-1). The removal of fluoride was about
50%. It is noted that the amount of fluoride adsorptionof the product was about 1/29 of the amount offluoride adsorption of magnesia (0.114 mmol ⋅ g-1) [6],and 1/250 of the amount of fluoride adsorption of
zirconia (1.0 mmol ⋅ g-1) [7].

Fluoride Adsorption
The fluoride adsorption ability of the productobtained by mechanochemical treatment for 10 min
using ϕ 5 mm balls was investigated.
The pH dependence of the fluoride adsorption forthe product was investigated, in comparison with thatof raw zircon sand. 0.1 mmol ⋅ L-1 KF solution withpH 1 - 7 was adjusted using HCl. 0.1 g of the raw sandor the product was added into the KF solution withdifferent pHs, and shaken for 12 h at room
temperature. After shaking, the mixture wascentrifuged, the pH of the supernatant was measured
with a pH meter, and the fluoride concentration in the468

GEOMATE – Melbourne, Australia, 11-13 November 2020

Φ 5mm
Φ 10mm
Φ 15mmRaw sand0.00750.006
Adsorption [mmol·g-1]

Adsorption [mmol·g-1]

0.01

0.005
0.0025

Product
Raw sand
0.004

0.002

0
0

0

10
20
30
40
50
60
Mechanochemical treatment time [min]

0

0.001

0.01

0.1

1

Seawater dilution ratio

Fig. 1 Fluoride adsorption amount of the productafter mechanochemical treatment using theball with various diameter.

Fig. 3 Fluoride adsorption amount of the productand raw sand in seawater.
Fig. 4 shows the adsorption isotherms of the
product and raw sand in fluoride solution at pH 2. As
the equilibrium concentration increased, the fluoride
adsorption increased sharply up to the equilibrium
concentration of 0.09 mmol ⋅ L-1 and 0.06 mmol ⋅ L-1
for the product and raw sand, respectively, and thengradually increased.

The pH dependence of the fluoride adsorption of
the product and raw sand is shown in Fig. 2. The
fluoride adsorption of the product increased withdecreasing pH, and the adsorption amount at pH 3.5
was 0.008 mmol ⋅ g-1, which was about 2 times higher
than that at pH 7, while adsorption of raw zircon sandat pH 3.2 was 0.007 mmol ⋅ g-1. It is reported that thefluoride adsorption amount of Zr adsorbent with ZrOH on the surface increases as the pH decreases [8].
It is considered that Zr-O-H is increased on thesurface of the product by mechanochemical treatment.

Adsorption [mmol·g-1]

0.1

Adsorption [mmol·g-1]

0.01
Product
Raw sand

0.008

Product
Raw sand

0.08
0.06
0.04
0.02
0

0.006

0

0.004
0.002

0.4
0.8
1.2
Equilibrium concentration [mmol·L-1]

Fig. 4 Fluoride adsorption isotherm of the productat pH 2.

0024Equilibrium pH

6

8

The experimental results obtained in Fig. 4 for the
product and raw sand are analyzed using theLangmuir adsorption model and the Freundlich
adsorption model to estimate the fluoride adsorption
behavior of the product. The liner equations ofLangmuir adsorption model and Freundlich
adsorption model are as follow:

Fig. 2 Effect of pH on fluoride adsorption amountof the product.
The fluoride adsorption amounts of the product andzircon sand in diluted seawater were shown in Fig. 3.
The product indicates higher fluoride adsorption than
raw sand and almost same adsorption amount in 1/10
– 1/1000 diluted seawater as in distilled water. This
result shows that product can selectively adsorb
fluoride in high saline water.

𝑪𝑪 ∙ 𝒒𝒒−𝟏𝟏 = (𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎 ∙ 𝑲𝑲𝑳𝑳 )−𝟏𝟏 + 𝑪𝑪 ∙ (𝒒𝒒𝒎𝒎𝒎𝒎𝒎𝒎 )−𝟏𝟏𝐥𝐥𝐥𝐥 𝒒𝒒 = 𝐥𝐥𝐥𝐥 𝑲𝑲𝑭𝑭 + 𝒏𝒏−𝟏𝟏∙ 𝐥𝐥𝐥𝐥 𝑪𝑪

469

(𝟑𝟑)
(𝟒𝟒)

GEOMATE – Melbourne, Australia, 11-13 November 2020

where q is adsorption amount [mmol ⋅ g-1], C isfluoride concentration [mmol ⋅ L-1], qmax is the
maximum adsorption amount [mmol ⋅ g-1], KL isadsorption equilibrium constant of Langmuir, KF andn are constant of Freundlich.
The results are shown in Table 2. According to thecorrelation
coefficient,
Langmuirequation(correlation coefficient: R2 = 0.98) could be more
realistic than that Freundlich's equation (R2 = 0.93).
The calculated maximum adsorption capacity of the
product using Langmuir equation is 0.09 mmol ⋅ g-1.
Table 2

Product
Raw
sand

Adsorption [mmol·g-1]

0.006

qmax

Freundlich
n
KF
R2

0.09

5.19

0.98

2.13

0.33

0.93

0.06

4.94

0.96

1.13

0.38

0.86

40℃

0.004

0.002

0

30
60
Time [min]90Fig. 5 Fluoride adsorption of the product at various
temperatures.
The experimental results obtained in Fig. 5 wereanalyzed by pseudo-first-order and pseudo-secondorder kinetics models to estimate the fluoride
adsorption behavior of the product in fluoride
solution. The linearized equations of pseudo-firstorder and pseudo-second-order kinetics models are as
follows;

The important feature of the Langmuir isotherm
can be written in form of a dimensionless quantity
known as separator factor (RL) which is given by (5).
𝑹𝑹𝑳𝑳
−𝟏𝟏
= �𝟏𝟏 + 𝑲𝑲𝑳𝑳 ∙ 𝑪𝑪𝟎𝟎,𝒎𝒎𝒎𝒎𝒎𝒎�

30℃

0The results of parameter from adsorption
isotherm of the product and raw sand.
Langmuir
KL
R2

20℃

𝐥𝐥𝐥𝐥(𝒒𝒒𝒆𝒆 − 𝒒𝒒𝒕𝒕 ) = 𝐥𝐥𝐥𝐥 𝒒𝒒𝒆𝒆 − 𝒌𝒌𝟏𝟏
∙ 𝒕𝒕

𝒕𝒕 ∙ 𝒒𝒒𝒕𝒕 −𝟏𝟏 = (𝒌𝒌𝟐𝟐 ∙ 𝒒𝒒𝟐𝟐𝒆𝒆 )−𝟏𝟏 + 𝒕𝒕
∙ 𝒒𝒒𝒆𝒆 −𝟏𝟏(𝟓𝟓)

where KL and C0,max are the adsorption equilibrium
constant of Langmuir model and maximum initialconcentration, respectively.
The value of RL determines nature of the isotherm
shape. It can either be favorable (0 < RL < 1),
unfavorable adsorption (RL > 1), liner (RL = 1) or
irreversible adsorption (RL = 0). The obtained valuesof RL to be 0.092 for fluoride adsorption by the
product suggest that the process is favorable.
The value of the Freundlich constant (n) indicatesthe affinity between the adsorbent and the adsorbate.
The adsorbate is easily adsorbed (0.1 < n-1 < 0.5), ordifficulty adsorbed (n-1 > 2) [9]. The value of n-1
obtained to be 0.469 for fluoride adsorption by the
product suggest that the process has affinity.

(𝟔𝟔)
(𝟕𝟕)

where qe, qt, k1, k2 and t are the maximum adsorption
amount (mmol ⋅ g-1), the adsorption amount at time
(mmol ⋅ g-1), the pseudo-first-order adsorption
kinetics constant (min-1), the pseudo-second-orderadsorption kinetics constant (g ⋅ mmol-1 ⋅ g-1), and time(min), respectively.
The results analyzed are shown in Table 3.
According to the correlation coefficient (R2), the data
fit much better to pseudo-second-order model than topseudo-first-order model. It was found that the
product has higher adsorption amount at lower
temperature, while reaction rates decrease.
Table 3. The results of parameters from adsorption
kinetics.

Figure 5 shows the fluoride adsorption of productafter mechanochemical treatment in fluoride solution
at various temperatures. As the stirring time increased,
the fluoride adsorption increased, and then be almost
constant. As the temperature decreased, the fluoride
adsorption amount increased.

Temp.

20 ℃
30 ℃
40 ℃

Pseudo-first-order
qe
R2
k1
0.003
0.92
0.034
0.002
0.025
0.83
0.002
0.023
0.82

Pseudo-second-order
qe
k2
R2
0.005
41
0.99
0.004
72
0.98
0.003 91
0.98

The activation energy for fluoride adsorption was
calculated by the Arrhenius equation as follows;

470

GEOMATE – Melbourne, Australia, 11-13 November 2020

(𝟖𝟖)

F- concentration[mg·L-1]

15

80
60

10

-

F effluent standard

40

5

20
0
0

50

100
150
-1
Dosage [g·L ]200

Fig. 7 Concentration and removal of fluoride fromhot spring water.
Figure 8 shows the fluoride concentration and thefluoride removal at each shaking time when the
product was added at 100 g ⋅ L-1. With increasing thereaction time, the fluoride concentration gradually
decreased to Japanese effluent standard, and theremoval reached to 49%, which indicates that fluoride
in the hot spring water can be removed to below theeffluent standard value by treating at 100 g ⋅ L-1 for
180 min.

4

ΔE = 7.53 kJ/mol

2
1
F- concentration [mg·L-1]

20

0
3.1

3.2

3.3

3.4

3.5

1/T × 103

Fig. 6 Arrhenius plot for fluoride adsorption on theadsorbent.
Fluoride Adsorption From Hot Spring Water

F⁻ concentration [mg・L⁻¹]
Removal [%]

15

80
60

10
40
F- effluent standard
5

20
0

0
0

Figure 7 shows the fluoride ion concentration andthe fluoride removal from the hot spring water after
treatment for 3 h by adding various amounts of the
product. It is noted that the Japanese fluoride effluent
standard value is 8.0 mg/L. With increasing theaddition of the product, fluoride concentration
decreases, and be lower than effluent standard. The
removal was 34 % with more than 100 g/L addition.

100
Removal [%]ln (k2)F⁻ concentration [mg・L⁻¹]
Removal [%]

0

5

3

100

20

where ΔE is the activation energy [kJ ⋅ mol ], A isthe frequency factor, T is the absolute temperature [K],and R is the gas constant. From the plot of ln(k2) vs.
1/T (Fig. 6), the activation energy for the adsorptionof fluoride was found to be 7.53 kJ ⋅ mol-1.
The magnitude of Δ E indicates the type ofadsorption, which can be physical or chemical. The
physical adsorption process has lower ΔE values (5- 40 kJ ⋅ mol-1), while the chemical adsorption has
higher ΔE values (40 - 800 kJ ⋅ mol-1) [10]. It is notedthat the activation energy of fluoride chemical
adsorption of Mg-Al oxide was 73.3 kJ ⋅ mol-1 [11],and the activation energy of fluoride physicaladsorption of tetrametallic oxide adsorbent (Fe-AlCe-Ni) was 1.1 kJ ⋅ mol-1 [10]. The activation energyof ordinary physical adsorption supports the high Faﬃnity of the product and the feasibility of thereaction.
-1

Removal[%]𝒌𝒌𝟐𝟐 = 𝑨𝑨 ∙ 𝐞𝐞𝐞𝐞𝐞𝐞(−𝜟𝜟𝑬𝑬 ∙ (𝑹𝑹 ∙ 𝑻𝑻)−𝟏𝟏 )30

60 90 120 150 180
Contact time [min]Fig. 8 Concentration and removal of fluoride during
the treatment.
The chemical composition of hot spring water andtreated water with 100 g ⋅ L-1 addition for 3 h is shownin Table 4. While 35 % of F- was removed in the hotspring water after the treatment, other elements were
removed less than 16 %. It suggests that fluoride in
hot spring water can be selectively removed using
mechanochemically treated zircon sand.

471

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 4pH
FClSO42Na+
K+
Mg2+
Ca2+
Fe2+
Al3+

REFERENCES

pHs and chemical compositions of hot
spring water and treated water.
Hot spring water
1.6
11.6
5101
12720
210
140
103
24.7
149
122

[1] NEC facilities Co., Ltd., Law and treatment
technology for boron and fluorine in wastewater,
Chemical Equipment, Industry ResearchCommittee, Ed., 2010, pp. 68-79[2] Halla L., Velazquez J., Robert H., Juan M. andJose R. R., Zirconium – Carbon Hybrid Sorbent
for Removal of Fluoride from Water: Oxalic
Acid Mediated Zr(Ⅳ) Assembly and Adsorption
Mechanism, Environmental Science Technology,
Vol. 48, No. 2, 2014, pp.1166 – 1174
[3] Munemiya S., Manufacturing method andapplications of zirconia grain, Newsletter of theJapan Institute of Metals and Materials, Vol. 23,
No. 2, 1984, pp.97-103[4] Hirota K. and Wajima T., Preparation of Highly
Selective Phosphorus Adsorbent From Zircon
Sand by Mechanochemical Treatment, J. Ion.
Exch., Vol. 29, 2018, pp.158-162[5] Hirota K. and Wajima T., Selective Removal of
Phosphorus
fromSeawater
Using
Mechanochemical Treated Zircon Sand,
International Journal of Environmental Scienceand Development, Vol. 11, No. 5, pp. 263-267[6] Meenaksh S., Sundaran C. and Natrayasamy V.,
Defluoridation of Water using Magnesia/
Chitosan Composite, Journal of Hazardous
Materials, Vol. 163, issue 2-3, 2008, pp.618-624[7] Blackwell A. and Carr P.M., Study of theFluoride Adsorption Characteristics of Porous
Microparticulate Zirconium Oxide, Journal of
Chromatography, A 549, 1991, pp. 43-57[8] Shiomi H., Mukai H., Masui M., Hashidume Y.
and Akita Y., Development of Phosphorous
Adsorbent using Mechano-chemically TreatedCalcium Silicate, Journal of the Society ofMaterial Science, Japan, Vol. 53, No. 6, 2004, pp.
618-622[9] Ogata F., Tominaga H., Kangawa M., Inoue K.
and Kawasaki N., Adsorption Capacity of Dye in
the Presence of Dying Assistant Auxiliaries by
Carbonaceous Material Produced from Cotton,
Journal of the Surface Science Society of Japan,
Vol. 32, No. 12, 2011, pp. 804-808[10] Raghav S. and Kumar D., Adsorption
Equilibrium, Kinetics, and Thermodynamic
Studies of Fluoride Adsorbed by Tetrametallic
Oxide Adsorbent, Journal of Chemical &Engineering data, Vol. 63, 2018, pp. 1682-1697[11] Kameda T., Oba J. and Yoshioka T., Kinetics andEquilibrium Studies on Mg-Al Oxide for
Removal of Fluoride in Aqueous Solution and its
Use in Recycling, Journal of Environmental
Management, Vol. 56, 2015, pp. 252-256

Treated water
1.7
7.6
5167
10739
221
117
105
27.8
151
125

CONCLUSIONS
Highly selective fluoride adsorbent was attempted
to be prepared from zircon sand by mechanochemicaltreatment using a planetary ball mill, and the fluoride
adsorption ability of the product, such as fluoride
adsorption amount, pH dependence, fluoride
adsorption isotherms and kinetics, was investigated.
The results are as follows;
1. The amount of fluoride adsorption of zircon sandcan be increased rapidly within 10 min of
mechanochemical treatment.
2. The amount of fluoride adsorption of the productincreased with decreasing pH.
3. The fluoride adsorption of the product in highsaline water was higher than raw sand.
4. The adsorption isotherm at pH 2 was fitted toLangmuir model better than Freundlich model, andthe calculated maximum adsorption amount is 0.09
mmol ⋅ g-1.
5. The adsorption kinetics of the fluoride in the
fluoride solution fit to pseudo-second-order model
better than pseudo-first-order model. The productindicates higher adsorption amount at lower
temperature, while the reaction kinetics decrease.
6. Fluoride can be adsorbed selectively from hot
spring water below Japanese effluent standard for
fluoride (8.0 mg ⋅ L-1).
These results indicate that a novel product withhigh selective fluoride adsorption ability can beprepared by simple mechanochemical treatment, andcan be applied to remove fluoride from hot springwater.

472

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

COMPARISON OF THE NATIONAL PARK MANAGEMENT SYSTEMIN THE UNITED STATES AND JAPANTakashi Kuchii, Atsushi Nakashima
PhD ABD, Faculty of Systems Engineering, Wakayama University, Japan
Professor, Faculty of Systems Engineering, Wakayama University, Japan
ABSTRACT"National Parks" have been introduced in various countries since Yellowstone National Park was designatedin the US in 1872 as the first National park. National Parks can be symbols of a country. In some developing
regions, where institutional design and management for nature conservation are not matured and where theeconomy tends to take precedence over nature conservation, it is important for authorities to have a common
understanding based on accurate information. The United States and Japan have leading examples of Nationalparks and their management systems. Yellowstone area in the US and Yakushima area in Japan are iconic
National Park areas. Each of those parks have different climate, geology, geography, fauna and flora. However,both have a state of the ‘National Park management system’ and something precious act human beings try topreserve. From data survey, interview survey, and comparison on both areas, the following has been revealed.
(1) The management systems are different, (2) Differences between the two systems are related to land systems,
history, etc. (3) There are other kinds of conservation systems, not only the National Parks system. Since eachcountry has its own circumstances, it does not make sense to create ‘uniform standards’ or ‘common systems’.
Organize comparisons more along the lines of a Nature Conservation system to deepen our understanding deeper
and accommodate National park diversity.
Keywords: National park, Yellow stone, Yakushima, Conservation and Utilizationcooperating with some other stake holders tomanage the park.
With the Exclusive type, the park authority has
power to manage all or most of the land. U.S.,
Canada, Tanzania are among the countries with thistype. The park authority has total control exclusive
power to manage the park.
In this full paper, comparison is done between
Yellowstone National Park in U.S. as ‘Exclusive
type’ and Yakushima National Park in Japan as‘Non-Exclusive type’. Both National Parks areincluded in Man and the Biosphere site (MAB
Biosphere Reserve) and registered on the World
Heritage List as the first ones in their respective
countries by UNESCO.
UNESCO designated 27 MAB Biosphere
Reserve site as first ones in the US in 1976. Amongthe first 27, 14 were withdrawn and only 13 remain
as MAB Biosphere Reserves at this moment.
UNESCO designated 4 sites as the first in Japan in
1980. UNESCO registered Yellowstone as Natural
World Heritage in 1978 as the only one in the US.
UNESCO registered Yakushima as a first one inJapan in 1993. Both areas are considered important
areas from the aspect of the world nature network.
Both National Parks and the irrespective regions
have high reputations and are recognized as
successful examples of nature conservation and park
management.

INTRODUCTION
More than 150 countries have a National Park
System [1]. The National Park System conserves
many of the country's outstanding landscapes andprecious nature. The Nepalese side of the world's
highest mountain, Mt. Everest is designated as
Sagarmatha National Park. Africa's highest
mountain is in Kilimanjaro National Park. North
America’s is Denali National Park, Australia’s is
Kosciuszko National Park and Europe’s highest
mountain, Mt. El Brace is in Prielbrusye National
Park.
The National Park system is different in eachcountry. U.S. has 59 National Parks, Australia hasmore than 500. The first National park among
Japan’s 34 was designated in 1934. On the other
hand, it was in 2016, Three River Source National
Park was designated in China as their first National
Park, 144 years after Yellowstone.
Also the management systems and management
authorities vary.
National Park Management System in the world
can be divided into 2 types; Non-Exclusive type andExclusive type [2].
In the case of the Non-Exclusive type, the park
authority doesn’t have the land ownership or land
management power directly. UK, Japan, Korea areamong the countries who adopt this type. Here, thepark authority needs to put more priority on

473

GEOMATE – Melbourne, Australia, 11-13 November 2020

MATERIAL AND METHOD

refer to local court.
At National Park in U.S. there are entry point
gates where visitors pay an entrance fee, so for
visitors it is clear where the National Park begins.
Throughout the park the main roads are paved for
visitors and parking areas are set at viewpoints ortrailheads.
In National Parks forestry, mining, hunting andprivate commercial business activities are
principally not allowed.

This comparison was focused on the park
management systems of Yellowstone National Parkand Yakushima National Park.
These researches have been done throughvisiting parks, hearing from park rangers, checking
website, papers and books.
Yellowstone National Park and Management of
U.S. National Park
Yellowstone National Park is located in the
Rocky Mountains in the U.S. (Wyoming, Montana,
Idaho states; Latitude 44.42, Longitude -110.58). It
was designated on March 1, 1872 as the U.S. first
National Park (Also the first National Park in theworld). The area covers to 8,983 ㎢, the number ofvisitors in 2017 was 4,116,000. In 1976 it was
registered as MAB Biosphere reserve, then in 1978
as Natural World Heritage site both as first ones in
U.S. by UNESCO. This park was also listed as‘World Heritage in Danger’ during 1995-2003
because of increasing tourists and invasive species.
The management authority of this park is the U.S.
National Park Service.
Yellowstone National Park is surrounded byGrand Teton National Park, Jhon.D. Rockefeller ,Jr.
Memorial Parkway, 6 National Forests (Gallatin,
Custer, Beaverhead-Deerlodge, Caribou-Targhee,
Bridger-Teton, Shoshone; managed by the U.S.
National Forest Service), 3 National Wild Life
Refuges (National Elk Refuge, Red Rock Lakes,
Grays Lake; managed by the U.S. Fish and Wildlife
Service) . In the western U.S., the Bureau of Land
Management manages a vast area (one eighth of the
U.S.'s land), but not around Yellowstone National
Park.
Yellowstone National Park has some specificnatural features. This is an active volcanic area, andhalf of World Geysers are in Yellowstone National
Park. There is a Continental Divide, which divides
the river to the Pacific and to the Gulf of Mexico.
Wolves were reintroduced into this area. Bison are
still living in the wild, but the number of bison has
been controlled [3][4].
Park Rangers are on duty in National Parks fornature conservation, visitor protection, visitor
interpretation, facility management and so on. In
Yellowstone National Park, approximately 780 Park
Rangers are employed. Among them, 190 park
rangers have regular life time employment [5]. Somelaw enforcement Park Rangers carry guns and have
not only the authority to arrest, but also authority toYakushima National Park and Management ofJapan National Park
Yakushima National Park is located in Yaku
Island, South West Japan (Kagoshima Prefecture;
Latitude 30.20, Longitude 130.30).
National Park law was introduced in 1931 and 8
parks were designated in 1934 in Japan.
Yakushima was incorporated with Kirishima
National Park in 1969 and designated as Yakushima
National Park independently in 2012. The area
covers 245.66 ㎢, the number of visitors in 2017 was
169,000. In 1981 it was registered as an MAB
Biosphere reserve, then in 1993 part of the park was
registered as Natural World Heritage site,
registrations by UNESCO were first for Japan.
The Ministry of Environment in Japan manages
the National Park as the park authority.
Yakushima
NationalPark
occupies
approximately 45% of the land area of Yaku island.
More than 85% of the National Park land property is
National Forest which is managed by the Forestry
Agency of Japan. The Ministry of Environment inJapan designated Wilderness areas, Gensei-ShizenKankyo Hozen-Chiiki, just beside Yakushima
National Park as a precious area to be preserved.
Yakushima National Park also has some specificnatural features. It’s a small island of 541 ㎢, but has
a 1935m peak, so it’s called ‘a high peak standing by
the sea’. The island is in middle of the flow of theKuroshio currents, one of the world's largest ocean
currents and receives a high level of precipitation. At
the sea side, there is 4,500mm of precipitation, andat the mountain side, 10,000mm per year. Mountainpeaks are subarctic, on the other hand the sea sideareas are subtropical. In this region cedar trees growslowly and live long, some cedar lives more than
2,000 years [6][7] .
Park Rangers are on duty in National Parks fornature conservation, visitor protection, visitor
interpretation, facility management and so on. In
Yakushima National Park, approximately 7 Park474

GEOMATE – Melbourne, Australia, 11-13 November 2020

Rangers are employed. Among them, 5 park rangers
are employed on a full time basis. They aregovernment officers. However they don’t haveauthority to arrest and prosecute.
At National Parks in Japan there are no entrance
gates. Visitors don’t pay an entrance fee, so for
visitors it is not clear where the National Park begins.
Some people are living in the National Park andsome people own land inside the National Park.
Roads are used by both visitors and local people
every day. Many roads are managed by local
government, not by the park authority.
In the National Parks, forestry and some otherbusiness activities are possible with permissions.
The Forest Agency of Japan is established forforestry mainly.
There are many differences between U.S. andJapan National Parks.

Special Protection Zone
1st Protection

3rd Protection Zone
Land

Sea
Normal Area
Sea Park Area

Designated

1st March 1872 as
US and the World
first National Parkby President
Ulysses S.Grant

Original
management
Act
Visitors
2017
Area
Number ofRangers
MAB
World
Heritage
Other
relatedsysteminside thePark1894 Park
Protection Act

Yakushima National
Park
Ministry ofEnvironment
Forestry Agency,
Prefecture, Private,
etc.
Incorporated to
Kirishima National
Park in 1969 anddesignatedindependently in20121931 Natural ParkAct

4,116,000

169,000

8983km2
780

541km27 in Park Authority(60 in related)1981 a first in Japan
1993 a first in Japan

Other
relatedsystemaround thepark

National Forest,
National Wild Life
Refuge, National
Park Way,
National Park,
Wilderness Area
No

Park
Authority
Land
Authority

Zoning inpark

1976 a first in U.S.
1978 a first in U.S.

Designated Park Area

Designated Protection Area

Fig.1 Concept of zoning system of National Park in
Japan
Japan National Parks have a zoning system forthe land area of the National Park System. The zones
are Special Protection Zone, 1st Protection Zone,
2nd Protection Zone, 3rd Protection Zone andNormal Zone. Special Protection Zone is the corearea where no one can do commercial activities.
Fig.1 shows the image of zoning system of
National park in Japan. Special protection Zone and
1st Protection zone are often treated as the core area.

Table.1 Comparison of Yellowstone National Parkand Yakushima National Park
Yellow Stone
National Park
U,S. National Park
ServiceU.S. National Park
Service

2nd Protection ZoneRESULTS
Comparison between Yellowstone National Parkand Yakushima National Park from the prospectiveof park management revealed differences below.
Firstly, U.S. National Parks have only one
authority for land and park management. Japan
National Parks have some authority for land
management and some authority for specificobjective management inside the park.
Secondly, Japan National Parks have a small
number of Park Rangers. However, National Parks
are managed not only by the National Park authority
in Japan. In Yakushima’s case, the Forestry agency,
Kagoshima Prefecture, Yaku Town and the Ministryof Environment are managing Yakushima National
Park cooperatively. Total number of public officers
who work for Yakushima National Park area isaround 60 [8].
Thirdly, Japan National Park has the zoning
system in National Park areas. The U.S. National
Parks don’t have such designated zoning system
inside National Parks. However, this doesn’t mean
the U.S. Nature conservation system doesn’t apply
the concept of Core Zone and Buffer Zone.
The U.S. Nature Conservation system often
regards National Park areas or National Monument
areas as core preservation area. Additionally,
National Forest, National Wild Life Refuge,

Forest Ecosystem
Reserve (National
Forest), Special
Natural Monument,
Prefectural
Management Area
Wilderness Area

Yes

475

GEOMATE – Melbourne, Australia, 11-13 November 2020

manages National Park and Park Way area. These
areas do not overlap
On the other hand, Yakushima Management
System is multi layered and overlapping.
A place is preserved or managed as Special
Natural Monument by the Ministry of Education,
Culture, Sports, Science and Technology (MEXT),as Forest Ecosystem Reserve by the Forestry
Agency and as National Park by the Ministry ofEnvironment. Three Authorities for one land.

National Grass Land, National Conservation Area,
National Sea Shore, National Lake Shore, State Parkand some other management areas usually play arole as a buffer zone to protect the core zones.
(Fig.2)
MAB Biosphere Reserve
Gallatin N.F.

National Park Area

Custer N.F.
Yellow Stone N.P.

BeaverheadDeerlodge N.F.

Shoshone N.F.

Red Rock Lakes

USFWS (*1)

Jhon.D.Rockefeller,
Jr. Memorial Parkway

Grand Teton

3 N.W.L.R(*2)
6 National
Forests

USFS (*3)
Bridger Teton N.F.

Caribou-Targhee
Grays Lake

NPS (*4)

2 National Parks

National Elk Refuge

Wilderness
Area

U.S. Congress

Fig2. Yellowstone National Park is surrounded by
National Forests and National Wild Life Refuges.

World
Heritage

UNESCO
MAB

Japan has Wilderness Areas designated by the
Ministry of Environment under the Nature
Conservation Act. In Japan, Wilderness areas arepreserved more strictly than National Parks. U.S.
also has Wilderness Area designated by the UnitedStates Congress under the Wilderness Act.
Wilderness Areas are designated over National
Forests, National Wild Life Refuges, National
Monuments, lands managed by the Bureau of Land
Management and some National Parks etc toencourage the management authority to prioritizepreservation rather than utilization. The term
Wilderness Area in U.S. and in Japan have a
different meaning.
Wilderness Area

(*5)

(*1)
(*2)
(*3)
(*4)
(*5)

↑
Authorities

USFWS : U.S. Fish and Wildlife Service
N.W.L.R : National Wild Life Refuge
USFS : US Forest Service
NPS : National Park Service
Jhon. D. Rockefeller, Jr. Memorial Parkway

Fig.4 Yellowstone Management Authority andManagement system
Local
Government

(*6)

MEXT
(*7)

Special Natural
Monument
Forest Ecosystem Reserve

Forestry Agency

National Forest

National Park Area

UNESCO↑
Authorities

Wilderness
Area

National Park

MOE (*8)

RamsarWorld Heritage
MAB(*6) Prefectural Birds, Mammals and Hunting Management Area(*7) MEXT : Ministry of Education, Culture, Sports, Scienceand Technology(*8) MOE : Ministry of Environment

Fig.5 Yakushima Management
Management system

Authority and

DISCUSSION
The concept of National Park system wasinvented in U.S. and Japanese government imported
National Park system from U.S. Where do thedifferences come from?

Fig.3 National Park Area and Wilderness Area in
Yaku island.
The following charts show the difference ofManagement Authority and Management system of
Yellowstone National Park area (Fig.4) andYakushima National Park area (Fig.5).
Yellowstone Management system looks simple.
One authority for one land. USFWS manage
National Wild Life Refuge area. U.S. Forest Service
manages National Forest area. National Park Service

History of Yellowstone National Park
Before deepening discussion, following show
some background by reviewing the history regarding
management of both National Parks.
476

GEOMATE – Melbourne, Australia, 11-13 November 2020

History of Yakushima National Park

11000 years ago Native Americans began to hunt
and fish in the region
1848 Gold Rush started in West coast of U.S.
1862 Home Stead Act by Abraham Lincoln
1860s Explorations started
1871 Ferdinand V. Hayden explored and wrote
comprehensive reports
1872 President Ulysses S.Grant sign 'The Act of
Dedication' which created Yellow Stone National
Park' as pleasure ground
1870s-1880s
Native American excluded from
National Park
1886 U.S. Army built Camp Sheridan at Mammoth
Hot Spring -1916 Army management
1894 Park Protection Act (6 important people
including Theodore Roosevelt)
1900 Lacy Act (Prosecute poachers)
1916 U.S. National Park Service is created
1917
National Park Service took over the
management from Army
1929 The first Park Area designation
1929-1930s Great Depression
1933-1942 CCC New Deal relief Agency madefacilities in the Park
1976 Registered UNESCO MAB Reserve
1978 Registered UNESCO World Heritage site
[3][4][5][10][11]Small numbers of local people were resident in thearea
1595 Shimazu Clan direct management started
1642 Forestry industry boomed in Yakushima
1879 Land become a National property by the Land
Tax Reform at Meiji Restoration
1891
The Forest Management Force wasestablished and prohibited local people from cuttingtrees
1904-1920 Local people challenged in court, andlost1921 Yakushima Constitutional declaration. Local
people are allowed to use Yakushima forest in a
specific ways
1931 Natural Park Act is established. (Yakushima is
not National Park yet)1957 The Forestry Agency made management planof Yakushima forestry. Then commercial forestry
boomed again
1964 Yakushima is incorporated into Kirishima
National Park and Park is managed by Ministry of
Health and Welfare. Land management controlled
by the Forestry Agency.
1966 The peak of forestry activity.
1971 The Environment Agency is established andpark management transferred.
1975 Designated as Wilderness area
1981 Registered on MAB Reserves
1992
The Forest Agency designated Forest
Ecosystem Reserve1993 Registered on World Nature Heritage2001 Ministry of Environment established.
2005 Registered as a Ramsar Site (Nagata hama)2012 Independently the Ministry of Environment
designated as Yakushima National Park[7][8] [9]At the early stage in governance of the area,
Yellowstone became a National Park and Native
Americans who might have some right or claim to
the land have been excluded from the area. Only the
US Army and National Park Service have been themain authority in this area.
Yellowstone was designated for the purpose ofbeing pleasure ground at the beginning. Civilian
Conservation Corp (CCC) constructed park facilities
such as tourist roads and visitor centers after thegreat depression.

Fig.7 Joumon Sugi, Yaku Cider in
National Park

Fig.6 Buffalos in Yellowstone National Park

477

Yakushima

GEOMATE – Melbourne, Australia, 11-13 November 2020

to conserve, so sometimes the decision makings taketime and sometimes the authority can’t decidewithout compromise.
(2) Differences between the two systems are related
to land systems, history, etc. Intensive utilization ofland in Japan invented zoning system over National
Park management system.
(3) There are other kinds of conservation systems,
not only the National Parks system. Both countries
have Wilderness area, but they are different systems.

From the beginning in Japan, the National Park
Authorities didn’t have power over land
management. The Forestry Agency had land
management power and rights to timber. The
management plan was made by the Forestry Agency
originally in 1957.
And also local people had rights to use theforests based on ‘Yakushima Constitutional
declaration’ of 1921.
Yakushima was incorporated into National parkin 1964 after many things were confirmed. The
Agency of Environment was established after theland use for forestry boomed.
Japan is small and populated, so there have been
intensive utilization of land. Rights on land becomecomplicated. For example, In Japan’s Civil Code, 10
types of land rights are prescribed. Land occupying
right, land ownership right, land cultivating right,
land collateral right and so on.

Conclusive Remarks
Authority of National Park management in U.S.
is the National Park Service. The National Park
Service manages the park based on land
management power and laws. Outside of Parks in
U.S., there are some other systems which conserveand utilize nature, such as National forest, National
grass land, and so on. All systems are based on land
management power. 4 major authorities aremanaging public land for Nature conservation andutilization at the federal level. These 4 authorities
are U.S. National Park Service, U.S. National Forest
Service, U.S. Fish and Wildlife Service and Bureauof Land Management.
Authorities of National Park in Japan are multilayered. The responsibilities are divided into
multiple authorities and these authorities cooperatewith each other to manage the park areas together.
Important decisions regarding park managementin Japan can take time, more than U.S. National
Parks. When the authorities have different interestson the same assets, conflict occurs. Often it is notclear who has the final decision power regarding thearea or the issues.
However, sometimes the fact that time takes for
decision making can work to prevent rapid
developing. It can also allow impact of damage to begreater.
Comparisons between Yellowstone National
Park and Yakushima National Park revealed
differences and these differences are derived from
history and land management power.
To manage a park, it’s better for park authority tohave exclusive power to manage. However, different
from U.S. management, Japan has a complicatedland right system and the governor’s policy was forforestry for a long time. So currently the park
authority doesn’t have exclusive power to manage.
UK, Japan, Korea or some other counties adopt
Non-Exclusive Park Management system. However,Difference of National Park Management System
Japan introduced a National Park System from the
U.S. and developed it along with Japanese society.
Even though Japan’s Park Authority didn’t havedirect management right on the land, they developed
their own system and build a cooperate system fromdifferent aspects of preservation, conservation or
utilization on a land. It leads to multi-layered system.
And the park authority was faced with the necessityof having a zoning system in National Parks.
The power of authorities of U.S. National Parks
and other nature conservation systems are mainly
based on land management power. So at the
National Parks, authorities can preserve withoutcompromise core.
However, recently the different type of
movement is occurring in U.S. National Park.
Gateway Arch National Park was designated in 2018.
The main attraction of the park is an artificial arch in
St. Louis city in MISSOURI.
The system can be changed by the respective
country’s policy. It will be interesting to watch
closely the future developments in U.S. National
Parks.
CONCLUSION
This research made clear the difference of
National Park Management System between U.S.
and Japan.
(1) The National Park management systems of U.S.
and Japan are different. U.S. National parks aremanaged by U.S. National Park Service. In Japan,
management authorities are multi layered on a land478

GEOMATE – Melbourne, Australia, 11-13 November 2020[2] Toshinori TANAKA, National Park System inJapan as non-exclusive management, Yowai
Chiikisei to shiteno nihon no kokuritu kouen
seido, Hokkaido Journal of New Global Law
and Policy, July 2012, pp6[3] Yellowstone Resources and Issues Handbook,
U.S. Department of the Interior, National Park
Service, Yellowstone National Park, pp.1-p104[4] Foundation Document Yellowstone National
Park, U.S. Department of the Interior, National
Park Service, 2014, pp.1-81[5] U.S. National Service, Yellow Stone National
Park
Website,https://www.nps.gov/yell/index.htm, 4th June
2020
[6] Hisae TOKUMARU, Nature Conservation on
Yakushima Island: Kagoshima Prefecture’s
Efforts, Global Environmental Research of
Association of International Research Initiatives
for Environmental Studies Vol.7 No.1 , Issue
2003, pp.103-111[7] Yakusugi Museum, Yakusima Yakusugi
Monogatari, Yaku Island Ceder Story, Yakusugi
Museun, 2000, pp44-45[8] Masashi NORIHISA, 20 years of Yakushima
the World Heritage, Report for a meeting by
Kagoshima Prefecture, 2012, pp.3-9,21,26
http://www.env.go.jp/nature/isan/kento/conf02/
03/mat02.pdf[9] Ministry of Environment, Yakushima Website,
http://www.env.go.jp/park/yakushima/, 4th June
2020,[10] Shigeyuki OKAJIMA, Nature Conservation
Movement in U.S., Amerika no Kankyou Hogo
Undou, Iwanami Shinsho, Issue 1990, pp1-212[11] Alfred Runte, NATIONAL PARKS, TAYLOR
TRADE PIBLISHING, 1947, pp29-41

their histories are also different, so comparison
research including them can be meaningful.
A simple comparison of National Park
Management Systems was not enough tocomprehensively realize the difference ofManagement system regarding Nature Conservationand Utilization in both countries. Organizing
comparisons focused more in the field of Nature
Conservation systems will make our understanding
deeper. Since each country has its own
circumstances, it does not make sense to create‘uniform standards’ or ‘common systems’.
It is important for park authorities andstakeholders in some developing regions, where
institutional design and management for nature
conservation are not matured, and where theeconomy tends to take precedence over nature
conservation, to have a common understanding
based on accurate information.
ACKNOWLEDGEMENTS
We thank all the people of the Graduate School of
Systems Engineering, Wakayama University, Mr.
Tsutomu Asoshina, Ex-Manager of Nagano Nature
Conservation Office and Mrs. Hiroko Kuchii, thepresident of Kailash Co., Ltd. for assistance in thissurvey.
REFERENCES[1] Hiroshi ONODERA, Munehiro ABE, Tsunao
WATANABE, Keiji NAKAJIMA, Toshio
TORII, Katsuji BANSHO, Tamaki NOMURA,
Michiharu KONO, Yuuki IWASA, Mitsuyuki
OKAMOTO, Kei OSADA, Katsumi UEMATSU,
Toshinori TANAKA, Kokuritsu Kouen Ron Kokuritsu kouen no hachijuunen wo touProfessionals Discussion on National Parks review of 80 years history of National Park inJapan-, Nanpou Shinsha, Issue 2017, pp14479

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DIVERSITY OF NATURE CONSERVATION SYSTEM IN EACHREGION AND CHALLENGE OF CREATING GLOBAL NATURECONSERVATION SYSTEM
Takashi Kuchii1, Hiroko Kuchii2 and Atsushi Nakashima3
1

PhD ABD, Faculty of Systems Engineering, Wakayama University, Japan,
2
President, Kailash Co., Ltd, Japan
3
Professor, Faculty of Systems Engineering, Wakayama University, Japan
ABSTRACTThe National Park system is one of the oldest of a variety of Nature Conservation Systems or International
Nature designation. Over 150 [1] countries have National Parks and the first National Park was born in 1872.
National Parks are popular tourist attractions all over the world. Authorities of National Parks try to balance
nature conservation with utilization. In some countries such as Tanzania, Nepal and Ecuador where Nature
conservation systems are developing, the countermeasures to an increasing number of visitors has also become
an urgent matter. They need to utilize such assets for their daily life. From experience and information byvisiting, interviewing and reading books and full papers, this research makes points clear, such as differencesamong ‘National Parks’ in 10 countries and their related systems. Efforts have been made to grasp the Nationalpark from a global prospective by International organizations, but it requires more research and trials. Nature
Conservation Systems including the National Park System are new to humans. We currently stand at the entranceof Nature Conservation from a Global view. Mutual international cooperation is necessary. It is also important toprioritize economic aspects creating a global standard for National Parks and Nature Conservation.
Keywords: National Park, IUCN Categories, Conservation and Utilization, Nature Conservation

China, a world leader in many ways, designatedtheir first National Park in 2016. Beijing Great Wall
National Park is popular tourist place and registeredas World Cultural Heritage. Rapa Nui, Easter Island
is designated as Chilean National Park and is also
registered as World Cultural Heritage.
National Park systems vary in each country.
Some try to preserve primitive nature and some try
to protect monuments. Some National Parks put
priority on nature preservation, some on visitor
control and some on utilization for the tourist
industry.
Annapurna range in Nepal is very significant
place which has beautiful landscape. However, the
Nepalese government conserves it not as ‘National
Park’ but as ‘Conservation Area’.
Mt. Aconcagua, the tallest in South America
stands in Argentina and was designated as‘Provincial Park’, not as ‘National Park’.
In Australia they designate more than 600
National Parks and managed by local governments,
not by National government.
Standards for designation of National Parks lookdifferent in each country.
National Park system is a system to balance
conservation and utilization. However, each country
has different National Park system and standards.
International organization such as IUCN and other
Conventions are trying to categorize or standardize
regulation of National Parks and Nature

INTRODUCTION
National Parks are popular tourist’s attraction allover the world.
Kilimanjaro National Park is in Tanzania.
Climbing Mt. Kilimanjaro is an interesting tour. On
the way down from the top of Africa, a guide
proposed to go on Safari for big cat hunting.
Galapagos National Park is in Ecuador. It is wellknown Darwin’s theory of evolution. Endemic
species such as Galápagos giant tortoise, Galapagos
penguin, finches and more can be seen easily. To
enter the Island, visitors must get 20USD permission
before they leave for the Galapagos Islands and needto pay 100USD at the airport. Visitors must be clear
of invasive species and pass through a disinfection
mat on entry. Visitors can’t look around the National
Park without official guides.
Brijuni National Park in Croatia was a residenceof Josip Broz Tito, the President of the Socialist
Federal Republic of Yugoslavia. He welcomed many
country leaders to the Brijuni and accepted many
gifts of endangered animals and raised them in theisland. Brijuni is a very beautiful island and good
tourist attraction, but has no primeval nature.
Yellowstone National park was the only place inthe U.S. where wild bison existed. Recently theauthorities have controlled the numbers of bison.
Yellowstone is popular as the first National Park in
the world, which was designated in 1872.
480

GEOMATE – Melbourne, Australia, 11-13 November 2020

Conservation.
The overall objective of this paper is to grasp theexistence of difference among ‘National Parks’ andpresent the issue regarding international designationand the necessity for more research and cooperation
to categorize and make standards for National Parks,
which are widely accepted in the world as the main
system of Nature Conservation.

Category III: Natural Monument or Feature
To protect specific outstanding natural features
and their associated biodiversity and habitats.
Category IV: Habitat/Species Management Area
To maintain, conserve and restore species andhabitats.
Category V: Protected Landscape/SeascapeTo
protect
andsustainimportantlandscapes/seascapes and the associated nature
conservation and other values created by interactions
with humans through traditional management
practices.
Category VI: Protected area with sustainable use ofnatural resources
To protect natural ecosystems and use natural
resources sustainably, when conservation andsustainable use can be mutually beneficial. [2]MATERIAL AND METHOD
Among National Parks which we actually visitedand did some researches, we selected 10 National
Parks from 10 countries, 3 from Asia, 1 from North
America, 2 from South America, 1 from Oceania, 2from Europe and 1 from Africa. Studies have been
done by interview, book, papers and website
research.
Many National Parks are registered or designates
by international organizations. How international
Organizations are trying to categorize, recognize andcreate definitions on National Parks is stated infollowing section.

World Natural Heritage
The United Nations Educational, Scientific andCultural Organization (UNESCO) seek to encourage
the identification, protection and preservation ofcultural and natural heritage around the worldconsidered to be of outstanding value to humanity.
This is embodied in an international treaty called theConvention concerning the Protection of the World
Cultural and Natural Heritage, adopted by UNESCO
in 1972. 1121 properties are listed including 218
Nature Heritages and 39 mixed, as of June 2020. [3]Protected Area Categories
IUCN protected area management categories
classify protected areas according to their
management objectives. The categories arerecognized by international bodies such as theUnited Nations and by many national governments
as the global standard for defining and recording
protected areas and as such are increasingly being
incorporated into government legislation.
Category Ia: Strict Nature Reserve
To conserve regionally, nationally or globally
outstanding ecosystems, species (occurrences oraggregations) and/or geodiversity features: these
attributes will have been formed mostly or entirely
by non-human forces and will be degraded or
destroyed when subjected to all but very light humanimpact.
Category Ib: Wilderness Area
To protect the long-term ecological integrity ofnatural areas that are undisturbed by significant
human activity, free of modern infrastructure andwhere natural forces and processes predominate, sothat current and future generations have theopportunity to experience such areas.
Category II: National ParkTo protect natural biodiversity along with its
underlying ecological structure and supporting
environmental processes, and to promote education
and recreation.

World Network of Biosphere Reserves
The World Network of Biosphere Reserves ofthe MAB Programme was launched in 1971 by
UNESCO. The Network is one of the maininternational tools to develop and implement
sustainable development approaches in a wide arrayof contexts. There are 701 biosphere reserves in 124
countries, including 21 trans-boundary sites. [4]The Convention on Wetlands in Ramsar
The Convention on Wetlands is theintergovernmental treaty that provides theframework for the conservation and wise use of
wetlands and their resources. The Convention was
adopted in the Iranian city of Ramsar in 1971 andcame into force in 1975. There are 2,331 Ramsar
Sites in 170 countries, covering over 2,100,000
square kilometers. [5]481GEOMATE – Melbourne, Australia, 11-13 November 2020and nature conservation systems.
In some countries, the National Park System isthe main one to conserve nature. And in somecountries, there are a range of conservation systems
other than National Park.
Information as for the National Parks indeveloped countries was easier to obtain rather thanin the case of developing countries.
Table.1 shows comparison among 10 National
Parks in 10 countries. These National Parks are
popular National Parks in each country and are big
tourist attractions. They are designated not only as
National Parks, but also as World Heritage, MAB or
Ramsar. All 10 National Parks are assigned IUCN
Categories. Some parks charge expensive entrance
fees, and some are free of charge.

UNESCO Global Geoparks
UNESCO Global Geoparks (UGGp) began in
2001. They are single, unified geographical areas
where sites and landscapes of international
geological significance are managed with a holistic
concept of protection, education and sustainable
development. There are 147 UNESCO Global
Geoparks within 41 Member States, covering a total
area of 288,000 km². [6]
RESULTS AND DISCUSSION
This research on 10 National Parks was focused
not only on ‘Nature’ but on international designation

Table.1 Comparison of 10 National Parks in 10 countries
Entrance
Fee

World
Natural
Heritage

MAB
Reserve

RamsarSite

Geoparks

Area

1964

Free

2005

-

‐

‐

386km2

II

1980

18USD

1991

1977

‐

‐

1817km2

Nepal

II

1976

18USD

1984

-

2003

-

952km2

Lake district

U.K.

V

1951

Free

2017

-

1991

-

2362km2

Torronsuo

Finland

II

1990

Free

-

-

2004

-

30km2

1974

100USD
over

1987

-

-

-

1,668Km2

52,000594,660(2018)
275000(2018)
542,794(2015)

Designation
IUCN
Year
Categories

Park NameCountry

Shiretoko

Japan

Ia

KomodoIndonesia

Chitwan

Tanzania

Kilimanjaro

II

Denali

U.S.

V

1917

15USD

-

1976

-

-

19,185.79
km2

Galapagos

EcuadorII

1959

100USD

1979

1984

2002

-

7,970km2

Los Glaciers

Argentine

II

1937

22USD

1981

-

-

-

7,269km2

Francois Peron

Australia

II

1993

Free

1991

-

-

-

525km2

Table.2 International designation and conservation system in 10 countries

Visitor(Year)
1,760,000(2017)
45,000(2010)
37,097(2016)
19,380,000(2018)
17,900(2013)*( ) is heritage in danger

country

National
Parks

World
NatureHeritage

Japan

34

4

10

52

9

Ministry ofEnvironment

1934

Wilderness Area, Quasi-National Parks, Prefectural
Natural Parks, Forest Ecosystem Reserve and so on.

Indonesia

54

4(1)

16

7

4

Ministry ofEnvironment andForestry

1980

3 Nature Reserves and over 100 Marine protectedareas.

Nepal

12

2

0

10

0

Ministry of Forests
and Environment

1973

1 Wildlife Reserve, 1 Hunting Reserve, 6Conservation Areas, and 13 Buffer Zones, covering
23.39 % of the total country's land.

U.K.

15

6

7

175

7

15 own authority

1951

Area of Outstanding Natural Beauty (AONB) andsome other protected area from aspects such as
Biodiversity value, Geo diversity value or Scenic
value.

Finland

40

1

2

49

1

Metsähallitus

1956

19 Strict nature reserves and 12 Wilderness
Area.

Tanzania

22

4(1)

5

4

1

Tanzania National
Parks (TANAPA)

1951

1 Conservation Area, 2 Game reserves, 2 Marine
Park, 12 Forest Nature Reserves.

U.S.

62

13(1)

28

40

0

National Park
Service

1872

Variety kinds of Nature Conservation system inFederal. Every State also has its own conservation
systems.

Ecuador

11

2

7

19

1

Ministerio delAmbiente y Agua

1959

5 Nature Reserves of Ecological, Forest andBiological.

Argentine

37

5

15

23

0

Administración deParques Nacionales

1934

Provincial protected area.

Australia

685

16

9

66

0

6 by National,
others by states
government

1879

Antarctic Specially Protected Areas,
Commonwealth Marine reserves and many states
managed reserves.

MAB
Reserve

Ramsarsite

Geoparks

Park Authority

First
National
Park

Other Nature Conservation system

482

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table.2 shows nature conservation systems in the
10 countries. In 9 of these countries, the National
Park Authority is National Governments. In
Australia, the State Governments are in control.
The number of Ramsar sites in UK is the largest
in the world. And the number of National Park inAustralia is the largest in the world. The second
oldest National Park in the world is the Royal
National Park in Australia. All countries have theirown nature conservation system.
Through this study, we found some interesting
points. 10 National Parks are all categorized with
IUCN categories. And IUCN Categories have acategory II National Park, but some National Parks
are categorized in Ib and V, not II. In some National
Parks visitors need to pay to enter the park.
Developing countries have tendency to charge theentry fees of National Parks. The U.S. has a wide
range of nature conservation systems. So it seems
the U.S. is passionate about nature conservation, butinterestingly they don’t have Geoparks as of June2020.

to change the name of category II from ‘National
Park’.

Fig. 2 Lake District N.P.: IUCN Category V
If the Global standards or Systems are not simple,
confusion can easily occur. IUCN have been playing
and will play big roles in leading nature
conservation in the world. So IUCN has
responsibility to make their efforts easily
understandable.

Simplicity is important for global standards

Local Economy and National Parks

These IUCN categories are decided based on
application from authorities in each country. So
categories can be strongly influenced by how theydescribed the park on the application form. The
Category Assessment is re-applicable. [7]Among these 10 researched National parks, someNational Parks are categorized in Ib and V, not II.
The Denali National Park and The Lake District
National Park are both categorized in V. However,comparing the visit experience, The Denali National
Park and The Lake District National Park were very
different.

In Tanzania, adjacent to National Parks, there areGame Reserves where recreational hunting occurs.
Visitors can shoot a lion with rifles if they pay
10,000 USD. There are no fences between National
Parks and Game Reserves. This is a surprising fact
in 2020. They call Buffalos, leopards, lions,elephants and rhinoceros are promoted as the Big
Five of hunting. Entrance fees and Activity fees arevery important income especially for developing
countries. It’s easy to say for outsiders ‘Don’t killwild animals’, but things are not so easy.
The guides who proposed me big cat hunting
said that he didn’t want to kill lions but it was such
big income and if he was successful he wouldn’t
have to work for some years.

Fig.1 Denali N.P.: IUCN Category V
Perhaps it would be better for IUCN to review
the categories system so that National Parks canhave multiple-categories. That multiple-categorysystem is already adopted by UNESCO the World
Natural Heritage adopted. Another option would be

Fig. 3 Elephants in Tanzania Safari

483

GEOMATE – Melbourne, Australia, 11-13 November 2020

or national authority. Geoparks have 147 sitesaround the world. There are no Geoparks in The
United States, former Soviet Union territory, India
or Australia. The largest number of Geoparks is
China’s 33 out of the total 147. China designatedtheir first National Park in 2016. There might besome special reason for this, not scientific reasons.

To climb Mt. Kilimanjaro in Tanzania, a climberpays the park authority 810 USD for 6 nights 7 days
climbing. Climbers need to pay for guides, porters,
tent, food, etc, totally over 2,000USD + tips.
Tanzania’s GDP per person is 1,029USD in 2018.
A climber pays almost double the amount of one
person’s yearly income only for 7days. To climb Mt.
Annapurna South in Nepal, visitors pay over 6,000
USD. The trek to the Annapurna Base Camp costover 800 USD. Nepal’s GDP per person is1,034USD in 2018/ 2019.
For these countries, tourism is quite a large
industry. For reference, GDP per person in U.S. is59,531USD and in Japan is 39,303USD. [8]International standards to conserve nature should
pay attention to their local life.

Table.3 First National Park Designation in EachCountry
1870s

2

United States, Australia

1880s

2

Canada, New Zealand

1900s

2

Namibia, Sweden

1910s

3

1920s

7

1930s

16

1940s

7

1950s

15

1960s

18

1970s

18

1980s

14

1990s

3

2000s

5

2010s

1

Switzerland, Mexico, Spain
Italy, Zambia, South Africa, Zimbabwe,
Iceland, Algeria, Guyana
Chile, Argentina, Netherlands, Ireland,
Poland, Burundi, Japan, Romania, India,
Brazil, Uruguay, Venezuela, Sri Lanka,
Greece, Belarus, Bolivia
Morocco, Kenya, Georgia, North
Macedonia, Paraguay, Croatia, Slovakia
Tanzania, United Kingdom, Uganda,
Montenegro, Côte d'Ivoire, Burkina Faso,
Niger, Costa Rica, Guatemala, Finland,
Dominican Republic, Madagascar,
Turkey, Ethiopia , Ecuador
Mozambique, Serbia, Colombia,
Thailand, Slovenia, Peru, Vietnam,
Norway, Chad, Bulgaria, Czech Republic,
France, Israel, Bosnia and Herzegovina,
Malawi, Albania, South Korea, Botswana
Germany, Ghana, Estonia, Portugal,
Pakistan, Hungary, Seychelles, Nepal,
Latvia, Denmark, Iran, Lithuania,
Dominica, Kyrgyzstan, Panama, Gambia,
Nigeria, Nicaragua
Tunisia, Indonesia, Ukraine, Austria,
Myanmar, Egypt, Russia, Haiti, Taiwan,
Kazakhstan, Sierra Leone, Kosovo,
Belize, Fiji
Tonga, Cambodia, Mauritius
Belgium, Gabon, Azerbaijan, Malta,Afghanistan
China

1890s

Fig. 4 Annapurna Conservation Area
National Park spread widely
The first National Park was created in 1987 andspread over more than 150 countries. It spread very
fast all over the world.
The Table.3 shows the number of countries
which introduce National Park in each 10 years.
(113 countries information were found throughwebsite research.)
It is less than 150 years since ‘National Park’spread to 150 countries after the first designation in
1872. In the 1930s National Parks became popular.
The World War II interrupted this. Many countries
introduce National Parks between the 1950s and the1980s. In recent years the IUCN hosts ‘The World
Parks Congress’ to gather information and wisdom.
In the 1970s pollution and in the 1990s climatechange became big issues for humans all over theworld. National Park system was established before
these issues. However, National Park is still a newconcept in human history.
International organizations are trying to involve
many bodies in Nature Conservation activity.
However, usually the decision maker is each country

CONCLUSION
Rights of autonomy must be respected. However,
Nature doesn’t have a national border. Some Wild
animals move around. They are trans-boundary.
Forest fire in Australia in 2019 changed the surface
color of Glaciers in New Zealand. Yellow sands
from the Chinese deserts fly to Japan every year.
Asian garbage drifted to seashore in America. All
human beings are under the effect of global climatechange.
At this moment, Nature Conservation is484

GEOMATE – Melbourne, Australia, 11-13 November 2020

Yellowstone.
Human beings stand at the entrance of Nature
Conservation from Global view. More researchshould be done and more supporters should be
involved, and systems and categories should be
simple so that everyone can understand andcomprehend.

dependent on each country’s policy. When wild fires
occurred in Brazil in 2019, many countries or bodies
proposed support to the Brazil government.
However, they were rejected. No one outside Brazil
can force the Brazilian government to do somethingin Brazil. Recently it became more possible than
before to pressure governments by gathering public
opinion through SNS or other internet tools.
There is a definite necessity to cooperate overborders for the Nature Conservation. Internationalorganizations and nations are struggling. The historyof International Nature Conservation is not old.
Geoparks started in 2001, Ramsar in 1975, World
Heritage in 1972, MAB programme in 1971 andNational Park in 1872. IUCN organization wasestablished in 1948U and NESCO organization wasestablished in 1946.
Human beings are at the beginning of Global
Nature Conservation. It’s better to make simple
systems or standards for mutual understanding to
involve many bodies.
To unite to one Nature Conservation system is
impossible. Diversity is very highly valued when it
comes to nature conservation. However, showing asimple standard or definition can be possible.
Especially in the way of minimum standard
regulations or mandatory standard regulations
should be discussed and shared all over the world
quickly. We all expect IUCN to play this importantrole in global society.
In the developing countries, there is a priority
more on economic development or the tourism
industry rather than Nature Conservation. Theirdaily life is important and Nature Conservation is
also important. It’s ideal to categorize or to makestandards for Nature based on scientific knowledge.
Developing countries need to utilize their national
assets to create benefit as their countries developed.
So it may be best to show some economic benefitsof a global standard or global categorization for
Nature Conservation alongside scientific knowledge.
It must be remembered that in the U.S.
Yellowstone National Park was designated in 1872
as a pleasure ground. In Japan, there was an era
when local governments wanted to have National
Parks in their jurisdiction as tourism attractions, soin the 1940s Japan created Quasi-National Parks andPrefectural Nature Parks. Lions are hunted in
Tanzania and Bison numbers are controlled in

ACKNOWLEDGEMENTS
We thank all the people of the Graduate School of
Systems Engineering, Wakayama University forassistance in this survey.
REFERENCES[1] Hiroshi ONODERA, Munehiro ABE, Tsunao
WATANABE, Keiji NAKAJIMA, Toshio
TORII, Katsuji BANSHO, Tamaki NOMURA,
Michiharu KONO, Yuuki IWASA, Mitsuyuki
OKAMOTO, Kei OSADA, Katsumi UEMATSU,
Toshinori TANAKA, Kokuritsu Kouen Ron Kokuritsu kouen no hachijuunen wo touProfessionals Discussion on National Parks review of 80 years history of National Park inJapan-, Nanpou Shinsha, Issue 2017, pp14
[2] IUCN,
Protected
Areas
Website,https://www.iucn.org/theme/protectedareas/about/protected-area-categories, 12th June
2020
[3] UNESCO,World
Heritage
Website,https://whc.unesco.org/en/about/, 12th June
2020
[4] UNESCO, Biosphere Reserves Website,
https://en.unesco.org/biosphere/wnbr, 12th June
2020
[5] Ramsar
Convention,
RamsarWebsite,https://www.ramsar.org/, 12th June 2020
[6] UNESCO, UNESCO Global Geoparks Website,
http://www.unesco.org/new/en/naturalsciences/environment/earth-sciences/unescoglobal-geoparks/, 12th June 2020
[7] Dudley Nigel, Shadie Peter, Stolton Sue, IUCN
Guidelines for applying protected area
management categories, IUCN, 2013, pp.1-31[8] Ministry of Foreign Affairs, World Area
Website,https://www.mofa.go.jp/mofaj/area/index.html,
12th June 2020

485

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE ENVIRONMENTAL PARAMETERS CONTROLLING THE
HABITAT OF INVASIVE ALIEN SPECIES, SPARTINA
ALTERNIFLORA IDENTIFIED BY PREDICTIVE MODELING
1,2

Michiko Masuda1, Testuya Morioka2 and Fumitake Nishimura3
Faculty of Engineering, Nagoya Institute of Technology, Japan; 3 Faculty of Technology, Kyoto
University, Japan
ABSTRACT

Spartina alterniflora introduced worldwide has impacted local Japanese ecosystems. Then the management
methods of the species are seriously required. In 2019, the invasion of the species was reported at Aichi Prefecture,
Japan. The vegetation of the species was grown up to 449 m2 for 5 years. However, the relationship between the
habitation of the species and environmental factors remains unclear. Therefore, we made a habitat prediction modelusing GLM (general linear model) to define the environment of the S. alterniflora habitat of Aichi Pref.,incorporating data in 2019. That measured population density, location, ignition loss of physical environmental
data, pH, NaCl concentration and altitude over the sea level by us. Our analysis showed that the standardization
parameter altitude over the sea level was the main effect in the predictive model (estimate 4.158) and had thegreatest absolute value. And the Wald value is significantly large (42.426). Secondary the reciprocal relationshipexpression between the altitude and pH was high value, 1.007. The species likes the habitat that altitude is highand pH is 7.1-7.3. For the GLM, we made the distribution for the species, the reappearance is well done. Then the
avoidance of the species expansion, the bank of the tidal area should erase the higher riverbed.
Keywords: General Linear Model, Habitation, Invasive species, Management of Invasive Species
the Ministry of Land, Infrastructure, Transport andTourism, many companies and many volunteers
removed the species from the salt marsh and creek
from 2011 to 2013, then all the plant removed fromthis area. In 2018 the species was detected 50 km
away from where S. alterniflora was previously
discovered in Aichi Pref. From the Street View at
Google map the invasion of the species was observedat the map in 2013. For 5 years the vegetation of thespecies has been widespread until 2018.
In order to prevent the invasion, it is necessary tomonitor the invasion of the species, but it is
impossible to do it because of the necessity to monitor
a vast area along seashore. To conserve the local
natural ecosystems we must detect rapidly in order toremove them with low cost. We, therefore, made a
habitat prediction model using GLM (General linear
models) to further define the environment of S.
alterniflora habitat. The area of the invasion
prediction is defined, we can easily find out theinvasion of the species. In a protection plan of the salt
marsh and tidal area ecosystem, analysis using GLM
is carried out for relation about habitation density andenvironmental factors [9][10]. In this analysis, weconsidered the habitation restriction factors of the S.
alterniflora in Japan.

INTRODUCTION
Smooth cordgrass, Spartina alterniflora wasordinally distributed at seashore at North America
and the Gulf Coast of the Mexico. The species is aperennial plant at salt marsh and estuary. They
occupied rapidly into the tidal zone and theenvironmental condition of the tidal zone has beenchanged by the species [1]. The vegetation of thespecies reduces the energy of the sea wave and helps
stop the coastal erosion. In China and other countries
planted the species that grew up in North Carolina,
Georgia and Florida for the controlling the sea
erosion [2]-[5]. An S. Q. et al. (2007) showed that
introduced S. alterniflora in China wasapproximately 2.6 km2 until 1985, and that it has
increased more than 430 times after 15 years (1120km2), in 2000 [6].
Thus, rapid invasion showed significant adverse
impacts on natural ecosystems, this species listed by
Lows et al. (2000) among the 100 most hazardous
invasive species in the world [7]. In 2008, the species
was detected at Aichi Prefecture, Japan and next year
at Kumamoto Prefecture. The rapid expansion of thespecies changed the tidal area, salt marsh and creek
area in Aichi Prefecture. About 3 years later the creek
was filled by the plant and the water stream wasprevented and the creek is almost dry land. The
Ministry of the Environment declared the species aninvasive alien species (LAS) on the Invasive Alien
Species Act of Japan in 2014 [8]. In Aichi Prefecture,

MATERIALS AND METHODS
Study site486

GEOMATE – Melbourne, Australia, 11-13 November 2020

The place of the investigation was Horikawa river,located in center of Japan at Pacific Ocean side (Fig.
1). The creek is 1.08km for the agriculture drain. It
has the gate to prevent sea water flood about 500m
from the mouth of the river. The banks of the river are
made of concrete brock that have higher river bed.

made the quadrat (1m x 1m) and measured thevegetation cover percentage by photographs. And the
reproduction of seeds was counted on 20th Sep. 2019.
We set the quadrat (1m x 1m), and harvest all the
reproduction stems. After harvest, all stems put intopolyester bags with ethanol not to help expansion of
the species. The number of seeds at each stems wascounted at laboratory.
Environmental Factor Measurements
For the measurement of environmental factors, we
set 130 plots (2m x 2m) in Fig. 2 for the distributionof the species is edge of the river. The water vein isright side. The samplings and measurement werecarried out on 130 plots in summer (2nd Aug. to 20th
Sep. in 2019). The four environmental parameters,
altitude from sea level (h), pH (ph), loss on ignition(IL) and NaCl (psu) concentration were measured ineach plot. The altitude from sea level was measured
using measure pole for leveling. The water pH was
measured pH meter (TOA DDK). Loss on ignition
was measured by sampling mud (core sampling 5cm
diameter 5 cm depth). After sampling the mud wasinto the incubator at 120°C for 2days in order to loss
the water. After pretreatment, the weight wasmeasured and they were burned in the muffle furnace(800°C 4days) to measure loss on ignition.

300km

Fig. 1

Map of research locality

Study Plant
Spartina alterniflora commonly known as smooth
cordgrass or oyster grass, is a salt-tolerant perennial
grass native to the Atlantic Coast of North America.
It is also found on the Pacific Coast of the UnitedStates, where it is considered an invasive species.
Spartina species are among the few salt marshes
plants that have been introduced outside their native
range for erosion control due to their abilities tocolonize open areas, stabilize eroding shorelines, andreclaim land.
The species displays considerable genetic andgenotypic diversity, which affects growth andmorphology, and ecological functions [11]. In the lowintertidal zone, S. alterniflora is often the dominant
species because of its high tolerance of frequently
flooding and hypoxia conditions. Very few species
can survive the ecological challenge of high salt andtoo little oxygen. Even though salt marsh productivity
is very high, species diversity is very low. Salt marsh
communities are dominated by a few species of
halophytes, plants, that are adapted for growth andreproduction in a saline environment. The height ofthe species ranges from 100 to 200 cm along the creek
banks. Recent evidence suggests that the commonly
observed tall and short forms may be geneticallydistinct, numerous studies have demonstrated that thedifference in productivity between forms is a functionof environmental variables [12] [13].

20mFig. 2

Location of each plots.

Statistical Analysis
We performed analysis using GLM (using quickR: correspondence analysis program) to elucidate
habitation density of the S. alterniflora and its
relationship to environmental factors. The
Environmental parameters used in this study assumes
four kinds of measurement data. The population of S.
alterniflora at each plot was used the distribution
research data.
In this study, the response is the population

Distribution Research
In order to know the distribution of the species inHorikawa river, we mapped the species using the 50m
measure and GPS (Garmin) in 9th April, 2019,
through the river. When we detect the species, we
487

GEOMATE – Melbourne, Australia, 11-13 November 2020

density of S. alterniflora. Thus, we supposed that theprobability distribution of the response obeyed
negative binomial distribution. To explain the
distribution of density, our model assumes the main
effect term (when a factor acts alone) and aninteraction term (when two factors interact). In thecase of interaction terms, we performed centration for
2 variables beforehand and incorporated it as an
explanatory variable. In total, the model assumes 6
variables of interaction between 2 factors as
explanation variables, in addition to the main effectvariable (4 variables). As a standard to evaluate thegood model, we used by Akaike’s information
criterion (AIC).

percentage. The census was carried out from 0-100%
vegetation cover at 130 plots.
Cover percentage %
100
90
80
70
60
50
40
30
20
10
0

Fig. 5 Population density of S. alterniflora atmodeling area in Horikawa River.

RESULTSa)Distribution

Altitude from sea level (m)9080706050403020100-

Distribution of S. alterniflora was shown in Fig.
3. The total area was 499 m2, 93 populations weredetected. The population was not always linked,
430m distant was observed between two next
populations.

b)Horikawa Horikawa
RiverRiver

7.87.77.67.57.47.37.27.17.06.96.86.7-

430m

150m

S. alterniflora

S. alterniflora

20m

pH

Fig. 3 Distribution of S. alterniflora in Horikawa
River.

c)

Loss on Ignition %
20-25
15-20
10-15
5-10
0-5

The number of seed reproduction at each shootwas shown in Fig. 4. There were 4 times differences
about seed production at each shoot. The mean seedproduction was 19908/m2.

Frequency

10d)1.31.21.11.00.90.80.7-50NaCl (%)

100

200-

300-

400-

500-

600-

700-

The number of seeds per shootFig. 4 The histogram of seed production at each shootFig. 6 Population density of S. alterniflora atmodeling area in Horikawa River, a) The altitude
from sea level, b) pH, c) The loss of ignition, d) NaCl
percentage of water

Field Census
Fig. 5 showed the population density of thespecies. The unit of the range is vegetation cover area

Fig. 6 shows the environmental descriptors of488

GEOMATE – Melbourne, Australia, 11-13 November 2020

environmental factors in census area. The altitude
from sea level is 0 to 102m. The edge of the river ishigher because of higher river bed. The pH is ranged
from 6.8 to 7.9. The higher level is observed the rightside of the Fig. 6-b. The loss on ignition showed
distributions at the center of the research areas (in Fig.
6-c). The distribution of NaCl ranges from 0.7% to
1.4%, the NaCl level is lower than sea water.
Fig. 7 showed the relationships between thevegetation percentage of the species and fourparameters. Clear relationships between thevegetation cover percentage and four parameters
were not observed.
1.0

0.2

0.2

20

0.6

abundance

0.6

Prediction of vegetation cover percentage %

0.0

0.0

0

0
0

20

0

5

20

40 60 80
比高(cm)
Altitude (m)
40

60

80

100
90
80
70
60
50
40
30
20
10
0

6.8
7.0
7.2
7.4
7.6
7.8
100 6.8
7.0
7.2
7.4
7.6
7.8
pH
pH
100

1.0

1.0

100

60

abundance

0.4

40

0.2

0.2

20

0.6

0.8

0.8

80

0.4

abundance

0.4

40

0.6

Vegetation Cover Percentage (%)

60

0.4

0.8

0.8

80

abundance

Table 1 The new model analysis parameters by new
general linear model
parameter βSEWald z P value
value
α0.005 0.047 0.038
0.97h4.158 0.098 42.426 0.016***ph0.925 0.055 16.902 0.016***IL-0.446 0.071 -6.307 0.000***
psu-0.772 0.062 -12.49 0.016***h:ph1.007 0.12
9.831
0.016***
h:psu-0554 0.090 -6.144 0.000***
ph:IL-0.865 0.088 -9.790 0.016***
ph:psu0.208 0.051 4.64
0.000***
IL:psu-0.691 0.085 -8.116 0.000***

1.0

100

percentage.

Fig. 8 Suitable habitation model of S. alterniflora
predicted density. The unit of the range is percentage
of vegetation cover
Histogram of dd

0.0

0.0

0

0

10

15

20

5
10
15 20
強熱減量(%)
Loss on Ignition (%)

0.7 0.8 0.9 1.0
1.1 1.2 1.3 1.4
塩分濃度(%)
NaCl (%)
0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

80

Fig. 7 The relationships between the vegetation coverpercentage and four environmental parameters

80

The Habitation Model
60

60
40

40

Frequency
Frequency

20

20
0

0

A newly created S. alterniflora habitation modelof this area found the main relationship effect variable
on population density to be the standardization
parameter the altitude from sea level, with a value of
4.158. This was the biggest value by the absolutevalue of all effects calculated. The Wald statistic
value was 42.426 indicating significance. Secondary
the standardized pH value was also high, 0.925. The
Wald statistic value was 16.924. The statistics valueof the model was shown in Table 1.
Fig. 8 shows the habitation model from thegeneral linear model. The habitation model was madeof parameters shown in Table 1, that is altitude fromsea level (h), pH (ph), the loss of ignition (IL) and
NaCl concentration (psu), the relationships
parameters h:ph, h:spu, ph:IL, ph:psu and IL:spu.
Large parts of the calculated habitation model agreewith the observed distribution data. Form the data thecensus and the expectation model, Fig. 9 shows there
was a relationship between expected density and theobserved density. In 92% of the plots the differenceof the vegetation cover percentage is under 20-1.0 -80-0.8 -60-0.6 -40
-0.4
-0.2
0.2
0.4
0.6
1.0
-100
-20 0.00 20
40
60 0.880 100The differenceof予測値の
the prediction（％）
観測値と差

Fig. 9 The histogram is the differences of the
population between the predicted value and theobservation value.
DISCUTTION
From the data of Horikawa River, the area of S.
alterniflora was dredged for stinking ooze in 2013. It
489

GEOMATE – Melbourne, Australia, 11-13 November 2020is expected the invasion of the species is almost 10
years ago. Then the species has been widespread in
Aichi Pref. before 2013. The production of the seeds
expected 5176 seeds/m2 by our data, the total seedproduction of the area would be over 2500000 seeds.
Hayasaka (2020) reported the percentage of the seeds
is almost 60% then 1300000 seeds dispersed in 2020.
The invasive ability is serious problems [14]. In 2016
5 km away from this area, single population wasobserved in Agui River, near sea shore. The
population may be connected the Horikawa River
population.
The Agui River population was easily removed
because of the short time invasion, it was very luckyoccasion. In Toyohashi City the cost of the removal
was almost over 30 million yen. It is very importantthat we find out the population as soon as the invasion
occurrence of the species for the cost reduction. Our
Japanese land has long seashore line and over 40000
rivers. To find out the invasion rapidly, it is necessary
to monitor rivers.
In this study we performed analysis based on four
environmental parameters and the 6 relationships
between two factors. Some studies used the GLM
models and predicted the habitation of each species[15]-[18]. Each model used many environmental
factors from 8 to 10. Bur our environmental factors
were not so much, and the prediction was suite to theobservation distribution. The most impact factor isthe altitude from sea levels, secondary pH value. Boththe parameters can be easily measured, so we caneasily expect the invasion area of the species. The
altitude from sea level is 1m and concrete bank iseasily invaded by the species.
Though our model explains well the distribution,
there are some problems about the environmental
factor fluctuation. It was reported that S. alterniflorachanges a tideland into the grassy plain and it has a
big influence on a native species [19][20]. In ourstudy site, five years have already passed after S.
alterniflora invasion, and environmental condition
may change. The sand and mud were gathered at theroot of the plants and the height from sea level waschanged. After the invasion, it is necessary toinvestigate the environmental factors at once. Now,
the removal of the vegetation of the species andsludge was finished at October in 2019. The
environmental factors are changed and the seeds wasdispersed in 2018, maybe the invasion will be repeat
again. For the improvement of our model, theinvestigation of the environmental parameters at earlyinvasion stage is needed.

alterniflora, invasive plant in sea shore area.
2) However, the species changed theenvironmental conditions, so more precise
investigation needed in order to understandthe invasion of the species.
3) From the predictive model, we were able topredict an invasive condition for eenvironmental factors of the future.
Then our monitoring site should be done below
the site,
1) The altitude from the sea level is 0 to 1m andconcrete banks should be monitored once ayear.
2) Because of the different ecotypes, we should
research another area using the general linear
model.
ACKNOWLEDGMENTS
We send thanks to Prof. K. Serizawa who which
cooperated with the research. We also thank to Mr. T.
Hanai who helped us about field census. And we
thank the member of our laboratory about the help theresearch.
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Mexico, Vol. 25, Barkworth et. al. eds. Oxford
University Press, 2003, pp. 240-250.
[2] Xu H., Created environments voluntarilycolonized by Spartina alterniflora in coastal
Louisiana. Master’s thesis of LSU 2230, 2006,
https://digitalcommons.lsu.edu/gradschool_theses/2230/.
[3] Wan S., Qin P., Liu J. and Zhou H., The positive
and negative effects of exotic Spartina
alterniflora in China. Ecological Engineering,Vol. 35, Issue 4, 2009, pp. 444-452.
[4] Zhang R.S., Shen Y.M., Lu S.G., Wang Y.H., Li
J.L. and Zhang Z.L., Formation of Spartina
alterniflora salt marshes on the coast of Jiangsu
Province, China. Ecological Engineering, Vol. 23,
Issue 2, 2004, pp. 95-105.
[5] Feng J., Shou J., Cui X., Ning C. Wu H., Zhu X.
and Lin G., Effect of short-term invasion of
Spartina alterniflora and the sub sequent
restoration of native mangroves on the soilorganic carbon, nitrogen and phosphorus stock.
Chemosphere, Vol. 184, 2017, pp. 774-783.
[6] An S.Q., Gu B.H., Zhou C. and Wang Z., Spartina
invasion in China: Implications for invasive
species management and future research. Weed
Research, Vol. 47, Issue 3, 2007, pp. 183-191.
[7] Lowe S. Browne M. and Boudjelas S., 100 of theworld’s invasive alien species. Alien, Vol. 12,
2000, pp. 1-12.
[8] Ministry of the Environment, List of regulated

CONCLUTION
The following conclusions are drawn from thestudy.
1) General linear model is effective to more
fully understand the habitation area of S.
490

GEOMATE – Melbourne, Australia, 11-13 November 2020

living organisms under the Invasive Alien Species
Act,
2005,
https://www.env.go.jp/nature/intro/2outline/list/f
uka_plant.pdf[9] Christophe J., and Antoine G., Modelling the
distribution of bats in relation to landscapestructure in a temperate mountain environment.
Journal of Applied Ecology, Vol. 38, Issue 6,
2001, pp.1169-1181.
[10] Sugiyama Y., Nakamura M., Senda S., MasudaM., Environmental parameters controlling thehabitat of the blackish water clam Corbicula
japonica identified by the predictive modelling.
International Journal of GEOMATE, Vol. 17,
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[11] Proffitt C.E., Travis S.E. and Edwards K.R.,
genotype and elevation influence on Spartina
alterniflora colonization and growth in a createdsalt marsh. Ecological Applications, Vol. 13,
Issue 1, 2003, 180-192.
[12] Mendelssohn I. A., and MacKEE K.L., Spartina
alterniflora die-back in Louisiana time-course
investigation of soil waterlogging effects. Journalof Ecology, Vol. 76, Issue 3, 1988, pp. 509-521.
[13] Boyer K.E. and Zedler J. B., Nitrogen addition
could shift plant community composition in a
restored California salt marsh. Restoration
Ecology, Vol. 7, Issue 1, 1999, pp.74-85.
[14] Hayasaka D., Nakagawa M., Maebara Y.,
Kurazono T. and Hashimoto K., Seed germination
characteristics of invasive Spartina alterniflora

Loisel in Japan: implications for its effective
management. Scientific Reports ISSN 2045-2322,
Vol. 10, 2020, article number 2116.
[15] Christophe J., and Antoine G., Modelling the
distribution of bats in relation to landscapestructure in a temperate mountain environment.
Journal of Applied Ecology, Vol. 38, Issue 6,
2001, pp.1169-1181.
[16] Minami N., Cleridy E. L., The Analysis of Datawith Much Zero: Excessive Estimate of The
Tendency by The Negative Clause 2 Regression
Model. Proceedings of the Institute of Statistical
Mathematics, Vol. 61, Issue 2, 2013, pp.271-287.
[17] Zeileis A., Kleiber C., Jackman S., Regression
Models for Count Date in R. Journal of Statistical
Software, Vol. 27, Issue 8, 2008.
[18] Silveira T.C.L., Gama A.M.S., Alves T.P. and
Fontoura, N.F., Modeling habitat suitability of theinvasive clam Corbicula fluminea in a
Neotropical shallow lagoon, southern Brazil. Braz.
J. Biol., Vol. 76, No. 3, 2016, pp. 718-725.
[19] Mooring M.T., Cooper A.W. and Seneca E.D.,
Seed germination response and evidence forheight ecophenes in Spartina alterniflora from
North Carolina. American Journal of Botany, Vol
58, Issue 1,1971, pp.48-55.
[20] Lindau C.W. and Hossener L.R., Substratecharacterization of an experimental marsh andthree natural marshes. Soil Science Society ofAmerica Proceedings, Vol. 45, Issue 3, 1981, pp.
1171-1176.

491

10th Int. Conf. on Geotechnique, Construction Materials and Environment
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

PARADIGM SHIFT IN NATURAL RESOURCES
AND ENERGY POLICIES

1

Hisatoshi Tanaka1, Atsushi Nakashima2 and Nabil Maghrebi3
Graduate School of Systems Engineering, Wakayama University, Japan
2
Wakayama University, Japan
3
Wakayama University, Japan

ABSTRACT
Looking back at Japan's period of rapid economic growth (1955-1973), it is clear that it has much in common
with the current situation in emerging and developing countries. In particular, natural resources and energy policies,
which are essential for social and economic development, seem to be lacking in many developing countries, invery similar way to what Japan experienced before its shift toward economic modernization and industrialization
without natural resources. It is clear that resources management and energy policy require the existence of longterm strategies. The issues of resource and energy security, economic efficiency and environmental protection are
not necessarily mutually exclusive. However, it is a fundamental trilemma that is usually faced by many resourceand energy policymakers. Japan's economy has already plunged into a steady-state economy and it seems to beentangled in more than three decades of prolonged stagnation. This paper examines some aspects of the trilemma
based on energy related research and policy materials. It is argued that a paradigm shift is important because future
resources and energy policies may be unsustainable for industry and society if allowed to be pursued along
uncertain and risky paths.
Keywords: Paradigm shift, Energy policies, Energy security, Economic efficiency, Environmental protection

critical role of the public and private sectors inpromoting basic research and applied technologies tomeet the needs of society, including improving
energy efficiency and raising awareness of energy
education [5].
The purpose of this study is to re-examine thestate of Japanese society during the period of rapid
economic growth, and to redefine energy policy andidentify issues to be addressed in order to promotetechnological innovation in an environmentallyfriendly manner. It is hoped that the results of thisstudy will help emerging and developing countries toconsider policies related to economic growth.

INTRODUCTION
Today, the dramatic changes in the global
environment, especially the problem of global
warming and its solutions, are a common global
challenge. It is undeniable that the background to thisis deeply related to the resource and energy policies
of emerging and developing countries that aim tocatch up with the developed world. Meanwhile,developed countries, which have been degrading the
global environment, are beginning to review theirresource and energy policies to redesign theireconomic growth strategies to achieve a sustainable
society [1].
After World War II, Japan, as a latecomer toindustry, pushed forward to catch up with thedeveloped nations and succeeded in economic growth[2]. The image of Japan then overlaps with that oftoday's emerging nations. On the contrary, today
Japan, as one of the most developed countries, isbeing forced to work towards the realization of asustainable society.
At the same time as shifting resource and energy
policies, it is necessary to recognize that energy
security, economics and environmental protection are
three interrelated issues when considering thepotential impact of the energy system on theenvironment [3]-[4]. This is a basic trilemma thatmany energies policymakers face on a regular basis.
Therefore, there is an urgent need to redefine theJAPANESE HIGH ECONOMIC GROWTH
Japan lost much of the country's economic base in
World War II [6]. During the post-war reconstruction
period, American-style capitalism was adopted inmany fields in an effort to build a new industrial
society [7]. As a result, many Japanese companies
have converted their management methods, which
they learned and developed from Germany after the
Meiji era, to the American style. And a massconsumer society without initiative was born against
the background of mass production, massconsumption and disposable culture [8].
During the period of rapid economic growth(1955-1973), when the foundations of Japan were laid,
the country grew as an export-led economy using

492

GEOMATE – Melbourne, Australia, 11-13 November 2020

cheap labor and a single exchange rate of ¥360 to thedollar as two weapons of international
competitiveness [9]. In the process, the Japanese statealso promoted an aggressive industrial promotion
policy [10]-[11]. It could be described as a kind oftrickle-down economy, where the wealth of thedeveloped Western countries, the customers ofJapanese companies, was the starting point forincreasing the income of domestic workers throughthe circulation of capital and goods [12].
These were (1) strengthening the economic baseby investing in modern capital equipment in key
industries, (2) promoting exports, and (3) reinvesting
profits back into the country [13]. The acquisition ofvaluable foreign currency has also enabled thecountry to import cheap oil from the Middle East andto change its main energy source from coal to oil [14].
This optimistic "use lots of cheap oil" energy policy
lasted until the 1973 oil crisis [15].
As shown in Figure 1, the 1973 oil crisis was aturning point in energy conservation, especially in theindustrial sector, and became. This promoted
economic
growthwhile
reducing
energyconsumption [16]. Meanwhile, energy consumption
in the transport, household, business and other sectors
is rising slowly. In the 1990s, energy-saving measures
were implemented in the transportation sector and theeffects of these measures can be clearly seen [17][18].
The household sector has remained largely
unchanged since the beginning of the 21st century,
mainly due to the increased energy efficiency ofhome appliances [19]. In addition, the downward
trend since fiscal 2011 has been driven by the energysaving orientation after the Great East Japan
Earthquake (i.e., widespread use of LED light bulbs
and the speed of change (such as the spread of energyappliances) is accelerating. The growth in businessand other sectors continued through 2007, with thetertiary sector showing significant growth. In addition,
the global financial crisis has led to a noticeable
decline since fiscal 2007. The sharp increase in thebusiness and other sectors in 1990 was due to achange in the calculation method of theComprehensive Energy Statistics.

Fig. 1 Final energy consumption and real GDP
・Left-hand axis: Real GDP (unit: trillions of yen) ・Right-hand axis: Magnitude of energy (unit: 1018 J)
Source: Compiled by the author based on data from the Agency for Natural Resources and Energy's
Aggregate Energy Statistics, the Cabinet Office's National Accounts, and the Institute of Energy
Economics, Japan (IEEJ) Energy and Economic Statistics Handbook.
Note: The method of calculation of "General Energy Statistics" has been changed since 1990.
The rapid economic growth of the 1960s, sparked

by the technological innovations of the post-1955 era,493

GEOMATE – Melbourne, Australia, 11-13 November 2020

competitiveness and market prices, there has been
little awareness of the importance of investing in theenvironment. It was not until the end of the period ofhigh economic growth that the failure to take care of
the environment and manage the crisis had a serious
impact on the structure of the industry and market
value, which would bounce back on the survival of
the company [26].

led to two mainstream trends in the Japanese
government and business community [20]-[21].
There are two main types of public-private
partnerships: (1) centralization of power and (2)organization. This system of public-private
partnerships will create an affluent and egalitarian
image of the "100 million middle class" in Japanesesociety and It has become an escape route for theJapanese style management society and has
narrowed the choices of the people, which is theessence of the times [22]. As a result, despite the factthat "there is a human life and there is an economy","there is an economy and there is a human life areversed idea of "there is" has been created.
The essence of rapid economic growth is that theentire population is working towards the goal of
creating "prosperity" as envisioned by thegovernment. It was a matter of pushing forward. On
the one hand, the occurrence of external
diseconomies was noted early on, but any criticism
was rather silenced for the sake of economic
prosperity [23].

JAPAN’S NATURAL RESOURCES
PROBLEMSJapan faces significant resource problems as thenatural result of the fact that it is almost impossible
to acquire energy resources domestically. The reason
for this is that both coal and oil reserves are small
and unprofitable. There have been "self-developed
oil fields" overseas in the past, in which Japanese oil
development companies and trading companies haveacquired concessions in overseas concessions anddeveloped them. However, the small scale ofinvestment has prevented it from taking the lead in
acquiring resources, and as a result, many othercountries As well as, it has always been at the mercyof American trends. As a result, because energy
policy does not allow for the free drawing up of along-term vision, the resources on the market are
mainly purchased at spot prices, which tend to reflectdemand, and are subject to changes in international
conditions [27].
In 1955, at the beginning of Japan's rapid
economic growth, hydroelectric power was the main
source of electricity in Japan [28]. However, in order
to meet the increasing demand for electricity inconjunction with economic growth, it was necessary
to switch to thermal power generation. At the time,
the lack of foreign currency prevented theimportation of sufficient oil, and coal, which wasless efficient at generating electricity [29].
As a result, there was a movement to takeadvantage of the "peaceful use of nuclear power"
boom created by the United States to use nuclear
power first before oil-fired power. Japan's nuclear
power program began with the submission of abudget for nuclear research and development to theDiet in 1954 [30]. The Atomic Energy Basic Law
was enacted as early as December 19 of thefollowing year, in order to regulate the use of nuclear
power [31]. As a result, the introduction of nuclear
power generation in Japan was approved for the
purpose of providing a stable and inexpensive supply
of industrial power.
Meanwhile, at the end of the 1950s, most ofJapan's local governments were absorbed in the longphase of economic growth and waves of labour
migration toward urban cities that continued to drain
local populations. The rate of modernization of local
industries remained low, and some municipalities
sought to attract a new industry to replace the

EXTERNAL DISECONOMIES
From the 1960s to the 1970s, a number of social
contradictions erupted in Japan, including the
problem of pollution [24]. This was the result of anational policy that prioritized industrial growth
above all else as a latecomer to capitalism, a stovepiped administration that wanted to protect theinterests of ministries and agencies, and a value
system that emphasized the pursuit of corporate
profits and the disposable income of the people [25].
Resource and energy policies, which are essential
for economic growth, are strongly influenced by thenature of a country's administration. In other words,
in a democracy where the people can choose theirown government, we have a serious problem ofenvironmental destruction. Based on this, the way inwhich people choose to solve social, economic andenvironmental problems depends on whichgovernment they choose.
The Ministry of International Trade and Industry(now the Ministry of Economy, Trade and Industry)
provided administrative guidance for the economic
growth at that time. The Ministry of Trade andIndustry (MITI) guided the Japanese economy with
policies that were appropriate to the economic
conditions of the time. At the same time, theexistence of an intermediary organization (industrial
association) that links the ministry and business
community facilitated energy policy implementation.
For example, the Ministry of International Tradeand Industry (now the Ministry of Economy, Tradeand Industry) enacted a law to promote theautomotive parts industry as an important industry
development
measure.
However,
becauseenvironmental considerations are reflected in market
494

GEOMATE – Melbourne, Australia, 11-13 November 2020

companies.

declining coal industry. In 1974, when Japan's
research and development of nuclear power
generation under the guidance of the United States
progressed, the "Three Laws on Power Sources"were enacted. This has created a mechanism to
subsidize municipalities that attract nuclear power
plants. As a result, depopulated and financiallystrapped municipalities have become moreaggressive in attracting nuclear power plants,
including the Fukushima Daiichi nuclear power
plant, which suffered an accident in March 2011 [32].
At the time, the slogan of the town of Futaba inFukushima Prefecture, which invited the plant, was"Nuclear Power: Energy for a Brighter Future.”

RussiaIndia
Thailand
China
Non-OECD
The Middle East.
Indonesia
Korea
The entire world
CanadaUSA
OECD
Australia
EU28
FranceGermany
Japan
UK

ENVIRONMENTAL INVESTMENT
Environmental issues are very much an
economic issue. For example, in emissions trading,the environment is perceived as a market value andis material to trade. In other words, the environment
itself began to form part of the value of the product.
It is not the resources that are destroying theenvironment, but the economic behavior of thepeople who use them.
Within the current elemental technologies andmeasures, unless the mass-production and massconsumption economy is changed, the resource
neither the problem nor the environmental problem
can be fundamentally solved. Moreover, it is
impossible to solve environmental problems througheconomic rationality alone. This is because theeconomy is not just about productivity; there is also
an economy in consumption. In order to solve
environmental problems, there is a need to reformthe consumer market and consumer awareness at thesame time.
Figure 2 compares indicators of GDP productionand energy consumption (supply) with majorcountries in 2018. Taking Japan as the base value of
1, we can look at the GDP production efficiency ofother countries. A smaller value indicates a country's
energy use and value creation efficiency. In order tobalance the challenges of global economic growthand energy conservation, one option is to not only
demand reductions from more efficient countries,
but also to strongly demand improvements from lessefficient countries.
When environmental issues are examined from amanagerial perspective, it is possible to realize that
there are indeed many environmental business
opportunities. In the past, investment inenvironmental measures has been an economic
burden for companies, but investment fromtechnological innovation has it is a business
opportunity. Today, environmental issues havebecome the basis for evaluating the SDGs and ESGs
being considered in financial markets. It has become
one of the most important management issues for

6.295
4.991
4.840
4.426
4.148
3.917
3.154
3.075
2.529
2.179
1.821
1.510
1.313
1.242
1.238
1.116
1.000
0.923

0.000 2.000 4.000 6.000 8.000

Fig. 2 Major countries in primary energy
consumption per real GDP
Source: "Energy White Paper 2019" by the Ministryof Economy, Trade and Industry and the
Agency for Natural Resources and Energy(June 7, 2019)
CONCLUSION
Japan has prospered for more than half a century
since it became the world's second largest economy
in 1968 [33]. But it has been slow to change its
resource and energy policies, and fiscal conditions
may only be part of the explanation. There has beenrather little willingness to address the majorinternational challenges, including the global
environmental problems that the world is currently
facing.
There is a fundamental trilemma in formulating
an effective energy policy given the potential
tradeoffs between energy security, environmental
protection, and sustainable economic development.
Rebuilding a sustainable society will require
however a paradigm shift from high to sustainableeconomic growth, from unlimited greed to limited
wants, and from environmentally harmful energy
sources to new and renewable energy.
Thus, it is imperative to strengthen basic andapplied research, and achieve technology advancesin the energy sector in line with social policies,
economic orientations, environmental initiatives.
There is an urgent need to raise consumer awarenessthrough better energy education, and redefine the495

GEOMATE – Melbourne, Australia, 11-13 November 20202019.
[17] Energy Efficiency. and Conservation Division.,
Agency for Natural Resources and Energy, onenergy efficiency and conservation measures inthe transportation sector, 2020.
[18] Ministry of Land, Infrastructure, Transport andTourism (MLIT Japan), Environment. carbon
dioxide emissions in the transport sector, 2020.
[19] JXTG Nippon Oil & Energy Corporation., Oil
handbook, 2019.
[20] Takenori Inoki., Japan's Modern Era.7, The
Fruits of Economic Growth 1955-1972,
Chuokoron Shinsha, 2002.
[21] Haruto Takeda., A Reading of the National
Income Doubling Plan, Japan Economic Review,
2014.
[22] Haruto Takeda., The Japanese Economy in HighGrowth Period. What are the Conditions for HighGrowth?, Monozukuri Management Research
Series, The University of Tokyo, 2011.
[23] Environmental Restoration and Conservation
Agency (ERCA). Air Pollution in the First Halfof High Economic Growth 1945-1964, 2019.
[24] Environmental Restoration and Conservation
Agency (ERCA)., Rapid Economic Growth andthe Intensification of Pollution 1965-1974,[25] Akio Watanabe., Japanese Modernity 8. GreatPower Japan's Fluctuations 1972-, Chuo Koron
Shinsha, 2000.
[26] Environmental Restoration and Conservation
Agency (ERCA)., Air Pollution after the Oil
Crisis and Period of Stable Economic Growth,
1975-1984, 2019.
[27] Agency for Natural Resources and Energy,
Department of Natural Resources and Fuels.
Efforts to acquire resources from overseas anddevelop domestic resources, 2019.
[28] Agency for Natural Resources and Energy.,
Japan's 150-year history of energy, 2019.
[29] JXTG Nippon Oil & Energy Corporation,
Petroleum Handbook, 2019.
[30] Agency for Natural Resources and Energy,
Nuclear Power Generation, 2019.
[31] MinistryofInternal
AffairsandCommunications, Nuclear Regulation Authority
The Fundamental Law of Atomic Energy, 2019.
[32] The Federation Electric Power Companies ofJapan, Atomic Energy and Regional Societies,
and the Three Methods of Power Supply
Payment System, 2019.
[33] Agency for Natural Resources and Energy.,
Japan's 150-year history of energy

critical role of the public and private sectors inpromoting responsible investment. Japan can thentake the lead in simultaneously addressing thefundamental
energychallenges,
globalenvironmental problems and sustainable social andeconomic development.
REFERENCES[1] Agency for Natural Resources and Energy,
Energy Policies of Major Countries, Recent
Developments and Challenges Faced, 2019.
[2] Cabinet Office, Government of Japan,
Economic White Paper, 1954-1973, 2019.
[3] Agency for Natural Resources and Energy.,
Japan's Energy Problem, 2019.
[4] G20 Ministerial Meeting on Energy Transition
forSustainable
GrowthandGlobalEnvironment.
[5] Keidanren, Considering Energy, 2019.
[6] Haruhito Takeda., Japanese Economic History,
Yuhikaku, 2019.
[7] Takahide Nakamura., A History of Showa
Economy, Iwanami Seminar Books 17, 1986.
[8] Modern Japanese Economy, The Second Term,
Mass Consumption Society as History, What
Was High Growth?, Keio University Press,
2019.
[9] Takenori Inoki., A Postwar World Economic
History from the Perspective of Freedom and
Equality, Chuko Shinsho, 2009.
[10] National Archives, The Development of HighGrowth Policy, 2020.
[11] Tomotaka Kuwabara., Economic and Industrial
Policy Bureau, Ministry of Economy, Trade andIndustry, Changes in economic and industrial
policy, 2015.
[12] Edited by Keiji Fukao., Takashi Nakamura., andMasayuki Nakabayashi., Iwanami Koza,
History of the Japanese Economy 5,Contemporary 1, From the Sino-Japanese War
Period to the Period of Rapid Growth 19371972, 2018.
[13] Shibata Shinya., Foundations of Political
Economy, Sopfusha, 2011, pp.25-131.
[14] Agency for Natural Resources and Energy.,
Japan's 150-year history of energy, 2020.
[15] Agency for Natural Resources and Energy andNational Life and Economic Activity, 2019.
[16] Agency for Natural Resources and Energy,
What happens when the oil stops? ～Learningfrom History, A Risk to Japan's Energy Supply? ,496

10th Int. Conf. on Geotechnique, Construction Materials and Environment
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

TECHNOLOGICAL INNOVATION AND ESG INVESTMENT
IN THE JAPANESE ENERGY SECTOR
Hisatoshi Tanaka1, Atsushi Nakashima2 and Nabil Maghrebi3
Graduate School of Systems Engineering, Wakayama University, Japan;
2
Graduate School of Systems Engineering, Wakayama University, Japan;
3
Graduate School of Economics, Wakayama University, Japan
1

ABSTRACTIn recent years, the global assessment of the economic activities of companies has become more severe. Thisis due to the existence of agreements on global warming measures such as the Kyoto Protocol of COP3 in 1997
and the Paris Agreement of COP21 in 2015, as well as the global sharing of the concept of the Sustainable
Development Goals (SDGs) adopted at the UN Summit in 2015. In international financial markets, the term"sustainable finance" has become widespread in order to realize a sustainable society, and there is a gradual shift
away from "investments solely for profit" toward "ESG investments for social contribution.” In Japan as well there
is a growing emphasis in the energy sector on renewable energy using natural resources. At the same time, theconstruction of energy technological systems that enable a virtuous cycle of "what is gained from nature is returned
to nature" is underway. The objective of this paper is to identify and examine issues related to ESG investment in
the energy sector, analyze some relevant data, and provide some recommendations for the future of ESG in Japan.
The analytical evidence based on macroeconomic data related to the energy sector indicates that new ESG
investment is essential for technological innovation in response to the environmental needs of future generations.
Keywords: Market economy, SDGs, ESG, TCFD

industrial society [9], [10]. However, with therealization of a low-carbon society and the emphasis
on renewable and natural energy becoming aninternational trend, Japanese energy companies arebeing asked to adopt the SDGs and ESG perspectives
as part of their management strategies [11], [12].
The purpose of this study is to identify and discuss
issues related to ESG investment in the energy sector,
analyze some relevant data, and provide somerecommendations for the future of ESG in Japan. It is
hoped that the evidence will be helpful to companies
and investors who are considering the introduction of
ESG in the future.

INTRODUCTION
The market economy aims at economic growthand social development through the functioning of themarketplace of supply and demand [1]. There is amechanism in place to allocate resources to moreproductive industries [2]. An important part of that ispreventing the waste of resources [3]. In addition, thedecision on which type of resources to consume isessentially left to the free and voluntary actions of
economic agents including households andcompanies [4].
In a market economy, it is the consumer who
decides the actual allocation of resources, but it isthere that the actions of individual economic agentsare aggregated and lead to the allocation of resourcesin the economy as a whole [5]. On the other hand,
while market economies adapt quickly to shocks,
there is always a degree of instability built into them
[6].
The real economy and society have grownsignificantly on a global scale. As a result, instability
within existing market mechanisms has become
apparent [7]. Stability has broken down in theindustrial sector, especially in the developed
economies, and one of the problems has been thenegative impact on the environment that could not be
solved by the market economy [8].
In Japan, coal-fired power plants are operating as
one of the main energy infrastructures that support the

SDGS AND ESG CONCEPTS
Although the concepts of SDGs and ESG are
similar, their perspectives are different [13]. The
SDGs are global environmental and social goals
adopted by the United Nations. The SDGs consist of
17 goals and 169 targets, each of which has anenvironmental it has a policy of alleviating social
problems. For this reason, the SDGs are not a way of
thinking that is confined to the company's profit
opportunities or risk management. Pursuing a
positive impact outside the company is in line withthe purposes of the SDGs.
ESG stands for Environment, Social andGovernance. The key to this is the consideration of
the stakeholders surrounding the company.

497

GEOMATE – Melbourne, Australia, 11-13 November 2020

approach the level in Europe. On the other hand, there
is a large gap of just over 3% in Japan, and investors
in Japan are less interested in ESG than in Europe andthe United States. There are indications that ESG
investment is still in its infancy.

Stakeholders include not only shareholders andinvestors, but also customers, employees, business
partners, competitors, local communities and theenvironment.
Japanese companies have traditionally applied theSDGs framework to explain their CSR activities.
Similarly, the SDGs have been incorporated into ESG
activities and have become entrenched due to theiraffinity. ESG activities mean that a company can
avoid conflict with its stakeholders as much as
possible, while at the same time, it can help them tounderstand the interrelated social, economic andenvironmental challenges. It is an activity to meet thedemands and expectations of the company. Therefore,
companies that were actively engaged in the SDGs
were inevitably considered to have taken ESG
considerations into account in their economic
activities. As a result, investors viewed companies
working to solve environmental and social problems
as more socially aligned than those that simply
emphasized financial attractiveness, and they
expected to see stability in earnings. However, given
the original purpose of the SDGs, it is likely to be oflimited application.

ESG FROM INVESTORS’ PERSPECTIVE
Traditional investments have been profit-driven.
However, after the global economic crisis of 2008,
ESG has become more important, especially forinstitutional investors who manage pension funds[16]. ESG investment is a method of capitalmanagement that aims to reduce negative social
externalities through "non-financial information"
related to ESG as well as "financial information" suchas profits, dividends and tangible assets [17].
ESG Investment relative to Total Assets
60

52.6

50.6

50
37.8

40
30

21.6

20

ESG INVESTMENT STRATEGIES

3.4

0.8

Canada Australia Japan

Asia

10
0

In today's corporate management, investors(mainly institutional investors) are looking to the
SDGs and ESGs as "non-financial indicators" tomeasure corporate value [14]. As a result, companies
cannot ignore these trends in their economic
activities; rather, they are required to be actively
involved [15].
Some companies, especially multinational
corporations, are proactively incorporating the SDGs
into both their business goals and management issues.
There has been an emergence of a mindset that istrying to be embraced. In other words, companies
involved in the SDGs have created opportunities toimprove their position in the global market and toenter new markets by placing the SDGs at the centerof their business models, rather than just as a social
contribution. At the same time, ESG-conscious
management is being incorporated into management
strategies, as it would otherwise put firms at a
disadvantage in market competition.
From the international perspective, data from the
Global Sustainable Investment Alliance (GSIA) showthat the share of ESG investment in global investment
is increasing every year. Figure 1 shows ESG
investment as a percentage of total investment in2016. This shows that in the European region andAustralia, ESG investment already accounts for morethan 50% of total assets. Even in the U.S., where
public opinion on the environment is rather divided,
ESG investment accounts for more than 20% of thetotal. It is only when the US and Canada are put
together that the aggregate ESG investment canEuropeUSA

Fig. 1 ESG investment as a percentage of total assets(2016)
Source: data from GSIA Review 2016, prepared by
the authors.
This may encourage behavioral changes incompanies and investors. For example, diminishinglong-term risks with environmental issues in mind,
job opportunities through new revenue generation forcompanies increase, etc. As a result, it has thepotential to enhance sustainable growth and broaden
its contribution to society.
Use of ESG Informationin Investment Decisions
100%

97.9%

80%
60%
40%
20%

2.1%

0.0%

0%

Currently used No current use No current use
but potential and no plan foruse in future
future use

Fig.2 Use of ESG information in investment
decisions
498

GEOMATE – Melbourne, Australia, 11-13 November 2020

Source: Compiled by the author based on data fromthe Environmental Economics Office, Industrial
Science and Technology Policy and Environment
Bureau, Ministry of Economy, Trade and Industry.

CORPORATE ESG-PERSPECTIVESIn the era of globalization since 1990, thecapitalist economy in the era of globalization has
added the conditions of "energy and information" tothe prerequisites of "people, money and goods," andit is a system that cannot function without these
conditions constantly circulating [18]. As a result, thechallenge for corporate management is how to getcloser to the corporate value that stakeholders seek.

Japan's Ministry of Economy, Trade and Industry(METI) issued in December 2019 a "Survey forManagement Institutions on ESG Investment.” It is
possible to assess the efforts by managers andinvestors toward ESG investment based on the resultsof the survey. As shown in Figure 2, a large
proportion (98%) of the sample of Japanese
investment management institutions (48 respondentcompanies with a total of approximately ¥3988
trillion under management) do indeed use ESG
information in their investment decisions.

Number of TCFD-Sponsoring Organizations(as of December 23, 2019)
0Which international initiatives do you
consider to be important for ESG
investment decisions?

SwedenHolland

50

100

150

200

250

300

Other Organisations
Non-Financial Institutions
Financial Institutions

FranceNone

2.1%
CanadaOther initiatives

35.4%

30％ Club

Australia

43.8%

Climate Actiion 100+

USA

54.2%

TCFD

UK

93.8%

UN Global Compact

Japan

54.2%

SDGs

89.6%

PRI

95.8%
0%

Other countries

Fig. 4 Number of institutions supporting TCFD
Source: Compiled by the authors based on data fromthe Environmental Economics Office, Industrial
Science and Technology Policy and Environment
Bureau, Ministry of Economy, Trade and Industry.

20% 40% 60% 80% 100%

Fig. 3 Japanese ESG Investment Standards
Source: Compiled by the authors based on data fromthe Environmental Economics Office, Industrial
Science and Technology Policy and Environment
Bureau, Ministry of Economy, Trade and Industry.

For companies, a focus on ESG is an opportunity
to be evaluated on the non-financial aspects of the
future. Until now, companies have not consideredenvironmental measures because they are notmarketable [19]. However, environmental care andsocial contributions are now one of the mostimportant business strategies, as they enhance acompany's reputation and lead to increased corporate
value and sustainable growth [20].
There is no denying that the impact of ESG
valuations on corporate value is directly reflected in
the share price of companies, which in turn has had asignificant impact on the liquidity of direct funds
available to them [21]. Investment markets are
increasingly emphasizing the existence of social
value and assessing frm performance from an ethicalperspective [22].
ESG investors evaluate a company's ability tostrengthen its operating base for sustainable growth.
However, it is not the ultimate goal for companies toincorporate ESG indicators and ESG ratings. It's only

Figure 3 shows the ESG investment decisions inJapan. When using ESG information to makeinvestment decisions, 93.8% of asset managers said
they would use the Task Force for Climate-related
Financial Disclosures (TCFD). Other international
initiatives include the UN Principles for Responsible
Investment (PRI) and the SDGs.
Figure 4 shows the number of institutions
supporting TCFD. It is noted that Japan has thehighest number of institutions in the world that
support TCFD. With reference to Figure 5, theevidence suggests that only about 40% of theinstitutions regard TCFD as relevant for ESG
investment purposes. In the future, the number of
support organizations should be increased, and theinformation should be made public and its content
enhanced.
499

GEOMATE – Melbourne, Australia, 11-13 November 2020

ESG the Select Leaders Index (MSIC) has different
evaluation criteria. What they have in common is the
adoption of uniform standards worldwide.
Table 1 shows some of the items assessed by
FTSE and MSIC. For example, the themes
themselves are different in the assessment items on
environment and governance. This has led to adiscrepancy between the FTSE and MSCI ratings.

about long-term growth expectations and gaining
profits.
In order for Japanese companies to be properly
evaluated on a global scale, they need to simply
disclose information and obtain an ESG rating.
Companies need to show investors that they havelong-term growth potential and that they can generate
new profits.

Table 1 Environment and Governance Assessment

How do you assess the state of support for
TCFD among Japanese companies?
No response
Do not know or cannot
answerOther responses
Insufficient support and more
efforts needed
Sufficient support but more
disclosure needed
Sufficient support and proper
disclosure

ESG criteria Assessment items FTSE MSCI
Environment Climate Change
Natural Resources
Pollution and Waste
EnvironmentalMarketOpportunities
Biodiversity

Pollution and
Resource Use
Water SecuritySupply ChainGovernanceCorporate
GovernanceCorporate Behavior

Tax TransparencyRisk Management
Prevention of Decay 
Source: prepared by the authors from FTSE andMSCI references. The checkmark symbol denotes theapplicability of environmental or governanceassessment criteria for the stock market indices.

6.3%
2.1%
8.3%
39.6%
41.7%
2.1%

Fig. 5 Evaluation of Japanese companies' support for
TCFD
Source: Compiled by the authors based on data fromthe Environmental Economics Office, Industrial
Science and Technology Policy and Environment
Bureau, Ministry of Economy, Trade and Industry.
In order for companies to use ESG information forfurther development of their sustainability
management initiatives and for information
disclosure, it is important to consider the essential
significance of their ESG initiatives, clarify theirpriorities, and indicate their intentions. In doing so, itwould be more effective if it could be streamlinedwith data-driven analysis and scoring possible.
Even if companies are making progress on ESG
initiatives, the impact is not immediately apparent.
However, if a company's unique risk factors surface,it could cause significant damage to the company's
value. The company needs to pay attention to investor
relations (IR), ESG compliance, and otherinformation disclosure.
Until now, ESG measures have tended to beviewed as an extension of corporate social
responsibility (CSR) activities [23]. In the future,corporate managers will need to consider ESG
measures as a more important management issue.

Different ESG assessment bodies have different
ESG assessment criteria, which means there is room
for companies to get in that's what it means. Therefore,
companies that desire to enhance their ESG
evaluation should check in advance the items requiredby the evaluation organization and make sure thatthey make the appropriate efforts to disclose relevant
information. There are no legal issues with this typeof action, and it is not fraudulent. However, thecompany is required to be honest and accountable toinvestors and the market. Therefore, it is likely to takesome time for many investors to change theirperceptions about ESG.
CONCLUSIONAs Japan has entered a steady-state economy, it isdifficult to determine whether the ESG initiatives ofJapanese firms are the manifest of transitory effectsof competition or the result of structural changes inJapanese firms. Companies are free to evaluate theSDGs and ESGs in any way they wish. However, thechoice between remaining in the existing investment
market or entering the new ESG investment market is

ESG ASSESSMENT
Each evaluation organization uses its own
indicators to assess a company's ESG efforts. For
example, of the four ESG indicators designated by
Japan's GPIF, the comprehensive ESG indicator
FTSE Blossom Japan Index (FTSE) and MSCI Japan
500

GEOMATE – Melbourne, Australia, 11-13 November 2020

already made by companies on behalf of investors
and for the interest of investors.
In fact, ESG investment is gaining popularity
among institutional investors around the world
because the focus on the SDGs and ESGs by portfolio
managers reduces the risk of deterioration in future
corporate performance or business discontinuity, andit improves also long-term risk-adjusted returns. In
promoting the SDGs and ESGs, internationalorganizations and governments provide frameworks
and guidelines for information disclosure.
Particularly on the environmental front, with the
adoption of the Paris Agreement, it is expected thatthe assessment of inadequate companies will become
more severe.
As volatility is bound to rise in global equity
markets in 2020 given the increased economic
uncertainty, the SDGs and ESG, which are the focus
of institutional investors, such as insurancecompanies and pension funds, who invest for the longterm, can help them to have a consistent investment
policy even in a rapidly changing market environment.
ESG is no longer a merely ethical issue for companies
but has become an important issue that cannot beignored for the firm’s business continuity andsustainable growth.

[9] Agency for Natural Resources and Energy, Are
we continuing to utilize coal-fired power
generation? ～Energy Mix and Efforts to Achieve
CO2 Reduction in FY2030,[10] Agency for Natural Resources and Energy, Basic
Energy Plan, 2019, pp. 26ff.
[11] Cabinet Public Relations Office., SDGs Action
PLAN 2018 - Aiming to transmit Japan's SDG
model in 2019 ~,[12] Keidanren., Achieving the SDGs through theRealization of Society 5.0, 2018,[13] Mizuguchi Tsuyoshi., ESG Investment. The
Shape of New Capitalism, in Nihon Keizai
Shimbun Publishing, 2017.
[14] Kenji Sawami., The Latest Trends in NonFinancial Information Disclosure,
Information Sensor, Vol. 64, 2011, pp. 16-17.
[15] Matsuo Takami., Introduction System and Issuesof Non-Financial Indicators in Performance
Evaluation System of Strategic Thinking,
Journal of National Economy. 194.4, 2006, pp.
73-89.
[16] Lukasz Mamica., Pasquale Tridico., Economic
Policy and the Financial Crisis, Routledge, 2014,pp.6.
[17] Bank of Japan., Recent trends in ESG investment,
Bank of Japan Review, 2019, pp. 1-6.
[18] Shinya Shibata., Foundations of Political
Economy, Sopfusha, 2011, pp.25-131.
[19] Ministry of Economy, Trade and Industry(METI)., Circulation and Economy Vision 2020,
pp. 5-43,[20] Ministry of the Environment., Environment andManagement,[21] Daiwa Institute of Research Holdings Ltd., Does
an improved ESG rating affect stock prices?,
2019, pp.1-9.
[22] Daisuke
Okamoto.,Mitsuhiro
Umezu..
Corporate Evaluation + Corporate Ethics
Approach to CSR, Keio University Press, 2006,
253pp.
[23] Ministry of Internal Affairs and Communications,
Changes brought about by the spread of social
media, pp. 199- 214.

REFERENCES[1] Kikuo Iwata., Studying Economics, Chikuma
Shobo, 1994, pp.27.
[2] Kikuo Iwata., Studying Economics, Chikuma
Shobo, 1994, pp.229.
[3] Kikuo Iwata., Studying Economics, Chikuma
Shobo, 1994, pp.229.
[4] Iwao Nakatani., Economics, Shueisha, 2002,
pp.178.
[5] Motoshige Ito., Hajimete no Keizaigaku, Nihon
Keizai Shimbun Publications Inc, Part I, 2004,
pp.30.
[6] Nihon Keizai Shimbun (ed.), Easy Economics,
Nihon Keizai Shimbun, 2001, pp.20.
[7] Asahi Noguchi. and Hideomi Tanaka., Structural
Reform Theory Misunderstood, Toyo Keizai
Shinpo Sha, 2001, pp.32.
[8] Takeshi Nakano. and Keita Shibayama., The
True Story of Global Depression, 2011, pp.85-88,
pp.189ff.

501

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DESALINATION BEHAVIOR OF NATURAL ZEOLITE IN SEAWATER* Fumika Sekihata 1, and Takaaki Wajima 1
1

Department of Urban Environment Systems, 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 with addition of calcined Ca-Fe type layered doublehydroxide (LDH) to desalinate seawater for agricultural use. Mordenite type natural zeolite from Fukushima, Japanwas used. The salinity and pH of seawater used in this study were 3.61% and 8.0, respectively. When more than
250 g/L of natural zeolite was added, the salinity decreased from about 3.56% to about 2.92% (reduction : about
18.0%) after stirring for 1 h. With higher dosage of calcined Ca-Fe LDH, the reduction time of salinity and theincrease of pH became faster, while the reduction rate of salinity was almost same and pH value increased. Whenthe mixture was used at the mixing ratio of natural zeolite and calcined Ca-Fe LDH was 5:4, the salinity decreased
to 1.0% (reduction : about 70.0%) after stirring for 1 h, and the pH of the solution increased to 9.0 - 9.7. Radish
sprouts could be harvested using seawater treated with a mixture of natural zeolite and calcined Ca-Fe LDH (5 :4), while it was not possible to harvest using seawater and seawater treated with natural zeolite with lower additionof calcined Ca-Fe LDH.
Keywords: Natural Zeolite, Calcined Ca-Fe LDH, Desalination, Radish Sprouts

INTRODUCTION

develop a new desalination material that reduces thehigh concentration of sodium chloride in seawater,
causing salt damage to a level where crops can grow,using natural zeolite with calcined LDH. However,
little information can be available on the desalination
behaviors of natural zeolite with calcined Ca-Fe LDH.
In this study, the desalination behaviors of naturalzeolite, calcined Ca-Fe LDH and the mixture of thesewere examined to produce the solution for plant
growth from seawater.

In recent years, the demand for water has been
increasing due to the increase of the world population,
the improvement of economy and living standard.
The total amount of water resources on the earth isabout 1.3 billion km3. In water resources, seawater isabout 97.5%, freshwater is about 2.5%, and humanbeings can only use about 0.01% of the total water
resources due to the most freshwater present as
glacier, cloud, vapor and so on. About 70% of fresh
water for utilization of human being is used for
agriculture. Therefore, the development oftechnology for obtaining agricultural water fromhigh-salinity water, such as seawater, is underconsideration to secure food. The main seawater
desalination technologies are the multi-stage flash
evaporation method and the reverse osmosis
membrane method, which are methods for producing
high-purity water used for securing drinking water orindustrial water. Therefore, the production ofagricultural water using these methods is expensive,
and a simple new desalination method is desired. In
previous studies, seawater was treated with calcined
Mg-Al layered double hydroxide (LDH) and naturalzeolite in two steps to obtain a solution that can beused for germination of radish
sprouts fromseawater[1-3] and we have succeeded in cultivating
radish sprouts using seawater shaken for 5 h with amixture of mordenite-type natural zeolite (producedin Iizaka, Fukushima Prefecture, Japan) and calcined
Ca-Fe LDH (calcination at 500 oC ) (mixing ratio ofzeolite to LDH =5 : 4) [4]. There is a possibility to

EXPERIMENTAL
SamplesMordenite-type natural zeolite obtained from
Iizaka mine, Fukushima Prefecture, Japan was used
as a natural zeolite sample. The exchangeable cations
and cation exchange capacity of natural zeolite wereshown in Table 1 [5,6].
Table 1 Exchangeable cations and cation exchange
capacity of natural zeolite
Exchangeable cations(mmol/g)Na+K+
Mg2+ Ca2+
0.50 0.20 0.02 0.19

Cation exchange
capacity (mmol/g)
1.82

The calcined Ca-Fe LDH was prepared as follows.
A mixed solution of Ca2+ and Fe3+ (Ca/Fe = 2) (200502

GEOMATE – Melbourne, Australia, 11-13 November 2020mL) 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.
The
mineralogicalcompositions of natural zeolite and calcined Ca-Fe
LDH were shown in Fig.1.

The mixtures of natural zeolite (15 g) and calcined
Ca-Fe LDH (1, 6, 12 g) were added to 40 mL ofseawater, and stirring for a 0 - 24 h. After stirring or
shaking, the solution is filtered, the pH of the filtrateis measured by pH meter (Horiba, F-72), salinity ismeasured by salinity meter (LAQUAact, ES-71), theconcentrations of SO42-, Cl-, Na+, K+, Mg2+ and Ca2+in the filtrate were measured with an ion
chromatograph (Tosho, IC-2010) and Fe3+ content in
the filtrate was determined using an atomic
absorption spectrophotometer (Perkin Elmer,
AAnalyst 200). The reduction rate of each ion inseawater after treatment was calculated using
equation (1).

(b)▲：Calcite [CaCO3]

𝐶 −𝐶

𝑅= 0

(a)

𝐶0

●：Mordenite■：Quartz [SiO2]

× 100

(1)

where R is reduction rate (%), C0 is each ion
concentration in initial solution (mg/L), and C is eachion concentration in the solution after treatment(mg/L). The residue was dried, and the structure wasconfirmed by a powder X-ray diffraction apparatus(Rigaku, MiniFlex 600).
Growth Test

Fig. 1XRD pattern of (a) natural zeolite and (b)calcined Ca-Fe LDH

Radish sprouts (30 seeds) were used for 10 days
growth test. Seawater or seawater treated with the
mixtures of natural zeolite and calcined Ca-Fe LDH(5 : 2, 5 : 4) was given by a spray every day, andgerminations of radish sprouts were observed.

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.

RESULT AND DISCUSSION

Table 2 Chemical composition, salinity and pH ofseawater

Seawater Desalination with Natural Zeolites
Figure 2 shows the salinity and pH of seawater
during natural zeolite treatment with various dosages.
When 250 - 500 g/L of natural zeolite was added, thesalinity rapidly decreased within 15 minutes, and thenbe almost constant to about 3.0 % (R : about 15.5 19.1%).When 50 - 125 g/L of natural zeolite wasadded, the salinity gradually decreased to 3.11 3.16% for 60 minutes, and then be almost constant(R : 11.2 - 12.6%). The pH decreased from 8.0 of
seawater to 5.0 -7.5 in 60 minutes, and became almost
constant. With increasing the dosage of naturalzeolite, the decrease of pH was larger.
From the above results, when more than 250 g/L ofnatural zeolite was added, the reduction of salinity
was almost same after stirring for 1 h. Therefore, 375
g/L of natural zeolite was used for the subsequent
experimental condition.

Value
Content (mmol/L)
SO42−
Cl⁻
Na+
K+
Mg2+
Ca2+
Fe3+
Salinity(%)
pH

34
805
475
11
71
10
0
3.46
8.0

Desalination Ability
The desalting ability was evaluated as follows.
Raw natural zeolite was added to 40 mL of seawater,and stirred for 0 - 2 h. Calcined Ca-Fe LDH wasadded to 40 mL of seawater, and shaken for 0 - 2 h.
503

GEOMATE – Melbourne, Australia, 11-13 November 2020

4

SalinitypH

14

Seawater Desalination with Calcined Ca-Fe LDH
Figure 4 shows the salinity and pH of seawater
after calcined Ca-Fe LDH treatment. When 300 g/Lof calcined Ca-Fe LDH was added, the salinity
rapidly decreased from 3.61% to 3.26% (R : about10%) in 10 minutes, then gradually increased to
3.49%, the pH increased from 8.0 to 10 in 5 minutes
and then be almost constant. When 30 g/L of calcined
Ca-Fe LDH was added, the salinity decreased to 3.2%
after 30 minutes, then gradually increased, the pH
increased from 8.0 to 8.8 in 30 minutes and then be
almost constant. With higher dosage, the reduction
time of salinity decreased and increase of pH became
faster, while the reduction rate of salinity was almost
same and pH vale increased.

12

10
pH

3
8

6

2

4
0

Fig. 230

60
90
Time（min）

120

4

14
pH

Salinity

Salinity and pH of seawater after treatmentof natural zeolite

30 g/L13

300 g/L

Figure 3 shows amounts of each ion in seawater
during natural zeolite treatment. For 30 minutesstirring, the Na+ concentration decreased from 475
mmol/L to 274 mmol/L, and then became almost
constant (R : about 40%), while the Ca2+
concentration increased from 10 mmol/L to 71
mmol/L for 30-minutes stirring. For 60-minutes
stirring, Cl- decreased from 805 mmol/L to 500
mmol/L, and then be almost constant (R : about 27%).
The decrease of SO42- , K+, and Mg2+ concentrationand the increase of Fe3+ concentration wereconfirmed, and those amounts were very small
compared with those of Cl-, Na+ and Ca2+
concentration. The reduction of SO42- , K+ and Mg2+,
were 37%, 50% and 37%, respectively.

Salinity（%）

12
11
pH

Salinity（%）

500 g/L
375 g/L
250 g/L
125 g/L
50 g/L

3
10
9
8
2

1000

SO₄²⁻
Na⁺
Mg²⁺
Fe³⁺

900

Content (mmol/L)

800

Cl⁻
K⁺
Ca²⁺
Fig. 4

600

60

90

120

Salinity and pH of seawater after treatmentof calcined Ca-Fe LDH

Figure 5 shows amounts of each ion in seawater
during treatment at 300 g/L dosage. The Na+concentration decreased from 475 mmol/L to 365
mmol/L in 10 minutes (R : about 23%), and became
constant. The Cl- concentration decreased from 805
mmol/L to 644 mmol/L in 10 minutes (R : about 9%),then was almost constant for 1 h and increased after 1
hs. It is considered that Cl- was captured in the
calcined Ca-Fe LDH due to the reconstruction of CaFe LDH (checked by XRD) and Ca-Fe LDH released
the captured Cl- as weak anion by ion exchange
reaction of other anions, such as carbonate ion in thesolution. The reduction rates of SO42-, Mg2+ and K+ in

500
400
300
200
100
0

Fig. 330

Time（min）

700

0

7
0

30

60
90
120
Time(min)Concentrations of each content in seawater
after treatment of natural zeolite504

GEOMATE – Melbourne, Australia, 11-13 November 2020

the solution were about 26%, 24% and 17%,
respectively. The Ca2+ concentration increased from
10 mmol/L to 333 mmol/L after 1-h treatment, andthen became constant. The concentration of Fe3+
increased to 0.649 mmol/L within 1-h stirring. It isconsidered that Ca2+ and Fe3+ forming calcined Ca-Fe
LDH were dissolved into seawater within 1 h.

salinity was reduced to less than 1 % at which theplant could be cultivated using the mixture of naturalzeolite and calcined Ca-Fe LDH at the mixing ratio of
natural zeolite to LDH = 5 : 4.
4

12

900
800

SO₄²⁻

Cl⁻

Na⁺

K⁺

Mg²⁺

Ca²⁺

3

Salinity（%）

Fe³⁺

700
600

10

8
pH

1000

Content (mol/L)

14

2
6

500

4

1
400
300
0

200

Calcined Ca-Fe LDH Salinity pH
300 g/L
150 g/L
25 g/L
Sea Water
0100
Fig. 6
00
Fig. 530

60
90Time(min)

120

60

120
180
240
Time（min）

2

0
24 h
300

Salinity and pH of seawater after treatmentof the mixture of natural zeolite and calcined
Ca-Fe LDH

Concentrations SO42-, Cl-, Na+, K+, Mg2+, Ca2+ andFe3+ in seawater after treatment with a mixture of 15
g of natural zeolite and various amounts of calcined
Ca-Fe LDH were examined. The addition amount ofcalcined Ca-Fe LDH to seawater is (a) 1.0 g, (b) 6.0
g, and (c) 12.0 g.
Figure 7 shows amounts of each ion in seawater
during the treatment using each mixture. In all cases,
Cl- and Na+ were significantly reduced, SO42-, K- andMg2+, which were much lower concentrations inseawater than Cl- and Na+, were also reduced, andCa2+ and Fe3+ increased. The concentrations of allions became constant after 1 h stirring regardless of
mixing ratio.
In the case of 1.0 g and 6.0 g, Na+ and Cl- decreased
rapidly for 5 minutes and gradually decreased for 1 h.
After 1 h, the concentrations of Cl- and Na+ were 441
mmol/L and 280 mmol/L at 1.0 g, and those were 366
mmol/L and 181 mmol/L at 6.0 g, respectively. At
12.0 g, the Cl- and Na+ concentrations decreased
sharply for 5 minutes, and then gradually decreased
to 226 mmol/L and 60 mmol/L, respectively. The
concentration of Ca2+ in the solution after 1-hincreased with increasing the LDH addition.

Concentrations of each content in seawater
after treatment of calcined Ca-Fe LDH

Seawater Desalination with a Mixture of Natural
Zeolite and Calcined Ca-Fe LDH
In order to determine the effect of adding ratio of
calcined Ca-Fe LDH to natural zeolite on seawater
desalination, the mixtures of natural zeolite (15 g) andcalcined Ca-Fe LDH (1, 6, 12 g) were prepared, andtheir desalination behaviors were investigated.
Figure 6 shows the salinity and pH of seawater
during the treatment by each mixture. For all mixtures
the salinity reduced within 1 h, and then be almost
constant. The mixture of natural zeolite and calcined
Ca-Fe LDH (1 g) reduced the salinity to about 2.46%(R : about 32%) and pH became 8.1 after 1-htreatment. The mixture of natural zeolite and calcined
Ca-Fe LDH (6 g) reduced the salinity to about 2.1%
(R : about 42%) and pH became 9.3 after 1-htreatment. When a mixture of natural zeolite andcalcined Ca-Fe LDH (12 g) was added, the salinity
decreased to about 1.0% (R : about 70%) after 1 htreatment, then remained constant thereafter, and the
pH after the treatment was 9.7. From these results, the505

GEOMATE – Melbourne, Australia, 11-13 November 2020

1000

1000
(a)

SO₄²⁻
Cl⁻

Cl⁻

800

Na⁺

800

Na⁺

700

K⁺

700

K⁺

Mg²⁺
600

Ca²⁺

500

Fe³⁺

400

Mg²⁺

600

Ca²⁺
500

Fe³⁺

400

300

300

200

200

100

100
0

0
0

0

24 h
30 60 90 120 150 180 210 240

Fig. 7
1000
(b)

SO₄²⁻

900

30 60 90 120 150 180 210 24240hTime(min)Time(min)Cl⁻Content (mmol/L)

SO₄²⁻

(c)
900

Content (mmol/L)

Content (mmol/L)

900

800

Na⁺

700

K⁺

Concentrations of each content in seawater
after treatment of the mixture of naturalzeolite and calcined Ca-Fe LDH of (a) 1.0 g,(b) 6.0 g and (c) 12.0g

When desalting seawater with natural zeolite andcalcined Ca-Fe LDH, the concentrations of SO42-, Cl-,
Na+, K+, Mg2+ decreased, and Ca2+ and Fe3+ increased.
With increasing the LDH addition, these change islarger and the salinity of seawater decreases.

Mg²⁺

600

Cultivation of Radish Sprouts

Ca²⁺
500

Table 3 shows the ion contents, salinity and pH of(a) seawater, (b) seawater treated with the mixture ofnatural zeolite (15 g) and calcined Ca-Fe LDH (6 g)and (c) seawater treated with the mixture of naturalzeolite (15 g) and calcined Ca-Fe LDH (12 g). It isnoted that treatment time is1 h.
By desalting with a mixture of natural zeolite andcalcined Ca-Fe LDH, Cl- and Na+ were significantly
reduced from seawater concentrations. In addition,
the decrease rate increased as the amount of calcined
Ca-Fe LDH added increased. The concentration of
Ca2+ in the treated seawater increased as the amountof calcined Ca-Fe LDH added increased. Other SO42-,
K+ and Mg2+ were much smaller than those of Cl- andNa+, and with increasing calcined Ca-Fe LDH
addition these contents decreased. Fe3+ concentration
were zero regardless of the treatment.

Fe³⁺

400
300
200
100
0
0

30 60 90 120 150 180 210 24240hTime(min)506

GEOMATE – Melbourne, Australia, 11-13 November 2020seawater. By investigating the desalination behaviorof these, we attempted to develop desalinationmaterials to obtain the solution for agriculture fromseawater in order to stably supply agriculture water
resources.
When 250 - 500 g/L of natural zeolite was added,
the salinity rapidly decreased within 15 minutes toabout 3.0% (R : about 15.5-19.1%). When 300 g/L of
calcined Ca-Fe LDH was added, the salinity rapidly
decreased from 3.61% to 3.26% (R : about 10%) in
10 minutes, then gradually increased to 3.49%, thepH increased from 8.0 to 10 in 5 minutes and thenalmost constant.With higher dosage, the reduction
time of salinity and increase of pH became faster
while the reduction rate of salinity was almost sameand pH value increased. When a mixture of naturalzeolite (375 g/L) and calcined Ca-Fe LDH (300 g/L)
was added, the salinity decreased to about 1.0% (R :about 70%) after 1 h treatment, then remained
constant thereafter, and the pH after the treatment was
9.7. Germination of radish sprouts was confirmed inseawater treated with a mixture of mordenite typenatural zeolite (375 g/L) and calcined Ca-Fe LDH(300 g/L) for 1-h. From these results, the salinity was
reduced to less than 1 % at which the plant could becultivated using the mixture of natural zeolite andcalcined Ca-Fe LDH.

Table. 3 Each ion concentration, salinity and pH of(a) the seawater and (b) the seawater after
treatment of the mixture of the mixture ofnatural zeolite (15 g) and Calcined Ca-FeLDH (6 g) and (c) the seawater after
treatment of the mixture of natural zeolite(15 g) and calcined Ca-Fe LDH (12 g)
SampleContent (mmol/L)
SO42−
Cl⁻
Na+
K+
Mg2+
Ca2+
Fe3+
Salinity(%)
pH

（a）

（b）

（c）

34
805
475
11
71
10
0
3.61
8.0

15
333
187
6
51
259
0
2.10
9.3

12
226
60
4
36
354
0
1.10
9.7

Figure 8 shows observation of radish sprouts after
10 day growth using (a) seawater, (b) the seawater
treated with the mixture of natural zeolite (15 g) andcalcined Ca-Fe LDH (6 g), and (c) the seawater
treated with the mixture of natural zeolite (15 g) andcalcined Ca-Fe LDH (12 g). The germination ofradish sprouts could be confirmed in seawater treatedwith a mixture of natural zeolite (15 g) and calcined
Ca-Fe LDH (12 g), while radish sprouts cannot begerminated in seawater and seawater treated with amixture of natural zeolite (15 g) and calcined Ca-FeLDH (6 g).
(a)Fig. 8(b)

REFERENCES[1] 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.
[2] Wajima,T., Shimizu,T. and Ikegami,Y., New
simple proceed of making agricultural
cultivation solution from seawater. Bull. Soc.
Sea water Sci., Vol.60, 2006, pp.201-202.
[3] Wajima,T., Desalination behavior of calcinedhydrotalcite from seawater for preparation ofagricultural cultivation solution using naturalzeolite. Energy Environ. Res, Vol.4, 2014, pp.310.
[4] Sekihata, F. and Wajima,T., Desalination
behavior of natural zeolite in seawater. Int. J.
GEOMATE, Vol. 19, 2020, pp.123-129.
[5] Wajima,T. Shimizu,T. and Ikegami,Y., Ion
exchange properties of natural zeolite in the
preparation of an agricultural cultivation solution
from seawater. J. Ion. Exch., Vol. 18, 2007,
pp.201-202.
[6] Wajima,T., Ion exchange properties of Japanesenatural zeolite in seawater. Anal. Sci., Vol. 29,
2013, pp.139-141.

(c)

Observation of radish sprouts after 10 day
growth using (a) seawater, and (b) the
seawater treated with the mixture of naturalzeolite (15 g) and calcined Ca-Fe LDH (6 g)and (c) the seawater treated with the mixtureof natural zeolite (15 g) and calcined Ca-FeLDH (12 g)

CONCLUSION
Mordenite type natural zeolite mixed withcalcined Ca-Fe LDH were used for desalination of507

10th Int.[TypeConf. here]
on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ESTUARY ZONATION BOUNDARY BASED ON SEA SURFACESALINITY DISTRIBUTION IN CILETUH BAY, WEST JAVA

1

S. Supriatna1, Iqbal Putut Ash Shidiq1, Dennisa M. Tunjung1
Department of Geography, Faculty Mathematics & Natural Sciences, University of Indonesia

ABSTRACT
Estuary is the area where there is a mixing of the seawater with fresh water from the mainland. The estuary
region is unique because the mixing of the seawater with freshwater makes the formation of brackish water withfluctuating salinity. The estuary region is very dynamic because there is always a process of changing in thephysical and biological environments. The estuary region has a salinity ranging from 0,5-30 ‰ or ppt (part per
thousand). This research aims to analyze the Ciletuh estuary zonation based on surface water salinity and to know
the relationship of salinity with variables of rainfall (seasons), river discharge, and tides. The salinity value isobtained from Sentinel 2A, through image processing using the salinity estimation algorithms. The statisticalanalysis is carried out to get the algorithm calculation model, where spatial analysis conducted to obtain zonationwaters. The results show that the mapping of the salinity distribution is forming the estuary boundary, and thevariables of rainfall, river discharge, and tides are affecting the salinity value.
Keywords: Algorithm, Estuary, Salinity, Sentinel 2A, Zonation

Seawater salinity levels can be identified byremote sensing technology. This remote sensing
sensor can provide spatial and temporal data invarious resolutions as well as the scope required forcoastal identification and analysis, and also data withtemporal and high spatial diversity. Not only that, but
the remote sensing technology is also the mosteffective approach to observe coastal formations andseafront [7]. Remote sensing satellite techniques canbe applied to monitor salinity in coastal environments.
That freshwater discharge not only affects the level ofsalinity and patterns in normal conditions but is also
very important in restoring the salinity pattern tonormal conditions after a storm disorder [8]. The
results of distant sensing imagery on seasonal climatechange in the Chesapeake, Delaware, and MidAtlantic regions were based on better models of
results in the determination of salinity levels [9]. It
also found the right symptoms in the Mid-Atlantic
region over a period of six years. The process of
mapping estuary in different water areas has beenpreviously done in the north and south coast of Java
Island using remote sensing techniques. The locations
were in Cilamaya [10] and Cimandiri [2]. Those
previous studies also created regression models
between the observed sea surface salinity and remotesensing spectral bands. Models resulted from thosestudies were applied in this study for identifying theestuary zone in Ciletuh Bay. Hopefully, thedetermination of the estuary zone can help the local
government and society in managing the estuary
region.
This research was conducted in the waters of
estuaries in Ciletuh Bay, West Java. It is located on

INTRODUCTION
The estuary area is the meeting area between landand sea, which is an important component of the river
basin in the complex and dynamic coastal area [1].
This region is very dynamic because there is always aprocess of changing in the physical or biologicalenvironment. The mixing of seawater with freshwater
makes the estuaries uniquely formating the brackish
water with a fluctuating salinity [2]. The tides and theseason influence the changes that occurred in thesalinity. The process of water movement due to tidal
currents in transporting minerals, organic materials,
and sediment and the presence of freshwater flowsthat occur continuously from the upstream of the riveris a basic material that supports the productivity of thewaters in the region that exceeds the high marine
productivity and freshwater water [1]. Therefore, theestuarine region becomes the most productive area, so
it becomes exciting to be studied [3]. The increase of
rainfall in tropical areas related to global climatechange can affect the dynamics of metabolism on thesurface of water estuary [4]. Changes in seawater willcause negative ecological consequences, while thepresence of reclamation water has an adverse impact
on ecology for the estuary environment [5]. Estuaries
in temperate and tropical regions were influenced by
the source of CDOM (Cromophic Dissolved Organic
Matter), which streams mixed with seawatersignifying a negative connection between the CDOM
and its degree of saltiness [6]. Estuarine waters havelower salinity than oceans and higher than freshwater.
The range is 5-30 ppt [3].

508GEOMATE – Melbourne, Australia, 11-13 November 2020

Where S is the salinity (ppt), Ln is the natural log,
Blue is the blue spectral band, and Red is the red
spectral band.
The determination of zoning in the estuary will bebased on the salinity value of image processing results.
Salinity value of less than 0,5 ‰ will be classified as
the freshwater or limnetic zone. Salinity 0,5 to 5 ‰will be classified as the mexo-oligohaline, where 5 to18 ‰ will be included in the mexo-mesohaline class.
Meanwhile, 18 to 30 ‰ will be placed in the mexopolyhaline class, and 30 to 40 ‰ will be classified as
the euryhaline. Lastly, salinity with more than 40 ‰will be included in the hypersaline class [13].
Estuary zoning areas in this study were analyzed
based on the rainy season and the dry season. The
determination of the season is based on Oldeman's
classification, which states that the wet month is themonth with high rainfall (more than 200 mm/month),
and the dry month is the month with low rainfall (lessthan 100 mm/month) [2, 10].
Tides and ocean currents (direction and velocity)also influence salinity concentrations in estuarywaters. The fluctuation of the salinity is affected by
the tides and the ocean currents [2, 14].

the southern coast of Java island. The purpose of thisresearch is to create a map of the estuary zone basedon a rainy and dry season with a remote sensing
approach. The waters of Ciletuh are the estuary of
three streams, namely Ciletuh river, Cikanteh river,and Cimarinjung river. Ciletuh itself is a productive
area because it has a wealth of marine resources thatare quite high. One of the dominant fish commodities
is the production of Sidat fish. Sidat fish is a type offish that mostly live in tropical areas [11, 12]. The
estuary zone firstly needed to be determined before
calculating its potential productivity. Determinationof the zoning of the estuaries can be done byanalyzing the sea surface salinity distribution [3].
METHODSThis research was conducted in Ciletuh Bay,
Sukabumi Regency, West Java. This research refers
to the geographic approach using remote sensing,which is the interpretation of the Sentinel 2A
imageries. The Sentinel-2A imagery used was level1C acquired on March 26th, 2019 and August 23rd,
2019. In this research, image processing is employing
two algorithms from previous studies in Cimandiri [2]and Cilamaya [10].

RESULT AND DISCUSSIONS
The monthly average rainfall in Ciletuh estuary
showed typical rainy months from January until May
in 2019. The average rainfall during January-May
2019 is more than 200 mm/month. In contrast, June
until September 2019 is showing typical dry months,
with average rainfall below 100 mm/month [15]. Thiscondition signifies that the Ciletuh estuary area wasexperiencing the dry season during the period. The
transition period between the dry and rainy months
occurred in May, October, and November 2019
(Figure 1).

Cimandiri Algorithm
Cimandiri algorithm is an algorithm to model thesea surface salinity distribution in Indonesia. It wasmade from research on the utilization of remotesensing technology through the use of multi-temporal
data of LANDSAT 8 OLI-TIRS satellite imagery to
map the sea surface salinity of Pelabuhan Ratu Bay(south coast of Java Island). The formula is as follows[2]:

Precipitation (mm)S = 29,983 + (165,047 * Blue) - (260,77 * Green) +(2,609 * Red) .................................................... (1)Where S is the salinity (ppt), Blue is the blue spectral
band, Green is the green spectral band, and Red is thered spectral band.
Cilamaya Algorithm

500
400
300
200
100
0
January
February
March
April
May
June
July
AugustSeptember
OctoberNovember
DecemberCilamaya algorithm is an estimator algorithm ofsalinity distribution in Indonesia made from researchon the utilization of remote sensing technology
through the use of multi-temporal data of Sentinel 2A
satellite imagery to map the salinity distribution of
Cilamaya estuaries (north coast of Java Island). The
formula is as follows [10]:Fig 1. Graph of average monthly rainfall in 2019.
The tidal data of seawater at Ciletuh station in therainy season of 2019 showed an average of 0,51
meters. Meanwhile, the tide of seawater at Ciletuh
station in the dry season of 2019 was 0,33 meters. The

S = 139,566970 + (86,21318 * LnBlue) - (24,62518 *LnRed) .............................................................. (2)509

GEOMATE – Melbourne, Australia, 11-13 November 2020

tides of the seawater in Ciletuh is higher in the drymonths (Table 1).

salinity value ranged between 13 to 32 ppt, especiallyin the rainy months of 2019. The low

Table 1 Rainfall and tidal data [16]DateMarch 26th 2019
(rainy month)August 23rd 2019
(dry month)

Precipitation
(mm)
210,8

Tides
(m)
-0,38

18,8

-0,28
(b)
Fig2. March 26th, 2019 (rainy month), and (b) August
23rd, 2019 (dry month).

The sea surface salinity can be modeled using therelationship between the observed salinity value
towards remote sensing spectral. Previous studies
showed that Landsat 7 ETM+ imageries could be used
to estimate the concentration of chlorophyll-a,turbidity, and salinity [12, 17].
The algorithms of Cimandiri and Cilamaya areapplied to determine the distribution of salinity of thesea surfaces during the rainy months (March 26th,2019) and dry months (August 23rd, 2019) in the
Ciletuh estuary. The statistical methods have beentested, and it could accurately predict sea surface
salinity through its combination with the remotesensing approach, especially in the physical andecological applications [18]. Spatio-temporal changesof the sea surface salinity on the ocean are influenced
by the freshwater flow and river discharge, whichhave been studied using Meris, MODIS imagery, andriver discharge data [19].

salinity level is found on the shore of Ciletuh (Fig. 3a).
On the other hand, the sea surface salinity ranged
between 9 to 28 ppt, especially in the dry months of
2019, and it spread more towards the offshore (Fig.
3b). Based on the results, it is known that the lowest
salinity value occurred in August 2019, with thelowest salinity found at nine ppt.

Sea Surface Salinity from Cimandiri Algorithm
The distribution of sea surface salinity values in the
Ciletuh estuary, calculated using the Cimandiri
algorithm model, can be seen in Figure 2.

(a)

(a)
(b)
Fig. 2 Graphic distribution of sea surface salinity of
Ciletuh estuary from the Cimandiri algorithm. (a)Fig. 3 Distribution of Ciletuh Bay salinity based fromthe Cimandiri algorithm. (a) March 26th, 2019 (rainymonth), and (b) August 23rd, 2019 (dry month).

The sea surface salinity distribution from Sentinel 2A
imageries with the Cimandiri algorithm is shown in
Figure 3 below. From the figure, it is known that the510

GEOMATE – Melbourne, Australia, 11-13 November 2020

(a)
(a)

(b)
(b)
Fig. 5 Graphic distribution of sea surface salinity of
Ciletuh Estuary with Cilamaya Algorithm (a) March
26th, 2019 (rainy month) and (b) August 23rd, 2019
(dry month).

Fig. 4 Zoning of Ciletuh Bay estuary based on
Cimandiri Algorithm on (a) March 26th, 2019 (rainymonth), and (b) August 23rd, 2019 (dry month).

The sea surface salinity distribution from Sentinel
2A imageries with the Cilamaya algorithm is shownin Figure 6 below. From the figure, it is known thatthe salinity value ranged between 0,5 to 26 ppt,
especially in the rainy months of 2019 (Fig. 6a). On
the other hand, the sea surface salinity ranged
between 5 to 30 ppt, especially in the dry months of2019 (Fig. 6b). Based on the results, it is known thatthe lowest salinity value occurred in March 2019,with the lowest salinity found at 0,5 ppt.

Based on the determination of the estuary zoning,
March 2019 has three classes [13], which are themexo-meshohaline(163,2hectares),mexopolyhaline (2661,7 hectares), and euryhaline (1888,7
hectares). In August 2019, there were two classes,
which the mexo-meshohaline (592 hectares) andmexo-polyhaline (3121,6 hectares). Based on theprocess using the Cimandiri algorithm, the zoningclasses that formed during the rainy and dry season
are not having much different because the amount of
rainfall in March 2019 is lower than other rainymonths in 2019 (Fig. 1). In August 2019 theeuryhaline class is formed (Fig. 4).
Sea Surface Salinity from Cilamaya Algorithm
The distribution of sea surface salinity values in
the Ciletuh estuary, calculated using the Cilamaya
algorithm model, can be seen in Figure 5.

(a)Fig. 6 Distribution of Ciletuh Bay salinity based on
Cilamaya Algorithm on (a)511

GEOMATE – Melbourne, Australia, 11-13 November 2020

March 2019 is -0,38 m, while in August 2019 is -0,28.
The tide of seawater can affect the amount offreshwater and seawater mixed in the estuarine zone[10]. These results are temporally similar to theresults of [20] research at the mouth of the Ganges
showing a variation of salinity in Estuari Hooghly.
There are differences in the results of estuaries
zoning by using Cimandiri algorithm and Cilamaya
algorithm. Zones formed using the Cimandiri
algorithm are dominated by the Mexo-polyhalinezone, while using the Cilamaya algorithm the Mexomeshohaline zone is more dominant.
Differences in distribution of salinity and zoningat Ciletuh Bay Estuary are influenced by the season.
March is a wet month and is included in the rainyseason, so the amount of rainfall in this month ishigher than the month of August which is a dry monthor included in the dry season. The rainfall affects thefluctuations between salt water and fresh water. The
rainfall also affects the discharge of river water
entering the estuary. The region with maximum
turbidity in the region of the estuaries has been
examined by [21], here more tidal cycles play animportant role in the maximum turbidity meter zone
(MTZ) change rather than the seasonal cycle ofestuaries. In his research [22] said that increased
salinity in coastal waters could protect vegetation
species. The results of the study of the zone of
Estuary Ciletuh based on the results of the analysis of[23], the salinity value is carried out by using a remotesensing system and the results of the salinity
measuring algorithm, Muara Cimadur based on thesalinity classification zone is at a distance of about 34 km from the river mouth in the wet month, while 12 km from the river mouth in the dry month.

(b)
Fig.6 March 26th, 2019 (rainy month), (b) August 23rd,
2019 (dry month).

(a)

CONCLUSION
Based on the analysis in this research, there aredifferences in results obtained from the use of the
Cimandiri and the Cilamaya algorithms. Differences
distribution of salinity at Ciletuh Bay Estuary are
influenced by the season. In the rainy months, thewater area has a low salinity level (0,5-5 ppt and 5-18ppt), and it is wider than the dry season. The area withlow salinity based on the results of the Cilamaya
algorithm occurred in March 2019. Meanwhile, a low
salinity area from the Cimandiri algorithm was foundin August 2019. The low salinity area (0,5-5 ppt, 5-18
ppt, and 18-30 ppt) is the area of Ciletuh Bay.

(b)
Fig. 7 Zoning Salinity of Ciletuh Bay Based on
Cilamaya Algorithm on (a) March 26th, 2019 (rainymonth), (b) August 23rd, 2019 (dry month).
Based on the determination of the estuary zoning,
March 2019 has three classes [13], which are theMexo-oligahaline(17,7hectares),
Mexomeshohaline (3618,7 hectares), and Mexo-polyhaline(1077 hectares). While, August 2019 had two classes,
wich the Mexo-meshohaline (148,6 hectares), andMexo-polyhaline (4565 hectares). In rainy month the
Mexo-oligohaline zone is formed and the Mexomesohaline zone is formed more broadly. Tides in

ACKNOWLEDGMENTS
Thanks are due to Directorate Research andCommunity Services (DRPM) Universitas Indonesia,
which has supported and funded this PUTI grant
research in 2020.

512

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Conditions. Journal of Estuaries and Coasts, 33,
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J.T., Oliver M.J., Satellite-derived coastal oceanand estuarine salinity in the Mid-Atlantic. Journalof Continental Shelf Research, 63, 2013, pp. 235242.
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513

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EFFECTS OF RIPARIAN WORKS ON
ICHTHYOFAUNA IN THE NACHI RIVER, JAPANTaiki Mizumachi1, Kentarou Hirashima2 and Atsushi Nakashima3
Graduate School of Systems Engineering, Wakayama University, Japan;
2
Wakayama Prefectural Museum of Natural History, Japan;
3
Faculty of Systems Engineering, Wakayama University, Japan

1

ABSTRACT
Many studies on the effects of artificial river environment modification on fish have been conducted before
and after river environment modification, and few studies have been conducted during river environment
modification. In this study, the flood was caused by the passage of the typhoon Talas in 2011, and the Nachi Riverin the southern part of the Kii Peninsula, Japan where long-term riparian works are being carried out was selected
as the study site. We investigated the effects of riparian works on fish inhabiting rivers. From September 2013 toNovember 2015, fish were collected from three points in the Nachi River. From April 2017 to November 2019,fish were collected from four points in the Nachi River. We collected fish using a hand net (2 mm mesh) at eachsurvey point at night and also collected fish by fishing. During the survey period, 25 species were identified, and1707 individuals were collected. As a result, the number and population of diadromous fish species increased overtime. Many fish species could be identified even when the turbidity caused by riparian works was strong. On theother hand, after a large-scale terrain change due to riparian works, some fish species could not be identified, andother species could be newly identified. From this, it is thought that topographical changes due to riparian works
affect ichthyofauna more than turbidity.
Keywords: Ichthyofauna, Riparian works, Turbid water, Riverbed topography, Nachi River

INTRODUCTION

MATERIALS AND METHODS

Turbidity of rivers and changes in river
topography due to natural disasters and artificial
changes in the river environment is said to directly
affect fish and their habitat, or indirectly through foodresources. Various studies have been conducted on
the damage to the living body, repellent behavior,
growth rate, etc [1]-[3]. For example, large-scale
flood disturbance is known to reduce fish population
density [4]. According to Keizo Watanabe et al., theaccumulation of suspended solids on the riverbed due
to riparian works reduces boulders and loose stone
and adversely affects benthic fish [5]. However, inmost cases, research on natural disasters and artificial
changes in the river environment is a comparison of
before and after an alteration of the river environment,
and few studies have been conducted during thealteration of the river environment. Understanding the
response of fish communities to riparian works isimportant not only for the protection and conservationof fish but also as an indicator of the degree ofrecovery of fish populations from damage caused byenvironmental changes.
Therefore, in this study, we investigated the effectof riparian works on the fauna of fish in the Nachi
River, where major flood damage caused by typhoon
Talas occurred in 2011 and long-term riparian works
were carried out.

Survey Sites Overview
Japan is located in East Asia and is a region withhigh rainfall. Also, Japan has many mountains and a
few plains, so it is a steep slope river compared toforeign rivers. The Nachi River (estuary position: 33°
38' N, 135° 56' E) surveyed in this study is a river
originating from the Nachi Mountains in Wakayama
Prefecture, Japan. The basin area of 24.5 km2 and an
extension of the trunk river of about 8.5 km. The
climate is affected by the Kuroshio Current, and theannual average temperature is about 17 ℃, which iswarm throughout the year, and the average annual
rainfall is about 3,500 mm, which is the region with a
lot of precipitation in Japan. The typhoon Talas that
occurred at the end of August 2011 washed away
riverbed structures and bedrock of the Nachi River. In
addition, after the flood damage, large-scale riparian
works have been carried out at multiple locations
from the estuary to the upstream, and the river
topography and riverbed materials have changed
significantly. The riparian works caused turbidity inthe daytime, but the turbidity was mostly resolved atnight. The survey period is from September 2013 toNovember 2015 and from April 2017 to November
2019. In this study, a total of 39 surveys wereconducted.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Since St. 1 is located in the downstream area whenriparian works were carried out in the upstream area,
it was always under the influence of turbidity. The
depth of water in April 2017 when the survey started
was about 10 to 50 cm, and there were places where
the flow was slow and places where the flow was fast.
However, in January 2019, due to riparian works, theriverbed topography and riverbed material became
uniform, the water depth was almost constant andabout 10 to 20 cm, and the flow velocity increased. In
St. 1 from September 2017 to April 2018, loose stonesin the riverbed decreased due to the inflow andaccumulation of sediment due to riparian works, andmost of the riverbed material changed to sand andmud. After September 2018, the riverbed wasrenewed by flood, and the riverbed material became
sand and gravel.
St. 2 was the survey site that was most affected by
the riparian works. From March 2014 to February
2015, construction on the left bank was performed,
and the river water was always muddy during the day.
Sand and mud were deposited on the riverbed, andloose stones were reduced, but no significant change
was observed in the river topography. However, when
the construction of the right bank started after July
2015, the pool disappeared, the water surface width
became extremely narrow, the riverbed topographychanged to rapids, and the riverbed material was
gravel and boulders. In 2017, the riverbed topography
was riffle and the water depth remained shallow, but
after April 2018, a pool was gradually formed in thesame place as before.
St. 3 had a dam, and the area directly below it wasa pool with a depth of 1 to 1.5 m. From December
2013 to January of the following year, the river
topography about 40m downstream of the damchanged from rapid to riffle due to the riparian works.
In October 2014, the river topography returned to
rapids, but after September 2018, the river surface
was extremely narrowed due to the riparian works.
Until May 2019, the riparian works were ongoing.
After the construction, part of the riverbed waschanged to concrete and a dam was installed.
Most of the riverbed materials in St. 4 wereboulders. In October 2014, the removal of sedimentand boulders that had accumulated on the dam wasconfirmed, but the change in river topography wasminor. The removal of boulders in the river channel
was confirmed in March 2015, and part of the rapidchanged to riffle. In addition, riverbed material
became sand and gravel, but two months later it
became gravel and boulders. Around March 2018,
due to the riparian works, the riverbed material was
only boulders, the water surface width became
narrow, and the water depth increased. Around
September 2018, the river topography became riffle
and rapid again due to the riparian works, and theriverbed material became sand, gravel, and boulders.

Survey Methods
This study collected fish from three points on the
Nachi River from September 2013 to November 2015
and collected fish from four points on the Nachi River
from April 2017 to November 2019. Four survey
points were set at intervals of 1 to 2 km from themouth of the Nachi River, and St. 1, St. 2, St. 3, andSt. 4 were set from the downstream side (Fig. 1). At
each point, fish were collected by using a hand net(mesh size 2 mm), and fish were also collected byfishing at St. 2 and St. 3. The bait used for fishing was
mainly crustaceans captured at the survey point, andonly when the bait could not be captured,commercially available bait was used for fishing. The
survey time was 45 minutes at each point. The
number of fish and the total length of the fish
collected were recorded for each species. After
recording the number and total length of the collectedfish species, some fish were re-released at thecollection point except for others. Individuals who
could not identify the species at the site were broughthome and the species were identified in the laboratory.
The identification of fishes followed Nakabo (2013)[6]. A part of the sample brought back was used as aregistered sample (WMNH) of the Wakayama
Prefectural Museum of Natural History. In 2013,
when the survey started, the river water became
cloudy due to the riparian works, and the daytime
survey could only be conducted in limited places. For
this reason, all surveys were conducted at night.

Fig. 1

Map of study sites. Locations of the Nachi
Falls and four survey points (St. 1 - St. 4) areindicated. Water flows from the upper left tothe lower right.

RESULT
Environmental Conditions at Each Survey Point
Fig. 2 shows the changes in the environment at
each survey point. At each survey point, changes in
the riverbed topography due to riparian works wereobserved at least once.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 2 Changes over time in conditions at each survey point on the Nachi River.
asotus), which are large fish, were inhabited only inthat pool. We also confirmed the inhabitation of
freshwater fish Dark Chub (Nipponocypris
temminckii) and Chinese Minnow (Phoxinus
oxycephalus), but Chinese Minnow was only larvaeand juveniles than 30 mm. Ayu was also confirmed toinhabit after April, which is the time of the run-up
period, but most of the confirmed individuals were
small. From March 2014 to February 2015, due to theconstruction on the left bank, many fishes including
Giant Mottled Eel could be confirmed even after
turbidity occurred in the daytime river. On the other
hand, Barcheek Goby could not be confirmed during
the construction period. Also, due to the constructionof the right bank after July 2015, the pool disappeared
and the river topography changed. Floating Goby
(Gymnogobius petschiliensis) was the only fish
species that could be confirmed thereafter, and thenumber of individuals that could be visually confirmed
was very small. In 2017, the riverbed topography wasmonotonous as in St. 1, and the water depth was
uniform. However, we were able to reconfirm thehabitat of many fish, including benthic fish, which had
not been confirmed since July 2015. Also, as time
passed, a pool was formed. In the pool, we could once
again confirm the inhabitation of Giant Mottled Eel
and Catfish, which are large fish. In addition, after
2019, when the river topography from the estuary to
St. 2 became monotonous, many Striped Mullet larvaeand juveniles (total length of 30-40 mm) have been
confirmed. In St. 2, the river was still turbid during thesurvey, but even in that case, it was possible to confirm
the inhabitation of Giant Mottled Eel, catfish, andbenthic fish.
In St. 3, 15 species were confirmed and 154
individuals were collected. The benthic fishes Monk
Goby (Sicyopterus Japonicus), Floating Goby, andCommon Freshwater Goby (Rhinogobius nagoyae)
have appeared stably, and Ayu was confirmed toinhabit during the run-up period from April to
September. In addition, after May 2015, it was
confirmed that Catfish, a large fish, lived in the pool
under the dam, and after July 2015 that Giant Mottled
Eel lived. A large amount of benthic fish was
confirmed in riffle, and freshwater fish such as Dark

Ichthyofauna confirmed in the Nachi River
Table 1 shows a list of fish confirmed in the Nachi
River during the survey period. During the survey
period, 25 species were confirmed and 1707
individuals were collected. Many fish could be
confirmed at each point even if turbidity occurred dueto the riparian works. The number of fish species andthe number of individuals that have been confirmed
have increased year by year since the start of the study.
In St. 1, 18 species were confirmed and 205
individuals were collected. In St. 1, much
amphidromous fish such as Barcheek Goby
(Rhinogobius similis) were confirmed, and also
peripheral fish such as Striped Mullet (Mugil
cephalus) and Grass Puffer (Takifugu alboplumbeus)
were confirmed. A Grass Puffer could not be
confirmed after the water became shallow and the flow
became fast. In July and September 2017, it was
confirmed that large Giant Mottled Eel (Anguilla
marmorata) inhabited, and the Giant Mottled Eel was
active at night. After the river depth became shallow
due to the riparian works, small individuals were only
confirmed in the gap between the boulder and theriverbed. From December 2018 to January 2019, theriverbed topography became monotonous due toriparian works, the water depth became shallow, andsand on the riverbed decreased. After that, Yellowfin
Goby (Acanthogobius flavimanus) was not confirmed.
Moreover, after the water became shallow, we could
not confirm Grass Puffer and Striped Mullet over 100
mm. Only in January 2019, Ayu (Plecoglossus
altivelis altivelis) with a total length of more than 100
mm were confirmed. Ayu is known as an annual fishand dies after the spawning season (October toNovember) [7]. In general, individuals with a total
length of around 100 mm are often seen during the runup period from the sea, from May to June. Therefore,
it was different from the time when it originallyappeared.
In St. 2, 16 species were confirmed and 1076
individuals were collected. In St. 2, there were a poolwhere the tetrapod sank and a pool formed of boulders
and bedrock. Giant Mottled Eel and Catfish (Silurus
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Table 1 The fishes appeared in Nachi River. The number indicates the collected number of individuals. Blackcircles indicate visual observations.
St.1 Family

Scientific nameCollected
Size
number range(mm)

Month
2017

2018

2019

Apr. May Jul. Aug. Sep. Dec. Apr. Sep. Nov. Jan. Mar. Apr. May Jun. Jul. Oct. Nov.
Anguillidae●

Anguilla japonica●

Anguilla marmorata
Cyprinidae

Candidia temminckii

Plecoglossidae

Tribolodon hakonensis
Plecoglossus
altivelis altivelis

Mugilidae
Sparidae
Kuhliidae
Gobiidae

●

●

1

1

2

Mugil cephalus cephalus

1

2

6

10

●

●

41

4

10

Kuhila marginata

2

2

1

1

2

1

11

47 - 115

1

11

2

1

2

5

80

24 - 1251

1

1

Sicyopterus japonicus

2

1

1

1

3

2

1

1

3

4

4

Rhinogobius nagoyae

1

31

1

6

36 - 752

14

31 - 109

1

3

3

3

3

5

4

3
2

1

2

1

Rhinogobius mizunoi

4
3

10

1

3

1

1

Tridentiger brevispinis

1

●

4

2

Tridentiger obscurus1

Takifugu alboplumbeus

1

2

3

●

1

2

1

1

2

18 Species

St.2 Family

26 - 68●

Acanthopagrus schlegelii

Gymnogobius
petschiliensis
Gymnogobius breunigii
Acanthogobius
flavimanus
Rhinogobius similis

Tetraodontidae

2

●

5

44 - 5313

56 - 15238

27 - 985

27 - 65182

8

49 - 983

33 - 6110

108 - 169

total 205

Scientific nameCollected
Size
number range(mm)

Month
2013

2014

2015

2017

2018

2019

Aug. Sep. Oct. Nov. Dec. Jan. Mar. Apr. May Jun. Jul. Oct. Nov. Dec. Jan. Feb. Mar. May Jul. Sep. Oct. Nov. Apr. May Jul. Aug. Sep. Dec. Apr. Sep. Nov. Jan. Mar. Apr. May Jun. Jul. Oct. Nov.
Anguillidae

Anguilla marmorata

Cyprinidae

Cyprinus carpio●

Candidia temminckii
Phoxinus
oxycephalus jouyi
Tribolodon hakonensis●

Silurus asotus
Plecoglossus
altivelis altivelis●

Siluridae
Plecoglossidae
Mugilidae

Mugil cephalus cephalus

Kuhliidae

Kuhila marginata

GobiidaeSicyopterus japonicus
Gymnogobius
petschiliensis
Rhinogobius similis

●

1

●

1

1

●

1

●

1

●

●

1

2

2

2

●

3

1

1

2

1

6

1

5

●

1
1

1

1

●

1
2

1

1

1

3

1

1

5

●

2

Rhinogobius nagoyae

1

Rhinogobius mizunoi

1

3

2

1

●

2

●

2

●

●

1
3

●

1

●
●

1

2

2

1

●

●

3

●

1

2

1

1

●

●

●
1

Tridentiger brevispinis
Tridentiger obscurus

●
●

2
●

1

1
1

2
●

●

2
7

●

1

●

1

●

●

1
●

1

1

●

1

157

3

552 - 585547 - 85
17 - 52

33

924

1

1

34

●

5

46 - 1033

1

26

32 - 108

3

8

12

4

11

51

25 - 902

12

26 - 54

1

●

1

1

2

3

1

16 Species

St.3 Family

20 - 24

1

●

1

1

450
31 - 108

2

1

1

●

1

●

226 386 235 35

675 - 1090

1
22

1

41

15

42 - 85137

total 1076

Scientific nameCollected
Size
number range(mm)

Month
2013

2014

2015

2019

Aug. Sep. Oct. Nov. Dec. Jan. Mar. Apr. May Jun. Jul. Oct. Nov. Dec. Jan. Feb. Mar. May Jul. Sep. Oct. Nov. Apr. May Jul. Aug. Sep. Dec. Apr. Sep. Nov. Jan. Mar. Apr. May Jun. Jul. Oct. Nov.
Anguillidae●

Anguilla japonica
Anguilla marmorata

Cyprinidae

1●

Candidia temminckii
Phoxinus
oxycephalus jouyi
Tribolodon hakonensis

2

9

●

1
●

1

●

●

6

8

1

●

●

1

●

●

4

1

2

1

●

●

1

3

●

●

●

●

6

●

●

6

●

1

860

3

46

24 - 162

1122 - 36●●●

Siluridae

Silurus asotus
Plecoglossus
altivelis altivelis
Adrianichthyidae Oryzias latipes●

Plecoglossidae

Gobiidae

●

1

●

●

●

●

●

●

1

●

●

14

●

●

1

●

1

1

1

1

1

1

2

1

1

1

2

●

7

2

1

●

1

1

●

1

Sicyopterus japonicus
Gymnogobius
petschiliensis
Rhinogobius similis

4

Rhinogobius nagoyae

1

●
●

●
2

●
2

3

1

●
1

2

●

1

●

1

●

●

●
●

1

●

●

●

●
4

●

2

3

3

●

1

1

31

1

10

42 - 84

1●

40

40 - 109●1

34

32 - 967

65 - 834

64 - 87

1
●

●
2

1

4

●

●

1

1

●

Rhinogobius fluviatilis●

Rhinogobius mizunoi
Tridentiger brevispinis

●

1

3

1

●

1

2

1

●

●

●

15 Species

St.4 Family

total 154

Scientific nameCollected
Size
number range(mm)

Month
2013

2014

2015

2017

2018

2019

Aug. Sep. Oct. Nov. Dec. Jan. Mar. Apr. May Jun. Jul. Oct. Nov. Dec. Jan. Feb. Mar. May Jul. Sep. Oct. Nov. Apr. May Jul. Aug. Sep. Dec. Apr. Sep. Nov. Jan. Mar. Apr. May Jun. Jul. Oct. Nov.
Anguillidae

Anguilla japonica

Cyprinidae

Candidia temminckii
Phoxinus
oxycephalus jouyi
Oncorhynchus
masou ishikawae
Sicyopterus japonicus
Gymnogobius
petschiliensis
Rhinogobius nagoyae

Salmonidae
Gobiidae

●
2

●

2

2
4

2

2

3

3

3

2

●

1

●

1

●
13

4

●

2

●

●

10

7

2
10

2

2

4

5

3

2

9

12

61

25

5

5

8

33 - 156206

7 - 1343

47 - 75

●
●

●

●

●

1

2

1

●

5

2

1

1

●
●

1

●

1

Rhinogobius fluviatilis
Rhinogobius mizunoi

4

●

●

1
1

●

●

1

●
1

●

1

●

Rhinogobius brunneus

1

10 Species

1

●

2

●

1

1
2

1
4

5

3

1

2

2

7

1

1

18

42 - 95

1

5

67 - 1142

31

62 - 107

1

94

total 272

517

GEOMATE – Melbourne, Australia, 11-13 November 2020

Chub and Chinese Minnow were confirmed in the pooland a place where the flow was gentle. In addition,
while the confirmed Chinese Minnow was a small
individual of less than 30 mm, Dark Chub was
confirmed from a small individual to a large individual
exceeding 100 mm. The number of fish species that
could be confirmed decreased after December 2017
when the river topography changed significantly due
to the flood, but many fish species could be confirmed
again after April 2019.
In St. 4, 10 species were confirmed and 272
individuals were collected. There has been littlechange over time since the survey started. In St. 4,
many Dark Chub and Chinese Minnow wereconfirmed in comparison with other survey points, andmuch large freshwater fish was confirmed incomparison with other points. On the other hand, thenumber of fish species confirmed in St. 4 was smaller
than in other survey points.

survey points. From this, it became clear that many
invading individuals from the sea live or are
temporarily used. In St. 2, many fishes could be
confirmed when the physical environment of thechannel before the riparian works was complicatedand diverse. On the other hand, after the river
topography became monotonous due to the riparian
works, the number of fish species and the number offish that could be confirmed clearly decreased. Thissuggests that changes in river topography have asignificant impact on fish. However, after returning tothe original diverse river topography, many fishes such
as Giant Mottled Eel could be confirmed again.
Therefore, by making the river topography
complicated and diversified, the population of manyfish species could be increased. It was suggested thatthe recovery would occur in a relatively short periodof time. This has supported the existing study [10]. In
St. 3, large Giant Mottled Eel and Catfish wereconfirmed after the riparian works were started in St.
2. This suggests that large fish such as Giant Mottled
Eel may have moved to seek suitable habitat due toriparian works. In recent years, rivers are often divided
by artificial structures such as dams. In such rivers, themovement of fish is restricted by the structures [11],and the impact of riparian works on fish may become
more prominent. In St. 4, there was no significant
change in the fish species that appeared compared tothe other survey points. From this, it was suggested
that the impact on fish is minor when the riparian
works are short-term and the topographical changes
are small.
In this study, it was clarified that abrupt changes inriver topography have more direct and short-term
effects on fish than turbidity associated with riparian
works. Rivers in Japan are often more transparent thanrivers in the world and are in a rare river condition in
the world called rapids [12]. Among them, thespeciation of fish inhabiting Japan has become
established. Due to the high transparency of rivers,
studies on river turbidity have been actively conductedin Japan [3]. However, the related species of fish thatlive in the muddy water rivers of the Asian also inhabit
Japan [13]. From this, it was considered that thecomplicated river topography created by the rapid
stream is more important than the transparent water
quality. On the other hand, in this study, we confirmedthat Ayu lived at a time different from the original
appearance time and that freshwater fish that lived in
the downstream region were mostly smaller. Fromthese facts, it could not be denied that the cloudinessof rivers could impede the growth of fish. In addition,
the Nachi River that we surveyed this time was asmall-scale river, and it was a river where turbidity
could easily subside. In addition, floods occurred in

DISCUSSION
Since 19 of the 25 species confirmed in this survey
were amphidromous fish and peripheral fish, it was
speculated that a large number of invaders from the sea
were inhabited. Even in the place where construction
was done during the daytime and the place that wasstrongly affected by turbidity, it was confirmed thatmany fish species such as Giant Mottled Eel, Dark
Chub, and Floating Goby, were observed at night.
Therefore, it became clear that many fish species
inhabit the river even during the riparian works. In
addition, the presence of many fish was confirmed
even before the turbidity of the river subsided,suggesting that the habitat was present even during thestrong turbidity of the river during the daytime.
Inhabitation of Ayu, which is said to be weak against
turbidity, was also confirmed [8], but most of themwere small. Ayu, an annual fish, generally lays eggs
and dies from October to November [7]. However, St.
1 confirmed Ayu with a total length of over 100 mm
in January 2019. Although it is suggested that these
individuals were immature during the spawningseason, it was not possible to clarify whether they were
due to the riparian works due to the small number ofconfirmed individuals. However, since it is known thatthe sedimentation of fine-grained soil due to riparian
works reduces the production of attached algae [9], itis fully possible that riparian works hindered thegrowth of Ayu through bait resources. In addition,
since sedimentation of riverbed decreases loose stones,
which are the spawning grounds for fish, it isconsidered that riparian works may hinder therecovery of fish populations.
At St. 1, the closest distance from the river mouth,
more amphidromous fish and peripheral fish wereconfirmed during the survey period than at other518

GEOMATE – Melbourne, Australia, 11-13 November 2020

[5] Keizo Watanabe, Futoshi Nakamura, Kunishige
Kamura, Hiroyuki Yamada, Yasuharu Watanabe,and Susumu Tsuchiya, Influence of strem
alteration on the abundance and distribution ofbenthic fish, Ecol. Civil Eng, Vol.4, No.2, 2001, pp.
133-146.
[6] Nakabo, T. (ed)., Fishers of Japan with Pictorial
Keys to the Species. Third Edition., Tokyo:Tokai
University Press., 2013, pp. 1-2428.
[7] Rikizo Ishida, On The Spawning of the Ayu,
Plcoglossus Altivelis T. & S.-Ⅰ. Structure of The
Spawning Shoal and Spawning Behavior, Bulletinof the Japanese Society of Scientific Fisheries,
Vol.25, No.4, 1959, pp. 259-268.
[8] Satoshi Awata, Hirohiko Takeshima, Tetsuya
Tsuruta, Takashi Yamada, and Kei’ichiro Iguchi,
Stress Hormone Response to Long-or Short-time
Exposure to Suspended Solids in Ayu
Plecoglossus altivelis, Aquaculture Sci., Vol.58,
No.3, 2010, pp. 425-427.
[9] Graham. A. A, Siltation of stone-surface
periphyton in rivers by clay-sized particiles fromlow concentrations in suspension, Hydrobiologia,
Vol.199, 1990, pp. 107-115.
[10] Teruo Toyoshima, Shigeru Nakano, Mikio Inoue,
Yugo Ono, Yoshimasa Kurashige, Fishpopulation responses to stream habitat
improvement in a concrete-lined channel, Japnese
Journal of Ecology, Vol.46, 1996, pp. 9-20.
[11] Shugo Kikuchi, and Mikio Inoue, Population
fragmentation of a stream-resident salmonid bydams: downstream progress of extinctions fromheadwaters, Ecol. Civil Eng, Vol.17, No.1, 2014,
pp. 17-28.
[12] Yoshimasa Kurashige, and Takeshi Takenaka,
Visual Water Clarity and Suspended Substanceunder Baseflow Conditions in the Nishibetsu River,Japan, Jpn.J.Limnol, Vol.56, No.3, 1995, pp. 195203.
[13] Atsushi Doi, A review of taxonomic studies ofcypriniform fishes in South-east Asia, Japan J.
Ichthyol., Vol.44, No.1, 1997, pp. 1-33.

the summer due to typhoons, and the riverbed wasrenewed. From these facts, it is unlikely that asufficient reaction appeared for all fishes. It is
expected that turbidity caused by riparian works andlong-term sediment accumulation on the riverbed willcause great damage to fish with loose stones asspawning sites. Therefore, it is necessary to continue
the investigation in order to clarify the impact of the
inflow of suspended solids into the river due to theriparian works.
ACKNOWLEDGMENTS
We thank all the members of the Graduate Schoolof Systems Engineering, Wakayama University forassistance in the field survey.
REFERENCES[1] Berg, L. and T.G. Northcote, Changes in
Territorial, Gill-flaring, and Feeding Behavior inJuvenile Coho Salmon (Oncorhynchus kisutch)
following Short-term Pulses of Suspended
Sediment, Can. J. Fish Aquat. Sci, Vol.42, 1985,
pp.1410-1417.
[2] Bunt, C. M., S. J. Cooke, J. F. Schreer and D. P.
Philipp, Effects of incremental increases in siltload on the cardiovascular performance ofriverine and lacustrine rock bass, Ambloplites
rupestris, Environmental Pollution, Vol.128,
2004, pp. 437-444.
[3] Sutherland, A. B. and J. L. Meyer, Effects ofincreased suspended sediment on growth rate andgill condition of two southern Appalachianminnows, Environ. Biol. Fish., Vol.80, 2007, pp.
389-403.
[4] Kousuke Sumida, Yo Miyake, Yuya Watanabe,
and Mikio Inoue, Responses of stream invertebrate
and fish assemblages to an extreme flood event in
the Shigenobu River, southwestern Japan, Ecl.
Civil Eng, Vol.22, No.1, 2019, pp. 35-49.

519

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Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EARLY FOREST RESTORATION ON THE CLOSED SKI SLOPE ISENCHANCED BY NATURAL REGENERATION AND ARTIFICIAL
THINNING
Tetsuoh Shirota1, Chizuru Iwasaki1, Tetsuo Okano1, and Shinjiro Oya2
1
Faculty of Agriculture, Shinshu University, Japan; 2Nagano Prefecture Forestry Research Center, Japan
ABSTRACT
Recently, there are many ski resorts that have been closed due to management difficulties in Japan. Forest
restoration on the abandoned ski slope is necessary from the viewpoints of landscape management, prevention ofavalanche, and one of the landslide disasters. However, it is difficult to establish forests due to poor nutrients bytopsoil removals at the time of ski slope development. In this study, we evaluate the effect of thinning to easecompetition for nutrients. The study site is in Mt. Tateshina, central Japan. The ski slope was closed in 1997.
Natural regeneration of larch was observed in 1998 and the artificial thinning was done in 2003. Two thinned andnine un-thinned quadrate plots were set. DBH with the height of all living and dead trees were measured. To clarify
the growth process, nine sample trees were selected and their tree ring at 1 m interval in height was analyzed. The
un-thinned plot was characterized by high density, small tree size, and many dead trees, suggesting that severe
competition has occurred. On the contrary, the thinned plot was characterized by the small density, large tree size,and less dead trees. According to the tree ring analysis, the size-order of individuals in the un-thinned plot wasdetermined at 5-year-old, while one of them in the thinned plot was changed after thinning. It was concluded thatthe artificial thinning of natural forest on the ski slope is useful to accelerate tree growth and early forest
development.
Keywords: Abandoned ski slope, Forest restoration, Topsoil disturbance, Lessening of competition

avalanche, topsoil erosion, and landslide disasters[2,4].
The early forest restoration on the abandoned ski
slope is necessary from the viewpoints of theconservation of ecosystem services. However,artificial reforestation is impractical due to the low
capital strength. The natural regeneration is possible
but entail uncertainty [6]. The amount of seeds supply
is directly affected by the masting event insurrounding forests. In some cases, the patches oflarge herbaceous perennial plant or of shrub species
inhibit the regeneration and development of tree
species [4,7,8]. In another cases, the topsoil removals
cause the insufficient of nutrients and the limitationof initial growth of trees [3,4,9].
In this study, we evaluate the effects of artificial
thinning on the topsoil removed ski slope. In this site,
the natural regeneration of Japanese larch was
succeeded [4]. Because number of saplings wasenough, it would be difficult to avoid theircompetition in poor nutrient condition. We examined
the hypothesis that artificial thinning will ease thecompetition among trees and accelerate their growth.

INTRODUCTION
Ski resorts facilities have been developed in themountainous regions of the world. The ski slope isusually constructed on forest area in Japan, whereas
on pastureland and heathland in European Alps [1].
This causes that the Japanese civil engineering workof the ski slope is accompanied by a drastic change inlandscape: the forest ecosystems was separated by thegrass and dwarf shrub ecosystem [1,2].
In the ski slope development process, all of trees
were harvested and their stumps and large rocks werepull off. Furthermore, huge amount of topsoil was
removed to smooth ski slope [2-4]. In many case, theexotic or local meadow species were introduced andcontrolled. According to the previous research inJapan, the following characteristics are common on
the ski slope vegetation: the existence of theintroduced exotic grasses, the development of unvegetated patches, and the restriction of woody plantestablishments [1,2]. These vegetation characteristics
are thought to be the results of the civil engineering
works and the artificial maintenances of the ski slopes.
Recently, there are many Japanese ski resorts
which have been closed due to management
difficulties [5]. The ski slopes are left derelict because
these ski resorts companies lack adequate capital.
These abandoned ski slopes continue to separate theforest ecosystems and deteriorate the landscapequality. Additionally, they increase the risk of

STUDY SITE AND METHODS
Study Site
Study site was located on the north facing slope(36.1216 N, 138.3405 E, alt. 1,800 m) of Mt.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Tateshina, Nagano prefecture, central Japan (Figure1). The closed Tateshina Associates Ski Resorts hasborrowed this area from National Forest until 1997.
The ski slope was surrounded by the artificial
forests of Japanese larch (Larix kaempferi). There
was the long step surface with from 1 to 2 m height
between the ski slope and the surrounding larch
forests. This step suggests that the civil engineering
work to construct ski slope was severe.
Just after the close of the ski resorts, the firstnatural regeneration of larch has occurred in 1998. In
2002, the density of larch was 70,000 trees/ha and ofbroad-leaved pioneer tree species (e.g. Betula
platyphylla, Alnus filma, Alnus masumurae, and Salix
sachalinensis) was 20,000 trees/ha (Dr. Y.Koyama,
personal communication). In 2003, when trees were5-year-old, the National Forest thinned them to 3,000
trees/ha. The National Forest assumed that the severe
competition causes the growth stunting.
In 2016, we found small un-thinned larch patches,
which age is 18-, 11-, and 6-year-old. The 18-yearold patch was located in the middle of the slope. This
rectangle patch had 10 m and 15m sides and wassurrounded by thinned larch and broad-leaved tree
stand. We also found another 18-year-old un-thinned
patch nearby the road in the lower part of the ski slope.
In this patch, the density of larch was smaller due tothe coverage of dwarf bamboo (Sasa senanensis). The
11-year-old patches were near the top of the ski slope.
To construct the starting terraces, the topsoil of thisposition was removed until about 3 m depth. On theother hand, the 6-year-old patches were establishedbeside the parking place for construction vehicles,
which surface was tamped down.

size of the quadrate plot was determined considering
the patch size and the stand density.
Two 18-year-old thinned (T18-1, T18-2) and unthinned plots (C18-1, C18-2) were set on theconstructed ski slope. In addition, an un-thinned plot
was set nearby road (S18-1).
Additionally, three un-thinned 11- year-old (C111, C11-2, and C11-3) and 6- year-old(C6-1, C6-2,
and C6-3) plots, which were 2 m and 1 m squares,
respectively, were set. There was no coveragevegetation in 11-year- old plots, whereas herbaceous
plants covered the 6-year-old plots.
Measurements
For each plot, tree height and diameter at breast
height (DBH) of every tree were measured withspecies identification. The tree height was measured
by the height meter (Measurement Pole, SK Co. or
Vertex IV, Haglov Co.). DBH was measured by steel
tape.

Quadrate plots
To discuss the developmental stage using thechronosequence method, we set eleven quadrate plotsin the patches that were different in age (Table 1). The
Table 1

Fig. 1The studied abandoned ski slope covered bylarch trees.

Outline of quadrate plots

Plot

Treatment

Regeneration

Age

Size
[m2]

Locationon slope

Vegetation ofground surface

Dead
tree

Thinned
stump

T18-1
T18-2

Thinned1998

18

10 x 10MiddleMeadow

-

Many

C18-1
C18-2

Un-thinned

1998

18

5x5

Middle

-

Many

-

S18-1

Un-thinned

1998

18

10 x 10

BottomDwarfbamboo

Few

-

C11-1
C11-2
C11-3

Un-thinned

2005

11

2x3

Top

-

Many

-

C6-1
C6-2
C6-3

Un-thinned

2010

6

1x1

BottomHerbaceous
plants--521

GEOMATE – Melbourne, Australia, 11-13 November 2020

To trace the growing process, the tree rings ofstem were analyzed. The disk samples from 1 minterval cross-section of the stem of nine trees werecollected in thinned (T18-1) and un-thinned (C18-1)
plots, respectively. These sample trees were selectedconsidering size classification in each plot. The
annual ring width on disk samples was analyzed
through four radial directions.
RESULTS
Stand StructureStand density of each plot was shown in Figure 2.
Stand density of T18 (Triangle), S18 (Solid circle),and C18 (Open circle) plots were about 3,000, 6,000,
and 12,000 tree/ha, respectively. On the other hand,those of C6 and C11 (Open circle) was ranged from30,000 to 80,000 and from 27,000 to 60,000 tree/ha,
respectively.
The canopy height of each plot was represented in
Figure 3. The dashed line represents a height growth
curve for the lowest-grade site index. We calculated
this curve by referring to the growth curve in the fifthgrade site of Nagano prefecture [10]. The canopy
height of un-thinned plots (C6, C11, and C18, Opencircle) was lower than those of the fifth-grade site
index. On the other hand, those of S18 (Solid circle)
and T18 (Triangle) plots were close to those of thefifth-grade site index. The average DBH represents
large value in T18 (Triangle) and S18 (Solid circle)
plots, and small value in C18 plots (Open circle).
The relationship between average D2H and standdensity was represented in Figure 4. The powerfunction was found in the data of C11, C18, and S18
plots, in which the power was approximately -1.5 (lnY = 18.49-1.411 ln X, r2=0.931). The values of C6
plots were obviously under this power function,
whereas the values of T18 plots were closely situated.
The height frequency of living (Open bar) anddead (Solid bar) trees in each plot were represented in
Figure 5. In C6 plots, the concentrated or unimodal
distribution patterns were found. In C11 plots, thebimodal distribution pattern was found and the small
trees were died. In C18 plots, the bimodal distribution
with positive skewness was observed, and the small
size trees were died. In S18 and T18 plots, unimodal
distribution with negative skewness was observed,
however, there were few dead trees.

Fig. 2Stand density of each quadrate plot.

Fig. 3

Canopy height and average DBH of eachquadrate plot.

Fig. 4

Relationship between average D2H andstand density.

Reconstructed Stem GrowthBased on the tree ring analysis, height growth wasreconstructed (Figure 6). In both plots, bimodal sizeclassification were found before thinning. But thegrowth of lower size class trees represented the quick
growth in the thinned plot. Finally, the height rangeof lower trees expanded in the thinned plot (T18-1).

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DISCUSSIONS
Natural Regeneration and Self-Thinning
The natural regeneration of larch was succeedingat this site in 1998, 2005, and 2010 (Table 1). This
interval supports that the masting cycle of Japaneselarch is from five to eight years. This long intervalalso suggests that the masting year is a valuable
opportunity for larch forest establishment.
The un-thinned plot was characterized by highdensity. The initial stand density is evaluated from60,000 to 80,000 tree/ha based on the first and third
regeneration (Figure 2, C6 plots). These values wereenough high for the natural establishment of forests.
However, the previous study reports that the standdensity of 4- to 7-year-old Japanese larch was from60,000 to 280,000 tree/ha in natural regeneration [6].
The density of this study is relatively lower than in
the previous study.
The high stand density in un-thinned plots causes
severe competition among trees which arecharacterized by many small tree sizes and by deadtrees (Figure 6). In C11, C18, and S18 plots, the deadtrees were found (Figure 6). The power function
between density and tree size in these plots suggeststhat the self-thinning [11] has occurred (Figure 5).
Oppositely, the competition in C6 plots is not thought
to be severe, because of without dead trees by the selfthinning and of unimodal height distribution (Figure5, 6).
Additionally, the un-thinned plots werecharacterized by a low growth rate in height anddiameter. Comparing to the lowest growth curve in
Nagano prefecture [10], their heights were smaller.
Usually, topsoil removal affects the vegetation andinhibits tree growth [3, 4, 9]. The more intensecompetition might be occurred not only for light but
also for underground resources. The heavy
competition with topsoil removal should be one of thereasons why the height growth was limited.

Fig. 5Size structure of each quadrate plot.

Fig. 6

Reconstructed height growth of nine trees inthinned and un-thinned plot.

Effects of Artificial Thinning
Thinning release the competition for light andunderground resources among trees. In fact, trees inthinned plots were larger than those of un-thinnedplots (Figures 2 and 3). Similar effects were found in
S-plot, where dwarf-bamboo decreased the larch tree
density. In this plot, small density eases thecompetition and accelerates tree growth until the local
lowest height growth curve [10].
Additionally, thinning affects the size structure.
The change of skewness from positive to negative
represents that the larger trees were increased in the
population (Figure 5). The growth of the lower-classtrees was improved by thinning, although sizeclassification was occurred before thinning. This
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GEOMATE – Melbourne, Australia, 11-13 November 2020

results that the smaller trees, which were stunted in
the un-thinned plot, could survive by their growth andresource acquisition in the thinned plot (Figure 6).
The artificial thinning contribute the improving
mortality and initial growth in a natural regenerated
dense stand. This effect may be more enhanced in thepoor soil condition such as the constructed ski slope.
However, we also should pay attention to thelimitation of this effects. At first, from the view ofcontrol the competition, the thinning should be planed
before a severe competition stage. On the second, theeffects may be limited in the severer soil condition.
The release effects of the thinning are based on theexistence of a certain amount of resources. If there
are no or little resources, it will be difficult to expect
the improvement effects.

[3] Nakamura T., Human Impacts on Vegetation andSoil of Skiing Area: I. A Case Study in Teine
Skiing Area, Hokkaido, Japan. J. Japan. Grassl.
Sci., 29, 1984, pp.331-340. (In Japanese withEnglish Summary)[4] Koyama Y., and Koyama K., Vegetation Change
for 20-Year Period at Abandoned Ski Slopes inNorthern Nagano Prefecture, Central Japan. Bull.
Inst. Natur. Edu. Shiga Heights, Shinshu Univ.
Vol.39, 2002, pp.1-6. (In Japanese with English
Summary)[5] Kureha, M., Changing ski tourism in Japan: From
mass
tourismtoecotourism?
GlobalEnvironmental Research, Vol.12, 2008, pp.137144.
[6] Igarashi T., Yajima T., Matsuda K., Natsume S.,
Takikawa S., Natural Regeneration in theJapanese Larch Plantation, Bull. Exp. For.
Hokkaido Univ., Vol.44, 1987, pp.1019-1040. (InJapanese with English Summary)[7] Saito I.T., Miscanthus sinensis Community as an
Indicator for Forecasting Tree Regeneration on
Abandoned Skislope in the Lowland of Hokkaido,
Nerthern Japan. Vege. Sci. Vol.29, 2012, 41-48.
[8] Iwasaki C., Shirota T., Okano T., Oya S. A
Difficulty of Ski Slope Reforestation with the Tall
Glass Community. Bull. Shinhu Univ. AFC.
Vol.17, 2019, pp.26-36. (In Japanese with English
Summary)[9] Nakagawa M., Hasui H., Ishihara N., Takiya M.,
Ohno Y., and Yasama M., Effects of Top Soil
Removal for Natural Regeneration on Growth ofJapanese Larch – A Case Study in Higashikagura
Town-. Boreal Forest Research Vol.60, 2012,
pp.117-119. (In Japanese)[10] Katakura M., Yamauchi M., and Furukawa H., A
Study on Long-Harvesting-Cycled Managementof Japanese Cypress and Japanese Larch. Bulletinof Nagano Prefecture Forest Research Center, Vol.
19, 2005, pp.1-16. (In Japanese)[11] Yoda, K., Kira, T., Ogawa, H. & Hozumi, K.,
Self-thinning in Overcrowded Pure Stands under
Cultivated and Natural Conditions: IntraspecificCompetition among Higher Plants XI. J. Biol.
Osaka City Univ. Vol.14, 1963, pp.107-129.

CONCLUSION
In some cases, the civil engineering work on ski
slope has affects for long time including thereforestation stage. However, the parts of them arepossible to be improved by the adequate stand density
control, not only by the natural regeneration.
ACKNOWLEDGMENTS
We thank Toshin Forest Station and Chubu
Regional Forest Office for their support to our using
the study site. We also thank Dr. Yasuhiro Koyama
for his helpful suggestions and the previous study
data on this site. This work is partly supported by
grants from the Ministry of Education, Science, andCulture of Japan (KAKENHI No. 26450222).
REFERENCES[1] Titus H.J. and Tsuyuzaki S. Ski Slope Vegetation
at Snoqualmie Pass, Washington State, USA, anda Comparison with Ski Slope Vegetation in
Temperate Coniferous Forest Zones. Ecol. Res.
Vol.13, 1998, pp.97-104.
[2] Tsuyuzaki S., Environmental Deterioration
Resulting from Ski-Resort Construction in Japan.
Environmental Conservation, Vol.21, 1994,
pp.121-125.

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THE ADEQUACY OF HOUSEHOLDS SANITATION LEVELIN THE CITY OF JAKARTA:A MICRODATA ANALYSIS FROM SUSENAS 2017
Noviyati Valentina Sidabutar1 and Chotib2
School of Strategic and Global Studies, University of Indonesia, Indonesia

1,2

ABSTRACTAs a metropolitan city, Jakarta faces the problems of uncontrolled migrant populations and limited housing
availability. This results in the uncontrolled growth of organic settlements with a lack of adequate sanitation
facilities. Furthermore, open defecation in public spaces is still practiced in some areas. Using the multinomial
logistic regression method, this research aims to identify the adequacy of household sanitation in Jakarta, focusing
on human waste disposal facilities in households. The dependent variable (adequacy of sanitation facilities) is
divided into three categories; adequate private sanitation (used by one household), adequate shared sanitation (usedby multiple households), and inadequate sanitation. This research also aims to identify the correlation of household
characteristics as predisposition factors towards the trends in the sanitation adequacy. The household
characteristics analyzed in this study are income, employment status, level of education, marital status, gender,
number of household members, and migrant status. This research uses microdata from the 2017 National SocioEconomic Survey. It was found that 85.64% of the household sanitation sampled in the research is adequate (ofwhich 68.81% are adequate private sanitation and 16.83% adequate shared sanitation), and 14.36% were classified
as inadequate. Sanitation adequacy is highly correlated with long term residency, high education, household
members ≥4 people, unemployed, married, female-headed household, and high expenditure. The government canutilize those predisposing factors in planning the design policies and interventions on sanitation to lower thepercentage of households with inadequate sanitation.
Keywords: Adequacy level, Households, Sanitation, Jakarta.
of the strategic issues in development in Indonesia [4].
Until now, Indonesia is still struggling to achieve theuniversal access to sanitation as mandated by theUnited Nation’s 6th Sustainable Development Goals(SDGs). Nevertheless, Jakarta targeted 100% properand safe sanitation by the year of 2024 [5], even when
there are a variety of obstacles to be faced with.
The main objective of this research paper is to
map the adequacy of household sanitation in Jakarta,
in order to achieve the SDG’s target as well as theregional targets. This research also aims to identify
the influence of household characteristics on theadequacy of household sanitation. This study focuseson household sanitation facilities for feces disposal in
Jakarta, using microdata from the 2017 National
Socio-Economic Survey.

INTRODUCTION
Jakarta as a megapolitan city has a massive
physical development that is a very strong magnet for
migrants seeking a better life in this city. Therefore,
Jakarta's population growth is strongly influenced by
the influx of migrants. According to the 2019 statistic[1], the average population of Jakarta in the last two
years has increased by 247 people every day or equal
to 10 people every hour. This data illustrates the
exponential population growth of Jakarta, which
increases housing needs. As a result of the housing
scarcity, some low-income migrant resorts to organic
settlements. These organic settlements, with dense
and slum characteristics, and also poverty, are oftenlabeled as an urban-diseases.
Most households in Jakarta still use traditional
latrines in their sanitation facilities. However,
Jakarta’s Health Office reported that in January of
2019, there are 115,185 households who are stillpracticing open defecation [2], or latrines that dump
feces directly to the environment.
Lack of sanitation facilities and the practice ofopen defecation are strongly related to human andenvironmental health. It can cause water and land
pollution, and increase the infection rate of various
diseases such as diarrhea. On the other hand, this also
results in increased economic burden for cities toaddress public health problems.
These sanitation issues, have been declared as one

LITERATUR REVIEWHuman beings can only be healthy in a healthy
environment [6]. Dahlgren and Whitehead argued
that environmental health is an important componentof public health, where the individual lifestyle to thesocio-economic and cultural environment takes partas major determinants [6]. It covers all aspects ofhuman behavior in relation to the living environment,with the aim of improving and maintaining thehighest level of human health by modifying socialand environmental factors. On the other hand,environmental health is in actuality a byproduct of525

GEOMATE – Melbourne, Australia, 11-13 November 2020

in determining the ability of households to adopt anadequate sanitation facility. There have been
numerous research that found a positive correlation
between latrine ownership and high socio-economic
status [10,11]. Those research found that richhouseholds have a strong will to build their own
latrines, even a latent demand for improving
sanitation comes from them [12]. In addition, poor
households tend to prioritize the fulfillment of basic
needs above the procurement of adequate sanitation
facilities [13,14]. Poverty has also been proven to
reduce the demand for sanitation [15-17], and that
individuals with low-income tend to not use latrines[18].
Migration status has a potential correlation in
improving household welfare [21] and social status[20]. Most migration is done to increase the current
value of the expected lifetime income [22]. Migrants
in the new urban environment have better working
conditions and quality of life. They are usually
women, well educated, older, living in longer
migration periods [23], unmarried, and have informaljobs [20].
Education and employment status of a household
are reliable indicators for household welfare [19].
Employment provides opportunities to gain income,while education is a way to develop knowledge andget better employment. However, individuals withinformal employment status in Jakarta has been foundto have higher welfare level due to migration [20].
Education has a significant influence on the use ofhousehold latrines [18,16]. Household heads withhigher education have more knowledge of healthyliving [14]. A better understanding of the healthimpact of poor sanitation is a driving factor forinvestment in adequate sanitation, which declines thetrend in open defecation [27].
Gender also influences sanitation preferences.
Numerous research has shown that females tend tohave high preferences for the health and non-healthbenefits of sanitation such as hygiene and privacy[10,25-27]. Research shows that women have an
important role in determining sanitation choices andare able to persuade their partners to invest in
adequate sanitation facilities [25].
Household size also determines the needs for
sanitation facilities, especially the size of a septic tank.
Commonly, households with four or more household
members have a higher chance of having a latrine [28].
Efforts to improve access to sanitation facilities at
the household level is expected to reduce theenvironmental and economic burden of a city.
Indirectly, it can improve welfare, helps reducepoverty levels by reducing health costs, productivity
gains associate morbidity and mortality, increase
gender equality, environmental sustainability, andwater resource [12,30]. Ultimately, sanitation
adequacy can improve health and save lives.

human activities. Therefore, human activities are
keys to the public health.
According to Lawrence Blum, there are four
factors that influence public health: 1) environmental
factors (physical, biological and socio-cultural), 2)
behavioral factors (attitudes and lifestyles), 3) healthservice factors, and 4) heredity. Green divided thedeterminants of health behavior into three main
factors consisting of predisposing factors, enabling
factors, and reinforcing factors [7].
One of physical component that supports theenvironmental health is the availability of adequate
sanitation in households. According to WHO-Unicef
Joint Monitoring Program (JMP), the criteria for
global measurement of sanitation services known as
the sanitation ladders consists of safely managed
sanitation, basic sanitation, shared sanitation,
unimproved sanitation, and open defecation [3]. The
sanitation service refers to the management of excreta
from facilities including emptying and transporting
process of the excreta from the facilities. This
sanitation issues have been declared as The United
Nation’s 6th Sustainable Development Goal. The goaltargets to achieve universal access to basic sanitation
services and progressively improving sanitation tosafely managed sanitation by the year of 2030. The
target can be achieved if all levels of society have
healthy sanitation behavior.
According to the theory of Thoughts and Feelingby WHO [8], there are four main factors that shape aperson's behavior, one of which is a resource reason
which includes facilities, money, time, energy.
Resources can have a positive or negative effect onhuman behavior. On the other hand, the availabilityof resources is strongly influenced by three reasons:1) the level of understanding and consideration (thepresence of knowledge, beliefs, and attitudes), 2)
influence from important people in their lives, and 3)
culture. These factors shape the way of life in urban
communities.
The Function theory expressed by Katz [8],considers that changes in individual behavior canoccur when individuals feel a need. The need revealed
the ability of individuals to deal with the surrounding
environment. On the other hand, individual behavior
also depends on the individual’s character, which isshaped in the household.
From the theories explained above, we can further
hypothesize that an individual’s decision to provide
adequate sanitation is influenced by the individual’s
resources (especially financial), knowledge, and need.
Household income illustrates the economic
condition and welfare of the household, the ability tofulfill all basic and non-basic needs, and the ability tomaintain their standard of living. Household welfare
can be assumed to be in line with the proportion ofhousehold expenditure which is spent on non-foodneeds [9].
Household income is also an important predictor
526

GEOMATE – Melbourne, Australia, 11-13 November 2020

METHODS

results in statistical modeling as follows:This research uses the microdata from the 2017
National Socio-Economic Survey which is conductedby The Indonesia’s Central Bureau of Statistics. The
survey, also known as Susenas, do annual collectionof data relating to the socio-economic conditions of
the community, including health, education, fertility,
family planning, housing and other socio-economic
conditions in all provinces in Indonesia. Data andindicators from Susenas have been widely used andare seen as important pieces of evidence useful forplanning, monitoring, and evaluating government
development programs. The 2017 survey sampled
3,483 households spread in six regions in Jakarta.
This research uses a quantitative approach tofulfill the research aims. Descriptive analysis isperformed for data analysis using a cross-tabulation
method, to map the adequacy of household sanitationin Jakarta. While the inferential analysis is performed
using a multinomial logistic regression method, toidentify the influence of household characteristics on
the adequacy of household sanitation.
The research begins with examining the influenceof resources (illustrated by the level of income andemployment status of households) on sanitation
adequacy. The research then examines the level ofunderstanding (illustrated by the level of education),
as well as other variables (marital status, gender,
household size and migration status of households)
and their correlation to sanitation adequacy.
The dependent variable in this research is theadequacy of household sanitation, adopted fromsanitation ladder of The JMP [3]. The sanitation
ladder was simplified into three categories: (1) level
0: inadequate sanitation (unimproved sanitation andopen defecation), (2) level 1: adequate sharedsanitation (shared sanitation), (3) level 2: adequate
private sanitation (safely managed sanitation danbasic sanitation).
The independent variables are the predisposing
factors that consist of household income (measured
by expenditure per-capita (exp_cap)), employment
status (workstat), education (educ), marital status(maritalstat), gender (gend), number of household
member (art), and migration status (risenmig andlifetimemig). Pearson correlation method is used todefine the correlation between each of independent
variables. The result indicates that there is no
multicollinearity between the independent variables.

ln �𝑃𝑃𝑠𝑠ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎
� = 𝛽𝛽0 + 𝛽𝛽1 𝑒𝑒𝑒𝑒𝑝𝑝_𝑐𝑐𝑐𝑐𝑐𝑐 + 𝛽𝛽2 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤
𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖+ 𝛽𝛽3 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 + 𝛽𝛽4 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚+ 𝛽𝛽5 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 + 𝛽𝛽6 𝑎𝑎𝑎𝑎𝑎𝑎 + 𝛽𝛽7 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟+ 𝛽𝛽8 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 + 𝜀𝜀
𝑃𝑃𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝
ln �
� = 𝛽𝛽0 + 𝛽𝛽1 𝑒𝑒𝑒𝑒𝑝𝑝_𝑐𝑐𝑐𝑐𝑐𝑐 + 𝛽𝛽2 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤
𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖+ 𝛽𝛽3 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 + 𝛽𝛽4 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚+ 𝛽𝛽5 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 + 𝛽𝛽6 𝑎𝑎𝑎𝑎𝑎𝑎 + 𝛽𝛽7 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟+ 𝛽𝛽8 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 + 𝜀𝜀

with:Pinadequate= opportunity for inadequate sanitation
category
Pshared= opportunity for adequate sharedsanitation category
Pprivate= opportunity for adequate private
sanitation category
Iteration log shows how fast the model isconverged. The log likelihood value (-2.546,679),
Chi-square likelihood ratio of 674.84 with p-value<0.0001, prob>chi2 value <0.05, Pseudo R2 value of
11.7%, and a large sample size shows that the model
describes the quality of household sanitation facilities
adequately. Partial effect was used in this research,which estimated by the Average Marginal Effect(AME), to obtain additional probabilities in eachdependent variable.
RESULT
In this research, 85.64% of the sampled
households in Jakarta owned adequate sanitation, of
which 68.81% are private sanitation and 16.83% areshared sanitation. The rest of the households(14.36%) owned inadequate sanitation. Figure 1
shows the distribution of household sanitation
adequacy in six regions of Jakarta. East Jakarta has
the highest percentage of adequate private sanitation(78.95%), Central Jakarta has the highest percentageof adequate shared sanitation (34.43%), and theThousand Islands has the highest percentage of
inadequate sanitation (38.03%).

Research Model
The dependent variables are treated in
multinomial forms with three categories that do nothave a natural sequence. The level labeled 0 (Y=0) set
as the reference category that forms the basis of thelogit function to compare other categories (Y=1 and
Y=2) against Y=0. Modification of the variables

Fig.1

527

Distribution of Households
Adequacy Level in Jakarta

Sanitation

GEOMATE – Melbourne, Australia, 11-13 November 2020

This research found that households in East
Jakarta tend to have the longest duration of stay, havea private home, with better financial ability. The
households in Central Jakarta have a higher
percentage of migrant households who live in rental
houses. On the other hand, households in Thousand
Islands district tend to have a longer duration of stay,
have a private home, but have a lower financial ability.

sanitation category are household members <4people (14,6%), formal employment status(11,2%), not married (9,6%), migrant lifetime(4,4%), and junior/senior high school education(0,7%). While gender, expenditure, and recent
migrant status show no significant effect.
2. The variables which significantly influence theincrease from inadequate to adequate private
sanitation category are non-migrant recent(26,2%), higher level of education/university(19,7%), unemployment (17,9%), household
members ≥4 people (16,4%), married (11,4%),
female-headed (8,6%), and high expenditure(0,6%). The lifetime migrant status shows nosignificant effect.

Table 1 Summary of Descriptive Statistic
Dependent

Independent
GenderAdequacy level of sanitation facilities in households
Inadequ
ate

11,86
Male
14,88
Number of household member<4 person
16≥4 person
12,97
Marital status
Not married
13,44
Married
14,69
Education
Uneducated20,36
Primary
21,4
Middle sch.
13,89
Univ.
6,27
Employment status
Unemployed
9,28
Informal
15,68
Formal15,68
Expenditure per capita(Rp 100.000)
16,232
Recent migrantNon-migrant
14,02
Migrant
21,8Lifetime migrantNon-migrant13,22
Migrant
15,24
FemaleTotal

Adq.
SharedAdq.
Private

%

n

15,72
17,06

72,42
68,06

100
100

328.6191.565.697

27,1
8,19

56,9
78,83

100
100

865.327
1.028.989

22,84
14,64

63,72
70,67

100
100

506.461
1.387.855

18,67
19,08
18,78
7,60

60,98
59,52
67,33
86,13

100
100
100
100

148.999330.771
1.077.312
337.234

8,76
17,32
20,05

81,96
67,00
64,26

100
100
100

392.172
613.269
888.875

18,03524,725

-

-

15,2
52,45

70,78
25,74

100
100

1.811.487
82.829

11,87
20,67

74,91
64,1

100
100

826.159
1.068.157

Table 1 shows the summary of the descriptive
statistics in this research. It provides some initialinsight into the household characteristics and its
correlation to the quality of sanitation. The
independent variables with a strong influence on theprivate sanitation adequacy are (1) female (72,42%),(2) household member ≥4 people (78,83%), (3)married (70,67%), (4) college education (86,13%),(5) unemployed (81,96%), (6) high expenditure, (7)non-migrant recent (70,78%), and (8) non-migrant
lifetime (74,91%). While the inadequate sanitation isstrongly related with (1) male (14,88%), (2)household member <4 people (16%), (3) married(14,69%), (4) low education (primary) (21,4%), (5)informal and formal sector (15,68%), (6) low
expenditure, (7) recent migrant (21,8), (8) lifetime
migrant (15,24%).
Table 2 shows the inferential analysis results ofthe significant relationship between researchvariables and the comparison within the adequacy ofhousehold sanitation. The results of the inferential
analysis are in line with the descriptive results. The
comparison result can be summarized in two point:
1. the variables which significantly influence theincrease from inadequate to adequate shared

Table 2 Multinomial Logistic Regression Analysis
Adequate Shared sanitation
Independent
Variables

Coef.

Gender(0 = Female, 1
0,17= Male)Number of household member(0 = <4 person,-0,90***
1 = ≥4 person)
Marital status(0 = notmarried, 1 =-0,54***
married)Education
primary (0 =uneducated, 1
0,03= primary)Mid.school (0= uneducated,
0,56***
1 = mid.sch)
Univ. (0 =uneducated, 1
0,50= univ.)
Employment status
Informal (0 =unemployment,
0,10
1= informal)
Formal (0 =unemployment,
0,34*
1= formal)
Expenditure per capita(Rp.100.000)-0,01
Recent migrant(0 = non-mig, 1
0,33= migrant)
Lifetime migrant(0 = non-mig, 10,35***= migrant)
_cons
0,03

Number of obs
LR chi2(22)Prob > chi2
Pseudo R2
Log likelihood

Adequate Private sanitation

Std.
err.

Part.
effect

Coef.

Std.
err.

Part.
effect0,23

0,06-0,40**

0,21

-0,09

0,14

-0,15

0,48***

0,11

0,16

0,20

-0,10

0,37**

0,18

0,12

0,23

-0,00

0,04

0,18

0,01

0,22

0,01

0,68***

0,17

0,08

0,32

-0,06

1,38***

0,25

0,20

0,20

0,07

-0,75***

0,16

-0,13

0,20

0,11

-0,89***

0,16

-0,18

0,01

-0,00

0,03***

0,00

0,01

0,27

0,15

-1,30***

0,29

-0,26

0,13

0,04

-0,04

0,10

-0,04

0,97***

0,21

0,27

3.483
674,840,000
0,117-2.546,6791

Note : *) significant at 10%; **) significant at 5%; ***) significant at 1%

DISCUSSION
This research found a statistical correlation
between household characteristics with sanitation
quality. In this research, longer duration of stay, ahigher level of education, and unemployment status

528

GEOMATE – Melbourne, Australia, 11-13 November 2020

safety factors. Even, the rate of open defecation islower in female than in male [21].
Jakarta is targeted to achieve 100% access toproper and safe sanitation by 2024 [4]. Eradicating
14.36% of the inadequate category is a heavy task.
The biggest challenge is presented by the practice ofopen defecation, inadequate sanitation facility inhouseholds, and inadequate clean water andsanitation service coverage. Susenas data show thatthe coverage of clean water services in 2017 is only
60% [5]. It can be ascertained that the remaining 40%
still uses ground water sources to fulfill their waterneeds. Besides that, the coverage of wastewater
management services in 2017 is 24.33% [12], andonly targeted to be 35% by 2022 [29]. The lack ofaccess to water resources may have influenced thehousehold sanitation adequacy. Heads of households
need strong reasons for the importance of adequate
sanitation to decide to adopt adequate sanitation.

have a significant correlation with the highpercentage of marginal effect to the adequate private
sanitation category. When households have been
living longer in Jakarta, it can be assumed that they
can adapt to the urban environment and improve theirstandards of living. On the other hand, education(formal and informal) can catalyze this process of
behavior change, especially in increasing theknowledge on the importance of adequate sanitation,
as well as improving the chance to have a better job
opportunity. On the other hand, the unemployment
status does not describe poor financial status. The
samples tend to have a good financial ability eventhough they are unemployed.
From further descriptive investigation, thisresearch found a statistical correlation between somevariables:
1. female-headed households tend to have a higher
correlation to adequate sanitation, withcharacteristics of not married, unemployed, andlive in their own house together with children/sonin-law/grandchildren.
2. male-headed households tend to have a higher
correlation to inadequate sanitation, withcharacteristics of not married, working in theinformal sector, living alone in a rental house with
uninhabitable condition.
The operational definition of not married status inthis research is divided into three categories: single,
widowed by law, and widowed by death. There are
two possibilities why females who are not married
tend to have adequate sanitation; (1) the female is anelder in her house at the time of the survey, andindirectly appointed as the head of the family, and (2)the females who are widowed have sufficient monthly
living expenses or have inheritance so that they arefinancially capable even though they are unemployed.
On the other hand, the inadequate category
indicates a correlation with the status of recent
migrants, who live in rental houses, with
uninhabitable conditions. Some of the migrants whocome to Jakarta may have limited resources orfinancial ability. They try to survive by settling
temporarily in a densely populated area. Many ofthem are from villages. They may still carry traits andhabits that they develop while living in their previous
dwelling. The transition between traditional rural
culture to modern urban culture can create problems
in their daily life, such as continually practicing open
defecation [31]. According to research, thesehousehold characteristics found in Central Jakarta
area. As for the Thousand Islands, our conjecture isrelated to low education and low welfare.
The other variables that also have a significant
correlation to the adequate private sanitation category
are being married, household members of ≥4 people,
and female-headed households. Females tend to havea high preference for private defecation facilities in
the household due to privacy needs, hygiene, andCONCLUSION
Research findings shows that 85.64% ofhousehold sanitation facilities in Jakarta haveadequate sanitation (68.81% are adequate private
sanitation and 16.83% adequate shared sanitation,
while 14.36% are inadequate. The household
characteristics of inadequate sanitations are maleheaded household, household members <4 people,
married, with low education and income, working in
informal sector, migrant (recent migrant and lifetime
migrant), and living rented house in low living
condition. The household characteristics thatsignificantly influence the change from inadequate toadequate private sanitation are non-migrant recent,university education, household members ≥4 people,
unemployed, married, female-headed household, andhigh expenditure. The problem of low-quality
sanitation in households in Jakarta can be overcome
by incorporating these correlated household
characteristics in the policymaking process,especially to lower the percentage of inadequate
sanitation.
Limitations of the Study
The analysis in this study is dependent on theavailability of variables in the Susenas data. A
comprehensive analysis conducting a household
survey specifically to study the feasibility of disposal
facilities at the household level in Jakarta is strongly
recommended. It will strongly support the analysis
results.
Acknowledgments
This paper is part of the University of Indonesia
Research
Grant
2020:
Social-Humanities
International Indexed Publications (Q4).
529

GEOMATE – Melbourne, Australia, 11-13 November 2020

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Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SPATIAL PATTERN OF EARTHQUAKE PRONE AREAS OF BANTULREGENCY, INDONESIA
1

Susiloningtyas, D1, Della Ayu Lestari1, Hengki Tasman2, Tuty Handayani1, Annacletus Ari Dartoyo3
Geography Department, Faculty of Mathemathics and Natural Science, Universitas Indonesia, Indonesia
2
Mathemathics Department, Faculty of Mathematics and Natural Science, Universitas Indonesia
3
Geospatial Information Agency, Indonesia
ABSTRACT

Bantul Regency is located around the subduction zone between the Indo-Australian Plate and is passed by
the Opak Fault making it an earthquake-prone region. Earthquake disaster is a disaster that cannot be predicted sothat an earthquake disaster mitigation effort is needed by studying the historical earthquake that occurred. The
purpose of this study was to analyze Peak Ground Acceleration (PGA) both the average and maximum values in
Bantul District based on earthquakes that occurred from 1900 to 2017. calculations using the Gutenberg - Richtermethod with the Richter formula combined with the intensity attenuation formula Subardjo-Prih Harjadi which isa function of magnitude, epicenter distance, and earthquake intensit. The PGA was then analyzed using theoverlapping method with the built-up area. Based on the PGA average PGA distribution, the lowest average PGA
value is 33.81 gals and the highest is 36.23 gals. Whereas based on the maximum PGA, the highest PGA
distribution is in the northwest and southeast regions of Bantul Regency with the lowest maximum PGA value of
223.58 gals and the highest of 248.42 gals. The PGA value has the potential for an enormous risk of damage is on
the VII MMI Scale. Pajangan Subdistrict, Bambanglipuro Subdistrict and Pundong Subdistrict are on average highPGA and maximum PGA, so these sub-districts have a higher risk potential than other sub-districts.
Keywords: Earthquake, Magnitude, Peak Ground Acceleration (PGA), vulnerabilityof the earthquake from the Benioff zone sometimes
also occurs in this area. The source of the earthquakeon land has been identified as coming from the Opak
Fault. This is a fault that is considered causing the2006 Yogyakarta earthquake. Based on theearthquake distribution, there may be other active
faults besides the Opak fault. The maximum
magnitude of the earthquake originating from thesubduction zone was 8.1 Mw, while the Opak fault
was 6.8 Mw [4].
One of the most damaging earthquake evidences
occurred on Saturday, May 27, 2006 at 05.53 WIB
obtained from the Meteorological, Climatological,
and Geophysical Agency of Indonesia station, UnitedStates Geological Survey (USGS), and the station
owned by the Volcano Technology Research andDevelopment Center, Yogyakarta. Data from theVolcano Technology Research and Development
Center is then analyzed by the Geological Agency
Emergency Response Team. USGS noted that theepicenter was located at coordinates 8.01o South
Latitude & 110.29o East Longitude at a depth of 17.2
km, strength of 6.2 Mw, and intensity of damage to
VII MMI. According to BMKG, the earthquake
centered at 7.89o South Latitude & 110.37o East
Longitude and a depth of 40 km, with a magnitude of
6.2 Mw, and an intensity of VII MMI. Whileaccording to Meteorological, Climatological, andGeophysical Agency of Indonesia, the earthquake
was centered at 8.08o South Latitude & 110.31o East
Longitude, depth of 33 km, with a magnitude of 5.8

INTRODUCTION
Indonesia-based on its geographical location is
one of the regions prone to natural disasters. One of
the natural disasters that often happens is anearthquake. Data shows that Indonesia is a country
that is has a top level of seismicity in the world, overten times as much seismicity in the United States [5].
Indonesia geographically located between 6o North
Latitude-11o South Latitude and 95o East Longitude 141o East Longitude are three places the world’s
major plates are the Indo-Australian Plate, theEurasian Plate and the Pacific Plate. The activity ofthe three plates causes good soil movement small
earthquakes that are only detected by seismographs,
quakes that can be felt an average of about 70-100
times per year, and a quake cause damage between 12 times per year [5]Yogyakarta Province and its surroundings are on
two active plates, Indo Australia and Eurasia, which
stretches from the western hemisphere of Sumatra to
the southern hemisphere of Nusa Tenggara. This
causes the Yogyakarta region and its surroundings
very prone to tectonic earthquakes [4]. The source ofthe earthquake in this region is located in the sea fromthe subduction zone and on land from an active fault.
In addition, there is an earthquake distribution at the
top of the subduction zone or also known as the interplate zone, the accretionary prism zone. This
earthquake is characterized by the epicenter of
position in the sea but with shallow depth. The source
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GEOMATE – Melbourne, Australia, 11-13 November 2020

SR and intensity scale VII-IX MMI [4].

steps as follows [5] [9]:a. Calculate epicenter distance
The epicenter distance is determined by
calculating the distance from the epicenter to thepoint X, using the formula:
Cos ∆ = Cos Le Cos LX + Sin Le Sin LX Cos (λEλX)(1)Over 5,000 people die, huge property losses, anda lot of environmental damage. Several cracks in the
ground, liquefaction, and landslides occurred in the
Yogyakarta region, which showed that the earthquake
shocks were strong. The Bantul region experienced
the worst disaster, so that the May 27, 2006
earthquake can also be referred to as the “Bantul’s
Earthquake”. The 2006 Yogyakarta earthquake wasone of the earthquakes with the most casualties inIndonesia since 1612 [4].
This earthquake has made us aware of theimportance of mitigation efforts, both structural or
physical and non-structural or non-physicalmitigation [7] [10] [11]. Mitigation efforts must be
carried out to minimize the risk of earthquake andtsunami disasters in the Yogyakarta region, which
may be repeated in the future. Including non-physicalmitigation efforts, delivery of data and information
about the danger and impact of the earthquake so thatpeople can prepare for disasters. Various attempts
were made to reduce the impact that occurs when an
earthquake occurs. One of them is by knowing thePeak Ground Acceleration (PGA) both the averagevalue and the maximum value in Bantul Regency
based on earthquakes that occurred from 1900 to2020 [1] [3 . The PGA was then analyzed using theoverlapping method with the built-up area of Bantul
Regency. I expect the results of this study can be used
as a reference in disaster mitigation efforts anddisaster risk management plans.

Information:
Cos ∆ : Epicenter distance to point X
Lx: The geographical latitude of the point X,
for which the PGA value will be calculated;λx: Geographic longitude of point X, for which
the PGA value will be calculated;Le: Epicenter geographical latitude,λe: Epicenter geographical longitude∆: Distance between points E and X in degrees∆°: converted to kilometers, diverting 111.11
Km for every 1 °b. Calculate the intensity in the hypocenter with theformula;Io = 1.5 (M - 0.5)
(2)
Information
Io: Intensity at the source;M: Magnitude of body wave (Richter Scale)c. Calculate the intensity at point X with the formulaof the attenuation constant of Prih Haryadi andSubardjo:
I = (Io. Exp –b.∆)(3)
Information:I: Surface intensity
Io: Intensity at the source∆: Distance to the source of the earthquake(epicenter)b: The attenuation constant (energy decay),worth 0.0021
exp: Natural number, worth 2,786

METHODS
Earthquake wave vibration parameters recordedby seismographs generally the velocity deviation orvelocity in units of kine (cm/s). Besides velocity,
other parameters such as displacement (deviation in
micrometer units) and acceleration (acceleration in
gal units or cm/s2) can also be specified. Seismic orfrequent wave acceleration parameters called theacceleration of the ground is one of the importantparameters in science seismology [8]. Acceleration is
the parameter that states changes in speed from rest
to a certain speed. Peak Ground Acceleration is thevalue of the greatest ground vibration accelerationthat ever happened somewhere caused by earthquake
waves [6]. In this study, calculations using the
Gutenberg-Richter method with the Richter formula
combined with the intensity attenuation formula
Subardjo-Prih Harjadi which is a function ofmagnitude, epicenter distance, and earthquake
intensity. The greater the PGA that occurs, the greater
the risk. PGA data which is a parameter of the sourceof damage caused by an earthquake, obtained throughfunctions of magnitude, epicenter distance, andearthquake intensity earth (Ibrahim & Subardjo,
2005). The PGA calculation value is carried out thed. Calculate the PGA value at point X with theformula;Log α = (I / 3) - 0.5(4)
Information:α: Peak Ground Acceleration (PGA) in gal(cm / second)I: Surface intensity on the MMI scale and thenumbers 3 and 0.5 are constants.
The point of the earthquake taken in this study
was that an earthquake that had occurred in theSpecial Region of Yogyakarta with a coordinate limitof 7 ° 32 '30 "LS to 8 ° 12' 20" LS and 110 ° 0 '10 "BT
532

GEOMATE – Melbourne, Australia, 11-13 November 2020

destructive earthquake events in 1943, 2006 and 2010
were estimated to originate from the Opak fault. In
addition, Bantul Regency is located in the collision’s
vicinity zone (subduction zone) between the IndoAustralian Plate which is moving northward to theEurasian Continent Plate at a speed of ± 7cm / year.
This zone is about 200 - 250 km from the South Coastof Java and has the potential to cause an earthquake.
The earthquake that occurred in the collision zone
was one of the prime causes of local tsunamis, which
in a quick time had the potential to impact the South
Coast of Java, including the beaches in Bantul
Regency.

to 110 ° 50' 5" BT sourced from Earthquake USGS
and BMKG. Determination of the reference point isdone by making the center of gravity. Determinationof the emphasis is done on each village in the city of
Bengkulu, as many as 75 reference points. In one
earthquake event, the PGA value was calculated ateach center of gravity so that at each point a PGA
value was obtained, then the PGA value wascalculated for all reference points from the PGA
values, a maximum PGA value is sought at each point.
After getting the maximum PGA value in each village
in Bengkulu City, then the same point value will becalculated and will form a contour-like line using theinterpolation method. In this study earthquake data
was divided into two groups, i.e. maximum PGA andaverage PGA. The division of these two groups isbased on different patterns and levels of earthquake
risk. Then from the minimum and maximum valuesof each group are categorized into three classes withthe equal interval method each class has the sameinterval in the interpolation’s division class.
DISCUSSIONSDistribution of Earthquakes in Yogyakarta
The frequency of earthquakes in Yogyakarta is
not really high but destructive earthquakes, in the
period of 120 years, i.e. the period 1900 to 2020 there
were 34 earthquakes with a magnitude of 3.7 SR to
6.3 SR with a depth of less than 100 km (Fig 1) .
Earthquakes with a magnitude of less than 4 SR
occurred three times, earthquakes with a magnitudeof 4 SR up to 5 SR occurred 26 times and earthquakes
with a strength of over 5 SR occurred three times.
Significant and destructive earthquake occurred on
March 13, 1981 with the intensity in Yogyakarta VII
MMI with the resulting impact of the cracked wallsof the Ambarukmo Hotel. On 25 May 2001 withmagnitude 6.3 SR depth of 143 km and intensity in
Yogyakarta V MMI and minor damage in
Yogyakarta. On May 27 2006, with magnitude 5.9 SR
33 km and intensity VII MMI. This is characterized
by: shocks felt by everyone, including those inside thevehicle; crockery falling and breaking; old buildings
and buildings that are designed not to withstandearthquake shocks collapse and are damaged;liquefaction, land cracks, and landslides occur; turbid
community wells, gutters and irrigation channels
damaged; and the ceiling and the top of the building
are broken.
Geologically, Bantul Regency is bypassed by the
Opak Fault. Based on historical records of destructive
earthquake events from Supartoyo et al. (2014)
coupled with the events of 2015, the Yogyakarta area
has experienced at least seven destructive earthquake
events. One incident is thought to have caused atsunami, namely the earthquake in 1840. The

Fig. 1

Distribution of Magnitude (1900-2020)
Bantul Regency.

Based on these conditions, the May 27, 2008
earthquake had a very significant impact on Bantul
District with 4,143 fatalities, then 71,763 houses weredamaged, 71,372 houses were severely / moderately
damaged and 73,679 houses were lightly damaged.
For Bantul Regency itself the most severe damage
occurred in Jetis sub-district in 143 people, then
71,763 houses were damaged, 71,372 houses wereseverely damaged / moderately damaged and 73,679
houses were lightly damaged. For Bantul Regency
itself the most severe damage occurred in Jetis subdistrict in 143 people, then 71,763 houses weredamaged, 71,372 houses were severely damaged /moderately damaged and 73,679 houses were lightly
damaged. For Bantul Regency itself the most severe
damage occurred in Jetis sub-district in where 11,356
houses were totally damaged. Damage data in every
district is complete can be seen in Table 1. The
earthquake also caused Adi Sutjipto Airport to be
temporarily closed due communication disruptions,building damage and cracks on the runway, so that airtransportation was diverted to Semarang and Achmad
Yani Airport Adisumarmo, Solo Airport.

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Spasial Pattern of the Average of Peak GroundAcceleration, Year 1900 to 2020

needed to mitigate disasters so that they can recoverquickly in the event of an earthquake.

If assessed based on the PGA, the average PGA
distribution in Bantul Regency shows in Figure 2 withthe lowest average PGA value of 33.81 gals and thehighest of 36.23 gals. Based on [2] with an average
PGA value is in the class of 25-50 gals, the resulting
risk level is small. This average PGA distribution has
a pattern further north, so the average PGA is getting
lower (Fig.2). The average low PGA area (<34 gals)
is in the north of the Regency with an area of 5638.98
Ha or 11.03% of the Bantul Regency area is in the
sub-districts of Sedayu Sub-district, part of the
Kasihan Sub-district, part of the Banguntapan SubDistrict and part of the Pinyungan Sub-District.The
average PGA area (34-35 gals) is in the north of
Bantul Regency with an area of 11,133 hectares or 21,
78% of the Bantul Regency area is in some areas ofSedayu District, Pajangan District, Kasihan District,Sewon District, Banguntapan District, Pleret District
and Pinyungan District. Then for the PGA region the
average high (> 35 gals) is in the middle to the southof Bantul Regency with an area of 34,346, 65 Ha or
67.19% of the Bantul Regency area.

Table 1. Distribution of Built-up Area in each classof PGA Average
Built-up Area (Ha)District Name

Low

Moderate

High

PGA

PGA

PGA

Bambanglipuro
Banguntapan

79.72

Total

452.78452.78184.42

6.48

270.61

Bantul3.11

179.54

182.65

Dlingo3.26

965,57968.831077.99

1077.99657.23657.23

Imogiri
Jetis
Pity

376.91390.78

Clove
Display

37.57

PandakPiyungan

67.51

Pleret767.70373.28373.28251.54289.10451.95451.95200.95183.80194.52Pudong411.62411.62

Sanden220.30220.30

Sedayu10,72

268,45537.47

Sewon
SrambatTotal of Built-up Area

96.80
232.27634.27142.01374.28122.66

122.667718.22

Spasial Pattern of the Maximum of Peak GroundAcceleration, Year 1900 to 2020
If assessed based on the maximum PGA, PGA’s
distribution in Bantul Regency shows in Figure 3
with the lowest maximum PGA value of 223.58 gals
and the highest of 248.42 gals. Based on Fauzi, 2000
with a maximum PGA value which is in the class200-300 gals with a high level of risk. This isindicated from the earthquake on May 27, 2006 which
has intensity VII MMI. This is characterized by:shocks felt by everyone, including those inside thevehicle; crockery falling and breaking; old buildings
and buildings that are designed not to withstandearthquake shocks collapse and are heavily damaged;liquefaction, land cracks, and landslides occur; turbid
community wells, gutters and irrigation channels
damaged; and it breaks the ceiling and the top of thebuilding.

Fig. 2 Peak Ground Acceleartion (Average) Bantul
Regency
Table 1. shows the area of built-up area that is ineach average PGA class. The lowest risk districts are
in Sedayu District which has an area of 634.27 Hawith 537.47 Ha including the low class and 96.80 Ha
including the moderate class. Conversely, it can also
be seen that several sub-districts that are on a highaverage PGA with the entire area included in theclass. Such as Bambanglipuro Subdistrict, Imogiri
Subdistrict, Jetis Subdistrict, Jetis Subdistrict, Kretek
Subdistrict, Pandak Subdistrict, Pandak Subdistrict,Pudonhm Subdistrict, Sanden Subdistrict andSrambat Subdistrict. These sub-districts have a higher
risk potential than other sub-districts, so efforts are534

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2. Distribution of Built-up Area in each classof PGA Maximum
Built-up Area (Ha)District Name

Total

Low

Moderate

High

PGA

PGA

PGA

49.51

403.26452.78150.772.20

270.61109.11

73.54

182.65531.67231.61968.83

Imogiri

564.46

513,531077.99

Jetis

602.3054.93

657.23377.5320.26

767.70Clove

301.62

71.66

373.28Fig. 3 Peak Ground Acceleartion (Average) Bantul
Regency

Display

57.49

231.62289.10Pandak

448,163.79

451.95In general, this maximum PGA distribution has apattern going further north, the average PGA getting
lower. However, specifically for the maximum PGA
which is in the high class spread in the northwest andsoutheastern areas of Bantul Regency. This happened
because of the magnitude of the influence of theearthquake on May 25, 2001, with magnitude 6.3 SR
depth of 143 km which has an epicenter point in thewestern part of Yogyakarta that affects the northwest
region of Bantul Regency. Then the earthquake on
May 27, 2006, with a magnitude of 5.9 SR depth of
33 km which has an epicenter point in thesoutheastern Bantul Regency.

Piyungan

Bambanglipuro
Banguntapan

117.59BantulDlingoPity

205.55369.8065.06

268,45

Pleret194.52194.52Pudong1.04

410.57411.62180.4339.87

220.30120.188.29

634.27Sewon

261.49112.78374.28

Srambat122.66203.39Sanden
Sedayu506.02

Total of Built-up Area

122.667718.22

CONCLUSION

Table 2. shows the area of developed land is ineach of the maximum PGA classes. The district withthe lowest risk is in Pinyungan District which has anarea of 268.45 Ha with 203.39 Ha including the lowclass and 65.06 Ha including the moderate class.
Conversely, it can also be seen that some sub-districts
that are at the maximum PGA that are in the high classwith almost the entire area of the area included in theclass, such as Pleting District, Bambanglipuro
District and Pundong District. These districts have ahigher risk potential than other districts. This is in linewith the earthquake damage data of May 27, 2006, inthe sub-district which was damaged. The total
damaged houses in Pundong District were 6,793
houses, Pleting District as many as 1,228 houses and.
Bambanglipuro District 6,587 houses. These subdistricts also need more attention as a disastermitigation effort.

The frequency of earthquakes in Yogyakarta ishigh, in the period of 43 years, namely the period
1974 to 2017 there were 34 earthquakes with amagnitude of 3.7 SR to 6.3 SR with a depth of lessthan 100 km. Based on the PGA, the average PGA
distribution has a pattern that the further north theaverage PGA is lower with an average PGA value of
33.81 gals and the highest of 36.23 gals. Based on the
maximum PGA, the highest PGA distribution is in thenorthwest and southeast regions of Bantul Regency
with the lowest maximum PGA value of 223.58 gals
and the highest of 248.42 gals. The PGA value has thepotential for a large risk of damage which is on the
VII
MMI
Scale.
Pajangan
Subdistrict,Bambanglipuro Subdistrict and Pundong Subdistrict
are on average high PGA and maximum PGA as well,so these sub-districts have a higher risk potential thanother sub-districts. More attention is needed to these
sub-districts to mitigate disasters.

535

GEOMATE – Melbourne, Australia, 11-13 November 2020

a Bayesian Hierarchical Model for the Spatial
Distribution of Earthquake Peak GroundAcceleration, Journal of Soil Dynamics andEarthquake Engineering, vol.120, 2019, pp.369385[8] Silacheva N.V., Kulbayeva U.K., Kravchenko
N.A., Probabilistic Seismic Hazard Assessmentof Kazakhstan and Almaty City in Peak GroundAccelerations, Journal of Goedesy andGeodynamics, Vol.9, 2018, pp.131-141[9] Supriatna, Samedi, J.M., & Nurmala, C., PeakGround Acceleration (PGA) of Destructive
Earthquake in Cimandiri Fault, Sukabumi, West
Java. Paper Reference Number 17. Depok:
Department of Geography, Faculty ofMathematics and Natural Science University ofIndonesia. 2010[10] Wegener T., Goda K., Erdik M., Daniell J.,
Wenzel F., A Spatial Correlation Model of PeakGround Acceleration and Response Spectra
Based on Data of the Istanbul Earthquake Rapid
Response and Early Warning System. Journal of
Soil Dynamics and Earthquake Engineering,vol.85, 2016, pp.166-178[11] Zengi Z., Shuanggen J., and Lihong J., .Coseismic Deformation Following the 2007
Bengkulu Earthquake Constrained by GRACE
and GPS Obeservations. Journal of the EarthandPlanetary Interiors, vol. 280, 2018, pp. 20-31

REFERENCES[1] Emad G.S.M., Sarah L.G., Application of
GMPEs to Estimate the Minimum Magnitudeand Peak Ground Acceleration of Prehistoric
Earthquakes at Hollywood, Journal ofEngineering Geology, vol.214, 2016, pp.60-66[2] Fauzi, “Geographic Information System for
Natural Disaster Map in Indonesia”. 2000.
[3] Gandomi M., Soltanpour M., Zolfaghari M.R.,
Gandomi A.H., Prediction of Peak GroundAcceleration of Iran's Tectonic Regions Using a
Hybrid Soft Computing Technique, Journal of
Geoscience Frontiers, vol.7, 2016, pp.75-82[4] Geological Agency of Mineral Resources of the
Republic of Indonesia., 10 Years from the
Yogyakarta Earthquake, GEOMAGZ, Vol.6,
No.2. The Ministry of Energy and Mineral
Resources of the Republic of Indonesia, 2016,
pp.18-29[5] Ibrahim, G and
Subardjo, Pengetahuan
Seismologi. Jakarta: Badan Meteorologi dan
Geofisika. 2005
[6] Mase L.Z., Likitlersuang S., Tobita Tetsuo.,
Analysis of Seismic Ground Response Caused
During Strong Earthquake in Northen Thailand,
Journal of Soil Dynamics and Earthquake
Engineering, Vol.114, 2018, pp.126[7] Rahpeyma S., Halldorsson B., Hranskelsson B.,
Green R.A., Jonsson S., Site Effect Estimation
on Two Icelandic Strong-Motion Arrays Using536

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INFLUENCE OF LIGHT ON MICROBIAL RIVER WATERECOSYSTEM AND ITS SELF-PURIFICATION POTENTIAL
Fumitake Nishimura1, Yuki Koike2 and Michiko Masuda3
1

Department of Environmental Engineering, Kyoto University, Japan; 2 Department of Civil andEnvironmental Engineering, Ehime University, Japan; 3 Department of Civil Engineering, Nagoya Instituteof Technology, Japan
ABSTRACT
Self-purification at rivers is mainly conducted by microorganisms attached on river bed. In order to enhance
the self-purification capacity, several process or technologies have been proposed for improvement of river water
quality and preservation of bio-diversity. From the viewpoints of environmental engineering, finding influential
parameters and their effective control are effective to improve river water quality. However, there are a lot ofparameters affecting self-purification capacity at rivers, and quantitative evaluation of these parameters have notbeen completed. In this study, influence of light on both microbial river water ecosystem and its self-purification
capacity are investigated. There are several type of water purification facilities incorporated in rivers in order toimprove river water quality and maintain bio-diversity. Most of the cases, light (sun light) is not blocked, and thelight might affect microbial community and structure by photo synthesis.
Lab-scale experimental facilities (artificial river) are set, and artificial contaminated river water are introduced
to the system. Light irradiation stimulates algal activity and affects the composition of the other microorganisms
such as bacteria and zooplankton. By mass balance analysis of carbon, it is made clear that amount of organiccarbon accumulated in the river under lighting condition becomes almost double compared with the case underdark condition. It is suggested that clean river water condition can be affected by light more than contaminatedriver water condition because of the large relative intensity of organic carbon loading by photo synthesis.
Keywords: self-purification, river ecosystem, mass balance analysis, photosynthesis

water temperature, and so on. Light irradiation is oneof them. In the direct purification process to the river,
it is practically not designed to block sunlight. There
are some cases where light irradiation was studied asa design factor in the design of direct purification [6],[7], [8], but few cases, and studies including treatment
processes have not been sufficiently conducted.
However, the effects of light (mainly visible light) on
the treatment results should be fully considered taking
into account photosynthesis.
In this study, the effects of light irradiation on thebiota, which is the main component of purification,
during the treatment process was investigated using alaboratory-scale contact oxidation system. The
amount and number of microorganisms in thetreatment process, their activity, dissolved oxygen,and the concentration of organic and inorganic
substances in the treated water were monitored andevaluated the effects of light.

INTRODUCTION
In order to preserve water environment in bothurban section and the catchment area around thesection, sewerage is important facilities and plays
important roles. However, sewerage development
requires not only huge cost but also long time. For
example, it takes 38 years to increase its coverage
from 30%(at the end of FY1980) to 79%(at the end of
FY2018) in Japan [1]. In the areas of where thesewerage system has not been developed, the ratio ofdomestic contaminants to total contaminants load into
enclosed water areas such as lakes and inner bays ishigher [2]. It is difficult to reduce the discharge
amount of contaminants by individual households,
therefore, direct purification in rivers, channels, lakes
and bays is being conducted in order to reduce theloading rate until the sewerage system development
is completed [3], [4]. Among the direct purification
methods, the contact oxidation process has been
attracting attention and it is adopted in many places
because it enhances the natural purification function
and seems to be environment-friendly. has highhydrophilicity [5]. However, good results are notalways obtained because proper design and operating
factors have not been sufficiently clarified.
Operational and design parameters in the system are
inlet water quality, water flow rate, HRT, DO, theMATERIALS AND METHODS
The schematic diagram of the experimental
equipment is shown in Fig. 1. The experimental water
channel was made of acrylic and the width, height andlength of the channel are 1 cm, 4 cm, 200 cm,
respectively. In the water channel, a fiber carrier(made by Chisso Filter Co., Ltd., Japan, material:

537

GEOMATE – Melbourne, Australia, 11-13 November 2020

polyethylene/polypropylene mixture, fiber length 64
mm, specific gravity 0.90 to 0.96, that is easy to fix
for retaining biota) is packed. The thickness of theincorporated fiber carrier is 1 cm, and attached to thechannel bottom. The packing density of the fiber
carrier becomes 0.031 g/cm3. Organisms used in thisstudy was collected from Okawa, Miyamae River,and irrigation channels in Matsuyama City, Ehime
Prefecture, Japan, and they were inoculated at 25 cm
intervals from the upstream to the downstream. Threeexperimental channels were prepared, and two ofthem were set under the light conditions, (hereinafterreferred to as case L-1 and case L-2) and the otherwas set under dark condition. (hereinafter referred toas case D). These three channels were installed on
the same shelf, and the entire shelf was surrounded bya blackout curtain. Under light conditions, whitefluorescent lamps were used to irradiate the upper
part of the channel with an illumination intensity of3000 Lux every 12 hours. Under dark conditions, thewater channels were covered with aluminum foil toblock the light. The water temperature in the water
channel was set to 25°C by contacting silicon tubes
on both sides of the water channel (inner diameter: 8mm× outer diameter: 1 mm) and circulating water at
30°C inside the tube. The composition of theartificial wastewater used in these experiments wasshown in Table 1. The wastewater was continuously
flowed into the experimental channels with a flowrateof 100 mL/h. The DOC of the artificial wastewater
was set to 20 mgC/L, assuming a small-to-medium-sized river in a city where sewerage development isdelayed. The hydraulic retention time was estimated
188 minutes as a result of the tracer experiment using
NaCl, that means average water level of the channel
was 1.56cm. The experimental equipment was
operated for 90 days in winter season.
DOC was measured as the amount of dissolved
organic matter in each channel, and the number of
viable and total bacteria was measured as the amountof biomass over time. Then, on the 90th day, in orderto grasp the condition inside the channel in detail, thechannel was divided into four sections I to IV shownin Fig. 2 from the artificial wastewater inflow section.
For the division of the sections, visual observation ofboth the algae green density and water depth in thechannel on day 73 were considered. SS, viable cellcount, total cell count, zooplankton population, Chl-a
concentration, and total dehydrogenase activity ofeach category were measured. For the measurementof the number of bacteria and the number ofzooplankton population, the sampling points shownin Fig. 3 were provided in each compartment.
Syringe was used for the sampling. Samples mixed
with dissolved and suspended matters from both thefiber carrier and the channel wall were collected andused for the measurement of SS, Chl-a concentrationand total dehydrogenase activity.
Table 1 Composition of artificial wastewater
Substrate concentration
NaCl
1.32mg/L
KH2PO43.72mg/L
KCl
2.68mg/L
NH4Cl
19.1mg/L

fluorescent lamp
influentP

experimental water channel

effluent

RESULTS DISCUSSION

pump

Removal capacity of organic matter andaccumulated materials in channels
Fig. 4 shows the daily change of DOC in the inflow
and outflow water of each case. From the 3rd day
after the start of the experiment, it was almost in therange of 3 to 6 mgC/L, and there was no difference inthe removal efficiency of organic substances in theentire channel resulting from the effect of lightirradiation.

shelf
blackout curtain
Artificial
wastewater

Fig.1 Schematic diagram of experimental setup
Artificial Wastewater
II

I

III

IV

37.5
50.0
50.0
62.5
Fig.2 Section of the waterway (Unit:cm)

(1)

(2)

20
DOC(mgC/L)

Artificial Wastewater: Sampling Point(3)

Substrate
concentration
NaHCO338.3mg/L
1.64mg/L
MgSO4・7H2O
Dextrin15.2mg/L
Bacto peptone
32.7mg/L(4)

Case L-1 inf
Case L-2 inf.
Case D inf.
Case L-1 eff.
Case L-2 eff.
Case D eff.

10

0

50

0

100
100
Fig.3 Points of sampling (Unit:cm)

20

40
60
Time (days)

80

100

Fig.4 Time course of DOC in influent and effluent
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 5 shows the SS and its composition in eachsection. Under dark conditions, SS increased slightly
in section II but decreased as it flowed down. In thecases of light conditions, it did not decrease as muchas in dark condition, and in some section it increased
to the same level or in sections III-IV. The amount ofaccumulated organic matter in the entire canal was
120 to 130 mgC in the two cases under the light
condition, whereas the amount in the dark conditionis 50 to 60 mgC which was about 50% or less of thelight condition. It is suggested that organic matter iseasy to accumulate under light conditions due tophotosynthesis.

Fig. 7 shows the population of zooplankton. In
Section I, the population tended to be small in allcases. It is considered that it was difficult for
zooplankton to live in Section I because of its
anaerobic condition. The proportion of metazoans
increased as they flowed under both light and dark
conditions.
The Chl-a concentration at each division is shownin Fig. 8. More number of algae was confirmed in
section IV where the organic matter load was low.
The algae inoculated at the beginning of theexperiment remained in sections II to IV. It isconsidered that the main component of SS in section
I and IV was microbial communities and algae,
respectively.
The total dehydrogenase activity in each section
is shown in Fig. 9. The activity tended to be higher inlight conditions, and the difference in the sections III
and IV was obvious.
Carbon balance in the channel
Considering these experimental results, it can beconcluded that the treated water quality was notdifferent between light and dark condition, however,
the organic carbon amount accumulated in thechannel was higher in light conditions than in dark
condition due to photosynthesis. In particular, it wassuggested that the effect of light cannot be ignored

Case L-1, IV
Case L-2, IV
Case D, IV

Case L-1, III
Case L-2, III
Case D, III

Case L-1, II
Case L-2, II
Case D, II

metazoan
protozoa

Case and Section

1010

Fig.7 Population of zooplankton at each section
109

2500

108

Case L-1
Case L-2
Case D2000
Chl-a (μg/L)

Total or viable cell counts(cells/mL)

Case and Section
Fig.5 SS and its composition at each section

16000
14000
12000
10000
8000
6000
4000
2000
0

Case L-1, I
Case L-2, I
Case D, I

Population of zooplankton (N/mL)

Case L-1, IV
Case L-2, IV
Case D, IV

Case L-1, III
Case L-2, III
Case D, III

Ash(mg/L)
VSS(mg/L)

Case L-1, II
Case L-2, II
Case D, II

1400
1200
1000
800
600
400
200
0

Case L-1, I
Case L-2, I
Case D, I

Concentration (mg/L)

Biota in the channel
Fig. 6 shows the total and viable cell counts ateach section. There was no difference in total andviable cell counts under both dark and light
conditions, and the numbers decreased as they flowed
down. Between section I and section IV, the numberof viable cells decreased by one order and the total
number of cells decreased by two orders. In section
I, where the loading rate of organic matter load ishigher, the environment became anaerobic, and underlight conditions, the presence of purple bacteria wereobserved.

107
106

0

100
200
distance from the inlet(cm)Viable cell counts
Total cell counts

Case L-1
Case L-2
Case D
Case L-1
Case L-2
Case D

1500
1000
500
0

Ⅰ

Ⅱ

Ⅲ

SectionFig.6 Total and viable cell counts at each point

Fig.8 Concentration of Chl-a at each section
539

ⅣGEOMATE – Melbourne, Australia, 11-13 November 2020

when the amount of inflowing organic matter issmall and the load due to photosynthesis is large.
Fig. 10 and 11 show the carbon balances of caseL-2 and Case D in section IV, respectively. Boxes
indicate living organisms and abiotic organic matterthat compose the ecosystem, and arrows show carbon
transfer rate. The numbers attached to the arrows andboxes indicate the ranges of quantity in each process
or state valuables. The box size and the thickness of
the arrow are based on each quantity. The extant
amount was calculated using the carbon conversion
formula shown in Table 2 based on the measured
value. The inflow and outflow amounts were actually
measured values. The amount of carbon transfer was
calculated using the formula shown in Table 3 basedon each existing amount. Table 4 shows the literature
values used to determine each quantity.

Dt (TFμg/10mL)

100
Case L-1
Case L-2
Case D

80
60
40
20
0

Ⅰ

Ⅱ
Ⅲ
SectionⅣ

Fig.9 Dehydrogenase activity at each division
Table 2 Expression of carbon conversion ofstanding crop
Item
SymbolEquation
Algae
CP
CP=b･(P/1000)･(V/1000)
Bacteria
CB
CB=B･a･wB･V
Protozoa
CZ1
CZ1=Z1･a*･wZ1･V
metazoan
CZ2
CZ2=Z2･a*･wZ2･V
CO Abiotic organic matter CO=D1･(V/1000)*: (C/protozoan dry weight), (C/bacterial dry weight) wereTable 4 Literature values for the calculation of carbon
transfer and storage
SymbolContent
ValuesreferencekB
Rate constant of self
0.25(1/d)[9]
decomposition (Bacteria)kP
Rate constant of self
0.007-0.07(1/d)[9], [10]decomposition (Algae)
kZ1 Rate constant of self
0.15(1/d)[12]
decomposition(protozoa:
Ciliata)kZ2 Rate constant of self
0.15(1/d)[12]
decomposition(metazoan: Rotatoria)
r1
Intake activity of organic 1.2-7.7[13]compounds by bacteria (×10-12mgC/(cell･d))r2
Filtration rate of rotifer[14]
0.6～1.5(1/(mgDW･d))r3
Carbon transfer rate with
0.000045[15]
intake of algae by(mgC/d)zooplanktonr4
Primary production rate
53.0
[16]
(mg-carbohydrate/(mgChl-a･d))r5
Predation rate of bacteria
0.000012[17]
by zooplankton(mgBacteria/(Nzooplankton･d)wB
Dry weight of bacteria[18](Escherichia coli)
4.0×10-10(mg/cell)(Alcaligenes faecalis) 5.56×10-10(mg/cell)wZ1
Dry weight of protozoa[18](Ciliata)
1.20×10-6(mg/N)(Colpoda steinii)
1.39×10-6(mg/N)(Tetrahymena
1.60×10-6(mg/N)
pyriformis)
3.85×10-6(mg/N)(Colpidium campylum)(Vorticella microstoma)
wZ2
Dry weight of protozoa
8.9×10-5(mg/N)[19](Ciliata)(Brachionus angularis)aC/(Dry weight of
0.531
[20]
bacteria)b
C/Chl-a49[21]
c
C/carbohydrate
1.1
[21]
e
Algal amount
0.0009(mgC/L)[15]

used for (C/metazoan dry weight)

Table 3 Carbon transfer process and theexpression
Process
Equations
Death of bacteria
R1 = kB･wB･a･B
Death of algae
R2 = kP･(P/1000)･(V/1000)･b
Death of protozoa
R3-1 = kZ1･wZ1･a･Z1･V
Death of metazoan
R3-2 = kZ2･wZ2･a･Z2･V
Degradation of abiotic
R4 = B･r1･V
organic matter by(assuming 1 cell = 1 CFU)
bacteria
Predation of algae by
R5 = a･r2･Z2･wZ2･V
metazoans
In case R5 > CP,
R5=A･r3･(V/1000)
Primary production by R6 = r4･c･(P/1000)･(V/1000)
algae
Bacterial predation by
R7 = r5･a･Z･V
zooplankton
Inflow into the section
R8 = D1･(Q/1000)･24
Outflow to the outside
R9 = D2･(Q/1000)･24
A: Algal biomass/algal biomass (literature value)( =CP/e)
D1: DOC at the previous section (mgC/L)
D2: DOC in the effluent (mgC/L)
P: Chl-a(μg/L)
B: viable bacteria count (CFU/mL)
Z1: Protozoa population (N/mL)
Z2: Metazoan population (N/mL)
Z: Zooplankton population (N/mL) (=Z1+Z2)
V: Volume of the section (mL)
Q: flow rate (mL/h)

540

GEOMATE – Melbourne, Australia, 11-13 November 2020

CO2

Minimum carbon
transfer (mgC/d)

14-22

Zooplankton

Maximum carbon
transfer (mgC/d)

7-19
influent
Metazoan
11-18

algae
16-20

protozoa
0.2-0.5

12-19

effluent

10-11

0.08-2

Maximum biomass
(mgC)

1-2

1-3
Dissolved organic matter
0.5-0.7

Minimum biomass(mgC)

bacteria

0.1-2

Fig.10 Mass balance of carbon (Case L-2, section Ⅳ)
CO2
1-2

Minimum carbon
transfer (mgC/d)

Zooplankton

algae
0.9-1

Maximum carbon
transfer (mgC/d)

0.02-0.03

0.006-0.07
influent

protozoa
0.01-0.05

5-7

effluent

Minimum biomass
(mgC)

12-13

Maximum biomass(mgC)

Metazoan
34-41

48-72
Dissolved
organic matter
0.5-0.8

2-3Bacteria
0.1-3

Fig.11 Mass balance of carbon (Case D, section Ⅳ)
carbon balance in the process of contact oxidation
treatment for wastewater containing organic matter
was discussed. The results obtained in this study are
shown as follows;
1) There were not clear effects of light irradiation on
the organic matter removal capacity of the entire
channel, but the amount of accumulated organic
matter in the channel under light condition wasalmost double compared to that of dark condition.
2) Light irradiation had an effect on the biota in thechannels,
includingthepresenceofphotosynthetic bacteria and the number of algae,
but no effect on the number of bacteria.
3) According to the carbon balance analysis in thechannel, it was suggested that the light irradiation

The abundance of algae was about 10 times larger
under light conditions than the dark condition. In
terms of carbon transfer, the primary production was
about 10 times, the predation of algae was 100-1000
times, and the death of algae was 10-1000 times
larger under light conditions than the dark condition.
It was suggested that light irradiation activates theprimary production by algae and increases theexisting amount. And the algae predation amount of
zooplankton and the production of abiotic organic
matter due to algae death increase accordingly.
CONCLUSIONSIn this study, the influence of light irradiation on the541

GEOMATE – Melbourne, Australia, 11-13 November 2020

effluent, Journal of Environmental Management,
Vol.260, No.15, 110134, 2020.
[9] Eckenfelder jr., W. W., and O'Connor, D. J.:
Biological Waste Treatment. Pergamon Press,
Oxford- London- New York- Paris, pp.22-42,
1961.
[10] DePinto, J.V. and Verhoff, F.H.: Nutrient
regeneration from aerobic decomposition ofgreen algae, Environmental Science andTechnology, 11, pp.371-377.1977.
[11] Takuya OKUBO, Mitsumasa, Akihiko
MURAKAMI: Characteristics of purification in
Small channels, Journal of Japan Society ofWater Environment, Vol.16, No.4, pp.261-269,
1993[12] Shigeo FUJII, Isao SOMIYA and Toshiyuki
SHIRAKI: Studies on the Effect ofPhytoplankton and Zooplankton on SelfPurification in Eutrophicated Water, Journal ofJapan Society of Water Environment, Vol.15,
No.6, p.347,1992.
[13] Edited by Isao Somiya: Purification mechanismof nature(Shizen no Joka Kiko, in Japanese),
p.127, Gihodo Shuppan, 1995.
[14] Edited by Yoshiro Iwasa: Engineering
limnology, p.318, Sankaido publishing, ISBN:
9784381008121 (in Japanese).
[15] Shigeo FUJII,Isao SOMIYA and Toshiyuki
SHIRAKI: Studies on the Effect ofPhytoplankton and Zooplankton on SelfPurification in Eutrophicated Water, Journal ofJapan Society of Water Environment, Vol.15,
No.6, p.397,1992.(in Japanese)[16] Edited by Yoshiro Iwasa: Engineering
limnology, p.322, Sankaido publishing, ISBN:
9784381008121 (in Japanese)[17] Ryuichi sudo: Biology for wastewater
treatment(Haisui syori no seibutu gaku, inJapanese), p.180, The Industrial Water Institute[18] Ryuichi sudo: Biology for wastewater
treatment(Haisui syori no seibutu gaku, inJapanese), p.180, The Industrial Water Institute,
1977.
[19] Jurgens et al: Effect of metazoan and protozoan
predation on the distribution of bacterial
biomass in microcosms, Journal of water andwaste, Vol.39, No.10, p.85, 1997.
[20] Edited by Isao Somiya: Purification mechanismof nature(Shizen no Joka Kiko, in Japanese),
p.19, Gihodo Shuppan, 1995.
[21] Edited by Yoshiro Iwasa: Engineering
limnology, p.320, Sankaido publishing, ISBN:
9784381008121. (in Japanese)increases the load of organic compounds due tophotosynthesis 10 times greater compared to thedark conditions, and the effect of light irradiation
cannot be ignored when the inflow amount oforganic compounds is relatively small. In the casenutrients such as nitrogen and phosphorus exist in
the water, internal production by algae gives
ACKNOWLEDGEMENTS
We wish to thank staff and students in laboratoryof Ecology, Faculty of Science, Ehime University for
their assistance of conducting the experiment.
REFERENCES[1] Ministry of Land, Infrastructure, Transport andTourism, Japan.
http://www1.mlit.go.jp/common/001273126.pd
f (in Japanese)[2] Ministry of the Environment, Japan.
https://www.env.go.jp/council/09water/y091701/mat05.pdf (in Japanese)[3] C. Junjun, M. Jian, J. Wei, C. Jinquan, L. Xuan,
J. Bohua, W. Haiming: Treatment of heavily
polluted river water by tidal-operated biofilterswith organic/inorganic media: Evaluation of
performance and bacterial community,
Bioresource Technology, Vol. 279, pp.34-42,
2019.
[4] Y. Sheng, Y. Qu, C. Ding, Q. Sun, R.J.
Mortimer: A combined application of different
engineering and biological techniques toremediate a heavily polluted river, Ecol. Eng.,
vol. 57, pp.1-7, 2013.
[5] Jibo Xiao, Shuyi Chu, Guangming Tian, Ronald
W. Thring, Lingzhou Cui: An Eco-tank system
containing microbes and different aquatic plant
species for the bioremediation of N,Ndimethylformamide polluted river waters,
Journal of Hazardous Materials, Vol.320,
pp.564-570, 2016.
[6] R.J. Craggs, A. Zwart, J.W. Nagels, R.J. DaviesColley: Modelling sunlight disinfection in ahigh rate pond: Ecological Engineering, Vol.22,
No.2, pp.113-122, 2004.
[7] C. Schultz-Fademrecht, M. Wichern, H. Horn:
The impact of sunlight on inactivation ofindicator microorganisms both in river waterand benthic biofilms, Water Research, Vol.42,
No.19, pp.4771-4779, 2008.
[8] Chawalit Chaiwong, Thammarat Koottatep,
Chongrak Polprasert: Comparative study on
attached-growth photobioreactors under blue
and red lights for treatment of septic tank542

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ABILITY AND WILLINGNESS TO PAY FOR WASTE WATERMANAGEMENT MAINTENNACE SERVICES (IPAL)*Marselina Djayasinga, Tsra Subianto, Tri Joko Prasetyo
Economic and Business Faculty, University of Lampung, Indonesia
ABSTRACT: Infrastructure sustainability must be maintained through public participation payment for theutilization of these public facilities. However, it is difficult to determine the appropriate tariff of public
utilization. Ability and Willingness to Pay (ATP and WTP) are used to get it. This study aims to estimate
ATP and WTP of beneficiary of waste water management maintenance services (IPAL). Using a purposive
random sampling and cluster technique from 300 RT, there are 153 are selected as samples. There are 3classiﬁcationof household:household native inhabitans , household comers without experience with

PDAMs, and household comers with experience PDAM. PDAM is another alternative of water supply
produced by government. The results of this study are, the highest ATP is household inhabitants compared tothe other 2 household categories. The WTP for IPAL is 30% higher than the rate set by the government
initially. Increasing WTP is affected by the level of education, reference cognition, level of income andsatisfaction. Continuous improvement in technology of water treatmenty, socialization to the community anduse of tariffs based on volume of usage must be carried out for the continuity of infrastructure.
Keywords: Willingness to pay, Ability to pay, Waste water management services
1. INTRODUCTION
Lack of public awareness ofwaste water
treatment has caused some areas to become a crisisof clean water and polluted the environment.
Recently, waste water management maintenanceservices is becoming an important issue in thewater protection in Indonesia. According to Hua(2010) the increasing population and reduced
water resources have caused the need for cleanwater to be a very pressing problem in almost allparts of the world and lack of awareness of proper
waste water.. This habit is driven by the low
charge even free of charge of this services. As apublic
good,
waste
water
management
maintenance services IPAL) in Indonesia, is noton market mechanism. The tariff cannot be
determine because no one can express theirpreference. So, it is needed the method to get the
tariff which reflect the ability and willingness to
pay of beneficiaries in order to maintenance thispublic facilities sustain for long time and aroseawareness community to protect the water.
Community is expected to respond for this public
facilities by expected to be willing to participate
through pay some cost to maintain thesustainability of this public infrastructure.
Langkapura Baru Sub district, in Bandar
Lampung City is one of the worst sanitation areas,
where domestic waste water, both black water andgray water, is thrown into the environment without
any prior treatment, thus polluting surrounding
groundwater and often experiencing drought. Now,

the village is being built a Waste Water Treatment
Plant, we called IPAL and immediately operating
precisely at RT. 05 LK.II Langkapura Baru. The
total population of households who will use theservice is 350 families.
The IPAL program aims to overcome theproblems of clean water and sanitation through theprovisionofassistanceto
Low-Income
Communities by building communal IPAL. Thispublic facility will treat household black water andgray water waste so that treated wastewater can be
returned safely to household and environment inaccordance with environmental quality standards.
This processed wastewater will be the next sourceof clean water. The regional government
cooperating with the Islamic Development Bank
(IDB) only build the IPAL infrastructure,meanwhile for operational and maintenance costs
are expected to come from community
participation. So that the amount of the tariff isneeded to cover this operational and maintenancecosts of the IPAL, which will be charged to thecommunity, especially the beneficiaries, which isin accordance with the ATP and WTP beneficiaryhouseholds
Now, the city government has set a tariff forCommunal IPAL in the Langkapura Baru Village
for operation and maintenance of this public
infrastructure
by IDR
100,000 / month /household. This tariff is set by the city government
with the consideration that the government will
provide a subsidy of IDR 50,000 / household, andhonestly this subsidy
can build the same543

GEOMATE – Melbourne, Australia, 11-13 November 2020

infrastructure in other regions if burden tocommunity. Allen Consulting Group (2003) statesthat one source of financing for public goods cancome from fees in the form of tariffs, where thelevy rates are influenced by the ability to pay(Ability to Pay) and Willingness to Pay. Based on
this background, it is a technique for estimating
tariffs that cover operational and maintenancecosts and encourage environmental awareness.
One of the methods to estimate is the Ability to
Pay (ATP) and Willing to Pay (WTP).
ATP is the ability to pay for public services
received based on income that is considered ideal.
According to Rubiani (2004), ATP is the amountof money that can be paid by the community toreplace the service costs it receives While Russel(1995) states that ATP is a consideration in
spending his income / expenditure to buy goods orother services. ATP is related to the limitations ofhousehold income, where household need makepriorities economically in choosing maximum
satisfaction. ATP is the rest of income after
deducted by essential expenditure, non-food
expenditure, non-essential expenditure.
According [14] one form of participation ofcommunity can be seen from their willingness topay.WTP according to Wedgwood. A (2003) is the
maximum amount of an individual's status forwillingness to pay for an item or service, Mankiw(2004) defines WTP as the highest price that eachbeneficiary is willing to pay using the consumer
surplus approach of the demand curve . The
consumer surplus is the willingness of a person to
pay an economic value less the value actually paidby him. Economists, psychologists, and marketing
researchers rely on measures of consumers'
willingness to pay (WTP) in estimating demandfor private and public goods and in designing
optimal price schedules [22] and customers pay
what they bid (s) so they must bid less than theirtrue WTP if they want to obtain surplus from thetransaction.. Using WTP to avoid the problem offree-riders in the determination of tariffs for public
goods services [3].

3. LITERATURE REVIEW
Public participation is essential t and may leadtoenormous
benefitsforsustainability
development [5, 22] agree that public goods arebuild by the government should be managed by
the community which requires active community
involvement. Involvement, participation and somecharacteristic of household determine their choices
in WTP. WTP is a technique to know thewillingness of individuals to pay for public goods
benefit in which the price of it cannot be
determined by market mechanism because of thelack of information or preferences. Some experts
also define WTP is the maximum pricebeneficiaries willing to pay for a given quantity of
product or services [21].
WTP as a way of
calculating the ability of each individual on an
aggregate basis to pay in order to improve
environmental conditions to conform to desired
standards. Economic valuation is an attempt toprovide a quantitative value to goods and services
produced by natural resources and environment
regardless of whether the market value is available
or not [17] and. CVM is done by asking directly tobenefit about the value of benefits of public goods
includes resources and environment. [12] foundthat almost 85% of respondents are willing to pay
a higher tariff and this WTP is effected by thelevel of education, family income This result also
supported by [11] that the average WTP of
consumers on waste treatment services is higher
than the tariffs paid.
WTP is based on the user's perception of the
public services. To get user perception, it can beused the Contingent Valuation Method (CVM).
According to [7] CVM is one technique toanalyze the valuation function that can provide
qualitative information that is difficult to
identify using other conventional valuation
techniques. CVM is conducted because people isunable to express its preference for economic,
such as for tourism hunting services, dumping
tariffs, pollution, waste management, utilizing
wildlife, environmental quality, etc. CVM is amethod of measuring someone's preference [8].
CVM is measuring WTP by eliciting stated user
preferences through direct surveys. The useris
directly asked about their WTP by using openended questions [17]. In these surveys, householdor beneficiaries are offered some alternative
services or condition with varying attributes. WTP
is inferred indirectly from their ranking or ratingsof these alternative services.
Some characteristics of community such as
education level, gender, age, lines of class, race,
ethnicity, house type, house distance, income andservice quality, awareness,
perception andhousehold satisfaction level toward the benefits of

2. OBJECTIVESThis study aims (1) to calculate the ability andwillingness to pay off household to waste water
management maintenance services(IPAL) inorder to get the tariff in which isresulting
which
cover
operationalandmaintenance cost and arouse the awareness ofhouseholds to be more responsible inwater
protection,(2) to investigate the relationshipbetween respondent’s characteristic with their
WTP. This study is expected to provide policy to
set tarrif in which keep infrastructure sustainable
and community awareness.

544

GEOMATE – Melbourne, Australia, 11-13 November 2020

public goods in correlating with WTP [4, 13].
Effects on WTP is high when they are interactions
with each other.. Community awareness of theenvironment is influenced by the level of educationand knowledge about the environment [7].
According to [17] household concerning will affect
WTP and it increases along with improving thequality of services. Income inequalities caninfluence environmental degradation. Inequalities
may affect the overall extent of environmental
quality so that the higher income the higher
WTP .High level of awareness and knowledge andpositive attitude will increase environmental
protection [2].

Scenario G

be flowed
times aweek
Water treatment will
be flowed every day( 7x a week)

150.000

The starting point of IPAL services scenarioatB, in which wastewater treatment will be
flowed to household 2 times a week with tariff
IDR 100.000. IPAL services is decreased orincreased by flowing water to household once aweek, Increased
scenario using IDR 10.000
every stage .If most respondents answer to one
scenario selected, then his scenario is the actual
WTP desired mostly

4. METHODOLOGY
4.1.Hypothesis Development

The location of IPAL construction is in the RT.
05 .LK II
Langkapura Baru, City of Bandar
Lampung. There are 350 households will receive
direct benefits from this infrastructure are 350
families. With a quota purposive samplingtechnique with the cluster method,158 respondents
were selected. Through structured questionnaires
and interview techniques, to determine the amountof tariff for operational and maintenance services
based on their ATP and WTP. The Contingent
Valuation Method (CVM) method with gamebidding techniques are conducted by giving
repeated questions about the desire to pay for
wastewater management maintenance services
with a number of services. The frequency of water
flowed into the household is service scenario. At
the starting point, tariffs are used and the service
promised by the government is IDR 100,000 /month / RT and water is supplied to household
twice a week. The 7 scenarios are designed asfollows:

To answer the second objective that therelationship between the characteristics of the
respondent with their WTP,
correlation
technique and crosstab analysis techniques areused. The respondent's characteristic consists of5elements, namely the number of family
members, the level of education, reference
cognition. Level of income and satisfaction of
the services. The hypothesis developed asfollows:
Ha (1):Numbers of Family has a positive
correlation with WTP.
Ha (2):Level of education has a positive
correlation with their WTP
Ha(3):The reference cognition has a positive
relationship with WTP.
Ha(4):Level of income level has a positive
relationship with WTP.
Ha(5):The level of satisfaction of respondents
has a positive relationship with
WTP.

Table 1. 7 Scenarios of Services IPAL
Scenarios

Services

Scenario A

Water will be flowed
to household once a
week
Water will be flowed
to household 2 times a
week
Water will be flowed
to household 3 times a
week
Water will be flowed
to household 4 times ha weekWater treatment will
be flowed 5 times a
week
Water treatment will

Scenario B(startingpoint)
Scenario C
Scenario D
Scenario E
Scenario F

5. RESULT

Tariff/month
90.000Descriptive Respondent
Questioner distributed to 350 households. There

100.000

are 158 households that returned and responded
the questioner or 45%. The purpose of the

110.000

household classified is to prove the correlation
between reference cognition with their

120.000

WTP. Itmay be correlated between perceptions of RTs
who have no reference at all about clean water

130.000

management, and households that have already felt
clean water management services such as IPAL,
namely140.000

545

PDAM

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2. Family Categories of Respondent

ATP dan WTP

Family Categories
Household stay formerly
Household comers without
experience with PDAM
service.
Household comers withexperience with PDAM
service.

The Ability to Pay for household stay formerly
on average, is lower than to ATP in householdcomers, but their willingness to pay (WTP) is
highest than to the other 2 categories ofhouseholds. This is presumably because the former
households
have experience having difficulty
obtaining water so they highly value water andrealize how difficult it is to obtain clean water.
While ATP for household comers with experience
using PDAM services is the highest compared tothe other two household categories, but they havethe lowest WTP compared to the other two
household categories. Household comers with
PDAM experience have had references to thequality and quantity of clean water provided by thecity government so far with cheaper rates because
PDAM tariffs have been subsidized by thegovernment.

KK
78
43

Percentage
49%
27%

37

24%

The original RT was obtained by 78
households, namely the RTs that settled in thatlocation and had never moved. During this timethey have experience in the difficulty of obtaining
water, especially during the dry season. The
second is 43 newcomer RT groups, namely RTs
who moved to that location and have settled inthat location and this household has neverexperienced water treatment services like theprevious PDAM. PDAM is a water treatment
service that is managed by the city government
where the source of the spring is from a river or
mountain or other source, then it is channeled to
RT. Third, namely immigrant households that livein that location but already have experience inexperiencing PDAM services. They already know
a little about the quality of PDAM water, thetariff, the frequency of the water flowing to theprevious household. The characteristics of
respondents based on the number of family
members and the level of education obtained thefollowing results.

Table 4. ATP and WTP IPAL ( IDR ,000)
Family Category
Household stay formerly
Household comers
without experience with
PDAM
Household comers withexperience with PDAM

A

B

40 %
60%

65% 60%35% 40%Level of Education< 9 years
9 ≤ x < 12 years≥ 12 years

40%
25%
35%

20% 15%25% 20%55% 65%

WTP
125
115

315

95

Based on the questionnaire, the average tariff for
PDAM water services, ranges from IDR 100,000- 300,000 / month with water can be used not
only for bathing and MCK but for drinking andcooking. Household comers consider thatwastewater management from IPAL are still
unsure about the quality of water produced Thiscondition happen because of the source ofwastewater from IPAL is come from black andgray water. Whether it is hygienic or not to be
consumed, the use of waste water that has beenprocessed by IPAL is still bog question. Basedon the IPAL experience, water is usage only
limited for non-eating, drinking purposes such as
washing clothes, flush for toilet, washingvehicles, flushing plants. Household who stayformerly do not really care about wastewater
quality. The important for them is, that watersupply is always there regularly every day. For
this condition
and they want to pay higher
because of the benefits of water and waterdifficulties so far.

Table 3. Descriptive of Respondents

Family member :< 5 person≥ 5 person

ATP
295
305

C

Note: A is household stay formerly and never move to otherlocation, B is household comers without PDAM service
experience, C is household comers with PDAM experience

Households which
stay formerly in thatlocation have a greater number of family members
than household comers with relatively lower levelsof education than comers. On the other hand,household comers is dominated by small family .
They have a family members less than 5 people,
but they have with a relatively higher level of
education than formerly household.

WTP

546

GEOMATE – Melbourne, Australia, 11-13 November 2020

Respondents with Their WTP

Of the 158 respondents who were filled with
the questionnaire, respondents were dominantly
chosen scenario F, by 35,7%. They desired thewastewater
management services working 6
times a week to flow the water. They agreed topay
IDR140.000 charged per month. If it
compares with government tariff which is IDR
100.000, this WTP can increases 40%. A totalof 31,4 % of respondents are willing to pay IDR
110.000 or scenario D, in whichwastewater
treatment is flowed to the household by fourtimes a week, A, B, C, E, F. The respondent's
choice is shown in

Characteristic
RespondentThe
familymemberLevel
Education
Reference
Cognition
Level
ofIncome
LevelofSatisfaction*) significant on 95%**) significant on 0,1%

Table 5. Validity of WTP of IPAL (IDR)
Scenario
A
B
C
D
E
F
G

Tarif WTP
90.000100.000
110.000
120.000
130.000
140.000
150.000

Valid (%)
10,5%
26,3%
30,8%
31,4%
24,2%
35,7 %
20,8%

coefficient

Sign

0,593

0,230

0,786

0,036*

0,155

0,025*

0,733

0.090**

0,740

0.010*

From 5 characteristics of respondent, only thefamily members has no correlation with their
WTP of IPAL, others such as level of education,level of income, reference cognition andsatisfaction of IPAL services have correlation
positively. The more the number of families is
not relevant to the more their WTP. The family
members has no correlation with WTP because
IPAL tariff setting initially, is fixed cost, does
not depend on the number of usage. Wastewater
treatment by IPAL is not equipped with a meteror water volume meter distributed such PDAM'sdid. Increasing WTP is also supported by level of
education and awareness of wastewater treatment
with coefficient correlation equal to 78,6%. The
higher level of education, the higher their WTP
of IPAL. With higher levels of education andknowledge, household tend to wish and better
understand about water protection. The more
knowledge about environmental, the higher their
WTP. Respondents who have good knowledge
about the benefits and damage of theenvironment, they will tend to be more likely to
be willing to pay [15]. Level of income is also
correlated with WTP with 73.3%. It means that
if the household has more income, the family has
more WTP, with a coefficient correlation of
56.8%.
Reference cognition is used as a variablebecause of past experiences either satisfied or un
satisfied of the services, has a big influence on
their WTP. For formerly household who often
difficult to get clean water especially in the dry
season, they have highest appreciate with
wastewater treatment, whatever its form. He [6]shows that experience is positively correlatedwith environmental awareness. People who haveenvironmental experiences are willing to pay
higher. Respondents will have more knowledge
that will not be environmentally damaging and

The actual WTP is that it is willing to pay IDR
140,000 a month or increase 40% as long as thequality of water treatment is improved, byincreasing the flow of water to household 6 times
a day. This result is higher than what [7] foundin WTP of water supply in Nigeria. It 's meanthat the WTP can be pushed to increase if theirsatisfaction of public services is increased.
5.1 Correlation Between the Characteristics ofRespondent with Their WTP of IPAL
The relationship of some characteristics of
respondents with their WTP used Pearson
Product Moment Test. Characteristics of
respondents consist of 5 elements, namely family
members, the level of education, reference
recognition, the level of income and satisfaction.
The result on
5.1 Correlation Between the Characteristics ofRespondent with Their WTP of IPAL
Relationship of some characteristics of
respondents with WTP used Pearson ProductMoment Test. Characteristics of respondents
consist of 5 elements, namely family members,
the level of education, reference recognition, thelevel of income and satisfaction. The result on
Table 6. Correlation Between Characteristics of547

GEOMATE – Melbourne, Australia, 11-13 November 2020are likely to be willing to make conservation
efforts environment.
Level satisfaction of household from IPAL
services, affects their WTP. Comers households
who had experienced the benefits of other water
sources, especially those provided by thegovernment, such as the PDAM, The satisfactionof household increases if the quality of water
treatment by implementing new technology will
increase the quality of wastewater treatment. He[10] shows that more services are added, more
WTP increases. Management service in Kampala
City, Uganda can be improved by improving thequality of waste services [3].
Level of income does not have a significant
effect with degree of freedom 5%, but significant
on its 10%. For households, water is a vital
goods, requirement and is determined by thegovernment in a fixed rate. So whatever the tariff
is charged to household, it will still to pays it
unaffected by its income.

same with other drinking water andgovernment promote it;(6) Tariff
perhousehold,shouldbedetermination as a variable costs or anumber of uses.
7.REFERENCES[1] Altaf MA, Haroon J, Whittington D.
1992. Willingnes to Pay for Water for
Rural Punjab.Pakisatan. World Bank
Water and Sanitation Programme.
[2]Afroz R, Using a Contingent Valuation
Approach for Improved Solid Waste
Management Facility. Journal Elsevier
Waste Management, Vol.31,2011,pp.800808.
[3] Meinrad Z, Sharifah ZB, Sayed ZASH,
Mahyar Sakari., Relationship Between
Awareness, Knowledge and Attitudes
Towards
Environmental
Education
Among Secondary School Students in
Malaysia, World Applied Sciences
Journal, 22 (9),2013, pp. 1326-1333.
[4] Banga,Margaret, Households Willingness
to Pay for Improved Solid Waste
Collection Services in Kampala City,
Uganda.The Journal of Environment andDevelopment, 2011.
[5] Brahim, Djemaci, Using a Contingent
Valuation Approach for
Improved
Household Solid Waste Management in
Algeria. Munich Personal. 2015.
[6] Chutarat Chompunth, Role of Public
Participation in
Environmental Impact
Assessment in Thailand,International
Journalof GEOMATE, Volume12, Issue
33, 2017.
[7] Finger, M, From Knowledge toAction?
Exploring the Relationship Between
Environmental Experiences,Learning, andBehavior, Journal of Social Issues 50(3),1994, pp.141-160.
[8]Fonta.W.Metall. Using a Contingent
Valuation Approach for Improved Solid
Waste Management Facility: Evidence
from Enugu State Nigeria. Journal of
African Economics. 2008.
[9] Ghanem Samar Khairy, The Relationship
BetweenPopulation
andThe
Environment andIts
Impact on
Sustainable Development in Egypt Usinga MultiEquation Model ,Environment,
Development
And
Sustainability,
Vol.20,2018,
Issue1pp.
305-340.
Hagos,Households Willingness To Pay for
Improved Urban Waste Management in
Mekelle City, Ethiopia, Discussion Paper
Series,2012,
EfDDP12-06

6. CONCLUSIONS(1) The ATP of household which stay formerlyon the location, , on average is lower thanothers the ATP from other householdclassification, but their WTP is relatively
higher than the 2 other categories ofhouseholds, because they highly value waterand know how difficult it is to obtain cleanwater when dry season arrives;(2) The average ATP of household comers who
have never had the experience of using
PDAM services is relatively highest
compared to the other two categories ofhousehold, but on average, their WTPs are
medium, because they feel the importance ofwater and feel the quality of wastewater
quality treats IPAL is quality standard;(3) Household comers that have had experience
using PDAM services, their ATP is medium,but their WTP are lowest, compared to 2
sample categories of household. They have
had experience enjoying water supplied by
the government that has better quality andthis household is still unsure, is the qualityof wastewater of IPAL to be supplied is highsuch as PDAM did;(4) Reference cognitive is positively correlatedwith their WTP because previous references
are mainly on experiences with PDAMs with
better quality and with tariffs so far;(5) The city government needs to improve
continually wastewater treatment by IPAL
using high technology so that the quality of
wastewater is quality standards, at least the548

GEOMATE – Melbourne, Australia, 11-13 November 2020

[10]Hua Wang. Water pricing with household
surveys: A study of acceptability andwillingness to pay in Chongqing, China.
China Economic Review . 21(1):136149 · March. 2010[11]James K. Boyce., Inequality andEnvironmental Protection. Paper, Political
Economy Research Institute MIT,2003.Ladiyance,Sand
Yuliana,
L.,
Variable
Effected Willingness To Pay
Household at Bidaracina Jatinegara, East
Jakarta, Widya Journal.Vol 2.No 2., 2014.
[12]Marselina,
Djayasinga., Ria Virsa.
Willingness to Pay (WTP) By Contingent
Valuation Method (Case Study: Waste
Management
Services.
Journalof
GEOMATE, Vol 17, Issue 62, pp 5964.2019.
[13]Steven Russel. Julian Fox Rusbhi. Dina
Arhin.. Ability to Pay for Health Care:
Concepts and Evidence. Health Policy andPlanning, 1996; 11(3):219-37
Kumakawa,
Altruism,and[14]Takehisa
Willingness to Pay for Environmental

Goods: A Contingent valuation study,
Journal of Geoscience and Environment
Protection, 5, 63-68, 2017.
[15]Tolulope J. Akeju. Gbenga J. Oladehinde
and Kasali Abubakar, An Analysis of
Willingness to Pay (WTP) for Improved
Water Supply in Owo Local Government,
Ondo State, Nigeria, Asian Research
Journal of Arts & Social Sciences 5(3): 115, 2018.
[16]Whittington D, Briscoe J, Mu X, BarronW. 1990.Estimating the willingness to pay
for water services in developing countries:
A case study of the use of contingent
valuation surveys in Southern Haiti.
Economic Development and Cultural
Change38 (2):293-312.
[17]WASH (Water and Sanitation for Health).
1988. Guidelines for Conducting
Willingness-To-Pay Studies for Improved
Water Services in Developing Countries.
WASH Field Report No. 306, October
1988.Washington
DC:
USAID.

549

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

LIFE CYCLE ASSESSMENT APPROACH FOR PREDICTING
GREENHOUSE GAS EMISSION FROM COLUMN, BEAM AND PLATEIN A REAL ESTATE CONSTRUCTION
Yatnanta Padma Devia1, Indradi Wijatmiko1, Bernadus Martino1, and Annisa Nur Rakhmawati
1
Faculty of Engineering, Brawijaya University, Indonesia
ABSTRACT
Buildings and their construction together contribute annually 36% of global energy use and 39% of energyrelated carbon dioxide emissions. In energy use per m2, buildings sector energy intensity constantly to improveat an annual average rate of around 1.5%. Building growth in ASEAN countries, include Indonesia, are predicted
rapid to double by 2040. Indonesia, is committing to 29% reduction in greenhouse gas (GHG) emissions byapplying the Intended Nationally Determined Contribution (INDC). Emission reduction in many sector should
be contributed, include construction sector. This study estimates the GHG emission of the use of column, beamand plate in a real estate project in Malang – Indonesia, based on their life cycle assessment (LCA). The stages
of LCA consist of goal and scope definition, life cycle inventory, life cycle impact assessment, and interpretationof the result. Each an individual construction material of column, beam and plate plus transport and electricity
related to project is analyzed by using software tools SimaPro 9.0 and GaBi. Then, an individual construction
material that contributes most significant to GHG emission is discussed. It is shown that the individual materialof column and beam in a conventional process namely cement, reinforced bar, and sand contribute +50%, 2644%, and +10% GHG emission, respectively. In a ready mix process, the highest GHG emission for two substructural materials (beam and plate) are ready mix concrete, reinforced bar, and wooden board. The dimension
and quantities of aforementioned sub-structural material also make in impact to GHG emission.

Keywords: Beam, Column, Greenhouse Gas (GHG) emission, Life Cycle Assessment (LCA), Plate[2]. Rey and Yepes [3] by their literature review,
mentioned that concrete structures using steel frames
had a greater impact when compared to wooden
frames and bamboo frames. Moreover, the frame
materials manufacturing caused greater energy andemission impact than the construction process. For
different construction units, it was observed that thegreater environmental impact related to concreteslab [3]. The three construction materials that
produced the largest percentages of carbon dioxide
emissions were cement, ceramics and steel in 30.3%,
20.3% and 18.75 per m2 of building, respectively [4].
According to Basbagill et.al. [5], the impacts foreach building component especially column andbeams was 0.27% (minimum) until 19.18%(maximum) of total impact. The environmental
impacts of conventional reinforced concrete, withproduction as critical stage, were 3.5 times moredamage to ecosystem, 32% more extraction ofnatural resources, and 53% effect to the humanhealth [6].
Indonesia, is committing to 29% reduction in
GHG emissions by applying Intended Nationally
Determined Contribution (INDC). Emission
reduction in many sector should be contributed,
include construction sector. Indonesia government
has a blue print Indonesia Construction 2030 as a
grand design and grand strategy in construction
sector. In the document stated that Indonesia must
be oriented not to contribute to environmental

INTRODUCTION
According to the United Nations Environment
Program [1], buildings and their construction
together contribute annually 36% of global energy
use and 39% of energy-related carbon dioxide
emissions
CO2 emissions
frombuildings construction increase continually, from 3.1
Gt CO2 in 2010 to around 3.7 Gt CO2 in 2016 [1].
Buildings sector energy intensity (in terms of energy
use per m2) constantly to improve at an annual
average rate of around 1.5% [1]. Building growth in
ASEAN country, include Indonesia, is expected
rapid to double by 2040. The environmental impact
of building growth should be investigated.
Many investigations were conducted to mitigate
greenhouse gas (GHG) emission or environmental
impacts in the buildings and construction industries.
Ragheb [2] determined which life cycle phase(manufacturing of materials, construction, use,maintenance, and demolition) contributes the mostto the total impact and investigated key assembly
systems (foundation, structure, walls, floors, roofs)
affect its environmental impact in Michigan. Theirstudy resulted that the use phase of the building has
the highest total impact (more than 90%). For
building assembly system, especially structure
system (beams and column) has the highest
contribution to fossil fuel consumption and to
eutrophication, namely 31% and 56%, respectively
550

GEOMATE – Melbourne, Australia, 11-13 November 2020

damage, but instead became a pioneer in the
improvement of environmental quality in the entire
habitat of Indonesia inhabited by humans and allother creatures in a symbiotic mutualism. However,there are still lack numbers of research in Indonesiathat investigated GHG emission of construction
material and assembly process in detail from its life
cycle especially at early design stages. Hendra [7]investigated an average carbon emission in the small
housing cluster construction was below thresholdenvironmental balance (314 ppm) with the ratio of
simultaneously construction: gradually construction
was 2.1:1.7. Sapulete et.al. [8], using cradle to cradle
in a small laboratory scope, has confirmed relationof higher cement consumption will produce higher
carbon dioxide emission for laboratory concrete
waste. Prior research in Indonesia has not includes
the most environmental impact in Life Cycle
Assessment (LCA) of material or process
construction. The sensitivity analysis has also
neglected in previous research in Indonesia.
Some methods for estimating GHG emission are
LCA [5] [6], Strategic Environmental Assessment(SEA), Environment Impact Assessment (EIA),
Environmental Risk Assessment (ERA), Cost
Benefit Analysis (CBA), Material Flow Analysis(MFA), Ecological Footprint and Carbon Footprint[9], supply chain method that applied actual
corporate and published data [10]. Some researchers
have integrated LCA with Building Information
Modeling (BIM), energy analysis, cost, embodied
energy and sensitivity analysis [4] [5]. According toHermawan et.al. [11], the advantage of LCA are atool in the construction industry manufacturing
where this LCA can describe the needs andenvironmental impacts resulting from each stage andhelp in making environmental impact reduction
decisions. A number of software tools have also
been used such as DProfiler for Building
Information Model, eQuest for energy simulation,
SimaPro and Athena Ecocalculator for operational
CO2eq [5], LCA Manager 1.3 and database
Ecoinvent 2.0 [6], and Gabi. In this research,
SimaPro 9.0 and Gabi will be used. The strength of
SimaPro 9.0 compared to previous version are new
and update data in Ecoinvent 3.5 and in method
especially ReCiPe 2016 that implemented aregionalized water consumption category (inendpoint), inclusion of normalization (endpoint andmidpoint) and weighting set (endpoint). The LCA
community recommended the ReCiPe method forseveral impact categories and promoted consistency
the midpoint and the endpoint methods [12].
This study purpose is to estimate environmental
impact of the use of column, beam and plate at realestate project in Malang City, East Java Province,
Indonesia. Based on LCA, the environmental
impacts are measured in GHG emissions that thesum of the emission during raw material procuration,

construction material production, the transportationand on site construction using SimaPro 9.0 and Gabi
software.
METHODS
A typical house 216 m2 is used in this research.
The location is in Malang City, East Java, Indonesia.
It is a two-storey house with four types column andbeam which detail information are presented inTable 1 and Table 2, respectively. All columns andlintel beam were constructed by conventional
methods, while ready mix concrete were utilized forother type of beams and plate. All of data collectedby learning the design and visiting site project.
Table 1 Details information of the column
Column dimension
LengthWidth Height
(cm)
(cm)
(m)
25
25
4
20
20
4
15
20
4
15
15
4

Numberofcolumn7
6
1273

Quantity
Volume
(m3)
1.75
0.96
91.44
6.57

Table 2 Detail information of the beam
Beam dimension
Length (cm)
Width
(cm)
25
50
15
30
15
15
15
15

Height
(m)
30.80
98.40
5.05
13.26

Quantity
Volume
(m3)
3.85
4.43
0.11
0.33*

Note: *lintel beam

To estimate GHG emission, the detail information of
LCA of column, beam and plate plus transport andelectricity related to project were analyzed by using
software tools SimaPro 9.0 and GaBi.
RESULT AND DISCUSSION
Goal and Scope Definition
The first goal is to estimate construction material
impact, related with greenhouse gas emission in theterm of global warming potential. The parameter iscarbon dioxide equivalents (CO2-eq) using therelevant 100-year global warming potential, whichmeasure the sum of the emission during raw material
procuration, construction material production,transportation, and on site construction. Furthermore,
the following goal is to determine which stage of
construction material contributes most significantly

551

GEOMATE – Melbourne, Australia, 11-13 November 2020

to environmental impact.
The scope of LCA that applied in this research iscradle to grave, which means life cycle from the raw
material to the operation process. This scopeconsists of raw material procuration, construction
material production, transportation, and on site
construction. Figure 1 schematically represents thelife cycle of a typical house.

15 x 20

Transportation

15 x 15
Rawmaterial
procuration

Construction
material
production

On siteconstruction

Fig 1. House project life cycle stages for estimating
impact of early phase designs.

Multiplex
Wooden board
Wooden block
Concrete wire
CementSand
GravelReinforced bar φ8
Reinforced bar φ12
Wooden block
Concrete wire
CementSand
GravelReinforced bar φ8
Reinforced bar φ10

4.60
0.9
0.19
4.01
489.72
0.70
1.02
13.60
15.40
1.14
23.92
2,922.92
4.18
6.07
49.02
68.40

sheet
m3
m3
kg
kg
m3
m3
bar
bar
m3
kg
kg
m3
m3
bar
bar

Material

Quantity

Unit

Multiplex
Wooden block
Concrete wire
Ready mix
Reinforced bar φ8
Reinforced bar φ12
Reinforced bar φ13
Multiplex
Wooden block
Concrete wire
Ready mix
Reinforced bar φ8
Reinforced bar φ10
Reinforced bar φ12
Multiplex
Wooden block
Concrete wire
Ready mix
Reinforced bar φ8
Reinforced bar φ10
Multiplex
Wooden block
Concrete wire
CementSand
GravelReinforced bar φ8
Reinforced bar φ10

12.41
20.22
13.30
3.85
27.19
12.27
27.60
11.78
18.14
9.47
4.43
32.55
13.38
26.75
3.29
5.07
2.64
0.11
31.03
43.29
50.20
3.77
40.34
5.791.41
9.32
13.99
4.00
4.70

sheet
m3
kg
m3
bar
bar
bar
sheet
m3
kg
m3
bar
bar
bar
sheet
m3
kg
m3
bar
bar
sheet
m3
kg
kg
m3
m3
bar
bar

Life Cycle Inventory

Table 4 Material in each beam

The second phase of life cycle assessment ismaking an inventory of input and output related tothe study scope. At the input stage, each column andbeam is divided into several sub materials resources
need and its transportation. Those resources needdefined into process, product stages and systemdescription. The resources need and transportation
will map into emission rate that described as theoutput. Materials input in each column, beam andplate are shown in Table 3, Table 4, and Table 5
respectively.

Type
Beam
25 x 50

15 x 30

Table 3 Material in each column
Type
Column25 x 25

20 x 20

Material
Multiplex
Wooden board
Wooden block
Concrete wire
CementSand
GravelReinforced bar φ8
Reinforced bar φ12
Reinforced bar φ13
Multiplex
Wooden board
Wooden block
Concrete wire
CementSand
GravelReinforced bar φ8
Reinforced bar φ13

Quantity

Unit

12.99
1.48
0.56
7.42
973.88
1.36
1.40
17.43
6.30
18.90
8.90
1.02
0.38
5.09
667.80
0.93
1.40
8.82
8.40

sheet
m3
m3
kg
kg
m3
m3
bar
bar
bar
sheet
m3
m3
kg
kg
m3
m3
bar
bar

15 x 1515 x 15(lintelbeam)

Table 5 Material in plate

552

GEOMATE – Melbourne, Australia, 11-13 November 2020

Material
Scaffolding (bamboo)
Wooden block
Multiplex
Reinforced bar
Concrete wire
Ready mix
Vibrator electricity

dimensions with different quantities and methods.
The dimensions of column are 25 x 50, 20 x 20, 15 x
20, and 15 x 15, while beam are categorized 25 x 50,
15 x 30, 15 x 15, and lintel beam 15 x 15. While inthis research also separate nine individual material
categories for column, beam and plate:multiplex/wooden board, wooden block, concretewire, cement, sand, gravel, and reinforced bar (φ8,
φ10, φ12, and φ13), ready mix concrete and bamboo
plus transport to project.
In this phase of impact assessment, the individualmaterials were analyzed in network flow and tablefor each type column, beam and plate. Moreover, theimpact assessment for general column, beam andplate were also calculated. Table 7 show the impact
assessment in the form of greenhouse gas ofindividual materials for each type column, beam andplate, as indicator of global warming impact.

Quantity Unit
26.90
m3
2.40
m3
54.00 sheet
604.00 bar
29.30
kg
17.3
m3
11,268.60 kWh

The calculation of quantity of materials used tobuild columns, beam and plate are considering 16
kg/sheet for multiplex and wooden board, 600 kg/m3
for wooden block, 500 kg/m3 for bamboo, 1,800kg/m3 for sand, 1,400 kg/m3 for gravel and 2,330
kg/m3 for ready mix. To calculate weight ofreinforced bar, the weight of each bar are used,which 4.74 kg/bar, 7.4 kg/bar, 10.66 kg/bar, and12.48 kg/bar for reinforced bar φ8, φ10, φ12, andφ13, respectively. The results of quantities in kg
were converted to kg CO2-eq using conversion
emission factor.
The other emission source that should beconsidered is transportation. Table 6 shows the
distance between raw material place and project
location. The distances in km were changed to kg
CO2-eq.

Table 7 Greenhouse gas as global warming impact
for each type column, beam and plate
Type

Material

Column25 x 25

Multiplex
Wooden block
Concrete wire
CementSand
GravelReinforced bar
Multiplex
Wooden block
Concrete wire
CementSand
GravelReinforced bar
Multiplex
Wooden block
Concrete wire
CementSand
GravelReinforced bar
Wooden block
Concrete wire
CementSand
GravelReinforced bar
Multiplex
Wooden block
Concrete wire

Table 6 Distances from raw material place toproject for calculating transportation
emission
Material
Multiplex
Wooden board
Wooden block
Concrete wire
Nail
CementSand
GravelReinforced bar φ8
Reinforced bar φ10
Reinforced bar φ12
Reinforced bar φ13
Ready Mix
BambooColumn20 x 20

Distance (km)
5.00
8.80
8.80
5.30
5.3
1.10
17.00
17.00
6.60
6.60
6.60
6.60
11.00
8.80

Column15 x 20

Column15 x 15

Life Cycle Impact Assessment
In this phase, the life cycle inventory result areevaluated their impact potency. Then, the impact
results are interpreted in the final phase. In thisresearch,
ReCipe2016
Midpoint
withcharacterization impact assessment is selected in
SimaPro software.
Each column and beam is classified into 4 types

Beam
25 x 50

553

Greenhouse gas(kg CO2-eq)-96.2065.90
16.90
932.00201.0068.30
753-66.0045.20
11.60
639.00138.0046.80
286.00-55.5018.80
9.14
469.00104.0034.00
447.00112.0054.50
2,800.00
618.00203.001,440.00-10.702,390.00
30.30

GEOMATE – Melbourne, Australia, 11-13 November 2020

Type

Beam
15 x 30Beam
15 x 15

Lintel15 x 15

Plate

Material
Ready mix
Reinforced bar
Multiplex
Wooden block
Concrete wire
Ready mix
Reinforced bar
Multiplex
Wooden block
Concrete wire
Ready mix
Reinforced bar
Multiplex
Wooden block
Concrete wire
CementSand
GravelReinforced bar
Scaffolding
Wooden block
Multiplex
Reinforced bar
Concrete wire
Ready mix
Vibrator elect.

arrow indicates the emissions from each material orprocess, while the green arrow indicates recyclable
material or no negative impact on the environment.
Fig. 2, Fig. 3 and Fig. 4 show the network flow to
calculate GHG emission as the global warming
impact for whole column, beam and plate,respectively.

Greenhouse gas(kg CO2-eq)
1,020.00
1,180.00-10.202,150.00
21.60
1,170.00
1,050.00-2.85
600.006.03
30.00
913.00
-0.94
9.64
1.99
120.0029.90
10.10
283.0023.30
1.54 x 105-1,740.00
8,800.00
66.80
1.01 x 107
2.38 x 104

Interpretation
By using SimaPro 9.0, the dominant GHGs
almost came from unvaried materials from eachtypes of column due to the same conventional
construction method. Based on Table 7, threematerials that produce highest GHGs on four types
column came from cement 469 kg CO2-eq – 2,800
kg CO2-eq (45% - 58%), reinforced bar 28 kg CO2eq - 753 kg CO2-eq (26% - 44%) and sand 104 kg
CO2-eq - 618 kg CO2-eq (10% - 13%). Similarly, on
the conventional beam construction (lintel beam 15x 15), the highest GHGs produced from cement
5,420 kg CO2-eq (58.80%), gravel 1,400 kg CO2-eq(15.20%) and sand 1,380 kg CO2-eq (15%). The
reinforced bar on lintel beam which installed above
doors and windows, do not give high GHG due todimension and quantities.
On the contrary, the three highest GHG
emissions on ready mix process for beam and plate
are varied sources depend on dimension andquantities. Ready mix concrete on beam and plateproduced GHG on 983 kg CO2-eq – 1,130 kg CO2eq (20% - 45%) and 4,570 kg CO2-eq (87.30%),
respectively. Reinforced bar contribute GHG 913 kg
CO2-eq – 1,180 kg CO2-eq (25% - 58%) on beam
construction and 440.90 kg CO2-eq (8.43%) on plate
process.

Multiplex, as shown as Table 7, gives a negative
greenhouse gas emission. Multiplex material does
not have an impact on the environment because thismaterial can be recycled. In network flow, the red

Fig 2. Network flow and emission GHG of all column type554

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig 3. Network flow and emission GHG of all beam typeFig 4. Network flow and emission GHG of plate
According to column network flow analysis(Fig. 2), it seen clearly that cement, reinforced bar,and sand emitted three highest GHG as global
warming emission at 4,840 kg CO2-eq (52%), 2,926
kg CO2-eq (31.50%), and 1,061 kg CO2-eq(11.40%), respectively. A high emission from
cement could be explained by cement production
and transportation. It originated from fuelcombustion and from the calcination reaction. The
rest of CO2 emissions emerge from grinding andtransport [13].
Meanwhile, the beam works consist of two
method, a conventional process and ready mix
process. As a consequence, beam network flow
analysis (Fig. 3) resulted not only cement (120 kg
CO2-eq, 1.09%) and reinforced bar (3,426 kg CO2eq, 31.20%) produced GHG emission, but also
ready mix concrete at 2,200 kg CO2-eq (20.20%)
and wooden block 5,150 kg CO2-eq (46.90%). Fig.

4 shows plate network flow analysis that dominatedby emission from ready mix concrete at 1.01 x 107
kg CO2-eq (98.40%). This emission included
emission from transportation and electricity.
Furthermore, in comparison to SimaPro 9.0, theimpact assessment also calculated using GaBi
software. The calculation indicates that there is no
significant difference in both of software result,especially on GHG as global warming indicator.
CONCLUSIONSThe emission of GHGs from conventional
process of column and beam in a real estate
construction derived from cement, reinforced bar,and sand at +50%, 26-44%, and +10% GHG
emission, respectively. In a ready mix process, thehighest GHG emission for beam and plate are readymix concrete, reinforced bar, and wooden board.
555

GEOMATE – Melbourne, Australia, 11-13 November 2020

The varied sources that contribute dominant GHG
depend on dimension and quantities of structural
material.

Architecture and Civil Engineering 2 (11),
2015,
pp.
21-31.
DOI
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Teknologi Terapan IV, Institut Teknologi Adhi
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EmissionsinThe
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ACKNOWLEDGMENTS
We would like to acknowledge the Faculty ofEngineering, Brawijaya University for supporting
financial grant for this research.
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A.F.,
Towards
Environmental
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Improvement
Potential.
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

TRENDS IN THE USE OF STREET TREES IN JAPANMasaaki Furuno 1, Taizo Uchida 2, Daisuke Hayasaka 3, Xue Jun Huan 4 and Teruo Arase 5
1

Graduate School of Engineering, Kyusyu Sangyo University, Japan; 2 Faculty of Architecture and Civil
Engineering, Kyusyu Sangyo University, Japan; 3 Faculty of Agriculture, Kindai University, Japan; 4
Faculty of Landscape Engineering, Suzhou Polytechnic Institute of Agriculture, China; 5 Faculty ofAgriculture, Shinshu University, Japan;
ABSTRACT

Street trees are one of the important components of green infrastructure in urban area. This study surveyed
trends in the introduction of street trees across Japan. The results showed that Japanese street trees tended to bemainly broad-leaved rather than coniferous, and many of these were native species. In addition, we found thatstreet trees not only fulfill the roles of disaster prevention and disaster reduction, but also contribute to citizen
welfare and recreation because a relatively large number were flowering trees such as sakura, camellia anddogwood. On the other hand, species composition of street trees was not monotonous across Japanese, which werebroadly categorized into four groups, and each group had species such as Picea glehnii, Quercus acuta, Aucuba
japonica and Planchonella obovata that were unique to that group. Each group had correlation with latitude,
longitude, Kira’s Warmth Index, annual precipitation, etc. Also, there were only five species that were common
to all groups: Rosaceae sp., Lythraceae sp., Theaceae sp., Juniperus chinensis, Fabaceae sp. It was suggested thatthese five species are the street trees that represent Japan. Incidentally, weeds which invade the narrow soil beds
at the base of high trees were managed by installation of shrubs, garden plants such as flowers and artificial coversuch as concrete in addition to weeding in japan.
Keywords: Street trees, Green infrastructure, Trend in Japan, Management

INTRODUCTION
MATERIALS AND METHODS
Urban areas are facing big problems such as theheat island phenomenon, air pollution, and increased
risk of disasters due to climate change [1]–[10], [23],[24]. Green infrastructure in urban areas has recently
attracted attention as a way to combat some of these
problems [11]–[14], [20], [22]. Green infrastructure
is a nature-based solution that contributes to disasterprevention and reduction by incorporating the naturalenvironment into normal infrastructure [11]–[14],[20], [22]. Trees planted along streets (street trees) are
one example of green infrastructure that is actively
introduced because they fulfill the roles of preventing
the spread of fires and reducing earthquake damage[15], [16]. It is difficult to introduce new large green
infrastructure such as parks in urban areas because oflimited space and the high costs associated withconstruction and maintenance; however, more street
trees are expected to be introduced in the future
because they are smaller and cheaper than full parks.
Although there are various reports on the use ofstreet trees in various prefectures and urban areas inJapan, there is no clear study on trends in the
introduction of street trees across Japan (e.g. native
species ratio) [17]–[19], [25], [26]. Clarifying thistendency would be useful for promoting green
infrastructure in the future. Therefore, this study
analyzed trends in the introduction of street trees
across Japan based on a collection of reports on street
tree use in various parts of Japan.

In this study, the top 50 high tree species in
number planted in each Japanese prefectures (47prefectures × 50 species = 2350 species) weretargeted. Information on species planted was obtained
from “The Roadside Trees of Japan VIII” issued by
the National Institute for Land and InfrastructureManagement Ministry of Land, Infrastructure,
Transport and Tourism, Japan [17]. We tallied theratio of native species, evergreen broad-leaved,
deciduous broad-leaved, coniferous, flowering (i.e.
trees that are generally grown for the enjoyment of
their flowers such as sakura, camellia and dogwood)and edible (i.e. trees with generally edible fruits suchas ginkgo, olive and orange) trees. We also
determined in what areas these street trees were usedfrom the classification of prefectures by Two Way
Indicator Species Analysis (TWINSPAN), andDetrended Correspondence Analysis (DCA).
In addition, we examined street tree managementfrom a survey of 1629 high trees randomly selectedin Fukuoka City, Fukuoka Prefecture, and located inwestern Japan and one of the five biggest cities inJapan. Street tree management was inferred byexamining the narrow soil beds at the base of high557

GEOMATE – Melbourne, Australia, 11-13 November 2020

96 species of street trees, Group B included 20
prefectures and had 141 species of street trees, GroupC included 22 prefectures and had 155 species ofstreet trees, and Group D included 1 prefecture andhad 50 species of street trees. There was a significant
difference in the rate of evergreen broad-leaved,
deciduous broad-leaved and coniferous trees among
these groups. Group A had the most deciduous broadleaved and coniferous trees, followed by Group B,
Group C, and Group D, respectively (Table 1).
Each group had its own unique street trees thatwere used only in that group (Appendix 1). Group A
had 27 unique species such as Picea glehnii, Abiessachalinensis and Populus suaveolens, Group B had
21 uniq ue spec ie s suc h as Quercu s acut a ,Chamaecyparis obtusa and Larix kaempferi, GroupC had 44 unique species such as Aucuba japonica,
Aphananthe aspera and Cinnamomum daphnoides,
and Group D had unique 29 species such as
Planchonella obovata, Satakentia liukiuensis andMillettia pinnatas. Incidentally, there were also fivespecies that were common to all groups: Rosaceae sp.,trees planted along the sidewalk.
TWINSPAN and DCA were analyzed using PCORD Ver.4.25 (MjM Software Design). Also,
relationship between the stand scores extracted from
DCA and environmental variables (latitude, longitude,
Kira’s Warmth Index, annual precipitation, daylight
hours and snowfall of each prefecture [21]) wereanalyzed by Pearson’s correlation test.
RESULTS
Trends in street trees used in Japan
Native species accounted for 57.1% of street trees
in this study (Fig.1). 46.0% of street trees were
deciduous broad-leaved, 40.1% were evergreen
broad-leaved, 13.9% were coniferous, 24.6% were
flowering and 5.6% were edible (Fig.1).
Japanese prefectures were broadly categorized
into four groups (A, B, C and D) based on the species
composition of street trees by TWINSPAN (Fig.2 andTable 1). Group A included four prefectures and had

Native †.

Non-native

Evergreenbroad-leaved.

Deciduous
broad-leaved

Flowering.

Group D

n=47

ConiferJapan

Non-edible

0

Table 1

Group B (n=20)

Non-flowering. EdibleFig. 1

Group A (n=4)
Group C (n=22)

20

40

60

80

100 (%)

3

2

1

Division level

Trends in Japanese street trees.
252 species were analyzed, excluding
duplicates from 2350 species in the researchtarget.
†Varieties were included.

Fig. 2 Classification of prefectures by TWINSPAN.
Top 50 high tree plant species in number
used in each Japanese prefectures and thenumber of each planted was set as a variable.

Tendencies in street trees for each group.
Group

P-value ‡A (n=4)B (n=20)

C (n=22)

D (n=1)

Total number of species

96

141

155

50

-

†

Native (%)
Evergreen broad-leaved (%)

56.3

64.5

61.3

52.0

.360

7.3

30.4

49.0

68.0

.000

Deciduous broad-leaved (%)

71.9

54.6

42.6

24.0

.000

Coniferous (%)

20.8

14.8

8.4

8.0

.022

Flowering (%)

25.0

24.1

23.2

34.0

.481

Edible (%)

5.2

5.0

5.8

6.0

.986

†Varieties were included.
‡Pearson's χ2558

GEOMATE – Melbourne, Australia, 11-13 November 2020

□ Group A

300

Table 2
Table 2△ Group B

Correlation between DCA stand scores andenvironmental variables.

● Group C■ Group D

Axis 2

200

Axis 1 ‡Axis 2 ‡Latitude-0.760**-0.697**

Longitude-0.614**-0.634**

Kira’s Warmth Index†

0.793**

0.668**

Annual precipitation †0.379**

0.278-0.0900.344*-0.282-0.685**100

Daylight hours (h)†Snowfall (cm) ††Data were obtained from the Automated Meteorological Data Acquisition System(2009-2018).
‡Pearson's correlation coefficient.

n=47
0
0

200

400

600

*p< 0.05, **p< 0.01.

Axis 1

Fig. 3

Ranking of Japanese prefectures by DCA.
Japanese prefectures and urban areas [17]–[19], [25],[26]. However, there is no clear summary of trends instreet tree use across Japan. This study collected andanalyzed information on street tree trends acrossJapan. Results showed that evergreen broad-leaved
and deciduous broad-leaved species accounted fornearly 90% of street trees in Japan and that many
street trees were native species (Fig.1). Therefore, we
considerd that Japanese street trees tended to consistof mainly broad-leaved and native species. In
addition, we found that street trees as a green
infrastructure not only fulfill the roles of disasterprevention and disaster reduction, but also contributeto citizen welfare and recreation because a relatively
large number were flowering trees (Fig.1). Since few
edible street trees were found, it was suggested thatthe role of street trees as edible was small (Fig.1).
Although there were some similarities, species
composition of street trees was not monotonous
across Japanese prefectures, which were broadly
categorized into four groups (Fig.2). Group A tended
to have the most deciduous broad-leaved andconiferous trees, and few evergreen broad-leaved
trees, followed by Group B, C, and D, respectively(Table 1). Also, it was suggested that Group A had
high latitude, cold temperatures, heavy snowfall, lowannual precipitation and low daylight hours followed
by Group B, C, and D, respectively (Fig.3 and Table2). On the other hand, each group had species thatwere unique to that group (Appendix 1). From above
results, it was suggested that Japanese street trees
have different species composition based on thelatitude, temperature and annual precipitation of eacharea.
On the other hand, each group had also fivespecies (Rosaceae sp., Lythraceae sp., Theaceae sp.,
Juniperus chinensis, and Fabaceae sp.) that werecommon to all Groups regardless of latitude,
temperature and annual precipitation (Appendix 1), it
was suggested that these five species are the street
trees that represent Japan.

Lythraceae sp., Theaceae sp., Juniperus chinensis,
Fabaceae sp. (Appendix 1).
Each group was ranked on two axes by DCA(“after-the-fact coefficient of determination” was
0.536) (Fig.3). Each group was arranged in the orderof Group A, B, C, D on the left side (i.e. the first axis).
Each group had a high positive correlation with
Kira’s Warmth Index and annual precipitation, and anegative correlation with latitude and longitude on thefirst axis (Table 2). Namely, it was suggested GroupA had higher latitude, colder temperatures and lower
annual precipitation followed by Group B, C, and D,
respectively. For the second axis, each group wasarranged in the order of Group A, B, C, and D along
the bottom side. Each group had a high positive
correlation with Kira’s Warmth Index and daylight
hours, and a negative correlation with latitude,
snowfall and longitude on the second axis (Table 2).
The groups had same tendency as the first axis, andGroup A had high snowfall and low daylight hours
followed by Group B, C, and D, respectively.
Street tree management
Results of the survey of the narrow soil beds at thebase of high trees showed the following five types ofmanagement: no installation (n=614), installation of
artificial cover such as concrete and iron (n=322),
installation of garden plants such as flowers (n=177),
installation of shrubs (n=341) and others (n=175)(Fig.4 and Photo 1). No installation was defined as
nothing installed on the narrow soil beds at the baseof high trees. Signal devices, garbage cans, road signs,
billboards, etc. were included in “Others” (Photo 1).
DISCUSSION
Trends in street trees used in Japan
There are various reports on street trees used in
559

GEOMATE – Melbourne, Australia, 11-13 November 2020

consider that Japanese street tree management tends
also to prevent invasion of weeds by installation of
shrubs, garden plants and artificial cover, because
they accounted for 51.2% of street tree management.
Incidentally, it was suggested that “Installation ofgarden plants” fulfilled also the role of recreation for
citizens and improving the urban landscape. Overall,
weeds which invade the narrow soil beds at the baseof high trees were managed by installation of shrubs,
garden plants such as flowers and artificial cover suchas concrete in addition to weeding in japan.

Street tree management
This study inferred street tree management by
examining the narrow soil beds at the base of hightrees. The results showed that Japanese street tree had
five patterns of no installation, installation of garden
plants such as flowers, installation of artificial coversuch as concrete and iron, installation of shrubs, andothers (Fig.4 and Photo 1). “No installat ion”
account ed for 37.7% of st reet t ree. In "No
installation", weeds often invaded (the authors
frequently observe this) (Photo 1). Therefore, it wassuggested that weeds are allowed to invade thenarrow soil beds at the base of high trees and are thenmanaged by weeding in japan. In addition, we

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No Installation0
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0
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100

200

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600

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[14] HP of European Commission: https://ec.europa.e
u/environment/nature/ecosystems/index_en.htm,
Referenced in 2020.6.30[15] Kida Y. and Kaniwa M., Urban disast er
prevention: "Especially about disaster prevention
abilit y of trees and their maintenance and561

GEOMATE – Melbourne, Australia, 11-13 November 2020

Appendix 1

Street trees present in each group.
Species

FamilyRosaceae sp (Cerasus x yedoensis )‡Lythraceae sp (Lagerstroemia indica )‡†Theaceae sp (Camellia japonica )Juniperus chinensis *Fabaceae sp (Acacia dealbata )Picea glehnii*

Aria alnifolia‡

Malus prunifolia‡

Abies homolepis*

Quercus dentata‡†

Bignoniaceae sp (Catalpa ovata )‡Populus alba

Picea pungens
Others*

Quercus acuta†

Linnaeaceae sp (Abelia x grandiflora )†Carpinus tschonoskii ‡‡Deutzia crenata
Ilex serrata

‡

Ⅴ

Ⅴ

Ⅴ

Ⅲ

Ⅴ

Ⅴ

Ⅴ

●

Cupressaceae

Ⅳ

Ⅳ

Ⅴ

Ⅴ

●

Ⅱ

Ⅱ

Ⅴ

*

Chamaecyparis obtusa*

Larix kaempferi

Fabaceae

Ⅲ
Ⅲ●

Rosaceae

Ⅲ●

Rosaceae

Ⅲ

Pinaceae

Ⅲ

●

Ⅲ

●

‡

Erythrina crista-galli

Juglandaceae sp (Pterocarya rhoifolia )Arecaceae sp (Phoenix dactylifera )Podocarpus macrophyllus*

Aucubaceae sp (Aucuba japonica )Ficus superba†

Malvaviscus penduliflorus††

Olea europaea†

Dendropanax trifidus†

Others†‡†

Palaquium formosanum†

Hibiscus tiliaceus †Ⅲ

Bombax ceiba‡

Millettia pinnata
Cocos nucifera††

Araucaria heterophylla
Thespesia populnea
OthersFirmiana simplex*†

Fraxinus lanuginosa
Bischofia javanica†

Carpinus laxiflora‡‡(18 species)(8 species)
Ⅰ

Ulmus parvifolia ‡Thujopsis dolabrata
Pieris japonica*

Ⅰ

Linnaeaceae

Ⅰ

Betulaceae

Ⅰ●

Hydrangeaceae

Ⅰ

●

Ⅰ

●

*

Ⅰ

PinaceaeⅠ●(12 species)(6 species)●

Fabaceae

Ⅱ

Juglandaceae

Ⅱ

Arecaceae

Ⅱ

Podocarpaceae

Ⅱ

Aucubaceae

Ⅰ●

MoraceaeⅠ●

Malvaceae

Ⅰ

Oleaceae

Ⅰ

Fabaceae

Ⅰ‡

Acer rubrum
Others (117 species)●●(17 species) (7 species)

Sapotaceae

Ⅴ
Ⅴ

Malvaceae

Ⅴ

Fabaceae

Ⅴ

Arecaceae

Ⅴ

Araucariaceae

Ⅴ

Oleaceae

Ⅱ

Ⅰ

●
●
●
●

Ⅴ

●

●

(8 species)(8 species)

Ⅴ

●

Ⅱ

Ⅰ

Ⅴ

Ⅴ

Ⅴ

●

Ⅴ

Ⅳ

●

Ⅰ

●

●

Ⅱ
Ⅰ

Ⅱ

Ⅰ

-

-

Occurrence frequency: I, <20; II, <40; III, <60; IV, <80; V, ≦100
†Evergreen broad-leaved.
‡Deciduous broad-leaved.
*Coniferous.

562(1 species)

●

Ⅱ

Ⅳ

●

●
Ⅰ

Ericaceae
Aceraceae-●(20 species)
Ⅲ(1 species)

●

Ⅴ

Ⅲ

●

●
Ⅴ

Ⅲ

-

●

(35 species)

Sterculiaceae(3 species)●

Sapotaceae

Cupressaceae●

Ⅰ

Ulmaceae†(1 species)●

Cupressaceae

Betulaceae
Pinaceae(3 species)●

Sapindaceae

Euphorbiaceae

Pinaceae sp (Pinus densiflora )●●Malvaceae
-

‡

●
●

Ⅲ

Malvaceae
Bignoniaceae‡

Tabebuia rosea

●

Ⅲ

-

Planchonella obovata

●

Ⅲ

-

Others

Edible●Pinaceae

Aquifoliaceae
Cupressaceae*

Chamaecyparis pisifera●

Ⅳ

Pinaceae
Pinaceae‡

Flowering●

Theaceae

Fagaceae

Aesculus x carnea

NativeLythraceae

Salicaceae*

Abies sachalinensis

D (n=1)
Ⅴ

Rosaceae

Fagaceae
Bignoniaceae

‡

Group
B (n=20)
C (n=22)Ⅴ
Ⅴ

A (n=4)
Ⅴ

●

-

-

●

(75 species) (26 species)(7 species)

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE RELATIONSHIP BETWEEN LAND USE AND TRANSPORTPERFORMANCE: A CASE STUDY OF PADANG, INDONESIA

1

Aleksander Purba1, Nikko Yulianda2, Shahnaz Nabila Fuady3
Faculty of Engineering, Lampung University, Indonesia; 2,3 Urban and Regional Planning Department,
Insitut Teknologi Sumatera, Indonesia

ABSTRACT
Padang on the west coast of Indonesia is one of the oldest cities with a history of more than a century. As thewest gate of Indonesia from the direction of the Indian ocean, its existence is known as a port city with the mainfunction as warehousing and logistics center. After continuing to develop as a city of trade and services, Padang
is now considered as one of the most congested cities in Indonesia. A series of secondary data has been collected
including satellite imagery and both qualitative and quantitative methods were used simultaneously. The patternof urban space has not changed significantly for decades, even though the number of trips has continued to increase.
The population of close to 1 million and land use with more diverse functions results in the road network being
overwhelmed to serve vehicle flow. Uncontrolled on-street parking contributes to reducing transport performance.
Case study found the degree of saturation and wide road occupied for parking variables, and the speed and numberof warehouse building variables are having strong correlation each other.
Keywords: Land use, transport, relationship, Padang, Indonesia

continuing depletion of green space. The sameproblem is also experienced by Lop Buri province,
Thailand [5] and even Georgia (USA) due to thedevelopment of interstate highway [6], though 10%
increase in a city’s initial stock of highway causes
about 1.5% increase in its employment over a 20-year
period in the USA [7].
This paper evaluates the long experience of one ofthe oldest cities in Indonesia; Padang city based on
the National Spatial Planning document is directed to
become a National Activity Center and will be
developed into the PALAPA (PAdang-Lubuk AlungPAriaman) Metropolitan Core City. As the capital ofwest Sumatera province the largest city on the west
coast of Sumatra is the west gate of Indonesia from
Indian ocean. With a land area of almost 700 km2 anda population of nearly 1 million, Padang, which was
hit by a major tsunami disaster at the end of 2009, is
now ranked fourth as the most congested city inIndonesia according to the INRIX 2017 Global
Traffic Scorecard [8]. Integrated of land use andtransport is still a big challenge in the operational
agency settings and integrating it with transportsystems [9], tension between bottom up and top down[10], [11], and the need to bridge the gap between the
proliferation of activity-based travel demand andtheir integration with operational models in practice[12]; the challenges became more complex if the city
sustainability issue is associated with energy scarcity
and climate change [13]. The more advanced
approaches using a relatively new database (LEHD)
to explore land-use-transportation relationships
conducted by Horner and Schleith [14], usage of

INTRODUCTION
City paradigm in Indonesia according to Law No.
26/2007 governing Spatial Planning emphasizes thatcities are areas with denser population concentrations
compared to surrounding areas due to functional
centralization related to the activities of the
population for work, commerce, social, culture,
recreation and education. It must be acknowledgedthat the model of city growth in developing countries
is not by design as is common in developed countries,
for example Eindhoven, the Netherland [1], andWellington, New Zealand [2], but develops naturally,
sporadically and spreads irregularly beyond
administrative boundaries. Daily activities andresidences are also further apart, because owning ahouse in the city center is not affordable for most city
residents. Rapid city growth and high dependence ofauto ultimately triggered new urban problems in theform of imbalance between land use from the demandside and the ability of the transportation system fromthe supply side to accommodate the movement ofpeople and goods and services as experienced by
Shanghai [3]. The situation in almost all cities inIndonesia is increasingly worrisome because theauthority is still focused on approaching the supply
side by building flyovers, underpasses, LRT and eventhe metro and is still very limited in steps related toreducing demand side. Lack of reference to bestpractices in the region as conducted by Denver in the
USA [4], loose and out of date regulations and theabsence of the role of civil society have triggered thecity of more suffering, marked by, among others, the563

GEOMATE – Melbourne, Australia, 11-13 November 2020

mobile phones to indicate the social function of land
use in Singapore [15], and applying a spatial
composite index to portray an overview of thesustainability performance [16].

m (Lubuk Kilangan) and coastal areas with elevations
from 0 to 8 m (Padang Barat). Administratively, thiscity besides dealing directly with Indian ocean in thewest is flanked by three regencies, each of Padang
Pariaman in the north, Solok in the east and Pesisir
Selatan in the south as shown in Fig. 1. Based on
regulations, the city of Padang is classified as a largecity with a population of 927,168 (2017); with anaverage growth of 1.53% annually the population ispredicted to reach 1 million in 2022 and will position
it as a metropolitan city.

MATERIAL AND METHOD
Study Area
Initially the city consisted of 3 districts with 15
villages. In its current development Padang covers 11
districts with elevations varying from 25 m to 1,853

Fig. 1 Administrative map of Padang city
decreases during the 2013-2017 period as shown in
Fig. 2. As with other cities in Indonesia public
transportation service is dominated by paratransit
with capacity 12 people as many as 1.979 units
covering 40 routes.

Interestingly, the population distribution is also
diverse; Koto Tengah district has a density rate of 817
people/km2 and Padang Timur is much denser at
9,751 people/km2 with land use dominated byeducation center and middle class housing complex
and closer to the central business district despite thearea including in the tsunami alert zone. Based on
population structure data, productive age with a rangeof 15-55 years dominates with a percentage of 62%
indicating more than half of the population has thepotential to travel and daily urban activities such as
education, shopping, leisure, economic and financial
activity, social, and culture. City economic structure
is supported by wholesale and retail trade, car andmotorcycle repair and transportation, andwarehousing, respectively. While the number ofregistered vehicles tends to be constant and evenpassenger car

Registered vehicle

500,000

freight

400,000

motorcycle

300,000
200,000
100,000
0
2013

2014

2015

2016

2017

Year

Fig. 2 Number of motorized vehicle 2013-2017564

GEOMATE – Melbourne, Australia, 11-13 November 2020

A series of secondary data has been collected
including satellite imagery, population, registeredvehicle, road class, and road information, spatialplans, transport master plan, and traffic and roadtransport master plan. Both qualitative andquantitative methods were used simultaneously inorder to get convergent findings.

New bus service named Trans Padang with acapacity of 40 people serving two routes began tooperate since the last two years. At almost the sametime, downtown Padang is also connected by train to
the Minangkabau international airport initiated by thecentral government. The first train departs from
Padang station on 06:30 a.m. and the last one would
depart on 06:00 p.m. with totally six trips daily and45 minutes’ travel time. Although the transport
supply available varies, the facts in the field indicate
the opposite condition. Based on the results of INRIX
research, Padang ranks fourth as the most congested
city in Indonesia as shown in Fig. 3 below. Based on
Fig. 3 on average the city residents of Padang lost 45
hours annually due to congestion and delay.
Surabaya (10)Semarang (9)37 h/yr.
39 h/yr.

Pontianak (7)40h/yr.
42 h/yr.

Yogyakarta (5)45 h/yr.

Padang (4)45 h/yr.

Malang (3)45 h/yr.

Bandung (2)46 h/yr.

Jakarta (1)Land use development
The area that focuses on this study is Kampung
Pondok district; the historical development of thisregion began since 1915 and after more than a century
the spatial patterns almost resemble actual current
conditions. According to Topographiache Inrichting,
Batavia map (1915) Kampung Pondok was clearly
directed to support the commercial system of the citywhich at that time was functioning as a port. Its vitalfunction was triggered by the growth of the city andwas proven by permanent buildings that lined theestuary of the Batang Arau river as shown in Fig. 4.

37 h/yr.

Tarogong (8)Medan (6)RESULT AND FINDINGS

63 h/yr.

Fig. 3 Indonesia most congested cities (hours/year)
Data Sources
Primary data related to land use was conducted byclassified building function along the study corridor
based on regulation (Regulation of the Minister ofAgrarian Affairs and Spatial Planning / Head of
National Land Agency No 16/2018 concerning
Guidelines for Compiling Detailed Spatial Planning
and Zoning Regulation of Regency/City). According
to its regulation the building function to be classified
is a building that has conformity with definition,
purpose of determination and zone perform criterionof housing, trade and services, offices, industries,
public service entities, tourism, and others.
In order to understand road performance, thewhole information related to road section
characteristics is needed. Road characteristic data wascollected in a number of standard field form survey
for urban road i.e. geometric form, traffic flow, androad side activity. Geometric itself covering roadsection type, lane number and lane width, section
length, curb, shoulder, median, and pedestrian facility.
Hence, the way the traffic controlled was conducted
including parking management, split, and flow
distribution. Moving car observer method has beenused to count the traffic flow, velocity, and density
for sixteen road section entire research area.

Fig. 4 Topographiache Inrichting Map (1915)
Refer to the map permanent buildings associatedwith function perform as government offices, rockhouses and wood and bamboo houses. It is clearlyseen in the map that railway line has been available
direct to the port indicating an advance transportsystem.

565

GEOMATE – Melbourne, Australia, 11-13 November 2020

Pondok region performs a strategic function on thegrowth of urban areas at the time.
In 1945 the area was remapped to understand thedevelopment of urban land use after four centuries ofinteracting with various urban activities. As can beseen in Fig. 5 the built up area continues to growalong the river estuary and looks more concentrated
in the southern part of the city. Non-governmentbuildings are also seen beginning to fill the area thatwas previously green space, indicating the presenceof the port has an important contribution in driving
the development of land use towards the north of thecity
But the delta along the Batang Arau river is stillseen as a major trading area through its building
function characteristics, especially warehousing,logistics centers, banks, and markets. Along with theechoes of Indonesia's independence, cities aredeveloping more rapidly including Padang city. The
city reform at that time was more focused on
improving the road network to accommodate thedevelopment of land use and increasing population,
which implies an increase in travel demand. Based on
the comparison of maps in 1915 and 1945 withcurrent conditions, it shows that the road sections in
Kampung Pondok district have not changed
significantly.

Fig. 5 Map of Kampung Pondok district (1945)
Rapid urban activity around river estuary and its
surrounding coast encourage the growth of buildings
supporting port activities such as warehousing andlogistics centers. Hence, the existence of the port andits supporting facilities shows that the Kampung

Fig. 6 Open space of Kampung Pondok (2005-2019)
This indicates that the development of the region
has maintained a spatial pattern over the last century
dominated by commercial support activities in theform of warehousing, logistic centers, plus tradecenters and office buildings as markers of citymodernization. In order to identify the latest spatial
566

GEOMATE – Melbourne, Australia, 11-13 November 2020

patterns and future plans in Kampung Pondok district,the authors elaborated the city spatial planning 20102030 period which stated that the west Padang district
will focus on a strategic area for the development oftrade and services. Furthermore, more detailed
identification is done based on Ministry Spatial
Planning Act No 16/2018 enacted guidelines for
preparation of detailed spatial planning and theregency/city zoning regulations. As can be seen at thistime along the study corridor dominated by buildingsthat functioned as trade centers and mixed functions,in addition to part of the space functioned for
reduced green space. Hence, analyses focusing on
average travel speed, density, and level of service
analysis according to Indonesian Highway Capacity

education, religion and military. But the growth ofland use in the current condition is no longer
stimulated by the existence of ports; the area develops
because of the function directed as the CBD. Figure 6
shows changes in land use around Kampung Pondok
district over the past few years. The existence of smallroads seems to contribute to the degradation of theregion with increasing population and has an impact
on increasing demand for housing and roads. The next
figure shows more clearly the changes in the
Kampung Pondok district over the past decade. The
high demand for various urban activities, resulting in

Fig. 7a (left) and Fig. 7b (right) show average speed
during 12-2 and 4-6

Fig. 8a (left) and Fig. 8b (right) show density level
during 12-2 and 4-6

Manual (1997) [17]. There were sixteen road sections
observed in April 2019 during 12–2 and 4–6. Average
travel speed in the two observation periods is shownin Fig. 7a and Fig. 7b, respectively. The next Fig. 8a
and Fig. 8b express level of density (pcu/km) in thesame period. Almost the same with speed motion,level of density getting worse during 4-6 period.

Analysis result related to the level of service as theratio of volume to capacity can be seen in Fig. 9. As
the CBD growing more excessive to northern part ofentire city the level of service of road network was
more suffering compare to southern part.
Relationshipperformance

betweenactivity

and

road

This section elaborated correlation between
activity entire surrounding area and road performancein order to identify whether the land use and road
network influence each other. In this case theidentification of connectedness is done by the
relationship between level of service of road network
variables and land use variables.

567

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 shows the relationship between degree ofsaturation (DS) and land use function and Table 2
shows the relationship between speed and land use
function.
Table 1 Relationship between DS and land use function
No

Variable

1

DS vs no. of local scale oftrade and services building
DS vs no. of district scale oftrade and services building
DS vs no. of regional scale oftrade and services building
DS vs no. of public facilities
building
DS vs no. of residentialbuilding
DS vs no. of warehousebuilding
DS vs wide roadDS vs wide road occupied forparking
DS vs no. of lane
DS vs no. of traffic direction

2
3
4
5
6
7
8
9
10

Fig. 9 Level of service of road network
Table 2 Relationship between speed and land use function
No

Variable1

Speed vs no. of local scale of tradeand services building
Speed vs no. of district scale oftrade and services building
Speed vs no. of regional scale oftrade and services building
Speed vs no. of public facilities
bldg.

2
3
4
5
6
7
8
9
10

Speed vs no. of residential building
Speed vs no. of warehousebuilding
Speed vs wide road
Speed vs wide road occupied forparking
Speed vs no. of lane
Speed vs no. of traffic direction

Coeff. ofcorrelation-0.253

0.334

Sig.
level
N/S

-0.337

0.202

N/S

0.177

0.512

N/S

0.349

0.185

N/S

0.252

0.346

N/S

-0.0850.754

N/S-0.172-0.535

0.525
0.033

N/S
S

0.026
0.606

0.925
0.013

N/S
S

Sig.

-0.24

0.370

N/S

0.221

0.411

N/S

0.672

0.004

S

0.007
-0.083

0.980
0.759

N/S
N/S

except two road sections in northern part as can beseen in Fig. 8b. According to field observation a
combination of the low travel speed and a normal
counted of density pattern was due to the part of wide
road occupied for parking since there is no off street
parking available within surrounding of the district.
This fact is in-line with the findings expressed inTable 1 and Table 2, where the degree of saturation
and wide road occupied for parking variables, and the
speed and number of warehouse building variables
are having strong correlation each other.

0.132
0.423

0.626
0.103

N/S
N/S

REFERENCES

Coff. ofcorrelation-0.085

0.754

Sig.
level
N/S

Sig.

0.189

0.483

N/S

-0.143

0.598

N/S[1] Wang, Z., Han, Q., and de Vries, B., Land Use/Land
Cover and Accessibility: Implications of theCorrelations for Land Use and Transport Planning,
J. of Appl. Spatial Analysis, Vol. 12, 2019, pp.
923–940.
[2] Witten, K., Pearce, J., and Day, P., Neighbourhood
destination accessibility index: A GIS tool formeasuring
infrastructure
supportforneighbourhood physical activity,
J. ofEnvironment and Planning A, Vol. 43, No. 1, 2011,
pp. 205–223.
[3] Tian, L., Ge, B., and Li, Y., Impacts of state-led andbottom-up urbanization on land use change in theperi-urban areas of Shanghai: Planned growth oruncontrolled sprawl? J. of Cities, Vol. 60, 2017, pp.
476–486.
[4] Ratner, K. A., and Goetz, A. R., The reshaping ofland use and urban form in Denver through transitoriented development, J. of Cities, Vol. 30, 2013,
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[5] Patarasuk, R., Road network connectivity and landcover dynamics in Lop Buri province, Thailand. J.
of Transport Geography, Vol. 28, 2013, pp. 111–
123.
[6] Mothorpe, C., Hanson, A., and Schnier, K., The
impact of interstate highways on land use

CONCLUSION
In this research the authors explore and find that
Kampung Pondok district plays an important role in
the historical development of Padang city during adecade as a center of trade and services. In the current
condition the region is supported by the number of
local, district and regional scale trade buildings andbuilding services which are denser compare to otherareas within the city. Growing populations and land
use activities that continue to grow increase thenumber and intensity of travel in the region. As theicon of city, Kampung Pondok district experiencing
pressure in the form of high vehicle movement while
the ability of the road network to flow has notchanged significantly for the last few decades. Most
roads show the level of service of D and E indicating
the flow rate is almost reaching capacity level or
unstable flow. Average travel speed was less than 18
km/hour far below the standard average travel speed
in urban areas of 40 km/hour. Interesting to note thedensity pattern that was shown in Fig. 8a and Fig. 8b;
both figures expressed the density level varies from
20 to 60 pcu/km, which are classified as normal cases
568

GEOMATE – Melbourne, Australia, 11-13 November 2020

conversion, The Annals of Regional Science, Vol.
51, No. 3, 2013, pp. 833–870.
[7] Duranton, G., and Turner, M. A., Urban growth andtransportation J. of Review of Economic Studies,Vol. 79, No. 4, 2012, pp. 1407–1440.
[8] INRIX Global Traffic Scorecard, 2017. Retrieved
from https://english.kontan.co.id/news/jakartastraffic-worsens-in-2017-survey[9] Waddell, P., Integrated land use and transportation
planning and modelling: Addressing challenges in
research and practice, J. of Transport Reviews, Vol.
31, No. 2, 2011, pp. 209–229.
[10] Batty, M., and Marshall, S., Thinking organic,
acting civic: The paradox of planning for cities in
evolution, J. of Landscape and Urban Planning,
Vol. 166, 2017, pp. 4–14.
[11] Chaudhuri, G., and Clarke, K. C., On thespatiotemporal dynamics of the coupling between
land use and road networks: Does political history
matter? J. of Environment and Planning. B,
Planning & Design, Vol. 42, No. 1, 2015, pp. 133–
156.
[12] Acheampong, R. A., and Silva, E. A., Land use–transport interaction modeling: A review of theliterature and future research directions. J. of
Transport and Land Use, Vol. 8, No. 3, 2015, pp.
11–38.
[13] Simmonds, D., Waddell, P., and Wegener, M.,
Equilibrium versus dynamics in urban modelling.
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[14] Horner, M. W., and Schleith, D., Analyzing
temporal changes in land-use–transportation
relationships: A LEHD-based approach. J. of
Applied Geography, Vol. 35, No. 1–2, 2012, pp.
491–498.
[15] Pei, T., Sobolevsky, S., Ratti, C., Shaw, S. L., Li,
T., and Zhou, C., A new insight into land use
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[17] Ministry of Public Works, Indonesian Highway
Capacity Manual, Jakarta, 1997, pp. 1-564.

569

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

THE UTILIZATION OF BIOGAS SLUDGE TO LEAD ZERO WASTE
SYSTEM IN BIOGAS IMPLEMENTATION: THE EFFECT OF
VOLUME ON CARBON DIOXIDE AND METHANE CONTENT

1,3,4

Ambar Pertiwiningrum1, Margaretha Arnita Wuri2, Widyawati Luhur Pambudi3 and Lies Mira4
Faculty of Animal Science, Gadjah Mada University, Indonesia; 1,2Centre for Development of Sustainable
Region (CDSR), Indonesia

ABSTRACT
Biogas is a solution to slow the effects of global warming by converting and trapping methane of manure toenergy. To make biogas cleaner and more environmentally friendly, residue from biogas also should be treated.
The strategy is recycling biogas sludge to be an adsorbent for biogas purification because it will reduce waste
generation. It means the utilization of sludge leads to a zero waste biogas system. The study was to investigate
the use of biochar from biogas sludge and find the optimal condition for biogas purification. Five treatment ofbiochar based biogas sludge were be conducted, every column of adsorption filled with 100% of the volume
zeolite, 50% of the volume zeolite and biochar, 25% of the volume zeolite and 75% of the volume of biochar,and the last 100% of the volume biochar. The results showed that 5 treatment of biochar reduced carbon dioxide(impurity gas in biogas) and the largest reduction of carbon dioxide presented by 100% of the volume biochar.
The decrease in carbon dioxide by 100% volume of zeolite is almost the same as the decrease in carbon dioxideby 100% volume of biochar. But the system also decreased the methane content in proportion to the decrease incarbon dioxide. The reduction in carbon dioxide also affected the temperature of biogas combustion and calorificvalue. We agree that using 100% of the volume biochar from sludge can replace zeolite in biogas purification
but further modification is also needed to increase the value of methane.
Keywords: Biogas, Sludge, Biochar, Zero Waste, Methane

management in biogas implementation. The use of
biogas sludge as biochar also drives a circular
economy because biochar is not only can use inbiogas purification but also in agriculture, energy,
and waste management sectors. In a circular
economy system, biogas sludge can be utilized andrecycled as a valuable product in the biogas system[8].
Biochar is carbon-based material that is fromagricultural residue, animal waste, or forestry waste,that have carbon content [9]. Biochar has beenutilized for the removal of heavy metal fromwastewater, soil enrichment, and gas separation.
Many studies are reported that biochar can use forcarbon dioxide adsorption in biogas purification[10]-[12]. It’s also claimed to be comparable to othercommercial porous materials like zeolite andactivated carbon. Pore structures in biochar, like inzeolite or activated carbon, play an important role incarbon dioxide adsorption [13]. However, most ofthese commercial adsorbents are too expensive
because some rural areas don’t have access to the
distribution of commercial adsorbents. According toMinh-Viet et al. [11], biochar has advantages ofrelatively low cost, easy regeneration, and stable
cyclic performance. It can be concluded that biochar
is a green adsorbent. There is a need to develop
local-based potential resources of biochar to

INTRODUCTION
Biogas is one of many solutions to slow theeffects of global warming by converting andtrapping methane of manure to energy. The
implementation of biogas technology reduces
dependency on fossil fuel energies for cooking orelectricity in rural or urban areas [1]. By utilization
waste or manure from livestock, biogas becomescheap energy sources. In a rural area, adopting
biogas technology improves energy access, reduces
cost for purchasing firewood or fossil fuel, reduces
the workload of women for looking for firewood,and diminishes manure smells [2]-[3]. It’s more
beneficial if the dissemination of biogas applies the
bio-cycle system [4]. For example, biogas also
generates waste, bio-slurry, that it’s usually used asorganic fertilizer [5]-[6].
Bio-cycle is the cycle through which energy andessential substances transferred among species andbetween the biotic and abiotic parts of theenvironment. Recycling of biogas sludge as organic
fertilizer gives benefit not only to reduce biogas
waste but also to enhance soil productivity [7]. But
utilization biogas sludge as biochar for biogas
purification in biogas system is rare. Whereas withthe use of sludge generated from biogas production
for biogas purification leads to a zero waste
570

GEOMATE – Melbourne, Australia, 11-13 November 2020

minimize the cost distribution of adsorbent.
Moreover, local potential sources that are came frombiogas production lead to the bio-cycle system inbiogas and a zero waste biogas management [12].
Our previous study has been investigated thecapability of carbon dioxide adsorption from biogas
sludge-based biochar for biogas adsorption [8]. In
Ambar et al. [8] biogas sludge-based biochar was
combined with zeolite in each mass of 40 grams
every column. But we evaluated that utilization each
40 grams mass of biogas sludge-based biochar andzeolite is not to be comparable because both of them
have different specific mass. So in this study weevaluated the utilization mass of adsorbents withvolume ratio.

identified the composition of methane and carbon
dioxide.
Table 1 Formulations of the adsorbents in a column
adsorption
Biogas% volume of% volume of

Sampleszeolitebiogas sludge-based biochar

P0

0

0

P1

0

100

P2

75

25

P3

50

50

P4

25

75

P5

100

0

METHODOLOGYRESULTS AND DISCUSSION

The carbon dioxide adsorption column was madefrom stainless steel material and completed with two
flowmeters with measurement specification in arange of 25 LPM (liter per minute) at outlet and inlet
pipes, and the compressor. The inlet and outlet pipesof column adsorption were connected with biogas
holder that holding of raw biogas (biogas frombiodigester) and purified biogas (biogas fromcolumn adsorption) respectively. The biochar was
produced from biogas sludge that collected frombiogas production of dairy cattle manure in theCenter of Agro Technology Innovation, Universitas
Gadjah Mada.

Before and after biogas purification, biogas
samples are analyzed its composition, especiallymethane and carbon dioxide because it’s the largest
composition in biogas. Biogas composition was
analyzed and calculated by gas chromatography. The
relation of the capability of carbon dioxide
adsorption and surface area was investigated in thisstudy.
Surface Area Analysis
The feature of carbon-based material is having
many pores on its surface like biochar and zeolite.
The porous structure of biochar and zeolite wasdetermined from a nitrogen adsorption-desorption
isotherm method at low temperature (77 K). Porevolume, pore size, and specific surface area were
evaluated and showed in Table 3.

Biogas Sludge-Based Biochar Synthesis
Biogas sludge-based biochar was produced by
the pyrolysis process. Before the pyrolysis process,biogas sludge was dried under sunlight for four days
to reduce water content. The pyrolysis process wasgoing for four hours at the temperature of 255oC.
The heat from pyrolysis induced the oxidation ofcarbon and then formed stable carbon of biochar.
Biochar was characterized by a surface area analyzer
to identified the surface area, volume, and diameterof pores. Surface area analysis used a nitrogen
adsorption method at 77 K.

Table 3 Specific surface area, volume and pore sizeof natural zeolite and biogas sludge-based
biocharPorousSpecific

Mean

Total

Micro-

Meso-

Material

surface

pore

pore

pore

por

area

size

vomue

volumevolumeZeolite(m2/g)
47.66

(nm)
47.41

0.11

0.017

0.093

Biochar

34.06

39.15

0.067

0.013

0.054

Biogas Purification: Carbon Dioxide Adsorption
Carbon dioxide adsorption is conducted at room
temperature. Each of column adsorption contained avolume of zeolite and biochar. There were six
formulations of adsorbents that presented in Table 1.
The first step of biogas purification is biogas filledin adsorption column by the compressor with apressure range of 5-7 bar. Biogas that after throughadsorption column was held in purified biogas
holder to analyze the composition. Samples ofbiogas before and after carbon dioxide adsorption
were analyzed by Gas Chromatography (GC) to be(cc/g)

These results showed that zeolite has a higher
specific surface area, pore volume, dan size
compared to biochar. Both zeolite and biogas sludge
biochar-based biochar has a dominant pore size inthe mesopore zone (pore size range of 2 - 50 nm)
according to the International Union of Pure andApplied Chemistry (IUPAC). These results werealso strengthened by the bigger mesopore volume of571

GEOMATE – Melbourne, Australia, 11-13 November 2020

zeolite and biochar compared to micropore volume.
The relation between pore characteristics and thecapability to adsorb carbon dioxide was investigatedin this study.

resulted not only in the greater the capability incarbon dioxide adsorption but also in the methane
reduction in biogas. It can be explained that
dominant pores in biochar and zeolite, mesopore(see Table 3), played an important role in methane
adsorption. It’s happened due to methane molecules
are easier adsorbed in mesopore areas than
micropore areas [17].

Carbon Dioxide Composition
Carbon dioxide is the main impurity gas inbiogas, which range of 20-40% [14]. These resultsof carbon dioxide analysis raw and purified biogas
are shown in Table 2. The capability of zeolite andbiochar to adsorb carbon dioxide has been proven.
The results showed that all formulation ofadsorbents capable to reduce carbon dioxide
composition in biogas. The use of 100% volume ofbiochar reduced 26.54% carbon dioxide. The carbon
dioxide reduction performed by 100% volume ofzeolite was 26.65%. These results indicated thatbiogas sludge-based biochar has the same capability
in carbon dioxide capturing as zeolite. The featuresin pore structure and mineral contents such as K, Na,
Ca, Mg in biochar facilitate carbon dioxide
adsorption. The existence of mineral content is able
to increase alkalinity condition on biochar’s surface
and has implication to increase adsorbent affinity tocarbon dioxide that plays as acidic molecules [15].
Although this results show the ability of zeolite for
capturing carbon dioxide is better than biochar, thevalue is not significant. It’s caused by zeolite is not
activated to expand pore structures.

Table 4 The methane reduction (%) after biogas
purification
Sampling

P2

P3

P4

P5

1st

P1
36.61

0.07

16.99

1.04

26.39

2nd

29.48

30.79

20.10

3.25

24.40

3rd

11.00

20.86

22.66

10.40

29.16

Average

25.70

17.24

19.92

4.89

26.65

P3

P4

P5

6.53

16.90

14.22

25.65

2nd

20.10

15.44

17.86

17.58

23.67

3rd

30.08

2.67

19.14

12.28

29.45

Average

25.70

8.43

17.97

14.69

26.26

Based on surface area analysis, either zeolite orbiochar was included in mesopore-based material[18]. Mesopore size is capable to adsorb large size
molecule effectively like methane molecules [17].
Mesopore volume of zeolite or biochar can adsorb
not only carbon dioxide but also carbon dioxide,
which has a molecule size of 0.32 and 0.4 nm
respectively. Although there is methane reduction,
the value are still smaller than carbon dioxide
reduction because carbon dioxide molecules are
more reactive to interact with pore structures inbiochar.

Table 2 The carbon dioxide reduction (%) after
biogas purification
Sampling

P1
25.67

P2

1st

CONCLUSIONS
From these results we concluded that biogas
sludge-based biochar can be an alternative adsorbent
of natural zeolite in biogas purification. Further
research is required to improve the carbon dioxide
adsorption and methane enrichment capabilities.
Activation of adsorbent is recommended to enhancemicropore size in adsorbents so that increasing
carbon dioxide adsorption in biogas.

Methane Composition
Methane is the main component in biogas that
produces energy for cooking or electricity. By
carbon dioxide adsorption, methane in biogas should
be increase. But the study showed different results.
After biogas purification, methane composition inbiogas went down. The reduction of methane waspresented in Table 4.
The decrease in methane composition looked
directly proportional to the decrease in carbon
dioxide composition. The highest methane reduction
performed by 100% volume of zeolite, the same aswith carbon dioxide reduction. These results can be
explained. The physical characteristic of adsorbents
that are represented by specific surface area
contributes to the capability to carbon dioxide
adsorption [16]. The greater specific surface area

ACKNOWLEDGMENTS
We gratefully acknowledge the support from
BRIN - Ristek Grant through Research Directorateof Universitas Gadjah Mada with a contract numberof 2921/UN1.DITLIT/DIT-LIT/PT/2020. We also
thank for the Center of Agro Technology Innovation
for helpful for this research.
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in Central Uganda. Energy for Sustainable
Development, Vol. 37, 2017, pp.124-132.
[2] Debadayita R., Pinakeswar M., and Michele L.
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GEOMATE – Melbourne, Australia, 11-13 November 2020[10] Shou-Heng L. and Yi-Yang H., Valorization ofCoffeeGrounds
toBiochar-Deriveded
Adsorbents for CO2 Adsorption. Vol. 175, 2018,
pp.354-360.
[11] Minh-Viet N. and Byeong-Kyu L., A Novel
Removal of CO2 using Nitrogen Doped Biochar
Beads as A Green Adsorbent. Process Safety andEnvironmental Protection, Vol. 104, 2016,
pp.490-498.
[12] Ambar P., Margaretha A. W., Andang W. H.,
Rachmawan B., and Misri G., Heating Value
Enhancement by Biogas Purification Using
Natural Zeolite and Rice Straw-Based Biochar.
International Journal of GEOMATE, Vol. 16,
Issue 55, 2019, pp.80-85.
[13] Zhang X., Zhang S., Yang H., Shi T., Chen Y.,
and Chen H., Influence of NH3/CO2 Modification
on Characteristic of Biochar and The CO2
Capture. Bioenergy Resources, Vol. 6, 2013,
pp.1147-1153.
[14] Peter J. J., Digisource, 2009, pp.1-36.
[15] Danielle B., Mourad K., Patrick N., Murielle M.,
and Robert H., Advances in Principal Factors
Influencing Carbon Dioxide Adsorption on
Zeolites. Science and Technology of Advanced
Materials, Vol. 9, 2008, pp.1-7.
[16] Xiaoyun X., Yue K., Ling Z., and Xinde C.,
Chemical Transformation of CO2 during Its
Capture by Waste Biomass Derived Biochars.
Environmental Pollution, Vol. 213, 2016,
pp.533-540.
[17] Deneb P., Imen G., Cecile H., Camelia M. G.,
Mejdi J., and David B., CO2 and CH4 Adsorption
Behavior of Biomass Based Activated Carbons.
Energies, Vol. 11, 2018, pp.1-10.
[18] Nani H., Purwanto and Istadi, Study of
Activated Carbon and Zeolite Integrated
Application on Biomethane Production Based on
Biogas. Journal of Industrial Research, Vol. 8,
2014, pp:65-72

C., The Implementation od Decentralised Biogas
Plants in Assam, NE India: The Impact andEffectiveness of The National Biogas andManure Management Programme. Energy Policy,
Vol. 68, 2014, pp.80-91.
[3] Liu Y., Kuang Y., Huang N., Wu Z., and Xu L.,
Popularizing Household-Scale Biogas Digester
for Rural Sustainanable Energy Development
and Greennhouse Gas Mitigation. Renewable
Energy, Vol. 33, pp.2027-2035.
[4] Cahyono A., Bambang H. S., Bambang S.,
Ambar P., Wiratni, Iwan S., and Dibyo P., Biocycles Farming System for Production of Biogas
through Gama Digester, Gama Purification andGama Compressing. Journal of the Japan ofEnergy, Vol. 90, 2011, pp.1085-1089.
[5] Ambar P., Margaretha A. W., Dina S.,
Rachmawan B., Cahyono A. D. K., and Misri G.,
Improving Nutrients in Cattle Manure by
Converting It Into Biogas Sludge and Compost,in Proc. 2019 Int. Conf. on Resources andEnvironment Series, 2019, pp. 1-6.
[6] Ambar P., Ngesti H., and Siti S., The Use of
Sludge from Cow Manure Biodigester as
Fertilizer and Carrier of Cordyceps sp. For
White Grub Pest Control. Journal of Agricultural
Science and Technology A, Vol. 6, Issue 3, 2016,
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[7] J. Lehmann, and S. Joseph. Biochar for
Environmental Management: An Introduction,
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Earthscan, 2009, pp.1-9.
[8] Ambar P., Andang W. H., Margaretha A. W.,
Adam G., Rachmawan B., and Misri G., Organic
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573

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ENVIRONMENTAL IMPACT ANALYSIS IN HOTEL SECTOR:
A CASE STUDY IN THAILAND
Jarotwan Koiwanit1 and Viachaslau Filimonau2
1

Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520,
Thailand; 2Faculty of Management, Bournemouth University, Talbot Campus, Fern Barrow, BH12
5BB, Poole

ABSTRACT:
Climate change is a major environmental impact. With its 5% share, the tourism industry is an important
contributor to global greenhouse gas (GHG) emissions. To reduce this contribution, the tourism industry has
started looking into the measures of carbon footprint mitigation. In order to develop such measures, theenvironmental performance of tourism enterprises should first be accurately assessed. Accurate assessments ofthe environmental significance of tourism enterprises, such as tourist accommodation facilities, are howeverrare which is due to data availability and immaturity of appraisal approaches. This paper contributes toknowledge with a case study of one of the largest hotels in Thailand, King Paradise (Koh Phayam, Ranong)
whose environmental impacts have been comprehensively appraised by applying the method of life cycle
impact assessment (LCIA). The results showed that food waste composting, single-use shower toiletry bottles
switching and applying electric vehicles to Koh Phayam community can substantially decrease theenvironmental impacts. The adoption of electric vehicles showed the greatest carbon reduction potential
although food waste composting was cheapest for implementation. By integrating these environmental
innovations into its operations, King Paradise can minimise its emissions and waste and become a moreenvironmentally sustainable company.
Keywords: Tourism, Hotel, Greenhouse gas emissions, Energy efficiency, Waste reduction, Life Cycle
Assessment (LCA)
KING PARADISE’S BACKGROUND AND
ITS ENVIRONMENTAL PERFORMANCEINTRODUCTION
Despite a number of socio-economic benefits,
tourism generates significant environmental
externalities, such as carbon footprint [1]. However,the carbon footprint of tourism has not been
sufficiently researched, especially in the context ofdeveloping and transitional economies, such as
Thailand, where international and domestic tourism
is on the rise [2]. More studies are necessary toquantify the carbon impacts of tourism and its
particular sectors, such as hotels, in this country to
promote a sustainable low-carbon tourism industryin Thailand.
This study will assess the carbon footprint of
one of the largest hotels in Thailand, King Paradise(Koh Phayam, Ranong), by applying the method ofLife Cycle Assessment (LCA). The potential of
LCA to provide accurate, yet holistic, assessmentsof environmental impacts of hotels has long beenrecognised [3], but it has never been applied in thecontext of Thailand. This study will therefore
partially plug an important knowledge gap insustainable tourism research.

King Paradise is located in Koh Phayam,
Ranong, Thailand. Koh Phayam is surrounded bynatural environment with sandy beaches and clear
blue sea and is the undiscovered pearl of theAndaman Sea. Given that the Koh Phayam island
is located circa 35 km from Ranong (Fig. 1) [4],most visitors arrive by boat or speed boat. They
subsequently travel to the King Paradise hotel by
motorcycle or faint, depending on the size of thegroup. The hotel is located 5 km away from theboat terminal.

Fig. 1574

Koh Phayam map [5]GEOMATE – Melbourne, Australia, 11-13 November 2020

As can be seen from Table 1, the first threesuggestions are the most reliable, cheap, and easy
for an implementation and thus these are explainedin more details in Results and discussion section.

At King Paradise, there are 40 rooms which
include 2 types of rooms: 1) bungalows(maximum with 2 guests) and 2) houses (4guests). Most of the guests are from European
countries (70-80% of total) while the rest arenormally from Asian countries. In high season(November – May), the hotel has approximately
100 customers per day while there are around 40
customers per day during low season (June –October). As a result, there are approximately
27,000 guests per year.
In terms of King Paradise's environmental
performance, King Paradise has mostly focusedon waste reduction and they have been taking
different actions to manage and reduce solid waste,a large fraction of which is represented by foodwaste. Examples of actions/campaigns for waste
reduction are shown as follows:

In this study, the survey collected data for the2020 year of King Paradise’ operations in January –March 2020. In addition, the hotel owners were
further interviewed to rectify any data gaps andclarify any data inconsistencies identified in thesurvey as well as represent the ‘business-as-usual’operational performance of the studied hotel in thereference year of 2019. As a result, the interview
refined the survey results and confirmed that thecollected data were representative of the hotel’business-as-usual operations.

Food Waste Reduction

RESULTS AND DISCUSSION

The hotel is trying to reduce food waste by notbuying a lot of food at one time even though thefood will be a lot of cheaper when bought inRanong instead of buying in Koh Phayam. In thisway, the hotel will not produce a lot of food waste.

The environmental impacts of each suggestion
presented in this section will be evaluated using thelife cycle assessment (LCA) methodology. LCA isa methodology that identifies the whole life cycleof a product, often called the cradle-to-grave
approach, in which complex systems are broken
down into elementary flows. The GaBi softwaresystem together with CML 2001, a life cycle impact
assessment (LCIA) methodology, was applied inthis study. In this study, suggestions are used tointerpret the outcome as a comparative study whichhelps to identify important unit processes that affectthe most of the results to further improve theirprocesses.

RESEARCH METHOD

Solid Waste Management
The hotel together with their customers works
hard to clear the plastic debris and create a wastefree zone on its premises. In addition, the hotelruns a zero waste campaign to alter attitudes,
values and behaviors of the King Paradisecommunity in regards to proper waste disposal
habits. These can help the hotel to conserve andprotect the environment.

No Food Waste at the Consumption Stage Composting

Recycling Program
Food waste is continually to be the main issuesof concern globally. Food waste cause serious
environmental consequence as public outrage overfood waste grow. There are 1.3 billion tonnes of athird of the world’s food is lost or waste each year
while hungers account for almost a billion [7]. The
increasing attention has been paid to food waste
issues mainly due to its negative environmental,social, and economic impacts [8,9]. In Thailand,
many hotels have begun to launch many campaigns
to reduce food waste and, in meantime, enlarge its
benefits. King Paradise which is considered as theleading hotel in Koh Phayam, has also put moreconcerns on reductions in GHG emissions.
However, most of food waste end up in landfill
and emits GHG emissions once it is broken down orin the best case, it can be utilized to produce animal
feed or recover energy (e.g. anaerobic digestion)[13]. Therefore, in this study, composting is one
way of suggestions required in the transformation

There are a lot of recyclable wastes, forexamples water bottles, cans, beer bottles, etc. andto reduce wastes, the hotel collects all of them andsells to recycling company. However, in order tobecome more environmentally friendly, there aresome other areas the hotel can also focus on suchas energy efficiency, green transportation, etc.
SUGGESTIONS FOR IMPROVEMENTKing Paradise has put an effort in improving intheir environmental performance; however, during
an inspection visit to the hotel, there are also somerooms for improvement and the suggestions wereanalyzed based on criteria on the follow and thedescription of each criterion can be found in [6].
The five suggestions for improvement in this study
are shown in Table 1.

575

GEOMATE – Melbourne, Australia, 11-13 November 2020of the express to help in reducing overall foodwastage problems. The functional unit used in thisstudy is 1 year of food waste collected from King
Paradise’s restaurant. The observed results showedthat there was 2.07×103 kg CO2 eq. of GWP fromfood waste for disposal. As the GHG emissions oflandfilling food waste are, on average, 2 times
larger than the GHG emissions of composting [14]the GHG emissions of composting were 1.035×103
kg CO2 eq. The environmental concerns weresignificantly decreased and this helps King Paradise
to be more environmentally responsible and reducethe company’s impact on the environment.

working on reducing mini bottles of shampoo andother toiletries to reduce plastic waste [15-17].
Instead of using tiny, single-use shower toiletrybottles, this study offers King Paradise’s guests a
recyclable pump-designed dispenser which is oneof the favorable [18,19].
These include toiletry bottles of shampoo,
conditioner and bath gel. By this design and whichuses an average of 13,500 bathroom miniatures
every year in which the average person uses a halfof bottle per night. This study assumed that one
bottle generates 19 g of plastic waste [20] and thisproduces approximately 256.5 kg of plastic waste
per year or 777 kg CO2 eq. of GWP per year.

Table 1 Analysis of Suggestion for Improvement
Sugges- Retion
liabilpointsity
No foodwaste attheHighconsumption
stage
MotionSensorLight
High
Installati
onSingleuse
showertoiletry Highbottles
switchi
ng
Solar
Cells
Installat
ionElectric
Motorc
ycles

High

High

Applicability

High

Administrative
simplicity

High

High

MediumEasy

High

MediumHigh

MediumMedium

Easy

Cost

Time
to
adopt

PublicParties inpercepvolved
tion

Low

High

Now

-King
ParadiseMedium

Medi
um

1
week

High

Low

High

1
week

MediumCapacity to
reduceemissions

Knowledge

High

MediumEase ofimplementation

High Medium

High Medium

1
year

2-3
years

-King
Paradise-Construction
company-King
Paradise-King
Paradise-Construction
company
Motorcycle
rentalcompanies-Koh
Phayam

Me
diu
m

High

High

MediumHigh

MediumHigh

MediumHigh

MediumSwitching single-use shower toiletry bottles topump dispensers does not require time and too
much cost. It could take only up to a month before
the pump dispensers are implemented. Therefore,
this is considered as a long term suggestion for King
Paradise to improve their environmental
performance. As this transition is a big step in theright direction, the public perception of thistransition will be medium because there are someSingle-Use Shower Toiletry Bottles Switching
Since packaging usually ends up as waste, thepackaging of a product is also a very importantfactor to consider when it comes to environmental
impact.
Many
hotel
companies(e.g.
InterContinental
HotelsGroup,
MarriottInternational, Holiday Inn, Kimpton, Six Senses,
Crowne Plaza, Staybridge Suites) have been576

GEOMATE – Melbourne, Australia, 11-13 November 2020

who like to take them home and giving a complaint
at the first step of changing. This could be solved byeducation to the public. For a 0.4L refillable liquid
dispenser, it produces 0.3 kg per dispenser [21].
There are 40 rooms at King Paradise and it isestimated that King Paradise will produce 24 kg ofplastic waste in total. This study assumes that thedispenser will last as long as the hotel does [22]. As
a result, this study assumed that it will last 50 years.
This shows that King Paradise will produce 0.48 kgof plastic waste in total per year or 1.45 kg CO2 eq.
of GWP per year. This can reduce GWP by 775.55
kg CO2 eq. per year compared to using bathroom
miniatures.
Another suggestion for King Paradise to reduce
the environmental impact of the single used
containers is that the hotel could have any unused
items to homeless shelters so all the containers canbe used and this helps the hotel to be able to reduce
their waste and time to dispose them [23]. In
addition, the hotel may provide the smallest
practical amount in recyclable containers anddonate amenities left behind.
However, the more up-to-date innovative
concept of SOAPBOTTLE designed by Jonna
Breitenhuber is the packaging-free products inwhich the product itself is turning to its own
packaging [24]. The packaging is made from a
traditional piece of soap that has been hollowed out.
On the top of the packaging, there is a reusable
metal to allow users to open the top and pour out thesoap [25]. This concept can avoid waste as the soap
is made from natural resources and is biodegradable.
In addition, the packaging is slowly dissolved as
hand soap [26].

motorbicycle is from the combustion of gasoline
refined from crude oil [31]. In general, the average
number of passengers per riding is no more than two
in at any given time. Based on the interviews, thestudy assumed that each consumer will be driving
the vehicle one time per day for around 20 km
around the island. In this study, the baseline of thecomparisons is the car-renting use and the numberof passenger per riding is assumed to be 2. By theseassumptions, it is estimated that the driving distancefrom all the consumers will be 270,000 km per year.
According to The Sustainable City IX: Urban
Regeneration and Sustainability, the total GHG
emissions per km of a motorcycle are estimated to78.5 g CO2 eq. [32]. Based on The Sustainable City
IX: Urban Regeneration and Sustainability, thisshows that motorcycles in this study produce
21,195 kg CO2 eq. per year [32].
However, the GHG impacts from an electricvehicle (EV) in use phase were investigated andcompared with its conventional motorcycle in thisstudy. The operating parameters taken from several
literature reviews are shown in Table 2.
Table 2 Main operating parameters of an electricmotorcycle
Parameter

Electric Motorcycles
In Thailand, transportation was ranged as thesecond largest contributor to CO2 emissions [27, 28].
Not only does road transportation contribute to thehigh costs of fuel consumption, but it will also leadto high GHG emissions and other environmental
impacts. To cope with this problem, an electricvehicle is a part of alternative transportation mode
which has recently experienced considerable
growth and the use of electric vehicle has become
an important approach in ensuring lower emissions[29,30]. As electric vehicles services continue torise, this study aims to evaluate the environmental
impacts of electric motorcycles compared toconventional motorcycles use using the Life Cycle
Assessment (LCA) methodology.
During visit and from the interview, as most of
tourists coming to the hotel as a small group,
motorbicycle is preferably considered as the mostconvenient commuter transport to drive theircustomers way from King Paradise to Coastal rather
than faint or bicycle. The energy use for the

Value

Unit

Ref./
comment--

Cautious

-

-

City

-

-

Flat

0

m/s2

150.7

kg

[33]

0.015

-

[34]

35

°C

Expert’s
estimate

1.15

kg.m-3[35]- Driving
behavior- Type of road
- Slope of theroad-Acceleration- Total weight(kg)-Rolling
friction of thetires-Temperature
at time of thetravel-Density of air
at 35 °C
-Aerodynamic
dragcoefficient-Frontal areaof the vehicle

1.057 ×
0.699

m×m

[33]

-Velocity

16.7

m/s

Expert’s
estimate

0.65

[36]

In this study, the GWP is 16690 kg CO2 eq. in
gasoline and electric vehicles, respectively. The
results show that the GHG environmental impact
can be decreased up to 78.7% in the electricmotorcycle compared to the conventional
motorcycle. Though these vehicles are operated by577

GEOMATE – Melbourne, Australia, 11-13 November 2020

external providers, the hotel can work closely to thecommunities to cope with an environmental issue.

[3] Kuo N. and Chen P., Quantifying Energy Use,
Carbon Dioxide Emission, and otherEnvironmental Loads from Island Tourism
Based on a Life Cycle Assessment
Approach, Journal of Cleaner Production, Vol.
17, Issue 15, 2019, pp.1324-1330.
[4] Kohphayam, Kohpharam Island, Ranong,Thailand,2020.
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Hypothetical Electric Airport Transportation
Services in Thailand, Conference proceedings,
in IOP Conference Series: Earth andEnvironmental Science, 8th International
Conference on Future Environment and Energy(ICFEE 2018), 2018, pp.1-8.
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Retrieved from http://www.fao.org/food-lossand-food-waste/en/ (Accessed 18 March 2020)

CONCLUSION
Over the years, hotels have been undergoingchanges to become more environmentallyfriendly. King Paradise has set to create a positive
impact in the community being socially
responsible by respecting the environment. They
strive to reduce their environmental externalities
by investing into food waste reduction, solid
waste management, and recycling program,which reduces GHG emissions. This generates
significant cost savings and consequently
provides high returns on investment. In this study,
the energy efficiency and environmental
performance of King Paradise have been
analyzed. It can be shown that King Paradise has
put in a lot of effort to enhance theirenvironmental performance.
However, there are some areas of
improvement and this study has presented fivesuggestions for King Paradise’s further
improvement while the three of them wereselected as case studies. This study paid a visit to
the King Paradise to inspect the applicability of
our suggestions. The results showed that theoption that is the most reliable, cheap, and easy
for an implementation is the food waste
composting. However, transportation sector is
one of the main emitters of large quantities ofpollutions to the atmosphere and an electricvehicle is showing highest reduction in GHG
emissions, it becomes an important option for
Koh Phayam community to be considered. The
sensitivity analysis can be obtained as a future
work to identify important parameters that affectthe most of the results in each suggestion.

[8] Papargyropoulou E., Lozano R., Steinberger J. K.,
Wright N., and Ujang, Z.B, The Food Waste
Hierarchy as a Framework for the Management ofFood Surplus and Food Waste. Journal of Cleaner
Production, Vol.76, 2004, pp. 106-115.
[9] Salomone R., Saija G., Mondello G., Giannetto A.,
Fasulo S., and Savastano D., Environmental Impactof Food Waste Bioconversion by Insects: Applicationof Life Cycle Assessment to Process Using Hermetia
Illucens, Journal of Cleaner Production, Vol. 140,
2017, pp. 890-905.

[10] Corrado, S., Caldeira, C., Eriksson, M.,
Hanssen, O. J., Hauser, H., van Holsteijn, F.,
Liu, G., Östergren, K., Parry, A. and Secondi,
L., Food waste accounting methodologies:
Challenges,
opportunities,
andfurtheradvancements, Glob. Food Sec., Vol. 20, 2019.
[11] Parfitt J., Barthel M., and Macnaughton S.,
Food Waste within Food Supply Chains:
Quantification and Potential for Change to2050. Philosophical Transactions of the Royal
Society B, Vol. 365, 2010, pp. 3065-3081.

ACKNOWLEDGEMENT
We are grateful for the required informationfrom King Paradise for this project. This work
was supported by King Mongkut’s Institute ofTechnology Ladkrabang Academic Melting Pot[KREF206226].

[12] Galanakis C.M., Emerging Technologies for the
Production of Nutraceuticals from Agricultural ByProducts: A Viewpoint of Opportunities andChallenges, Food and Bioproducts Processing, Vol.
91, Issue. 4, 2013, pp.575-579.

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Springer Science & Business Media, London,
2012, pp. 1-103.
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Carbon Footprint of Food Waste Management
Options in the Waste Hierarchy – a Swedish
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578

GEOMATE – Melbourne, Australia, 11-13 November 2020

Bottles May Soon Be a Thing of the Past,
Huffpost,
2019.
Retrievedfromhttps://www.huffpost.com/entry/ihg-hotelshampoo-bottles-plasticwaste_n_5d41d2d2e4b0db8affb27022?guccounter=May 2020)[25] Segran E., The Future of Packaging could be as
Simple as a Bar of Soap: One Designer’s WaterSoluble Vision for Replacing all Those Plastic
Shampoo Bottles, Fast Company, 2019. Retrieved
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Soap,
2020.
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and Efficiency, Energy in Thailand Facts &Figures, Ministry of Energy, Bangkok, 2013.
[28] Energy Policy and Planning Office, Energy
statistics of Thailand, Ministry of Energy, Bangkok,
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[29] Ellingsen L.A., Singh B., and Strømman A.H.,
The Size and Range Effect: Lifecycle Greenhouse
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276-284.
[31] Pugliaresi L. and Pyziur M., Gasoline Blending
an EPRINC Primer, 2015. Retrieved fromhttps://eprinc.org/wpcontent/uploads/2015/06/Updated-Gasoline-

1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlL
mNvbS8&guce_referrer_sig=AQAAANZgjKWwaQ
N1Fb_DTcc4moq2Q8wqvLH2LpgqaW0p67HcHcS
Zy6_UfpzSxIXqhHxUTT7Qqr35Qk5cWjy5qOi7oIQ1cLKLch6FHNpHlwzYrGeObRxIdc82tka
Fnrdx-DYIbqj8HZlSOu4qYX4_tIvDET_fYI_pmTU6UtaWy
BKeO2 (Accessed 12 April 2020)[16] Bethesda M.D., Marriott International toeliminate single-used shower toiletry Bottles fromproperties worldwide, expanding successful 2018
initiative, Marriott international, 2019. Retrieved
fromhttps://news.marriott.com/news/2019/08/28/marrio
tt-international-to-eliminate-single-use-showertoiletry-bottles-from-properties-worldwideexpanding-successful-2018-initiative (Accessed 1
January 2020)[17] Associated Press, Holiday Inn to eliminate minibottles of shampoo and other toiletries, MarketWatch,
2019.
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reduction guidelines for hotels, TUI group sustainable
development sustainability, 2019. Retrieved fromhttps://www.tuigroup.com/damfiles/default/downloa
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and Bastianoni S., The Sustainable City IX:
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[33] Ultimatespecs, Hondawave 100 Specs, 2013.
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December 2019)[34] Nguyen V.-T. and Hwang P., Study of a Novel
Hybrid Powertrain system for Honda Wave
110cc Motorcycle with Rear Wheel Electric
Motor, International Journal of Innovative[20] Wastecare Corporation, PET – Plastic Water/
Soda Bottles – By Bale Weights, Size andRecycling Equipment, Wastecare Corporation,
Georgia,US,
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

TOWARD SUSTAINABLE COMMUNITIES: A COMPREHENSIVEENVIRONMENTAL IMPACT ASSESSMENT OF BUILDING
Nantamol Limphitakphong1,2 and Orathai Chavalparit2,3
1Interdisciplinary Program of Environment, Development and Sustainability Program, Chulalongkorn
University, Thailand; 2 Research Unit of Environmental Management and Sustainable Industry,
Chulalongkorn University, Thailand; 3 Department of Environmental Engineering, Chulalongkorn
University, Thailand
ABSTRACT

Coupling with the changing of climate naturally, an occurrence of excessive artificial environment has caused
many severe impacts widely. To cope with such emerging instability, sustainable city was presented as one of themost important actions among sustainable development goals because of a rapid growth of population andurbanization. Several attempts have been made to alleviate such issue substantially focusing on constructionindustry since it consumes numerous resources and effects various kinds of environment impact. Based on life
cycle assessment approach, this study therefore was aimed to assess the comprehensive environmental impacts ofbuilding by considering “cradle-to-grave” system boundary throughout its service life of 50 years. The results
demonstrated that majority of impact categories associated with all stages of building life involved global warming,
natural resource depletion, eutrophication, smog, water intake, human health - criteria air pollutants, andecotoxicity. Hotspot of such impacts was found during the operation phase of building rather than construction
and demolition phases. Of what findings, appropriate strategies for minimizing the environmental impacts andincreasing an effective use of the natural resources will consequently be suggested for designing sustainable low
carbon building in the future pertinently.
Keywords: Environmental impact; Educational building; Life cycle assessment; Global warming; Sustainability
NOMENCLATURE

effects various kinds of environment impact.
Evidently, buildings consumed 40% of either
global energy consumption or raw materials, and 12%of global potable water consumption, whilst emitted
1/3 of global greenhouse gas emissions and generated
4/10 of waste to landfill [2]. The nature resources
depletion and environment degradation had brought
the awareness to finding the methods to reduce its
consequent effects unceasingly. Kofoworola andGheewala [3] had analyzed the life cycle energy of atypical office building and emphasized importance ofconsidering embodied energy coupling with
operating energy for incorporation into the existingenergy code. Anastaselos [4] had evaluated theenvironmental performance of energy systems usedin residential buildings and emphasized the higher
annual impact savings in buildings using a natural gas
boiler for floor heating and applying poly-Si PV.
Moschetti [5] had explored the most influential
aspects for transition from zero-energy to zeroemission building and concluded that besides
renewable energy, the materials’ embodied energy
and emissions should be focused on for a fullcompensation of life cycle emission of building.
Thaipradit [6] had demonstrated the energy andenvironment performances of educational buildings
and revealed that autoclaved aerated concrete block,cellulose wall insulation and triple glazing were ofimportance in reducing life cycle energy and carbon

A.Eng CPDPEB
HVAC -

Architectural Engineering
Construction Phase
Demolition Phase
Educational Building
Heating,
Ventilation
andAir
Conditioning EngineeringOP- Operation Phase
S.Eng - Structural Engineering
SFPE
Sanitary and Fire Protection Engineering
TEI- Total Environmental Impacts
INTRODUCTION
Globally, the changing of climate both naturally
and artificially has caused several impacts severely.
In 2015, with agreement of all parties in the United
Nations to protect the planet from these economic,
social and environmental challenges, a blueprint of 17
sustainable development goals which intended to beachieved by 2030 has been established. Because of
the rapid urbanization, sustainable city is prioritizedfor making cities inclusive, safe, resilient andsustainable [1]. Since construction industry plays
important role in leading the growth of the country,
either developed or developing countries have put
several attempts to ease and to solve such issue
substantially as it consumes numerous resources and580

GEOMATE – Melbourne, Australia, 11-13 November 2020of building located in tropical country. Yim [7] had
analyzed buildings’ life cycle greenhouse gas
emissions in high-density cities and found that 85%of the life cycle GHG emission of the building casestudy occurred from the operating energy.
Nevertheless, most of previous studies focused
only on one aspect of environmental impacts
individually; for instance, primary energy
consumption, global warming potential. To pursue
sustainable development, all environmental impact
categories should be integrated into consideration.
This study, therefore, was aimed to explore solutions
for sustainable building transition regarding all
relevant environmental impacts using life cycle
assessment as a tool. An educational building of
Chulalongkorn University was selected as a casestudy to demonstrating a good practice as well as
enhancing capacity building with knowledge transfer
for communities and interested parties. The findingsof this study will be useful for building
owner/developer in designing sustainable low carbon
building pertinently.

four steps of life cycle assessment procedure aspresented in ISO14040-44:2006 [9]-[10] wereapplied in this study using SimaPro V.8.2 programwith Building for Environmental and Economic
Sustainability (BEES+) method [11].
Goal and scope
To moving towards sustainability, comprehensive
understanding of key points effecting environmental
challenges is necessary in order to alleviating and/or
preventing such damage that might be occurred
pertinently. In this study, goals of using LCA
approach were to quantify the level of environmental
performances of educational building demonstrating
through a study of Chulalongkorn University and to
identify hotspots causing each category ofenvironmental impacts.
As presented in Fig. 1, a scope of this study
involved three phases of building life cycle including
building construction (cradle), building operation
with a life span of 50 years (gate) and building
demolition (grave). Due to a complexity of materials
used throughout building service life, however,transportation occurred within and between eachphase was omitted.
To simplify an understand about environmental
performance related to this building case study as awhole, either dataset of building inventory or impact
was referred an educational building with a grossfloor area of 20,000 m2 and a reference study periodof 50 years as a functional unit [12].

RESEARCH METHODOLOGY
Building Description
Table 1 describes characteristics of educationalbuilding of Chulalongkorn University. The case study
building is located in the heart of Bangkok at 13.92
latitudes and 100.6 longitudes with a range of drybulb temperature of 19.0 – 37.2 °C and humidity ratioof 11.2 – 28.7 gw/kgda throughout the year [8]. For
indoor air temperature, air-conditioners are primarily
set at 25 °C as recommended by the Ministry ofenergy.

Life cycle inventory analysis
Based on data availability, firstly, a secondary
data recorded both type and quantity of construction
materials obtained from building owner was accessedto build an inventory of building material. Following
a class of construction work, the inventory of CP wasclassified into four groups including S.Eng, A.Eng,
HVAC, and SFPE. In total, 20 different types ofbuilding material represented a majority of massintensity and embodied energy [13] were selected todemonstrate a level of environmental impacts duringbuilding construction phase.
Secondly, an inventory of OP was built upon a
primary data of resources consumption andwaste/wastewater generation on a monthly basis. The
log sheet obtained from building manager provided
five dataset including electricity from grid, electricity
generated from rooftop PV system, tap water,
wastewater, and solid waste as illustrated in fig. 1.
Lastly, an inventory of DP was formulated either
in accordance to an interview with demolition worker
or literature reviews [14]-[15]. Building demolition
waste were classified based on methods of waste
management namely reuse, recycle, and final disposal(landfill).

Table 1 Characteristics of case study educationalbuilding.
Parameters
Building floors
Gross floor area
Structure
Envelope
Window-to-wall ratio
FloorsRoof
Solar PV installation

Specifications
12 floors above ground
20,000 m2
Concrete
Brick and curtain wall
combination (aluminium/
clear flat glass)0.169
Cast-in place concrete
Flat roof, concrete
25 kWLife Cycle Assessment (LCA)
Building life cycle could be roughly classified
into three main phases namely construction, operation,and demolition. To estimate environmental
performances of building throughout its life cycle,

581

GEOMATE – Melbourne, Australia, 11-13 November 2020

CONSTRUCTION PHASEOPERATION PHASE

DEMOLITION PHASE

Electricity (Grid & PV),
Tap Water
Structural Engineering(S.Eng)Reuse
Architectural Engineering(A.Eng)RecycleHeating, Ventilation and Air
Conditioning Engineering (HVAC)
Landfill
Sanitary and Fire Protection
Engineering (SFPE)
Solid Waste,
WastewaterFig 1. System boundary of this case study.
Life cycle impact assessment

operation phase annually (Pt). 50 indicates a numberof building service life, while i, j and k refer fourclasses of building materials used for building
construction, five datasets of input and output duringbuilding operation, and three types of waste
management for building demolition respectively.

The impact assessment was performed through
SimaPro V.8.2 using BEES+ method withstakeholder
panel
weightingtosupportenvironmentally preferable purchasing [16]. A single
score accounting from weighting results of allenvironmental performances throughout building life
cycle were performed to emphasize the overall impact
as a whole. Moreover, a contribution of eleven impact
categories including global warming, acidification,
human health – cancer, human health – non cancer,
human health - criteria air pollutants, eutrophication,
ecotoxicity, smog, nature resource depletion, water
intake, and ozone depletion in each phase of building
life was quantify to reveal a major effect related tobuilding.

RESULTS AND DISCUSSIONClassification of building materials

Environmental Impact
Construction Phase

Interpretation
Hotspots of environmental performances was
discussed in this procedure. Equation (1) is used toestimate the total amount of environmental impacts
for an entire service life of educational building casestudy. Of what findings, appropriate strategies in thecontext of educational building for reducing theenvironmental impact and increasing the natural
resources consumption were proposed.
𝑻𝑬𝑰𝑬𝑩 = ∑𝟒𝒊(𝟏 𝑻𝑬𝑰𝑪𝑷 + 𝟓𝟎 ∑𝟓𝒋(𝟏 𝑨𝑬𝑰𝑶𝑷 +∑𝟑𝒌(𝟏 𝑻𝑬𝑰𝑫𝑷

SFPE

431

HVAC

133

A.Eng

of

Building

During

4,767

S.Eng

9,822
Environmental Impact (Pt)Fig 2. Level of environment impacts during building
construction phase.
Figure 2 illustrates scores of TEICP classified bybuilding construction components with total score of
15,153 Pt. Undoubtedly, building materials used for a
purpose of S.Eng was the dominant contribution inthis activity, followed by a group of materials used
for A.Eng, SFPE and HVAC respectively.
Within S.Eng group, more than 90% of TEICP
contributed to concrete and reinforcing steel. It wasdifficult to decrease the amount of materials used for
S.Eng due to a concern of safety. Shams [17](1)Where TEIEB is total environmental impacts ofeducational building throughout its life cycle (Pt),
while TEICP and TEIDP are total environmental
impacts of educational building during construction
and demolition phase (Pt). AEIOP represents
environmental impacts of educational building during582

GEOMATE – Melbourne, Australia, 11-13 November 2020

Environmental Impact
Operation Phase

Classification of Building Operation
Sources

Solar PV Energyof

Building

Classification of Construction Waste
Management

suggested to use fly-ash or blast furnace slag for newbuilding construction instead of using virgin cement,
mortar, or concrete to reduce environmental impact
from structural materials. Autoclaved aerated
concrete block was recommended by Thaipradit [6]to replacing conventional concrete block even an
embodied carbon of the former was greater than thelatter, but for a long run throughout building service
life it helped reduce a huge amount of operationalenergy, resulting in minimizing TEI as a consequence.
Whilst a majority of TEICP within the A.Eng
group involved window frame – aluminium, gypsum
fibreboard, alkyd paint for primer and top coat, brick,and cement mortar. Unlike the S.Eng group, several
rooms were feasible for minimizing the impacts
generated by A.Eng materials. For instance, to getcloser to neutral impact, loft style had beenencouraged. According to the results of this study,
about 10% of TEICP from A.Eng would be reduced if
no painting applied.

41

Wastewater(825)

Tap water

753

Recycle(6,685)Landfill

647

Fig 4. Level of environment impacts during building
demolition phase.
Figure 4 illustrates scores of TEIDP classified byconstruction waste management with total score of 6,372 Pt. The negative value reflected benefits gained
from using used materials instead of the virgin one fora new cycle. The avoided impacts of construction anddemolition waste management from steel, glass andplastic was highlighted by [19]. Whilst 100%
recycling of ferrous and non-ferrous metals,
cardboard, plastic and glass were suggested for
maximizing both environmental and economic
benefits [20]. In this study, a concept of zero waste tolandfill was introduced in this phase, however, there
were still leftovers, yet to find better solutions for
disposal. Among that some can be used forbackfilling/land reclamation, reducing the TEIDP
occurred from direct landfill.

DuringElectricity(334)

Environmental Impact (Pt)

(2,227)

Solid waste

Reuse

Life Cycle Environmental Impacts of Educational
Building

52,414
Environmental Impact (Pt)

To sum up, the results of this study demonstrated
that the level of TEIEB was 58,937 Pt. The biggest
contribution to TEIEB occurred during building
operation phase (85%) followed by building
construction phase (26%), while the activity ofbuilding demolition phase helped compensate about
11% of the total impact. It emphasized that all phaseof building life cycle was necessary for taking into
account to understand its life cycle impact
comprehensively. The positive impacts occurredfrom demolition waste management could be traded
off for the negative impacts generated fromconstruction materials, whereas the negative impacts
contributed to using non-green energy could be
minimized by fully operating on green energy.
As illustrated in fig. 5, considering a contributionof environmental performances on each phase ofbuilding life cycle, global warming was the dominantconcern in every phases because it currently is abiggest challenge mentioned in every stage
domestically and internationally, followed by water
intake and natural resources depletion for CP and DP,
and by natural resources depletion, eutrophication

Fig 3. Level of environment impacts during building
operation phase.
Figure 3 illustrates scores of TEIOP classified bytype of input-output resources with total score of
50,156 Pt. Unlike CP, either positive or negative
impacts were found in this phase. Noticeably, a maincause affecting TEIOP was consumption of electricity.
The strongest recommendation for minimizing TEIOP,
therefore, was relying rather on green energy either
from on-site or off-site.
In addition, building energy management measure
was recommended since it had the largest potential in
reducing energy consumption and GHG emission[18]. Installation of external wall insulations was alsosuggested as it helped reduce a requirement of energy
consumption compared to the case with no insulation
installation [13].
Environmental Impact
Demolition Phaseof

Building

During583

GEOMATE – Melbourne, Australia, 11-13 November 2020and smog for OP. Water intake was not a major
concern during building operation phase because
besides the consumption of tap water, re-using treatedwastewater was taking into account as presented thepositive impacts in Fig. 3.
Global warming
HH noncancer
Ecotoxicity
Water intake

Acidification
HH criteria air pollutants
Smog
Ozone depletion

CONCLUSIONS
To moving towards sustainability, a case study ofeducational building of Chulalongkorn University
was assessed to quantify the level of environmental
impacts throughout building life cycle using life cycle
assessment approach and was analyzed to pinpoint
pathways for building sustainable building in the
future.
The results revealed that throughout building life
cycle, operation phase had contributed the highest
negative impacts (causing by a huge consumption of
non-green energy), followed by construction phase(mainly generating from a use of structural
engineering materials). Whilst the positive impacts
were occurred during demolition phase from reusing
and recycling materials for a new cycle, which could
be a trade-off for construction materials used inconstruction phase. For a full compensation of life
cycle impacts of building, building management
strategies was strongly suggested to focus on low
embodied impacts materials for construction phase
coupling with using green energy/resources for
operation phase and applying zero-waste to landfill
approach for demolition phase.

HH cancer
Eutrophication
Natural resource depletion

DEMOLITIONOPERATIONCONSTRUCTION

Note: construction and operation phases presented a contributionof total negative impacts, while a contribution of total positive
impacts was presented for demolition phase.

Fig 5. Contribution of environmental performances
throughout building life cycle.
As a whole, the environmental impact categories
of educational building could be classified into 4
main groups depending on a degree of damage as
super severe impacts – TEI ≥ 50% (global warming),
severe impacts – 20% ≤ TEI < 50% (natural
resources depletion) , moderate impacts - 5% ≤ TEI< 20% (eutrophication, smog, water intake), and mild
impacts – TEI < 5% (human health - criteria air
pollutants and ecotoxicity). To reduce the TEIEB,
global warming and natural resources depletionshould be prioritized.
In addition, to reduce the impact of global
warming, coupling with using low embodied carbon
materials [6], [13] or recycled materials [17],innovation on green construction materials wasneeded exceptionally. Water harvesting was also
recommended to minimize the impact of water intake
as well as natural resources depletion as aconsequence [21]. The impact of natural resources
depletion was merely possible to relieve since it was
related to the amount of materials used which could
not be deducted to preventing building collapse.
Using recycled materials instead of the virgin one [6],[17], building on loft design, and applying building
energymanagement
[18]
were
strongly
recommended for consuming least natural resources
as possible. In a case that all measures mentioned
above had been implementing, this case study
building will become a zero-carbon building,referring the definition firstly defined by the UK
government [22] with TEIEB reduction of 89%
approximately.
Avarietyofgreen
innovation/knowledge, however, is still purposefully
needed to demonstrate a case of sustainable building
successfully.

ACKNOWLEDGMENTS
Financial support from Thailand Research Fundthrough the Royal Golden Jubilee Ph.D. Program(Grant No. PHD/0071/2559) is acknowledged.
REFERENCES[1] United Nations, Cities – United Nations
Sustainable Development Actin 2015, UN Web
Services Section, Department of PublicInformation, 2015, http://www.un.org/sustainable
development/cities/.
[2] Intergovernmental Panel on Climate Change(IPCC), Climate Change 2014: Synthesis Report,
Contribution of Working Groups I, II and III to
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[6] Thaipradit P., Limphitakphong N., Kanchanapiya
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J.M.W., Comprehensive Evaluation of Carbon
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EXPERIMENTAL INVESTIGATION OF PHYSICAL PROPERTIES OFPEATS IN WESTERN PROVINCE, SRI LANKA
A.M.Z. Zimar1, M.C.M. Nasvi2, D. Robert3*, S.Jayakody4, J.R.R.N.Jayarathne5 and John V. Smith6
1,3,6
School of Civil and Infrastructure Engineering, RMIT University, Australia
1,4,5Faculty of Engineering, South Eastern University of Sri Lanka
2
Faculty of Engineering, University of Peradeniya, Sri Lanka*Corresponding Author
ABSTRACTPeat soils are extremely soft soils that exhibit high compressibility, low shear strength, and low bearing capacity.
There are about 2500 hectares of peatland in Sri Lanka, and a larger part is in the western coastal region of theisland. The soft quality of peat soils was a major challenge for the recently completed expressway constructions
where many ground improvement techniques were applied. Understanding the physical properties and developing
correlations among easily identifiable physical properties of the peat will be helpful for future construction works
in this region. This study describes the properties of Muthurajawela peats on the western coast of Sri Lanka. Peatsoil samples were collected from the Muthurajawela region, and physical properties were determined. Results
revealed that the Muthurajawela peats are fibric and hemic peats with high acidity. Also, the applicability ofexisting correlations was assessed and calibrated against the tested properties obtained from the current study.
Outcomes from this study are helpful in identifying design parameters and suitable correlations that are required
for evaluating the local peat soils, which will form the foundations for many planned upcoming infrastructuredevelopments in this region.
Keywords: Peat, Fiber content, Organic content, Hemic peats, Fibric peats[4]. Peat soils are classified according to physical,
chemical and physico-chemical properties such as
texture, organic content, ash content, fiber content,
pH, colour, water content, and degree ofdecomposition.
Peats are problematic soil due to the low shear
strength, high compressibility, and high water content.
It exhibits distinctive mechanical properties
compared to clay and sandy soils. The water saturated
environment within the peat deposits generates
anaerobic conditions and limits the decomposition of
fresh plant litter. Peatlands are divided into
fens/topogeneous and bogs/ombrogeneous. Fens are
areas where groundwater is fed or surface water is fed
by runoff from marginal sites, whereas bogs are areas
where water is fed only by rainwater. The formationof fen peat happens when plants die and accumulates
at the bottom of a lake. Generally, fen peat isunderlain by very soft organic mud. Bog peats are
typically fibric (fibrous) and acidic [5].
The previous practice was to remove the peat by
excavation or any displacement method when it was
encountered during construction. Nowadays, thistrend has been changed, and the knowledge of peatsoil behaviour has improved among engineers.
Several techniques such as surface reinforcement,
preloading and preloading with vertical drains, stone
columns, dynamic replacement, thermal precompression, deep stabilization, and chemical
stabilization are widely used for the peat soil
improvement in the world [1, 6].

INTRODUCTION
Peat occurs in over 4.5% of the total land coveragein the world [1]. In Sri Lanka, peat covers around
25,000 hectares [2], and Muthurajawela is the mostsignificant peat area on the island. The Southern
Highway is Sri Lanka’s first E Class (expressway)
highway that links the capital of Sri Lanka, Colombo
and Matara, a major city in the south of the island.
Many stretches of the highway went through flood
plains and wetlands such as the flood plains ofWelipenna river, Bentota river, and Gingaga riverareas, which consist of very soft peats [3]. Similarly,
the Colombo - Katunayake Expressway (E03) was thesecond E Class highway in Sr Lanka, which was
opened in October 2013. The proposed alignment waslaid on marshy lands starting from the brink of Kelani
River. It traverses the flood plains of the Kelani Riverand the marshy areas of Peliyagoda and
Muthurajawela before it reaches the Negombo lagoon
area. Soft peat was found down to 12 m depth incertain areas of the alignment, and several ground
improvements such as traditional preloading
techniques, prefabricated vertical drains, sandcompaction piles, driven piles, and stone columns
were carried out during the construction [3].
Peat soils form in the wetlands under suitableclimatic and topographic conditions. Peat contains
fragmented organic materials and partly decomposed
plants such as leaves and stems, which have beenchemically changed and fossilized from vegetation
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GEOMATE – Melbourne, Australia, 11-13 November 2020

However, peat soil should be well studied before
designing appropriate ground improvement methods.
There are many ongoing construction activities
around the Muthurajwela area, in the western
province of Sri Lanka. However, no major studies
have focused on the properties of peats in
Muthurajawela region. Hence, this research aims tocharacterize peats in the Muthurajawela region by
investigating their physical properties and checking
whether the existing correlations reported in theliterature are applicable to predict the properties ofpeats in the Muthurajawela region. The outcomes ofthis study will be useful for engineering designers tounderstand the characteristics of local peats in
Western Province, Sri Lanka.

mass of ash content was weighed. Organic matter
content of peat is represented by loss on ignition (%)
and derived using the Eqs. (1) and (2).
Ash Content (%) = (Ma x 100)/Md(1)

Where Ma is the mass of ash, and Md is oven-driedmass.
Organic content (%) = 100 – Ash content (%)(2)

Fiber content of the peat was determined using
ASTM D1997-91[8]. The test required 500 g of peat
sample to soak in 5% of Sodium-Hexametaphosphate
for about 15 hours. The soaked sample was washedby applying a gentle flow of water through a 150 µm
sieve, and the retained mass on the sieve was ovendried at a temperature of 105°C until a constant mass
was achieved. Fiber content was calculated using Eq.
(3).

EXPERIMENTAL METHODS
Physical Properties
Peat samples were collected from six locations in
the Muthurajawela area and designated as L1 to L6,
as shown in Fig. 1. The minimum distance between
two sampling locations was maintained as 800 m, andsamples were transported in sealed boxes to thelaboratory to maintain the in-situ moisture contents.
Physical properties such as natural moisture
content, specific gravity, bulk density, organic
content, and fiber content were investigated at thelaboratory. Each index property test was conducted
for five samples in order to ensure reproducibility for
all the physical properties investigated.
Field moisture content, organic content and ash
content of peat soils were determined as per ASTM
D2974-14 [7]. Oven-dried peat specimens were used
to find the organic content and ash content.
Specimens were placed inside a muffle furnace,
and the temperature was gradually raised to 440°C
until the samples were completely ignited. Then,remaining ash was cooled in a desiccator, and theFiber content (%) = Mf/Ms x 100(3)

Where Mf is dry mass of fibers and Ms is the initial
dry mass of soil.
Specific gravity and bulk density of the samples
were determined in accordance with ASTM D854-02[9] and ASTM D4531-86 [10] respectively. The pH
values of peat soils were measured in the laboratory
using a pH meter according to ASTM D2976−71 [11].
Classification of Peats
Peat was initially classified using the Von Post
classification and Munsell colour system at the site.
In the Von Post Classification, peat can be
categorized from H1 to H10 where H1 is considered
as fully undecomposed fibric peat, and H10 is forcompletely decomposed amorpous peat (sapric peat).
In this method, a handful of peat was taken and gently
squeezed to drain the water out. The condition of theFig. 1 Peat sample locations.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

squeezed peat was carefully observed and comparedwith the Munsell colour system for classification. The
Munsell colour system specifies colours based on
three colour dimensions: hue, value (lightness), andchroma (colour purity). Colour of the peat wascompared with the colour range given in the standard
Munsell colour chart.
The American Society for Testing and Materials(ASTM) Standards classifies peat based on fiber
content, ash content, and acidity of soil, as shown in
Table 1.

lightweight fibers, which results in low specificgravity values [14].
The fiber contents of peat samples varies from
55% to 82%, and it has been found that there isirregular structure of fibers and organic matter in peatin Muthurajawela, Sri Lanka [15]. The highest fiber
content was observed at location L4 while location L5
showed the least, which results in lower specificgravity value compared to other locations. Based on
the acidity test, it was concluded that the
Muthurajawela peat is highly acidic.

Table 1. Classification of peat based on ASTM
standard
Fibric: Peat with greater than 67%
fibers.
Fiber content
Hemic: Peat with between 33% and[8]67% fibers.

Classification of Muthurajawela Peat
Peat samples were classified by Von Post scale
method, which is a primary method of peatclassification used in the field by observing thedegree of humification. Dark brown muddy water
was observed when the peat was squeezed at location
L1 to L4, and plant structures were not easily
recognizable.
Therefore, peats from location L1 to L4 wereclassified as H4. Peat structure was easily identifiable,
and yellowish water was observed during squeezing
at location L1 and L2 which belongs to category H2
in the Von Post classification.
In addition, the colour of the peat soil samples wasobserved for classification as per the Munsell colour
system, as shown in Table 3. Colour of the peat oflocations L1, L2, L4 and L6 were black while
locations L3 and L5 showed very dark grey and very
dark brown respectively.
Fiber content, ash content and pH values wereused to categorize the peat soil according to the
ASTM standards, as shown in Table 1. Fiber content
tests of the peat samples from locations L1 to L4
indicate 33% to 67% of fibers, while the locations L5
and L6 showed more than 67% of fibers. Therefore,peats from locations L1 to L4 were considered as
Hemic while peat samples from L5 and L6 were
Fibric. This clearly means that the decomposition
process is being continued and conversion into theform of Sapric peats is expected with time.
Fibric and Hemic peats have higher water holding
capacity, permeability and compressibility comparedto Sapric peats. Therefore, the identified peat types in
Muthurajawela reflect high susceptibility to greater
settlement under the structural load. Ash content of
the peats from all six locations were more than 15%,
and the pH value was less than 4.5. Hence,
Muthurajawela peat is highly acidic with high ash
content.

Sapric: Peat with less than 33% fibers.
Low ash: Peat with less than 5% ash.
Ash content[7]Medium ash: Peat with between 5%
and 15% ash.
High ash: Peat with more than 15%ash.
Highly acidic: Peat with a pH lessthan 4.5.

Acidity [11]Moderate acidic: Peat with a pH
between 4.5 and 5.5.
Slightly acidic: Peat with a pH greaterthan 5.5 and less than 7.
Basic: Peat with a pH equal or greaterthan 7.

RESULTS AND DISCUSSION
Physical Properties
Table 2 shows the physical properties of six
different peat samples collected from locations L1 to
L6. The moisture content of the specimens varied
from 200% to 370%. while specific gravity values
ranged between 1.86 to 2.19. Both field moisture
content and specific gravity values are within therange that was reported for Sri Lankan peat [12]. The
observed higher specific gravity values imply higher
decomposition of peats under the prevalentenvironment. Bulk density of peat varied from 1025
to 1240 kg/m3, which was lower compared to othersoils due to the inertial gases present either inmacropores or in the micropores [13].
The average organic content of the peats fromlocations L3, L5, and L6 are 48.4%, 50.6%, and53.8% respectively, while the other locations show
values less than 30%. The organic content of the peatincreases with the presence of perforated and porous

Correlations Between Index Properties of
Muthurajawela Peat
Correlations are useful to predict complex
engineering properties from the physical properties

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Table 2. Physical properties of peat soil in the Western province of Sri Lanka
Samples location
Parameter

L1

L2

L3

L4

L5

L6

Moisture content (%)

370

320

202

211

244

264

Specific gravity (Gs)

2.19

2.05

1.99

2.17

1.89

1.86

Ash content (%)

77.3

72.3

51.6

77

49.4

46.2

Organic content (%)

22.7

27.7

48.4

23

50.6

53.8

pH value

3.3

1.6

3.9

3.8

3.1

3.3

Dry density (kg/m )

263

275

411

376

312

282

Bulk density (kg/m3)

1236

1151

1241

1168

1074

1026

Fiber content (%)

64.8

66.8

64.5

54.8

82.3

73.5

3

Table 3. Classification of peat soil samples in the Muthurajawela region in the Western
province of Sri Lanka
Classification
Parameter

Locations
L1

L2

L3

L4

L6

H4

L5
Very
dark
brown
H2

Color (Munsell
chart)

Black

Black

Very
dark greyBlack

Von post scale

H4

H4

H4

Fiber content

Hemic

Hemic

Hemic

Hemic

FibricFibricAsh content

High ashHigh ashHigh ashHigh ashHigh ashHigh ashAcidityHighlyacidicHighlyacidicHighlyacidicHighlyacidicHighlyacidicHighlyacidicBlackH2density of Muthurajawela peat based on in-situ
moisture content.
which could be easily be determined from laboratory
tests. The applicability of previously developed
correlations by Al-Raziqi et al., [16] and Kazeiman
et al., [17] were assessed by plotting the experimental
values against the computed results using theempirical Eqs. (4), (5) and (6). The analyses wereconducted for dry density, specific gravity andorganic content of Muthurajawela peat and are
discussed below;

ρd = 0.872 (w + 0.317)-0.982(4)Organic content (OC) vs specific gravity [Gs]
Figure 3 shows the relation between specificgravity and organic content (loss on ignition).
Muthurajawela peats satisfy the equation proposed by
Kazemian & Huat [17]. The close alignment between
the results illustrates the applicability of the Eq. (5)for the prediction of the specific gravity of
Muthurajawela peat by OC.

Moisture content [w] vs. dry density [ρd]Figure 2 illustrates the in-situ dry density (ρd) andin-situ moisture content (w) relationship for the
Muthurajawela peats.
The experimental dry density values are comparedwith the curve using the empirical Eq. (4) developedby Al-Raziqi et al. [17] for Malaysian and New
Zealand peats. Both experimental and calculated
results show a reasonable match between the results;
hence, Eq. (4) can be applicable to estimate the dry

Gs = 5.26(OC)-0.285(5)Bulk density [ρb] vs organic content [OC]Figure 4 shows the relationship between bulk
density [ρb] and organic content [OC]. The organic
content versus bulk density of the Muthurajawela peatfollows the equation proposed by Kazemian & Huat
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GEOMATE – Melbourne, Australia, 11-13 November 2020

0.5

2.5

0.3
0.2
Experimental results
Prediction Eq (4)0.1

2Specific gravity, Gs

Dry density (Mg/m3)

0.4

1.5
Experimental results
Prediction Eq (5)

1
0.5
0

0

20
200

250

300
350
Moisture content (%)

400

40

50

60

Organic content (%)

Fig .3 Specific gravity as a function of organic
content

Fig. 2 Dry density as a function of moisture content.
[17] for the same relationship for peat in West
Malaysia, as shown in Eq. (6). Hence, Eq. (6) can beused to predict the organic content of peat in
Muthurajawela region.

60
50

Organic content (%)

OC = 59.836 x ρb-4.64

30

(6)

CONCLUSIONS

An experimental study was conducted todetermine the physical properties of Muthurajawela
peat and to evaluate the applicability of the existingcorrelations reported in the literature to predict theproperties of Muthurajawela peat. The following
conclusions were drawn from the findings of thisstudy.
• Muthurajawela peat is categorized in the range of
H2 to H4 based on the Von Post classification.
• The moisture content of the Muthurajawela peat
ranges from 200% to 370% while the bulk
density ranges from 1026 - 1241 kg/m3.
• Peats in this region belong to hemic and fibric
type with high acidity and ash content.
• The available correlations in the literature werereasonable fits for the peats of Muthurajawela
when predicting the physical properties such thatdry/bulk density and specific gravity using insitu measures of moisture content and organic
content, respectively.
This study will improve the knowledge on
characteristics of peat soil in Muthurajawela region
which could be used to understand the behavior ofpeat and to propose possible ground improvement
practices for future construction works.

40
30
20
Experimental results

100Prediction Eq (6)
1

1.1

1.2

Bulk density (Mg/m3)

1.3

Fig. 4 Organic content as a function of bulk density
ACKNOWLEDGEMENT
The authors acknowledge South Eastern
University of Sri Lanka (SEUSL) for the support
provided under the University Research Grant
scheme to conduct this research study. Also, our
gratitude goes to the National Building Research
Organization (NBRO), Sri Lanka for their assistancein obtaining peat samples in Muthurajawela region
REFERENCES[1] Mesri, G., M.J.J.o.G. Ajlouni, and G.
Engineering, Engineering properties offibrous peats. Journal of GeotechnicalGeoenvironmental
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2007.
133(7): p. 850-866.
[2] Cooray, P., The geology of Sri Lanka,
National Museum of Sri Lanka Publ. 1984,
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[3] Karunawardena, A. and Toki M.
Performance of Highway Embankments
Constructed Over Sri Lankan Peaty Soils.
International
JournalofIntegrated
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GEOMATE – Melbourne, Australia, 11-13 November 2020

[4] Dhowian, A. and Edil AW, Consolidation
behavior of peats. 1980. Geotechnical
Testing Journal. 3(3): p. 105-114.
[5] Ovenden, L., Peat accumulation in northern
wetlands. J Quaternary research, 1990.
33(3): p. 377-386.
[6] Edil, T. Recent advances in geotechnicalcharacterization and construction over peats
and organic soils. in Proceedings 2nd
International Conference on Advances inSoft Soil Engineering and Technology.(Eds).
Huat et al. Malaysia: Putrajaya. 2003.
[7] ASTM D2974-14. Standard Test Methods
for Moisture, Ash, and Organic Matter ofPeat and Other Organic Soils. 2014: WestConshohocken, PA.
[8] ASTM D1997-91. Standard Test Method forLaboratory Determination of the FiberContent of Peat Samples by Dry Mass. .
2008: West Conshohocken, PA.
[9] ASTM D854 -02. Standard test methods for
specific gravity of soil solids by water
pycnometer. 2002: West Conshohocken, PA.
[10] ASTM D4531-86. Standard Test Methods
for Bulk Density of Peat and Peat Products.
1999: West Conshohocken, PA.
[11] ASTM D 2976-71(R98). Standard Test
Method for pH of Peat Materials. 1998:
West Conshohocken, PA.

[12] Karunawardena,
W.A.,
Consolidation
analysis of Sri Lankan peaty clay usingelasto-viscoplastic theory. 2007.
[13] Huang, J., J.J.G. Han, and Geomembranes,
3D coupled mechanical and hydraulicmodeling of a geosynthetic-reinforced deep
mixed column-supported embankment.
2009. 27(4): p. 272-280.
[14] Kaniraj, S.R. and R.R. Joseph. Geotechnicalbehavior of organic soils of North Sarawak.
in Fourth Int Conference on Soft Soil
Engineering. Vancouver, Canada. 2006.
[15] Zimar, A.M.Z, Nasvi, M.C.M., Jayakody., S.
Geotechnical Characterization of Peats in
Muthurajawela Region in the Western Coastof Sri Lanka. Geotechnical and GeologicalEngineering 2020: p. 1-15.
[16] Al-Raziqi, A., B. Huat, and H. Munzir.
Index properties of some tropical peat andorganic soils. in Proc 2nd int conference on
advances in soft soil engineering andtechnology, Putrajaya. University Putra
Malaysia Press, Malaysia. 2003.
[17] Kazemian, S. and B.B.J.E.J.o.G.E. Huat,
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2009. 14: p. 1-13.

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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SPATIAL MODELLING OF LARVA GLASS EEL AT CILETUH BAY,
WEST JAVA, INDONESIA
Illa Annisa, S. Supriatna, and Iqbal Putut Ash Shidiq
Department of Geography, Faculty Mathematics & Natural Sciences, University of Indonesia, Indonesia

ABSTRACT
Eel larva or Leptocephalus is the larval stage of the eel, one of the fish species which naturally grow at theriver freshwater into the brackish area such as bay or estuary, thus making the bay a potential area for the habitat.
The study located in Ciletuh Bay, south coast of the West Java province. The coastline is directly facing the Indian
Ocean, and it is one of the famous places for the eel catching spots. Eel is one of the essential and valuablecommodities in the area, which are being caught and traded by the local communities. The abundant amount of
the glass eel and the eel larva (Leptocephalus) influenced by the local condition and the characteristics of the bay
environment, specifically the dynamics of the water transition. The study aims to identify the spatio-temporal
dynamics of the eel larva potential habitat within the area of Ciletuh Bay. The spatio-temporal distribution of theeel larva is being identified with some of the oceanographic parameters, like the seasonal sea surface salinity andtides, water depth, and distance from the coastline. The sea surface salinity generated from a previously developed
algorithm, which is employing Sentinel 2A imagery. The result shows that the habitat of eel larva is at a depth offive meters, with a radius of one kilometer to the northwest, extends to two kilometers to the northeast. An estuarywith a type of sandy beach becomes a suitable area for the spawning the eel so that on the sandy estuary, theamount of larva glass eel in this area is very high. Furthermore, the spatial model for the habitat of the eel larva is
required to optimize the cultivation and conservation of the bay area and to achieve a sustainable rearing for the
species.
Keywords: Ciletuh Bay, Larva Glass Eel, Remote Sensing, Spatio-temporal.

of sediment and chlorophyll. It is also influencing thenature of saltwater, horizontally and vertically, as we
saw in the tidal phenomenon [4].
As mentioned before, the estuary is a transitionalzone between freshwater and saltwater withsignificant salinity differences. Nevertheless,biological productivity in the estuarine region tends
to be higher. The area is rich in nutrients that are
beneficial for the fish larvae [5].
The process of eel larva migration into the estuary
area is influenced by the local oceanic-hydrologicaland climatic factors [6]. The dynamics of the watertransitions are affected by local conditions and theenvironmental characteristics of the bay, which also
determine the abundance and distribution of eel
larvae. These species can tolerate the significant
environmental differences between freshwater andsaltwater in the estuary region. Other factors that
affect the abundance of the eel larvae are the seasonalsea surface salinity, tide, and water temperature, andalso water depth [7]. Harrison et al. [8] stated that inorder to identify eel through their life cycle, the mostimportant thing to be concerned about is their spatiotemporal dynamics on a local scale.
Ciletuh Bay is the estuary of two rivers, Ciletuh
and Cimarinjung. They are located on the south coastof West Java Province and directly facing the Indian
Ocean. Ciletuh Bay is a dynamic estuary, where there

INTRODUCTION
Eel (Anguilla spp.) is an elongated type of fish
species that live in an estuary region. Particularly inIndonesia, the ell mostly spreads within the Indian
Ocean, specifically from the west coast of theSumatra waters to the southern part of Java sea [1]. It
is a high-value commodity and profitable for the local
community. One of the unique characteristics of eel
is their habitat, from freshwater to saltwater and in thebrackish water, which is known as estuary. Their
habitat ideally influenced by the level of sea surface
salinity, tides, oxygen, seawater current, waves,freshwater disorders, and water depth. The unique life
cycle of eel starts with their mass migration from
freshwater to the ocean. From there, they produce alarva, which will grow and evolve into glass eel andmigrate back into the estuary [2]. Those life cycles areassociated with the nature of the coastal ecosystem inthe tropical region, where the abundance of fish in theriver delta with low surface salinity [3].
Eel is known to be a nomadic organism. In orderto survive, eel moves from a particular place to a moresuitable place, by adjusting on the environmental
conditions of the area. Their habitat is heavily
influenced by oceanographic parameters such as sea
surface temperature (SST) and sea surface salinity.
Sea surface salinity can be used to estimate the level592

GEOMATE – Melbourne, Australia, 11-13 November 2020are much freshwater going into the waters of the bay.
The estuary of the river is one of the places where thespecies are reproducing. The estuary is an area ofwater with the highest level of productivity in theworld that supports the natural ecological system
between seas and rivers. As a transitional area, theestuary of the river is well-protected natural zoning
and is also a habitat for the many species of living
creatures that have different characteristics. Fish is an
essential component of the estuaries that utilize theestuary as a habitat for its life cycle [9].

Sea surface salinity
In this study, the sea surface salinity extracted
from Sentinel 2A imageries. In order to map the
distribution of sea surface salinity, this study used apreviously developed algorithm employing spectral
value from satellite imageries. The estimation of thesea surface salinity refers to the following equation[12]:
𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 (𝒑𝒑𝒑𝒑𝒑𝒑) = 𝟐𝟐𝟐𝟐. 𝟗𝟗𝟗𝟗𝟗𝟗 + (𝟏𝟏𝟏𝟏𝟏𝟏. 𝟎𝟎𝟎𝟎𝟎𝟎 ∗ 𝑩𝑩𝑩𝑩𝑩𝑩𝑩𝑩) −(𝟐𝟐𝟐𝟐. 𝟐𝟐𝟐𝟐𝟐𝟐 ∗ 𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮𝑮) + (𝟐𝟐. 𝟔𝟔𝟔𝟔𝟔𝟔 ∗ 𝑹𝑹𝑹𝑹𝑹𝑹)(1)METHODSThe The study is located at Ciletuh Bay. This
study selects two specific sites located in Ciemas andCiracap sub-districts, Sukabumi, in southern WestJava (Figure 1). The absolute location is in the 7° 11’
0” South and 106° 27’ 0” East, and it is about 30
kilometers from the Pelabuhanratu Bay [10].

Fig. 1

where Blue, Green, and Red are the spectral bandsof the Sentinel 2A. Furtherly, the sea surface salinity
is being employed to delineate the boundary of theestuary zone.
Water depth
Information on water depth was extracted fromnational bathymetry data. The national bathymetry
data or BATNAS were generated from the data
inversion of the gravity anomaly resulted from the
combination of altimetry and echo-sounding data.
The information were readily collected fromwww.batnas.big.go.id.
Meteorological, oceanographic, and primary data
The climate data were obtained from theMeteorological Climatological and GeophysicalAgency (BMKG). The data contains the annual
rainfall information (2018-2020). As the supporting
data, the information of daily highest and lowest tidal
were obtained from the local authority in the Ciletuh
area. Lastly, primary data regarding the eel larva
catching spot, including fishing period and theamount of production, were gathered from the field
via a questionnaire-based interview with thefishermen around Ciletuh Bay.

Location of the study area.

Data Collection and Data Processing
The study includes data from 2018, 2019, and2020 to perform the temporal analysis. The samples
were taken in-situ in two zones, intertidal and subtidal.
The samples were randomly distributed in threedifferent clusters. The clusters were placed in ameasured distance (1, 2, and 5 kilometers) from theshore. Samples from the river were also taken,
specifically in the river body 500 meters from theintertidal zone. The seasonal condition is also
considered here; thus, data from the dry and wetmonths were collected in this study. These conditions
are influencing the spawning process of the fish. The
egg is transported to the shallow water, and it will
hatch into the larvae [11].

RESULT AND DISCUSSION
Ciletuh Bay has a unique geological condition.
The beach-side is controlled by the activity of theEurasian and the Indian-Australian plate. Thisgeological activity makes Ciletuh Bay have somevarieties of rocks with different characteristics.
Besides that, it is also in the estuary zone of Ciletuh
and Cimarinjung rivers, which are affecting thephysical and chemical condition of Ciletuh Bay.
Ciletuh Bay is one of the coastal waters in thesouthern part of West Java, which has the potential
for the eel habitat (including their migration). The eel
593

GEOMATE – Melbourne, Australia, 11-13 November 2020and eel larva fishing sites are in the estuary, which
points to the downstream portion of the river thatconnects directly to the sea [13].
The eel fish and eel larva collectors are the local
community residing at a radius of three to four
kilometers from the edge of the Ciletuh andCimarinjung river. The usual fishing time of thelarvae was at night to dawn (Figure 2). The eel larvaethat have been collected are mostly sold to collectors
who will then be marketed to Japan and South Korea.
Some the collectors are doing the fish farming by
cultivating the eel and marketed them at the Ciletuh
Geopark, in the form of fresh food. Some of thefishermen can sell the eel larvae for one million
rupiahs for 6,000 tails [14].
(a)Fig. 2(b)
Fig. 3 The sea surface salinity for the dry months.
(a) 27 September 2018 (b) 22 October 2019.
Based on the previously developed algorithm or
the Cimandiri algorithm [12], this study has been able
to map the sea surface salinity (Figure 3 and 4). As
mentioned before, the estimation of the sea surface
salinity is differentiated based on the dry and wetmonths. The classifications are shown in Table 1. The
value can be used to determine the boundary of theestuary.

The process of eel larva fishing in the study
area. (a) the night fishing and (b) the glass
eel larva captured around Ciletuh Bay.

(b)

Table 1 The image acquisition date for the dry andwet months
Classification
Dry months

Fig. 2(a)
The process of eel larva fishing in the study
area. (a) the night fishing and (b) the glass
eel larva captured around Ciletuh Bay.

Wet months

Image acquisition date
27 September 2018
22 October 201919 February 2018
5 January 201919 April 2020

Remote sensing-based studies and observations
on sea surface salinity in the tropical region have been
done previously [15]–[17]. Based on those studies, it
is known that the values of salinity are dynamicallyformed against time and influenced by monsoons,ocean currents, freshwater coming from rivers, andsea waves.
Based on the results (Figure 3 and 4), the sea
surface salinity in the dry and wet months are ranging
from 5 to 16 ppt. It will further group as the mexomesohaline zone [18]. In the estuary of the Ciletuh
river, the salinity ranges from 5 to16 ppt, whereas in
the estuary of Cimarinjung river salinity ranges from
6 to 16 ppt. It is assumed that the bigger size of the
Ciletuh river will bring more sediments to the estuary
and decreasing the salinity of the water.

(a)

594

GEOMATE – Melbourne, Australia, 11-13 November 2020

more freshwater to the river estuary, mixing the waterin the estuary and finally dominated water in theestuary.

(a)Fig. 5

Annual rainfall (mm) in 2018-2020 around
Ciletuh Bay

Freshwater flows play an important role in thehydrodynamic process [19]. It can be seen that in wet
months the salinity value will be less because of thehigh rainfall when the rainfall decreases, the salinity
increases.
The tidal factors also influence the mixture of
freshwater and salty water in the estuary. Not only
that, but the tide of the seawater will also influence
the eel larvae migration to the estuary. Then, it will
settle in the area and occupy it as the proper habitat.
The tidal wave phenomenon is one of the factors ofcoastal hydraulics, which is affecting the character ofthe flow in the estuary [17]. The tidal data is
associating with the image acquisition date in dry andwet months. The high tides will result in the greater
water mass that enters the river estuary so that the
mixing of salinity highly increases, causing a higher
sea surface salinity in the river estuary. In contrast, attimes of low tides, waves are followed by the low
salinity value in the estuary.

(b)

(c)
Fig. 4The sea surface salinity for the wet months.
(a) 19 February 2018, (b) 5 January 2019,and (c) 19 April 2020.

Fig. 6As this study observed the annual rainfall data, it
is known that during the last three years, the drymonth has an average rainfall of 19 mm, wherein thewet months the average rainfall is 419.65 mm (Figure5). Rainfall will affects the discharge to the river.
High rainfall is enlarging the probability of thestreamflow so that the mixture of freshwater (fromrainwater) will further neutralize the salinity of theseawater. The intensity of high rainfall will bring

Hourly tidal dynamics in 1 day (meter) ataround Ciletuh Bay.

Based on Figure 6, it is known that the highest
tides occurred on 5 January 2019 (0.653 m), while thelowest tides occurred on 19 February 2018 (-0.575 m).
The highest tide occurred at 13:00 (1:00 pm), and thelowest ebb occurred at night (19.00 or 7:00 pm). On
22 October 2019, tidal patterns differ by an averageof 0.3-0.4 meters and at a wave height of 0 meters.
However, there are no significant differences in the595

GEOMATE – Melbourne, Australia, 11-13 November 2020

Seawater with less salinity was found at 5 meters
depth. The salinity level of the bay is mostly foundfrom zero up to five ppt. Nevertheless, a higher rangeof salinity was found at the end of the river (from 5
up to 16 ppt). As the water gets deeper, the salinity
increase. The study found salinity at six up to 16 ppt
in the depth of 10 to 15 meters. Lastly, the highest
salinity was found at a depth greater than 15 meters(more than 16 ppt).
It is found that Ciletuh estuary has high sea
surface salinity because it is mostly located in the
deeper water. Regarding the seasonal aspect, thesalinity of the wet months were less than 30 ppt, andit was found at 5-20 meters depth. The excess offreshwater from the precipitation is dropping thesalinity, as can be seen from the wet month of 19
April 2020, where the lowest salinity (0-5 ppt) were
found in the depth of five meters. In the other rainymonths, it is found that the water with 0-16 ppt ofsalinity was extended towards the ocean until thedepth of 20 meters. Salinity value in the dry monthincreases with the value higher than 16 ppt, and it was
extended towards the bay and mouth of the river. For
example, on 27 September 2018, most of the water
was saline with a value of 17-30 ppt.
Figure 8 is showing a cross-section of the seafloor,
and temporal salinity value at 0-meter depth. From
the figure, it is known that even in the same depth,
salinity might be different each month. From thefigure, it also suggested that distance from theshoreline is also varying the salinity value.
On 19 February 2018, most of the catchment area
at Cimarinjung river have high salinity (12 ppt). As
the distance is getting far from the end of the river,the number of fishing spots decrease, while salinity
increase to 25-30 ppt. On 18 September 2018, thefishing area got smaller because of the zone
dominated by very saline water (29-30 ppt). On 5
January 2019, the fishing ground extended as thesalinity dropped to 11 ppt.
Similar patterns were found in the dry months. On
22 October 2019, the fishing ground has medium
saline water (15 ppt), but as it is getting far from theestuary, the salinity increase to 30-32 ppt. On 19 April
2020, salinity in the estuary found at five ppt. As it isgetting far from the estuary, the salinity increases to
16-46 ppt (Figure 8).

daily ups and downs of the tides during 2018-2020.
The eel larva migrates from the sea to the river atnight, which it utilizes the currents brought by the sea
breeze. The sea waves generated by the wind arebringing the seawater to the bay. At night the sea
breeze evokes waves until early morning. Based on
the tidal wave above (Figure 6), at night (21:00 or
9:00 pm) towards the morning (04:00 or 4:00 pm), theformed waves start to move up, so that fishers orcollectors of eel larva will fishing in that the earlymorning.
The presence of chlorophyll strongly influences
the life cycle of eel. Around the area of Pelabuhanratu
Bay, which is a large bay around Ciletuh Bay, thechlorophyll-a, surface salinity, and suspended solids
are tend to increase towards the west direction anddecrease towards the southwest or moving away from
Ciletuh Bay, especially in the rainy season. Apartfrom that, the sea surface temperature has the highest
value along the coastline, including Ciletuh Bay, but
this variable does not affect the chlorophyll content[20].
Based on the interview with fishers, the fishing
spots are usually located on Cimandiri estuary, which
10 kilometers from Ciletuh Bay. The fishing activities
starts at night (in between 7:00 pm to 5:00 am)
because it is easier to spot the glass eel and eel larva
with minimum lighting. The average amount of catch
is about 0.7 ounces per day. In the dry months, theaverage amount ranges from 1 to 1.2 ounces per day,while in the wet months, the average daily amount is
0.5 ounces [21].
The underwater topography around Ciletuh Bay is
being observed using bathymetry data. The seafloor
around the coastal area dominated by flat and slightly
steep terrains that extend from the estuary towards thewest direction to the ocean. Figure 7 displays
variations of water depth around Ciletuh Bay. The
depth is ranging from five to 30 meters below thewater.

(a)Fig. 7

Fig. 8Cross-section and temporal sea surface
salinity of Ciletuh Bay. (a)(b)

Bathymetry of the Ciletuh Bay.

596

GEOMATE – Melbourne, Australia, 11-13 November 2020

Faculty of Mathematics and Natural Science,
University of Indonesia which has facilitated theresearch activities.
Fig. 8REFERENCES

Cross-section of the seafloor and (b)temporal sea surface salinity in 0-meter
depth.

[1] Arai, T., Do we protect freshwater eels or do we
drive them to extinction. Springer Plus, 2014,
3:534.
[2] Linton E.D., Jonsson B., Noakes D.L.G. Effectsof water temperature on the swimming andclimbing behavior of glass eels, Anguilla sp.
Environmental Biology of Fishes, 2007, pp.
78:189-192.
[3] York J.K., G.B. McManus, W.J. Kimmerer, A.M.
Slaughter, and T.R. Ignoffo., Trophic links in theplankton in the low salinity zone of a large
temperate estuary: top-down effects of introduced
copepods. Estuaries and Coasts 37, 2014, pp.
576-588.
[4] Kang X., Xia M., Joseph P. S., & Chingbu P.,
Dynamics of water and salt exchange at Maryland
Coastal Bays. Estuarine, Coastal and Shelf
Science, 189, 2017, pp. 1-16.
[5] G. A. Knox and T. Miyabara. Coastal Zone
Resource Development and Conservation inSoutheast Asia, with Special Reference toIndonesia (East-West Center Resources Systems
Institute, Honolulu, Hawaii), 1983.
[6] Arribas C, Fernández-Delgado C, Oliva-Paterna
FJ, Drake P. Oceanic and local environmental
conditions as forcing mechanisms of the glass eel
recruitment to the southernmost European estuary.
Estuar. Coast. Shelf Sci, 2012, pp. 107:46–57[7] Guo, Hongyi, Xuguang Zhang, Ya Zhang,Wenqiao Tang, and Jiamin Wu. Effects ofEnvironmental Variables on Recruitment of
Anguilla Japonica Glass Eels in the Yangtze
Estuary, China. Fisheries Science, 2017, pp. 333–
410.
[8] Harrison Andrew J., Alan M. Walker, Adrian C.
Pinder, Cédric Briand, and Miran W. Aprahamian.
A Review of Glass Eel Migratory Behaviour,
Sampling Techniques and Abundance Estimatesin Estuaries: Implications for Assessing
Recruitment, Local Production and Exploitation.
Reviews in Fish Biology and Fisheries, 2014, pp.
967–83[9] Santos R. V., Ramos S., & Bonecker A. C.,
Environmental control on larval stages of fish
subject to specific salinity range in tropicalestuaries. Regional Studies in Marine Science, 13,2017, pp. 42-53[10] Global/Trans-national services. GeoHack-Teluk
Ciletuh, 2020.
[11] Machando I., Calliari D., Denicola A., & Grana
L. R., Coupling suitable prey field to in situ fishlarval condition and abundance in a subtropicalestuary. Estuarine, Coastal and Shelf Science, 187,

Based on the analysis, it can be said that the
habitat of the eel and eel larva is at five-meter depth
and in the radius of one kilometer to the northwest
and two kilometers to the northeast. An estuary withsandy-type each becomes a suitable breeding area forthe eel so that there is an abundance amount of eel inthis area. A gravely substrate with 81.4-100% sandy
fraction is mostly found in the sediments around
Ciletuh Bay [22].
The abundance of eel can be seen from the area
flooded at the highest tide [23]. The presence of eel
and glass eel influenced by topographical factors, andthe river's technology, as well as the physical andchemical properties of river waters. It is also
influenced by waste disposal from activities
surrounding the river, including agriculture,
plantation, and settlements.
Lastly, the Ciletuh estuaries are potential as eel
habitat. Therefore it is vital to maintainenvironmental sustainability and the culture process
as a livelihood for the people around, for the supplyof cultivation activities, and also as a tourist attraction.
A reasonable management effort with the monitoringof rivers will improve Ciletuh Bay as the preservationof the eel habitat.
CONCLUSIONS
Based on the results of the analysis, the eel larva
habitat in Ciletuh Bay is influenced by natural
conditions (dry and wet months). Different amountsof rainfall between those months are affecting sea
surface temperature and salinity, as it is also provenby results from remote sensing data. The study also
found a similar characteristic of tides between thosemonths. The potential habitat of the eel and eel larva
has been found between 5 to 20-meter depth. The
abundance of the eel and eel larva was found atnighttime with the production peak at the wet months(December-May), where less saline water is available.
ACKNOWLEDGMENTS
This study is supported and funded by the
Directorate Research and Community Services(DPRM), University of Indonesia under the Publikasi
Terindeks International (PUTI) 2020 research grant.
The authors also express their greatest gratitude andthe highest appreciation to Department of Geography,597

GEOMATE – Melbourne, Australia, 11-13 November 20202017, pp. 31-42[12] Supriatna L., Supriatna J., Koestoer R. H., &Takarina N. D., Algorithm model for thedetermination of Cimandiri Estuarine boundary
using remote sensing, AIP Conference
Proceedings, 1729, 2016, 020079.
[13] Triatmodjo, B. Teknik Pantai, second edition,
1999.
[14] Taufik., Menjaga Denyut Teluk Ciletuh Lewat
Konservasi dan Budidaya. Media: CNN Indonesia,
2018.
[15] Wang Y., Hua J., Pan H., Li S., Failler P., An
integrated model for marine fishery managementin the Pearl River Estuary: Linking socioeconomic systems and ecosystems. Journal of
Marine Policy, 64, 2016, pp. 135–147.
[16] Geiger E.F., Grossi M.D., Trembanis A.C.,
Kohut J.T., Oliver M.J., Satellite-derived coastal
ocean and estuarine salinity in the Mid-Atlantic.
Journal of Continental Shelf Research, 63, 2013,
pp. 235-242.
[17] Min J.E., Ryu J.H., Lee S., Son S.H., Monitoringof suspended sediment variation using Landsat
and MODIS in the Saemangeum coastal area of
Korea. Marine Pollution Bulletin, 64, 2012, pp.
382-390.

[18] Taupp T. & Wetzel M. A., Leaving the beaten
track-Approaches beyond the Venice System toclassify estuarine waters according to salinity,
Estuarine, Coastal and Shelf Science, 148, 2014,
pp. 27-35[19] Trinugroho, Muchamad Wahyu, Binod Bhatta,
and Muhammad Babur. The Seawater Intrusion
under Dam Failure in the Cimanuk River Estuary,
Indonesia, Regional Studies in Marine Science,
2020, 36: 101267.
[20] D S Irianto, Supriatna, and Tjiong Giok Pin.,
Distribution of glass eel by the water surface
salinity using landsat TM at Pelabuhanratu Bay,West Java, IOP Conference Series: Earth andEnvironmental, 47, 2016, 012016.
[21] Suhanda, D, Yuniarti, Ihsan, YN., Harahap, SA.,
Nutrient Concentration and Population ofMacrozoobenthos in Ciletuh Bay, SUkabumi
District, West Java, IOP Conference Series: Earthand Environmental, 406, 2014, 012014.
[22] Sriarti, Telaah struktur dan kelimpahan populasi
benih ikan sidat, Anguilla bicolor bicolor dimuara Sungai Cimandiri, Pelabuhan Ratu, Jawa
Barat, 1998.

598

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

NUMERICAL SIMULATION OF THREE-DIMENSIONAL FLOW
AND LOCAL SCOUR AROUND A CYLINDERINDUCED BY TSUNAMI RUN-UP
Yuki Kajikawa1 and Masamitsu Kuroiwa2
1,2Faculty of Engineering, Tottori University, Japan
ABSTRACTIn this paper, we have presented a numerical model of a fully three-dimensional flow and topography change
to predict local scour around cylindrical structures induced by a tsunami run-up. In the numerical model, thethree-dimensional Reynolds-averaged Navier–Stokes equations with the renormalization group theory k-ε
turbulence model using the Cartesian coordinate system was adopted. Moreover, the fractional area/volume
obstacle representation method was introduced into the governing equations such that the model could be applied
to complex topography. Furthermore, non-equilibrium bed-load and suspended-load transport were considered
for the topography change calculation. To validate this model, it was applied to a large-scale laboratory
experiment with a cylinder. Comparison between the calculated and experimental results showed that the model
accurately replicated the time variations of water levels and scour depths in the offshore side of the cylinder andnext to it. However, the model could not replicate the local scour on the land side of the cylinder. It wassuggested that introducing the effect of curling up of bed load and seepage of tsunami run-up flow into the modelis necessary to predict the local scour more accurately.
Keywords: Tsunami run-up, Cylindrical structure, Local scour, Numerical simulation, Three-dimensional model

INTRODUCTION

an extremely complex phenomenon as there is asignificant change in flow and sediment transport intime and space. Furthermore, the prediction becomes
more difficult owing to an upward flow development,
resulting in a strong non equilibrium sedimenttransport behind a cylinder [1], [4]. However, thedevelopment of a numerical model that can predict
this phenomenon with high accuracy is urgently
needed to prepare for tsunami disasters in the future.
In this paper, the authors present a numericalmodel that can estimate a fully 3D flow andtopography change to predict the local scour arounda cylinder caused by the tsunami run-up flow. In thismodel, the renormalization group theory (RNG) k-ε
turbulence model is adopted, and both nonequilibrium bed-load and suspended-load transport
are considered for the calculation of change in
topography. The model was validated with theexperimental data obtained from the local scour
around a cylinder using a large-scale tank [1].

When a tsunami strikes a shore area, local scour
is induced around cylindrical structures, such as an
oil tank and a wind power plant constructed in thearea, owing to a tsunami run-up. The local scour not
only decreases the stability of the structures, but alsocollapses the structures in a worst-case scenario.
Therefore, it is extremely important to quantitatively
evaluate the topography change including the local
scour around cylindrical structures due to thetsunami run-up, in advance.
Several studies on local scour around a cylinder
have been carried out experimentally [1], [2] andnumerically [3]-[6]. In particular, local scour in a
steady flow has been investigated over the past few
decades, mainly in the field of river engineering. It
can be predicted extremely accurately using a
numerical model consisting of fully threedimensional (3D) flow and non-equilibrium bedload transport [3]. Moreover, several numericalmodels predicting local scour due to tsunamis [4],[5] and waves [6] have also been proposed in recent
years, and these models have been validated bycomparing the results with those of the experimental
models.
Although several numerical models concerning
the local scour around a cylinder have been
proposed, there are extremely few models
concerning the local scour due to the run-up anddownflow of tsunamis. This is because the local
scour due to the run-up and downflow of tsunamis is

NUMERICAL MODEL
Governing Equations for Tsunami Flow
In the tsunami flow model, the 3D Reynoldsaveraged Navier–Stokes (RANS) equations using
the Cartesian coordinate system was adopted.
Further, the fractional area/volume obstacle
representation (FAVOR) method [7], which has theability to smoothly impose boundary conditions atcomplex boundaries, was introduced into the599

GEOMATE – Melbourne, Australia, 11-13 November 2020

the turbulent kinetic energy; ε is the turbulent
dissipation rate; and νt is the eddy viscosity
coefficient.
The following standard values were used foreach constant in the RNG k-ε turbulence model [8]:governing equations. Moreover, the RNG k-εturbulence model [8] was adopted to accurately
estimate the separation flows behind a cylinder asfollows:x3 (z)

 k = 1 1.39 ,   = 1 1.39
(6)
C 1 = 1.42, C 2 = 1.68, 0 = 4.38,  = 0.012
C = 0.0845,

Az

Ax
Ay
x1 (x)

Governing Equations for Topography Change

z

V
x

y

In the topography change model, both the bedload and suspended-load transport were considered.
The bed-load transport rate was estimated from thefollowing non-equilibrium bed-load transport model
proposed by Uchida and Fukuoka [9]:x2 (y)

Fig. 1 Definition directions of each fractional area
rate (Ax, Ay, Az) for 3D flow. Δx, Δy, and Δz are thegrid sizes in the x, y, and z directions, respectively.
 A( j )u j  = 0
x j

qBi 1  L( j )u Bj qBi + tS  x j


(1)

=

ui 1 1 P
 A( j )u j ui + 
 = − g 3i −
t V 
 xi
 x j  ui u j  
1  


+
+
 
 A( j ) ( +  t ) V x j 

x

x
j
i





ui  ui u j +−x j  x j xi (2) t   
 1  A( j )u j   1  + =
 A  +t V  x j  V x j  ( j )    x j C1 = C 1 −(4a)

ui ui u j ui+x j x j xi x j

(4b)

 (1 −  0 ) ;k=
1 +  3 t = Ck2

qB u Bi+ m hB (  ei −  i )

(7a)

(7b)

where qBi is the bed-load transport rate in the xi
direction (qB2 = qBiqBi); S is the movable bed area
rate on two-dimensional horizontal (2DH) bedplane; L(j) is the fractional line rate in the xj direction
shown in Fig. 2; uBj is the sediment particle velocity
of bed load in the xj direction (uB2 = uBjuBj); qBe is theequilibrium bed-load transport rate; ζ is the spatial
lag distance; ζe and uBei are the ζ and uBi forequilibrium condition, respectively; m* = μk s g cos
θb/(s +1 + CM); μk is the dynamic friction coefficientof sediment; s is the specific gravity of sediment inwater (= σ/ρ – 1, σ is the density of sediment); θb is
the maximum bed angle; CM is the coefficient ofadded mass (= 0.5); hB = qBuB; γi = qBi/qB; γei =qBei/qBe; qB* = qB/(sgd3)0.5; qBe* = qBe/(sgd3)0.5; and dis the sediment diameter.

(3) u  u u j 
2+C1  t i  i +− C 2k x j  x j xi 
k

e

−

u B sgd = 3.3(qB )1 3 , u Be sgd = 3.3(qBe )1 3
23
 hB d = 0.3(qB )2323 e d = 600( qBe ) ,  d = 600 max( qBe , qB ) t  k 
k 1  A( j )u j k  1  + = A( j )  +t V  x j  V x j 
 k  x j + tqBeu Bei

x2(y)

(5)

x
Solid boundary

where t is time; i = 1, 2, 3; j = 1, 2, 3; (x1, x2, x3) = (x,y, z) in the Cartesian coordinates (x, y, and z denotethe horizontal, cross, and vertical coordinates,
respectively); uj is the velocity in the xj direction [(u1,u2, u3) = (u, v, w)]; V is the fractional volume rate;
A(j) is the fractional area rate in the xj direction
shown in Fig. 1 [(A(1), A(2), A(3)) = (Ax, Ay, Az)]; g is
the gravitational acceleration; δ is the Kronecker’s
delta; ρ is the fluid density; P = p + 2/3k; p is thepressure; ν is the kinematic viscosity of fluid; k is

S

Ly

y

Lx
x1(x)
Fig. 2 Definition directions of each fractional line
rate (Lx, Ly) on 2DH bed-plane.
600

GEOMATE – Melbourne, Australia, 11-13 November 2020

The equilibrium bed-load transport rate qBe in Eq.
(7a) was calculated using the following formula
given by Ashida and Michiue [10]: K  K c c = 17 3 2  1 − c c   1 −  
sgd



qBe

3

discretized employing the fifth-order weighted
essentially non-oscillatory (WENO) scheme [15]and central differencing scheme, respectively. The
Adams-Bashforth method was used for time
integration. Moreover, the hybrid scheme [16] wasadopted to discretize Eqs. (3), (4), and (10) to avoid
numerical oscillations.
As the boundary conditions, the time variation of
the wave profile set in the experiment was provided
at the offshore boundary. According to thelogarithmic velocity distribution and assumption of
local equilibrium of turbulence, the wall function
was imposed at the solid wall. The free-slip
condition was applied to the amount of velocities
and turbulence at the water surface. Moreover,regarding the suspended load, the vertical flux was
zero at the water surface, and it was equal to thesuspended-load transport rate per unit area of the
bed.

(8)

where τ* and τ*c are the non-dimensional bed tractive
and critical bed tractive forces [11], respectively;
and Kc is the correction factor attributed to the
influence of bed inclination on sediment motion [12].
The suspended-load transport rate per unit area
qsu was defined using the following formula
proposed by Itakura and Kishi [13]:  −  gd
q su = K   
 − w f 

u





=

   
B

1







1







exp ( − 2 ) d 
exp ( − 2 ) d 

+2

(9a)


B

MODEL APPLICATION AND DISCUSSION−1(9b)Model Application
A numerical simulation was performed using
experimental data of change in topography with thelocal scour around a cylinder induced by the tsunami
run-up [1]. The elevated view and top view of theexperiment are shown in Fig. 3 and 4, respectively.

where wf is the settling velocity of the sedimentgiven by Rubey [14]; α' = B*/τ* – 2; α* = 0.14; K =0.008; and B* = 0.143.
The advection and diffusion processes of thesuspended load were calculated using the following
suspended-sediment transport equation, into which
the FAVOR method was introduced:

Cylinder2

c 1  A( j )u j c A( 3) w f c  1  
c 
+ 
−
=
 A( j ) Dt
 (10)
t V  x j
x3  V x j x j 

1:20 slope

2.45 mMean grain size: d = 0.35 mm
Manning’s roughness coefficient: n = 0.02 s/m1/3

where c is the concentration of the suspended load;
Dt is the eddy diffusion coefficient (= νt/σt); and σt isthe turbulent Schmidt number (= 0.8).
The topography change was calculated using thefollowing continuity equation of sediment transport
into which the FAVOR method was introduced: L( j ) qBj

zb
1
+
+ S ( qsu − w f cb )  = 0
t S (1 −  )  x j


3m

50

Fig. 3

40

30

20

10

0

1

0
–10 m

Elevated view of the experiment.
Solitary wave

0.5 m

Offshore : B(11)

C : Land 2 m
D0.35 mA

3mwhere zB is the bed elevation; λ is the porosity of the
bed material; and cb is the near-bed concentration of
the suspended load.

Fig. 4 Top view of the experiment: stations A, B,
and C indicate the locations of the wave gages.
Stations B, C, and D indicate the locations of thescour depth recorders.

Numerical Method
The governing equations were discretized using
the finite-volume method on a collocated grid
system. To satisfy local continuity and solvepressures, the highly simplified mark-and-cell(HSMAC) method was adopted in this model. The
convective terms and diffusion terms in Eq. (2) wereA large-scale sediment tank 135 m long, 2 mwide, and 5 m deep was used in the experiment [1].
A beach with a 1:20 slope was constructed using
well-graded sand (mean grain size 0.35 mm) and acylinder having 0.5 m diameter was vertically601

GEOMATE – Melbourne, Australia, 11-13 November 2020

Measured
Calcuated

2.6

2.4

5

10

15

20

25

Measured
Calculated

2.6
2.4

0

5

Station C

10

15

20

25

Measured
Calculated

2.8
2.6
2.4

2.2
-5

0

5

10

15

20

0
-5

-10
-20
10 -5
5

5

10

15

20

25

5

10

15

20

25

10

15

20

25

0
-10

Measured
Calculated

-15
-20
10 -5
5

0

Station D

0
-5

-10

Measured
Calculated

-15

-20
-5

0

5

Time t (s)Fig. 7 Comparison of calculated and measured
scour depths at various stations.

200 cm/s

200 cm/s

Δzb (cm)

200 cm/s

2

6

0
4

-2

0

-4

50

2

50

-8

0

-2

-4

0
-1

–8

-2

0

-4

0

–6

-6

–4

4

Land side ← Distance x (cm) → Offshore

0

Station C-525

Fig. 6 Comparison of calculated and measured
water levels at various stations.
200 cm/s

Measured
Calculated-15

Time t (s)100

Station B

5

Wall

0

Station B

2.8

10

Centre2.2
3.0 -52.2
3.0 -5

Water level (m)Scour depth (cm) Scour depth (cm)Station A
2.8

Scour depth (cm)

Water level (m)Water level (m)

3.0

2

-8

-50
0

-10

-100

1s

0

50

6s

100 0

Cross-shore distance (cm)

50

15 s

100 0

Cross-shore distance (cm)50

Cross-shore distance (cm)50

Final stage

100 0

Cross-shore distance (cm)

(a)

–2

-12

20 s

100 0

–2

–50

50

100

Cross-shore distance (cm)

-14

–100

Final stage

50
100
Cross-shore distance (cm)(b)

Fig. 8 Comparison of bed variation contour: (a) Calculated bed variation contour with near-bed flow velocity
vectors and (b) measured bed variation contour at the final stage.
calculation time step Δt was set to 0.002 s, andManning’s roughness coefficient n was set to 0.02
s/m1/3.

installed at the center of the beach. A solitary wave
was generated at the offshore end of the tank. The
experiments were carried out by systematicallychanging the water depth within a range of 2.25–
2.65 m and wave height within a range of 0.10–0.30
m. In this study, we tried to achieve the experimental
condition values of 2.45 m water depth and 0.22 m
wave height as shown in Fig. 5.

Comparison of Water Level and Scour Depth
Figure 6 shows a comparison of calculated andmeasured water levels at Station (St.) A, B, and C
indicated in Fig. 4. Here, t = 0 s indicates the time
when the wave front arrived at St. B. The water level
increases sharply when the wave front arrives, and it
decreases gradually once the wave front passes. The
calculated results are in good agreement with themeasured values.
Figure 7 shows a comparison of the calculated
and measured scour depths at St. B, C, and D, thelocations of which are indicated in Fig. 4. The
presented numerical model was found to accurately
reproduce the time variations of scour depths in theoffshore side of the cylinder (St. B) and next to it (St.
D). However, the model could not reproduce thescour depth on the land side (St. C). Although

Water level (m)

2.70
2.65
2.60

2.55
2.50

2.45
-10

Fig. 5-50Time (s)5

10Wave profile at the offshore boundary

The horizontal grid sizes (Δx and Δy) andvertical grid size (Δz) were set to 0.05 and 0.0125 m
as the calculation conditions, respectively. The
602

GEOMATE – Melbourne, Australia, 11-13 November 2020

0s

1s

6s

15 s

20 s

30 s: Final stage

Time variations from a bird’s-eye view of calculated topography and water surface.

250
230

c (ppm)

290 Transverse section 100 cm/s c (ppm)

6000

4000
3000
2000

50

5000

270

4000
3000

250

2000

1000

1000

0

100

6000

x = – 27.5 cm

5000

z (cm)

270

200 cm/s

Land sidez (cm)

290 Longitudinal section: y = 0 cm

Offshore side

Fig. 9

0

-50

-100

230

x (cm)← Cylinder side0

Wall side →50

y (cm)

0

100

(a)

230

c (ppm)

4000
3000
2000

50

6000

x = 27.5 cm

5000

5000

270

4000
3000

250

2000

1000
0

100

290 Transverse section 100 cm/s c (ppm)6000

z (cm)

250

Offshore side270

200 cm/s

Land sidez (cm)

290 Longitudinal section: y = 0 cm

0

-50

-100

x (cm)

1000

230← Cylinder side0

Wall side →50

y (cm)

0

100

(b)

Fig. 10 Calculated 3D flow velocity vectors in the longitudinal and the transversal sections: (a) run-up of
tsunami (t = 1 s) and (b) downflow of tsunami (t = 15 s).
erosion occurred on the land side of the cylinder inthe experiment, sediment deposition occurred in thecalculation.
The calculated bed variation contour with nearbed flow velocity vectors and measured bed
variation contour at the final stage are shown in
Figure 8(a) and 8(b), respectively. As shown in Fig.
8(a), at the time of the tsunami run-up (t = 1–6 s),
reverse flows occur on the offshore side of thecylinder due to the development of horseshoe
vortexes, and the scour hole is formed concentrically.
Moreover, a sediment deposition area is formed
simultaneously on the land side. At the time of thetsunami backflow (t = 15–20 s), the sedimentdeposition flows into the scour hole. This is because
the scour depth at St. C could not be reproduced, as
shown in Fig. 7. The calculated bed variation
contours at t = 15–20 s are relatively similar to themeasured one shown in Fig. 8(b). However, thecalculated bed variation contour at the final stage
differs from the measured contour as explained
above. The authors tried to calculate the bed

variation using various suspended-load transport rate
formulas to reproduce this local scour, neverthelessfailed to reproduce it.
Three-Dimensional Flow around Cylinder
Figure 9 illustrates the time variations from abird’s-eye view of the calculated topography andwater surface. It can be seen that the model canadequately calculate the flooding and drying bed in
the subcritical and supercritical flow fields causedby the tsunami run-up and downflow.
The calculated 3D flow velocity vectors in thelongitudinal and transverse sections at the time of
the tsunami run-up and downflow were shown in Fig.
10(a) and (b), respectively. The suspended-loadconcentration contours were also indicated in thesefigures. Here, the land side of the cylinder is focusedon, where the local scour could not be reproduced by
the presented model. As shown in the longitudinal
section of Fig. 10(a), an upward flow that induces
curling up of the bed-load is reproduced at the time603

GEOMATE – Melbourne, Australia, 11-13 November 2020of the tsunami run-up. This flow situation can also
be seen in the transverse section of Fig. 10(a).
Moreover, a horseshoe vortex that expedites theformation of the local scour is reproduced at the timeof the tsunami downflow shown in the longitudinal
section of Fig. 10(b). In other words, the presented
model can reproduce the flows, which have alreadybeen confirmed [1], on the land side of the cylinder,
and there is a possibility that the model can
reproduce the phenomenon more accurately byconsidering the mechanism of the curling up of the
bed-load. Furthermore, the seepage of the tsunami
run-up flow into the beach was not considered in thismodel. Therefore, a calculation considering seepage
flows is also needed to improve the prediction
accuracy.

tsunami wave run-up and effects on sedimentscour around a cylindrical pier, J. Eng. Mech.,Vol. 138, No. 10, 2012, pp.1224-1235.
[5] Larsen B. E., Fuhrman D. R., Baykal C., andSumer B. M., Tsunami-induced scour aroundmonopile foundations, Coast. Eng., Vol. 129,2017, pp.36-49.
[6] Quezada M., Tamburrino A., and Niño Y.,
Numerical simulation of scour around circularpiles due to unsteady currents and oscillatory
flows, Eng. Appl. Comput. Fluid Mech., Vol. 12,
No. 1, 2018, pp.354-374.
[7] Hirt C. W. and Sicilian J. M., A porosity
technique for the definition obstacle inrectangular cell meshes, Proc. 4th Int. Conf. on
Ship Hydrodynamics, Washington, DC, 1985.
[8] Yakhot V., Orszag S. A., Thangam S., Gatski T.
B., and Speziale C. G., Development of
turbulence models for shear flows by a double
expansion technique, Phys. Fluid. A: Fluid
Dynam., Vol. 4, No. 7, 1992, pp.1510-1520.
[9] Uchida T. and Fukuoka S., Numericalcalculation for bed variation in compoundmeandering channel using depth integrated
model without assumption of shallow water flow,
Adv. Water Resour., Vol. 72, 2014, pp.45-56.
[10] Ashida K. and Michiue M., Study on hydraulicresistance and bed-load transport rate in alluvial
stream, Proc. Japan Soc. Civil Eng., Vol. 206,
1972, pp.59-69.
[11] Iwagaki Y., Fundamental study on criticaltractive force (I) Hydrodynamical study on
critical tractive force, Trans. Japan Soc. Civil
Eng., Vol. 41, 1956, pp.1-17.
[12] Takebayashi H. and Okabe T., Geometriccharacteristics of braided channel on nonuniform sediment bed, The Int. Cong.
INTERPRAVENT 2002 in the Pacific Rim,
Congress publication, 1, 2002, pp.79-89.
[13] Itakura T. and Kishi T., Open channel flow withsuspended sediments, J. Hydraulic Division,
ASCE, Vol. 106 (HY8), 1980, pp.1325-1343.
[14] Rubey W. W., Settling velocity of gravel, sandand silt particles, Amer. J. Sci., Vol. 25, 1933,
pp.325-338.
[15] Liu X.-D., Osher S., and Chan, T., Weighted
essentially non-oscillatory scheme, J. Comput.
Phys., Vol. 115, 1994, pp.200-212.
[16] Spalding D. B., A novel finite difference
formulation
for differential
expressions
involving both first and second derivatives, Int.
J. Num. Methods Eng., Vol. 4, 1972, pp.551559.

CONCLUSIONSIn this study, we have proposed a fully 3D flow
and topography change model considering the nonequilibrium bed-load and suspended-load transports
with the final goal of a highly accurate prediction of
local scour around cylindrical structures induced bytsunami run-up. The model was applied to a largescale laboratory experiment using a cylinder that is
installed on a uniform slope beach. Moreover,comparisons
betweenthecalculated
andexperimental data were carried out. Consequently,
the proposed numerical model was found toaccurately reproduce the time variations of waterlevels and scour depths on the offshore side of thecylinder and next to it. However, the model could
not reproduce the local scour on the land side of thecylinder. In our next work, we intend to perform thecalculations by incorporating the curling up of bed
load and the seepage of tsunami run-up flow into the
topography change model.
REFERENCES[1] Tonkin S., Yeh H., Kato F., and Sato S.,
Tsunami scour around a cylinder, J. Fluid Mech.,Vol. 496, 2003, pp.165-192.
[2] Larsen B. E., Arbøll L. K., Kristoffersen S. F.,
Carstensen S., and Furhrman D. R.,
Experimental study of tsunami-induced scour
around a monopile foundation, Coast. Eng., Vol.
138, 2018, pp.9-21.
[3] Nagata N., Hosoda T., Nakato T., and MuramotoY., Three-dimensional numerical model for flow
and bed deformation around river hydraulicstructures, J. Hydraul. Eng., Vol. 131, 2005,
pp.1074-1087.
[4] Pan C. and Huang W., Numerical modeling of604

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

ON RELATIONSHIP BETWEEN MONUMENT AND THE STATIONBUILDING
Kaho HASEGAWA1, Kazunari TANAKA1
1Graduate Course in Architecture, Civil Engineering and Urban Design, Graduate School of Engineering Osaka

Institute of Technology,５-16-1Asahi-ku Osaka Japan
ABSTRACTThe relationship in this paper is an important element that shows not only the scale of a city, but also the image
it is trying to project. The structure acts as a gateway to the culture, history and industry of a town. In recent years,
by installing monuments in a public square, it has increased people's opportunity to be exposed to art in everyday
life. Whereby, growing people's interest in art. Furthermore, a monument can be a symbol of a station. However,it can conversely be a factor that disrupts the view of the station square. Therefore, planners need to think about
aspects such as location, position, size etc., of the monument. Previous studies have arranged the characteristics
and constitution of the surroundings of monuments. The purpose of this study is to clear the characteristics of themonuments in front of the stations. We have not had methods to determine placement of monument. We
conducted psychological evaluation surveys and we found the relationship between monuments and buildings
show psychological elements of impression. These results are very promising and show that monuments can be an
important factor of station squares without disturbing views.
Keywords: monument, psychological evaluation, symbol, landscape

INTRODUCTION

SELECT THE STATION SQUARES

The square in front of the station is the entrance tothe city and the symbol of the town. The monuments
there are one of the symbols, but they also haveaneffect on the cityscape. The space in front of thestation forms the daily lives of local residents andgives the tourist an image of the city. It is
indispensable to focus attention on people’s
consciousness to find a characteristic station squarewith a monument.

GroupAPURPOSE AND METHOD
The purpose of this study is to clarify thecharacteristics of the monuments in front of thestations. Up to now, we have not had a standard
method for the various decisions that need to be madewhen placing a monument at a station. The results of
the research will show the possibility of standardizing
the design of station buildings and public squares infront of them.
The method of research here is collect the squares infront of the stations where the following monuments
are located and group them. Next, an impression
evaluation experiment is conducted for the stationsquares with similar characteristics. From the results,
we can grasp how the station space is influenced by
the monuments.

GroupB

GroupCGroupDFig1. Classification of monuments
605

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table3. Difference of factors with and without themonument

In this research, we target two lines in the suburbs ofOsaka (JR Kobe / Kyoto line, Hankyu Kobe / Kyoto
main line) . Then, we conducted principal component
analysis using the indicators of the monument and thesurrounding environment. Next, we classified themonuments by cluster analysis based on the threeaxes up to the extracted eigenvalue of 1.19 andcumulative contribution rate of 53.3%.
The 10 stations（Table2:A~J） were the subject of
psychological evaluation, centering on themonuments that are strongly connected with thesurrounding environment.
Table2. Select Stations
A St.Koshienguchi
C St.Rokkomichi
E St.Nagaokakyo
G St.Katura
I St.Nada

B
D
F
H
J

St.Nnishinomiya
St.Mukomachi
St.Minase
St.Ibarakishi
St.Kobe

No

1

2

3

4

A

-1.19

-2.20

0.05

-1.08

B

2.05

2.57

0.82

1.88

C

-1.35

-2.72

-0.93

-0.90

D

-1.13

-1.71

-0.17

-0.44

E

-0.33

-0.59

-0.71

-0.04

F

0.12

-0.31

0.30

0.00

G

-0.53

-0.97

0.75

-0.33

H

1.44

1.62

0.55

-0.27

I

-1.09

-1.68

-0.70

-0.99

J

1.48

2.73

2.55

2.37

healing
12

PSYCHOLOGIXAL EVALUATIONS● with monument● without monument

11
10

G
E

B
9

D
H

8

C

I

A
A

D
F
F
G

J

I

B

E

J

C

H
7
8

9

10

11

12

13

14

15

curiosity
Fig3. Factor score for each station(comfort-neatness)neatness
29
28
27
26
25
24
23
22
21
20
19
18
17

Fig2.Photos used in the experiment (the upper :withmonument, the lower: without monument)
Next, from these initial evaluations, I madequestionnaires with the Semantic Differential method
for the pictures of the station squares. I carried out a
5-step evaluation questionnaire for 14 pairs ofadjectives. The adjective pairs used are as shown in
Table4. The collected questionnaire data wasanalyzed with factor analysis to survey theimpression of the images.

● with monument● without monumentB
E

F

I

G
G

J

A
J

F

I

B

D

A

D

E

C
H
C
H

8

9

10

11

12

13

14

15

comfortFig4. Factor score for each station (curiosity-healing)606

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table4. Adjective pairs used in the experiment
Difference of factors with and without the monument

frustratingrefreshingoverwhelm

exciting

ordinary

specialhatefavorite

dirty

clear

closed

open

cool

warm

simplecomplicated

noisy

quiet

heavy

light

hard

soft

crampedfree

dangerous

safe

scatteredtidyGroup1

Group2

Based on the results of the questionnaire, a factor
analysis was performed using the main factor
method, and factors up to a cumulative contribution
ratio of 49.4% and an eigenvalue of 1.35 wereadopted. Each factor was evaluated in order ofneatness, comfort, curiosity, and healing; and thefactor score of each station was calculated. The
category score of each station is shown in Fig 3,4.
Group3
RESULT AND DISCUSSION
Fig5. Monument group example

First off, we focus on the difference between thepresence and absence of monuments in the target
station square. There was a big difference in theimpressions depending on the presence or absence ofmonuments in the target station square. Furthermore,
we pay attention to how impressions differ
depending on the presence or absence ofmonuments. Then, there is a difference in
impression depending on ones that give a positive
impression
to the entire station square (which are regarded ashighly symbolic monuments), and ones that give anegative impression (regarded as discordantmonuments). On the other hand, those with no effecton impression can be regarded as inconspicuousmonuments. We named the groups 1, 2, 3 in theorder of monuments with high symbolism, thediscordant monuments, and the inconspicuousmonuments.

CONCLUSIONSIn this study, we grasped the current situation bypositioning monuments, and surveying andorganizing target stations. In addition, we extracted
the elements composing the target monuments
andthe installation environment, and performed
classification using principal component analysis.
Therefore, the monuments that affect the installation
environment were extracted, and the landscapeevaluation of those monuments and the station space
was extracted from the psychological impression by
the questionnaire. The results indicate that this study
should be classified into four groups from the two
indicators of symbolism and harmony. As a futuretask, it is necessary to understand the physicalelements related to these groups in the station space.
607

GEOMATE – Melbourne, Australia, 11-13 November 2020

REFERENCES
Permission System for Private Monuments, Journalof the City Plannning Institute of Japan No.45-3,
45~43, 2010.10[4]Takanori Ito,ShoTaira,Kosuke Narita:Utilizationof the City Symbol for Creating Place Brandings,
Journal of the Science of Design, 133~140,
2004.11[5]Shunsuke Saikai, Suguru Mori:A Basic Study ofTransition on the Orientation of Public Arts in
Hokkaido, Proceedings of AIJ Hokkaido
Architectural Research Conference, 351~354,
2012.6[1] YukikoYamamoto,Shinji
Ikaruga:LandscapeCharactheristics of Installation Space from theOpen-Air Sculpture Installation Project Process,
J.Archit.Plan.,AIJ VoL.75 No.653,1697-1706,
1697~1706, 2010.7[2] Katunori Mizushima, Fumika Anbo, Yoshio
Itoi:A Basic Study on The Expression Structureof Monuments in Station Squares,
J.Archit.Plan.,AIJ No.585, 133~140[3] TetsunobuYoshitake, Kohta Yoshimoto, Chikashi
Deguchi:A Study on Municipal Guidance and608

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

STUDY ON THE MEANING OF “QUAINT” SPACE
Yoshiki Shirafuji, Kenta Aoki, Koji Inoue, Dr. Kazunari Tanaka
Department of Civil Engineering and Urban Design, Osaka Institute of Technology, JapanABSYRACT
Since ancient times, there is a word “quaint” which represents a sense of beauty in Japan. Although there arestudies on the architectural history, on the aging of facade and on the quality of calmness of houses, there is no
study on the sense of “quaint” regarding spaces. We clarify what kind of impression “quaint” gives by using
statistical analysis methods. It would be useful to clarify the concept of “quaint”. The research method consistedof a SD method and Image experiment. The adjectives are 5 steps each in the SD method, and the Image experiment
was a picture questionnaire with 6 figures. The figures are brightness-edited parts of a standard picture. The method
is based on the statistical data obtained from experiments. The analysis consisted of the Distribution Probability,
Co-occurrence Network, and Factor Analysis. As a result, it appears there is difference from the Distribution
Probability between the forms of the spaces. From the Co-occurrence Network Analysis, it was clear that the dark
parts (or direction) and the bright parts (or direction) in spaces have an influence of feeling quaint. The two factorsof “fresh feeling” and “stylish atmosphere” by the Factor Analysis have a possibility to explain the feeling of“quaint”.
Keywords: Aji, Quaint, Distribution probability, Co-occurrence network, Factor analysis
INTRODUCTION

The quaintness things could be grouped into 5 groups
like figure.1 from KJ method. The targets were mostly
in a room, that’s why we choose space in a room.

The evaluation on authorizing it as engineering
works inheritance establishes weight in beauty andfriendly feeling particularly a technical aspect and therarity and evaluates it[1]. I bring about the newviewpoint and sense of values for maintenance, theprotection of the engineering works structure or a planand the design by adopting the evaluation from anangle unlike these and think that how to catch may
change.

NONFIGURATIVE QUAINT RESEARCH
We carried out the questionnaire survey by SD
method on an imagined space for about 76 people who
gave their impression on a space imagined freely by asubject. For statistical data, we performed analysis
with probability distribution, factor analysis and cooccurrence network analysis. From the probability
distribution, right and left came out symmetric which
validated the data for all adjective. Showing an
example table 1. A difference appeared in the spacethat felt quaint when we did probability distribution foreach form of the room. Showing an example table 2.

BASIC RESEARCH
Using a dictionary and the Internet, we extracted
expression of the Aji (quaint) to define the meaning ofquaint in this study and performed a questionnaire
survey for this with 73 subjects. As a result, Aji meansa thing that brings back pleasant feelings or memories.
We investigated this using the KJ method on 13 people
to grasp what kind of object people felt quaint with.

Table 1 probability distribution

clean
Figure 1

KJ method609

brightGEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2 Differences of probability distribution

Table 3 Factor Analysis
Factor
Factor 1

Factor 2

mordrnbland newFactor 3

An object and the relation of the form of the room wereanalyzed with co-occurrence network analysis. The
relations with the object are the thinnest, and theJapanese-style room is considered to have an
impression of being tidied up. This had the mostnumber of the groups, and there were few strong ties.
The Western-style room had least number of groups,
and the ties in each group were stronger.

Factor 4

Adjective

point

Modern0.610

Brand new

0.651

Bright

0.536

Clean

0.838

Coherence

0.640

Messy

0.677

Interesting

0.735

Showy

0.412

Unique0.676

Comfortable

0.751

Quietly

0.569

Messy

0.406

VISUALLY QUAINT RESEARCH
We evaluated the visually “quaint” feeling with
nonfigurative research. We carried out two kinds ofquestionnaire surveys. One was evaluation by the paircomparison using four pictures (Table 4 pictures 1)
where brightness and warmth were altered for 98
people. It turned out the picture that was made darker
and warmer was considered the most quaint. The otherevaluation was the six pictures method which alteredthe upper, lower, upper-side, lower-side etc., parts of apicture to warmer and/or darker (T able 5 pictures 2)[2]. As a result, a picture with many points that wereedited were considered more quaint. Next, we
examined the rank of the results, and it was revealed
that the spot which ranked the highest was the upperside part.
Table 4 pictures 1

Figure 2 co-occurrence network
For the factor analysis, we got 4 factors to show intable 2. We interpret it as factor 1 and factor 2 areconsidered to be relatively easy to explain. Factor 1
interpreted as "refreshing" and "new construction",and factor 2 as "united feeling", "atmosphere thatlooked good". From the result of these analyses, we
supposed imagined space. In this study we ignored thepoint less than 0.500.

610

light-warm

dark-warm

light-colddark-cold

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 5 pictures 2

upper

side

lower

upper-side

lower-side

upper-lower

Figure 3 Rader chart
to feel quaint. We believe this is because we didn’t
quantify it. In addition, it is necessary to spread tomunicipal facilities, community spaces, and toinvestigate objects based on these results.

SUMMARY
From basic research, Aji means a thing that bringsback pleasant feelings or memories. But in KJ method,
no one mention feelings or memories. Everyone
mentions a thing. We guess that they mention a thingwith a itself background like a worn-out wallet.
From nonfigurative quaint research, In all adjectives
that we used, we got the accuracy of data. In cooccurrence network, By the type of the rooms, adifference was greatly reflected on the relations withthe thing. In the factor analysis, we got a factor of theabstract quaint.
From visually quaint research in that pictures,warmly-darkly is the best to feel quaint. Making aradar chart with the ranking and score. And make aline between ‘upper-lower’ and ‘side’ on figure 3. Leftside of the line is easy to feel quaint. Right side is notimportant to feel quaint.

Figure 4 probability distribution

REFFRENCE

FUTURE ISSUE[1] 土木学会土木史研究委員会，『日本の近代土木遺In this study, we analyzed many aspects about“quaintness” and grasped the big picture regarding thewhat makes an image quaint. The object of thequestionnaire is about 20 generations, and a result may
change by the generation. Some disagreement
occurred between visually quaint and nonfigurative
quaint from the questionnaire. for example, figure 4 isshowing probability distribution of “bright” and“warm”. In this data, when people feel quaint, thespace should be brightly and warmly. But comparewith a result of visually quaint research, there is adisagreement about “bright”. An imaged quaint spaceis bright. but darkly space is better than brightly space

[2]

611

産（改定版）―現存する重要な土木構造物2800選』，
土木学会，2005
P.シール，船津孝行訳編『環境心理学6 環境研究の方法』誠信書房，1975

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SPATIAL CHARACTERISTICS OF FACTORS INFLUENCING THE
OCCURRENCE AND SCALE OF MULTIPLE SLOPE FAILURES
Takashi Wada1, Ryo Kodani2, Hiroshi Miwa1
1
Department of Social Systems and Civil Engineering, Tottori University, Japan;
2
Tottori City Office, Japan
ABSTRACTIn this study, we investigated the spatial characteristics of the factors influencing the occurrence and scale ofmultiple slope failures in areas with smaller grid sized than the unit size for the current emergency alert system for
sediment disasters in Japan. We used high-resolution data for the spatial distribution of topography and rainfall.
The study areas were located in southern Hiroshima Prefecture and southeastern Ehime Prefecture of Japan, wheremultiple slope failures occurred due to heavy rainfall in July 2018. With an increase in slope gradient, thefrequency of smaller-scale slope failures increased significantly in southeastern Ehime Prefecture, whereas thistendency was not as noticeable in southern Hiroshima Prefecture. Larger-scale slope failures occurred at the same
frequency on the area of mild (< 30 degree) and steep (≥ 30 degree) slope gradients. The reason for this is that thewide ranges of these slope failures expand to mild slopes closer to the mountain ridges. Total precipitation (i.e.,
long-term rainfall index) influenced the frequency of the occurrence of slope failure in southern Hiroshima
Prefecture. Whereas, Maximum hourly precipitation (i.e., short-term rainfall index) related to the occurrence ofslope failure in both study areas. Furthermore, we were able to estimate with a high possibility the occurrence ofslope failure using a smaller grid size than the unit size for the current system, by combining the topographic
indices (e.g., slope gradient) and rainfall indices (e.g., short- and long-term rainfall indices).
Keywords: Multiple slope failures, Slope gradient, Hourly precipitation, Total precipitation, GIS data

INTRODUCTION

characteristics of long-term rainfall indices (e.g., total
precipitation, daily precipitation) and local landfactors in the areas with multiple slope failures inJapan using GIS data and aerial photographs. Theirresults indicated that the occurrence of slope failure
and a slope gradients had a positive correlation. In
addition, the occurrence of slope failure and longterm rainfall indices were also positively correlated.
However, factors influencing the scale of slope
failure have hardly been investigated in the previous
studies.
It is widely accepted that high intensity and highduration rainfall events are the common important
meteorological trigger for slope failures worldwide[4]. As such, in recent decades, several researchers
have attempted to establish a rainfall-based alert
system for sediment disasters [5]. In Japan, a rainfallbased alert system for sediment disasters in Japan
uses a short-term rainfall index (e.g., hourly
precipitation) and a long-term rainfall index (e.g.,
antecedent rainfall, precipitation for several days, soil
water index) to determine the threshold for theoccurrence of slope failure, the so-called critical line(CL) [6]. However, since these systems use therainfall data obtained at the sparse observation points
spatially, the space resolution of the rainfall data islow. Therefore, it is difficult to investigate the local
relationships between the rainfall indices and thefactors influencing an occurrence of a slope failure
for small watersheds and districts. In addition, theRecently, the multiple sediment disasters, such as
the disaster caused by the heavy rainfall due to theseasonal rain front and Typhoon Prapiroon in July
2018, have increased in Japan. Since the heavy
rainfall occurred intensely and widely, and led to total
precipitation of over 400 mm, it caused the serious
sediment disasters in most part of Hiroshima andEhime Prefectures [1], [2]. The unit size of the current
emergency alert system for sediment disasters inJapan is too large (mainly a grid of 5 km) so that local
inhabitants have difficulties in perceiving an
increased possibility of nearby slope failure andsediment runoff by the emergency alert. Moreover,this unit size is not precise enough to consider local
conditions of surface topography and precipitation,
which may influence the occurrence of slope failures.
Considering the above-mentioned facts, it isnecessary to establish an emergency alert system for
sediment disasters on a smaller scale than that of thecurrent system. Before a new system is implemented,
it is necessary to investigate the spatial characteristics
of local land factors (e.g., surface topography,
geological features, land use conditions) andmeteorological triggers (e.g., short- and long-term
rainfall indices, wind direction), which can influence
the occurrence and scale of slope failures on a smaller
grid scale.
Iwahashi et al. [3] investigated the spatial
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GEOMATE – Melbourne, Australia, 11-13 November 2020

local land factors are not considered to determine thecritical line (CL) for the occurrence of slope failure inthese systems.
Herein, we investigate the spatial characteristicsof the factors influencing the occurrence and scale ofmultiple slope failures caused by the heavy rainfall in
July 2018. We scrutinize the characteristics on asmaller grid range than the one for the topographic
and rainfall data used in the previous studies and thecurrent alert system. For the study, the 10-m grid
Digital Elevation Model (DEM) data provided by the
Geospatial Information Authority of Japan and the250-m grid rainfall data from the extended radar
information network (XRAIN) in Japan were utilized.
These data were converted to the 15-m grid DEM data
and the 350-m grid rainfall data by averaging
spatially these data.

Fukuyama City
Fuchu City

Kumano Town

N

Higashi-Hiroshima City
Mihara City
Southern Hiroshima
Prefecture

Hiroshima

Slope gradient City
(deg.)

Onomichi City
Type for
failure scale

Kure City

Type 1
Type 2Type 3

Takehara City

Kaida Town
Saka Town
Edajima City(a) Southern Hiroshima Prefecture
Slope gradient(deg.)

N
Ohsu City

Yawatahama City
Seiyo CitySTUDY AREAS AND METHODS
Study area locations

Type for
failure scaleSouthern Hiroshima Prefecture and southeastern
Ehime Prefecture were selected as study areas. These
areas are characterized by multiple slope failures that
occurred by the heavy rainfall in July 2018 and led tothe serious sediment disasters with many humandamages. Fig. 1 shows the spatial distribution of slope
failures obtained by the location data of them fromthe Geospatial Information Authority of Japan andslope gradients calculated by the 15-m grid DEM datain both study areas. The slope failures were classified
using the methodology described below.

Type 1
Type 2Type 3

Uwajima City

Southeastern
Ehime Prefecture(b) Southeastern Ehime Prefecture
Spatial distribution of various slope failurescaused by the heavy rainfall in July 2018 in
two study areas.

Number of slope failures for each scale typeFig. 1

Classification of slope failures
We classified the slope failures surveyed by
Sasahara et al. [2] (a total of 73 failures) into threescale types: large-scale failure (Type 1), small-scale
failure (Type 2), and erosion of a mountain stream
bed (Type 3). Table 1 shows a comparison of the fieldsurvey results with the estimated results obtainedusing the aerial photographs by the Geospatial
Information Authority of Japan for each scale type ofslope failure. The slope failures classification was
based on the estimated failure volume, that is, afailure with over 1,000 m3 of the volume wasclassified as Type 1, a failure with under 1,000 m3 ofthe volume was classified as Type 2, and a sediment
runoff with a negligible failure area at its upstream
end was classified as Type 3. By investigating thescale and area of slope failure using the aerial
photographs, it was estimated that they werepositively correlated. On basis of the investigating
results, the areas of slope failures of Types 1, 2 and 3
were defined as over 1,000 m2, 100–1,000 m2 andunder 100 m2, respectively. Therefore, we classified
the other slope failures into three scale types with nofield survey on basis of their failure areas estimated

2500

59.1%

Type 1
Type 2Type 3

2000
40.4%

1500
1000

90.9%

500

Fig. 2

0

0.5%

6.2%

Southern Hiroshima
Prefecture(N = 3999)

Southeastern Ehime
Prefecture(N = 806)

2.9%

Number and percentage of slope failures pereach slope failure scale type.

by the aerial photographs in both study areas.
Fig. 1 shows the spatial distribution of all slope
failure scale types in both study areas based on theclassification method described above. Fig. 2 shows
the number and percentage of slope failures for eachscale type and study area. The failures of Type 3 were
almost absent in southeastern Ehime Prefecture,
whereas they were founded in abundance in southern
Hiroshima Prefecture. The percentage of Type 1
failures to all other types of failures was higher insoutheastern Ehime Prefecture than in southern613

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 An example of a comparison of the field survey results with the aerial photograph results for each slope
failure scale type in southeastern Ehime Prefecture, Japan.
Scale type of
Estimated results by the aerial photographs
Field survey results by Sasahara et al. [2]slope failure
after the disaster∙Failure depth;∙Failure head area;
About 7.8 m
About 2200 m2
Type 1∙Failure width;
About 45 m
Estimated range
Large scale ∙Length of failure surface;of failure head
About 75.5 m
About 2 m
failure∙Failure volume;
30 m
About 26500 m3∙Failure depth;
About 1.5 m
Type 2∙Failure width;
About 5 m
Small scale ∙Length of failure surface;
About 40 m
failureAbout 2 m
∙Failure volume;
About 300 m3∙Erosion depth;Type 3
About 2.5 m

Erosion ofmountainstream bed∙Failure head area;
About 100 m2

30 m∙Failure head area;
About 80 m2

Estimated rangeof stream head

About 2 m
30 m

Hiroshima Prefecture. These results indicate thatvarious scale types of slope failures are predominant
in some regions and are almost absent in other regions,despite being triggered by the same heavy rainfall.
One possible reason for this is that their local landfactors are different. As such, we investigated thespatial characteristics of topographic and rainfall
indices, which influence the occurrence of these slope
failures, and compared them between the study areas.
Herein, the topographic indices were the slope
gradient and slope-facing direction. The rainfall
indices were the total precipitation and maximum
hourly precipitation. Geological features, land use
conditions and wind directions were not considered inthis study because it was difficult to compare theirinfluence on the occurrence of slope failures between
two study areas by great difference between thecharacteristics of these factors in two study areas.
In order to investigate the effect of these indiceson slope failure, considering the frequency of theindex appearance in the study areas, we define thespatial frequency of slope failures for factor (F) using
the following equation:

F =  Af

Estimated range
of failure headAfrequencies of slope failure for the slope gradient,slope-facing direction, total precipitation andmaximum hourly precipitation, respectively.
RESULTS AND DISCUSSION
Occurrence and scale of slope failures vs.
topographic indicesFig. 3 shows the spatial frequency of slope failure
for slope gradient Fgr for all slope failure scale types
based on the 15-m grid-sized area. As slope gradientincreases, the frequency of the Type 2 and 3 slope
failure increases significantly in southeastern Ehime
Prefecture, whereas this tendency is not as evident insouthern Hiroshima Prefecture. We concluded that
other indices, such as the geological feature (strongly
weathered granite) and large total precipitation,
influence the occurrence of slope failure in southern
Hiroshima Prefecture. Larger-scale slope failuresoccurred at the same frequency on the area of mild (<30 degree) and steep (≥ 30 degree) slope gradients.
The reason for this is that the wide ranges of theseslope failures expand to mild slopes closer to themountain ridges. Accordingly, it is not necessarily topositively correlate a slope gradient to the occurrenceof slope failure because this relationship is likely
influenced by other factors (e.g., geological features,
total precipitation). Therefore, we conducted aninvestigation on the spatial characteristics of the(1)

where, Af is the total area of the target grids
including slope failures; A is the total area of thetarget grids in the target range, including all of themunicipalities where the slope failures occurred inJuly 2018. Fgr, Fdi, Ftp and Fhp are the spatial
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Spatial frequency of slope failures for slope gradient, Fgr0.030%
0.025%
0.020%

30%(a) Southern Hiroshima Prefecture
0°- 10°10°- 20°20°- 30°30°- 40°40°- 50°50°- 60°

Spatial frequency of slope failures for total precipitation, Ftp

0.035%

0.015%
0.010%

0.005%
0.000%
0.035%

0.030%
0.025%
0.020%

Type 1
Type 2Type 3(b) Southeastern Ehime Prefecture
0°- 10°10°- 20°20°- 30°30°- 40°40°- 50°50°- 60°

0.015%※No mesh over 550 mm

5%
0%
30%
25%
20%
15%
10%

Type 1
Type 2(b) Southeastern Ehime Prefecture

Type 3150-200 mm
200-250 mm
250-300 mm
300-350 mm
350-400 mm
400-450 mm
450-500 mm
500-550 mm※No mesh over 550 mm

Type 1

Type 1

Spatial frequency of slope failures for slope-facing direction, Fdi

10%Fig. 50.000%

Type 2Type 3

Spatial frequency of slope failures for slope
gradient (Fgr) for each slope failure scale
type based on the 15-m grid-sized area.

0.020%

15%

150-200 mm
200-250 mm
250-300 mm
300-350 mm
350-400 mm
400-450 mm
450-500 mm
500-550 mm

0%0.005%

0.025%

20%(a) Southern Hiroshima Prefecture

5%0.010%

Fig. 325%

Total
precipitation(mm)

Type 2Type 3

Spatial frequency of slope failures for total
precipitation (Ftp) for each slope failure scale
type based on the 350-m grid-sized area.
Fukuyama City
Fuchu City

Kumano Town

N

Higashi-Hiroshima City(a) Southern Hiroshima Prefecture

Mihara City

North (NorthWest, NorthEast)-facing
East (SouthEast, NorthEast)-facing
South (SouthEast, SouthWest)-facing
West (SouthWest, NorthWest)-facing

HiroshimaCity

0.015%

Onomichi City

0.010%

Type for
failure scale

Kure City

0.005%

Kaida Town
Saka Town
Edajima City

0.000%
0.025%
0.020%

Type 1
Type 2Type 3(b) Southeastern Ehime Prefecture

Fig. 6

North (NorthWest, NorthEast)-facing
East (SouthEast, NorthEast)-facing
South (SouthEast, SouthWest)-facing
West (SouthWest, NorthWest)-facing

0.015%
0.010%

Type 1
Type 2Type 3

Spatial distribution of slope failures for eachslope failure scale type and total
precipitation in southern Hiroshima
Prefecture.

seen in each study area. This indicates that theoccurrence of slope failure is unrelated to a slopefacing direction.

0.005%

0.000%

Type 1

Fig. 4

Takehara City

Type 2

Occurrence and scale of slope failures vs. rainfall
indices

Type 3

Spatial frequency of slope failures for slopefacing direction (Fdi) for each slope failure
scale type based on the 15-m grid-sized area.

Fig. 5 shows the spatial frequency of slope
failures for total precipitation Ftp for all slope failure
scale types based on the 350-m grid-sized area. In
southern Hiroshima Prefecture, as total precipitation
increases, the frequency of slope failure increases
significantly. However, the frequency of slope failure
for total precipitation range of 200–250 mm is
relatively high due to the occurrence of slope failurein the limited small area of 200–250 mm of total
precipitation. The special local land factor in this area
may influence their occurrence and should be

combined indices of local land factors andmeteorological triggers, which influence theoccurrence and scale of slope failures.
Fig. 4 shows the spatial frequency of slope failure
for slope-facing direction Fdi for each slope failure
scale types based on the 15-m grid-sized area. Fdi forall slope failure scale types are almost equal and theremarkable difference of Fdi for each scale type is not
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Spatial frequency of slope failures for maximum hourly precipitation, Fhp

investigated in future works. The frequency of slope
failure is high (> 10%) when total precipitation
exceeds 450 mm, except the range of 200–250 mm.
This is confirmed by overlapping of the high-density
area of slope failure with the high total precipitation
area in southern Hiroshima Prefecture (Fig. 6).
However, in southeastern Ehime Prefecture, total
precipitation and the frequency of slope failure are
not correlated relatively.
Fig. 7 shows the spatial frequency of slope failure
for maximum hourly precipitation Fhp for each slope
failure scale type based on the 350-m grid-sized area.
As maximum hourly precipitation increases, thefrequency of slope failures increases in both study
areas. Especially in southeastern Ehime Prefecture,
the frequency of slope failures is high (> 20%) whenmaximum hourly precipitation exceeds 80 mm/hr.
This is confirmed by overlapping of the high-density
area of slope failure with the high maximum hourly
precipitation area in southeastern Ehime Prefecture(Fig. 8).
These results indicate that the occurrence andscale of typical slope failures depend on the rainfall
indices with a different manner in each study area.
Total precipitation (i.e., long-term rainfall index) is
predominantly responsible for slope failure insouthern Hiroshima Prefecture, whereas maximum
hourly precipitation (i.e., short-term rainfall index)
affects slope failure in both study areas. The study
results also show that the 350-m grid-sized XRAIN
data for the rainfall index lead to a high possibility of
estimating the occurrence of slope failure in the gridsized area.

60%(a) Southern Hiroshima Prefecture
10-20 mm/hr
20-30 mm/hr
30-40 mm/hr
40-50 mm/hr
50-60 mm/hr
60-70 mm/hr
70-80 mm/hr
80-90 mm/hr
90-100 mm/hr

50%
40%
30%
20%
10%
0%

Type 160%Type 2Type 3(b) Southeastern Ehime Prefecture
10-20 mm/hr
20-30 mm/hr
30-40 mm/hr
40-50 mm/hr
50-60 mm/hr
60-70 mm/hr
70-80 mm/hr
80-90 mm/hr
90-100 mm/hr

50%
40%
30%
20%
10%
0%

Type 1

Fig. 7

Type 2Type 3

Spatial frequency of slope failures for
maximum hourly precipitation (Fhp) for eachslope failure scale type based on the 350-m
grid-sized area.

N
Ohsu City
Yawatahama City
Seiyo City
maximum hourly
precipitation(mm/hr)Type for
failure scale

Occurrence of slope failures and combined indicesof local land factors and meteorological triggers

Uwajima City

Table 2 shows the spatial frequency matrix ofslope failures for all slope failure scale types and the
combined indices of local land factors andmeteorological triggers based on the 15-m grid-sizedarea. Using all 15-m grid DEM data without
narrowing by any rainfall indices, the spatial
frequencies of slope failures for various slope
gradients (Fgr) in both study areas equal only 0.046%
and 0.017% at most, respectively. However, as the
maximum hourly precipitation and the total
precipitation increase, Fgr for all slope gradients
increases more in both study areas. As mentioned
above, the rainfall indices that influence Fgrremarkably are different in each study area. In
southern Hiroshima Prefecture, when a slope gradient
ranges between 30° and 40° and total precipitation isover 450 mm, Fgr equals 0.249%, which isapproximately 6.5 times higher than that without
narrowing by any rainfall indices. In southeasternarea Ehime Prefecture, when the slope gradient
ranges between 30° and 40° and maximum hourly
precipitation is over 90 mm/hr, Fgr equals 0.306%,

Fig. 8Type 1
Type 2Type 3

Spatial distribution of slope failures for eachslope failure scale type and maximum hourly
precipitation in southeastern Ehime
Prefecture.

which is approximately 18 times higher than thatwithout narrowing by any rainfall indices. As a result,by combining the topographic and rainfall indices, wemay be able to estimate with a high possibility theoccurrence of slope failure using a smaller grid size
than the unit size for the current system.
CONCLUSIONSIn this study, we used high-resolution dataobtained from two areas in Japan to reveal the spatial
characteristics of the topographic and rainfall indicesinfluencing the occurrence and scale of multiple slope
failures caused by the heavy rainfall in July 2018 inareas with smaller grid sized than the unit size for thecurrent emergency alert system for sediment disasters
in Japan.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2 Spatial frequency matrix of slope failures for all slope failure scale types for a slope gradient and rainfall
indices. The purple, red, orange, yellow and cream cell colors equal > 0.3%, > 0.2%, > 0.1%, > 0.05%
and > 0.01%.
Southern Hiroshima Prefecture

Slope gradient

Maximum hourly
precipitation

Total precipitation

Rainfall index.

Southeastern Ehime Prefecture

0°- 10° 10°- 20° 20°- 30° 30°- 40° 40°- 50° 50°- 60° 0°- 10° 10°- 20° 20°- 30° 30°- 40° 40°- 50° 50°- 60°

All data(Over 0 mm)

0.0026% 0.0179% 0.0459% 0.0382% 0.0131% 0.0186% 0.0007% 0.0045% 0.0096% 0.0163% 0.0167% 0.0034%

Over 300 mm

0.0026% 0.0180% 0.0465% 0.0386% 0.0132% 0.0189% 0.0011% 0.0082% 0.0182% 0.0297% 0.0297% 0.0071%

Over 400 mm

0.0037% 0.0404% 0.1027% 0.1002% 0.0512% 0.0565% 0.0014% 0.0032% 0.0075% 0.0102% 0.0133% 0.0000%

Over 450 mm

0.0043% 0.0732% 0.1888% 0.2490% 0.1928% 0.0000% 0.0000% 0.0000% 0.0010% 0.0000% 0.0000% 0.0000%

All data(Over 0 mm/hr)

0.0026% 0.0179% 0.0459% 0.0382% 0.0131% 0.0186% 0.0007% 0.0045% 0.0096% 0.0163% 0.0167% 0.0034%

Over 30 mm/hr

0.0029% 0.0214% 0.0561% 0.0489% 0.0181% 0.0203% 0.0031% 0.0184% 0.0389% 0.0629% 0.0602% 0.0127%

Over 60 mm/hr

0.0032% 0.0369% 0.0859% 0.1203% 0.0824% 0.1057% 0.0050% 0.0426% 0.1122% 0.1969% 0.1961% 0.0473%

Over 90 mm/hr

No rainfall data

0.0186% 0.1392% 0.2255% 0.3060% 0.1517% 0.0000%

The occurrence and scale of typical slope
failures depend on a slope gradient and total
precipitation (i.e., long-term rainfall index) with adifferent manner in each study area and can be
influenced by other factors, such as geologicalfeatures. Whereas maximum hourly precipitation (i.e.,
short-term rainfall index) affects slope failure in bothstudy areas.
We investigated the spatial frequency of slope
failure for all slope failure scale types by combining
the topographic and rainfall indices, which influence
the occurrence and scale of slope failure. The results
indicate that there is a high possibility to estimate theoccurrence of slope failure by combining these
indices using a smaller grid size than the unit size forthe current system.
Geological features and land use conditions were
not considered herein. Therefore, it is necessary toevaluate the spatial frequency of slope failure basedon these indices in both study areas in a future project.
It is also essential to estimate temporal changes in the
short-term and long-term rainfall indices for each
topographic index in areas with the smaller grid thanthe unit size for the current system, in order toestimate the occurrence and scale of slope failure
spatially and time-variably. Furthermore, theproposed methodology has to be verified by applying
it in areas with other multiple slope failures.

REFERENCES[1] Kaibori M., Hasegawa Y., Yamashita Y., Sakida
H., Nakai S., Kuwada S., Hiramatsu S., JitousonoT., Irasawa M., Shimizu O., Imaizumi F.,
Nakatani K., Kashiwabara Y., Kato N., Torita E.,
Hirakawa Y., Yoshinaga S., Tanaka K. andHayashi S., Sediment related disaster due toheavy rainfall in Hiroshima Prefecture in July,
2018, Journal of the Japan Society of Erosion
Control Engineering, Vol.71, No.4, 2018, pp.
49–60. (in Japanese with English abstract)[2] Sasahara K., Ikeda T., Iwai Y., Kakuta K.,
Kanazawa A., Gonda Y., Saitou Y., Shuin Y.,
Tagata S., Fujita M., Miyata S., Miwa H., Murata
I., Yamanoi K. and Wada T., Sediment Disasters
in Shikoku Region in July, 2018, Journal of theJapan Society of Erosion Control Engineering,
Vol.71, No.5, 2019, pp. 43–53. (in Japanese withEnglish abstract)[3] Iwahashi J., Yamagishi H., Kamiya I. and Sato H.,
Discriminant Analysis for Lndslides Caused by
the 2004 Niigata Heavy Rainfall in July and the
Mid Niigata Prefecture Earthquake in October,
Journal of the Japan Landslide Society, Vol.45,
No.1, 2008, pp. 1–12. (in Japanese with English
abstract)[4] Wieczorek G. F., Landslides triggering
mechanisms, In: Turner K. and Schuster R. L.
(Eds.), Landslides: Investigation and Mitigation,
National Research Council, Transportation
Research Board, Washington, 1996, pp. 76–90.
[5] Wieczorek G. F. and Glade T., Climatic Factors
Influencing Occurrence of Debris Flows, In:
Jakob, M. and Hunger, O. (Eds.), Debris-flow
Hazards and Related Phenomena, Praxis, Springer,
Berlin, 2005, pp. 325–362.
[6] Osanai, N., Shimizu, T., Kuramoto, K., Kojima,S., and Noro, T. (2010): Japanese early-warningfor debris flows and slope failures using rainfall
indices with Radial Basis Function Network,
Landslides, Vol. 7, pp. 325–338.

ACKNOWLEDGMENTS
The field survey results used in our study isprovided by the survey group of Japan Society of
Erosion Control Engineering for the sediment disasterin Shikoku region caused by “Heavy Rain Event ofJuly 2018”. The XRAIN dataset used in our study isprovided by the Ministry of Land, Infrastructure,
Transport and Tourism (MLIT) of Japan, and thisdataset is archived and provided under the frameworkof the Data Integration and Analysis System (DIAS)
funded by Ministry of Education, Culture, Sports,
Science and Technology (MEXT) of Japan.
617

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SPATIAL ENTRAPMENT OF WOMEN WORKERS IN METROPOLITAN
AREAS
Putri Halimah1, Chotib2
1,2School of Strategic and Global Studies, University of Indonesia, Indonesia
ABSTRACTIntercity integration in a metropolitan area forms a unified city that is interconnected l ike a mutualism
symbiosis. Metropolitan areas provide opportunities for people between regions or between cities to connect witheach other by doing non-permanent mobility. The purpose of this study is to identify how much potentiallywomen are trapped spatially than men in some metropolitan areas in Indonesia. Spatial Entrapment is a condition
that is more common in women who trip in shorter distance and times during commuting between place ofresidence and place of work. This study uses micro data from the 2018 National Labor Force Survey(SAKERNAS), which the survey was conducted by Indonesian Central Beurau of Statistics (BPS). The research
approach is quantitative by using descriptive analysis to see the relationship between the independent variable
and the dependent variable through bivariate cross tabulation analysis and graphics. Then, inferential analysis isused to determine the effect of independent variables on dependent variables inferentially. The inferential
analysis model used in this study is multinomial logistics regression.
Keywords: Spatial entrapment, work trip, job mobility, gender gap, women workers

INTRODUCTION

Based on that urban phenomenon, we research inthree metropolitan areas in Sumatera Island,
Indonesia. They are Mebidangro (Medan, Binjai,
Deliserdang, Karo); Palapa (Padang, Lubuk Alung,Pariaman);, and Patung Raya Agung (Palembang,
Banyuasin, Ogan Ilir, Ogan Komering Ilir). This
paper highlights the problem of spatial entrapment on
dual career households with dependent variables are
distance and time work trip. While, independent
variables are income, age, education, working status,
and transportation. To examine spatial entrapment,
we use multinomial logit regression by SPSS 26. Datafor the study is derived from raw data of National
Labor Force Survey (SAKERNAS) 2018, Central
Bureau of Statistics. The advantage of this study is
knowing the factors that influence the movement of
women workers. So, in the future, stake holder will
be able to plan urban development in terms oftransportation, wages and labor policies that provide
more comfortable, safe and fair space for women
workers. The limitations of this study are limited in
the metropolitan area of the Sumatra Island, and thevariables used are also still limited.

In many developing countries, migration fromrural areas to urban areas because of the necessity to
improve better life. Urban areas offer classier
education, better social services, also better jobs.
Gradually, urban areas are transformed into
metropolitan areas. Within metropolitan areas,
human mobility in terms of work happens quickly andeasily which is supported by infrastructure progress.
It increases of women labor market and advances dual
career household. Naturally, this condition changes
the social theory in the traditional society about menbreadwinner and women-homemaker [1].
This paper analyzes spatial entrapment of
women’s work trip in a household. Spatial
entrapment is a condition where women’s work trip
shorter than men in distance and time [2]. Women
tend to experience unwell-being in job mobility,
because they prefer to get low wages than having
travel further [3]. Spatial entrapment can prevent
workers from getting jobs better, or limit them atlower paying jobs [4].
Another approaches argues that women’s shorter
work trip because of their role as a mother [2], [5].
The presence of children in the household also
contributes to the reduction in income from trips
attended by women [6], and also reduce a women’s
work trip but not a man’s [7]. Some researchers findthat occupational or industrial segmentation is areason why women’s work trip shorter. In
metropolitan cities, labor market competition is
fierce, for entering labor market women’s skill areneeded [8]. It means, high educated women are morewanted. Then, work trip cost becomes an increasing
expenditure for them [9].

LITERATURE REVIEW
Research on gender relations and spatial space has
provided important information about the mobility ofwomen workers. Research continues to develop in
highlighting it so that a "spatial entrapment" thesis
emerges. The thesis shows the limitations of the labormarket and women's journey to work. Since the1970s, researchers have found various case related to
the relationship between travel behavior and gender.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Women have shorter distance and time of travel [10].
The differences of gender show that work trip
depends on where women work and live [3]. Thisdifference is due to the activities carried out by
women, their dual roles as mothers and moneyearners. They are segmented in local labor markets[11]. Women have no choice to get better job awayfrom home, or women’s experiences are limited in awork career compared to his male counterparts in theindustries.
On the other hand, because the labor market isindeed limited. when women try to enter the labormarket and fix things that give disadvantages for
women economically. First, it is about wages that
discriminate against women. The discriminatory
wage structure is triggered by the demand for labor in
the company is smaller, but the more offer by women
workers in certain sectors [12]. Although, workers arehomogeneous theoretically, many industries
hypothesize that women and men workers are totallydifferent. Some industries provide low-wage,
predominately female-dominated jobs. They hire
women workers around the residents with apreference for women in mobility and limited skills
have shown the entrapment of working bysegmentation of local labor market.
Women workers reduce the time of wok trip
because of their household responsibilities and theconcentration of jobs in suburban areas [7]. Wheatley[5] describes about school-age children reflect thetime or schedule of school will be entrapping women
spatially, even highly skilled. The presence ofchildren was significant for entrapping women
worker. McQuaid and Chen [6] explain that womenwith two or three children will have work trip shorter.
The age of the youngest child influences it, where theolder the child, the less of traveling 30 min or more.
Rapino [2] shows gender roles are significant in
women's spatial entrapment. For married women,
decreased of work trip time about 3% if having
children under the age of five, decreased of work trip
time about 11.5% if having children having children
more than the age of five, and decreased of work trip
about 5% if having no children in comparison withmarried men. Madden [3] explains that work trip
lengths would be longer if women had the same job
tenure, same wages, and same work hours.
Men have higher mobility in each marital status,
single or married. Interestingly, married men will
have higher mobility compared to unmarried women[13]. In the United Kingdom, Spain, and Australia,
the proportion of total work time in mobility is twice

more than women. While women do not experiencespatial entrapment in Finland [14]. Other studies
show there are no systematic differences between
men and women moving towards larger companies
when looking at potential experience, years of work,
education, and working status, and only a few minor
differences for some sectors, company age and parttime status [15]. Another study indicates that genderequality in work hours and in wage will make as longas work trip between women and men, or women’s
will be longer [3]. Gender work trip gap based on the
differences in jobs status and wage, where bigger
household responsibility does not indicate women's
length of the work trip become shorter [16]. It
supports another study that female workers have arelatively long work trip and that the presence ofadults or other children in the household does not
reduce women's work trip [17].
Gender also influence to work trip time andaccessibility of workers. According to the study,
women tend to spend more time on their commuting
and have a more limited spatial range in urban areas
compared to men. Mostly, women depend on public
transportation as the main mode of transportation[18], [19]. Transportation has the largest direct effecton the time of workers' trip so that people with privatetransportation (drive own cars) has significantly
shorter work trip times than those who use public
transportation. There is reducing the genderdifference in work trip time because men and womenequally use public transportation. On the other hand,in the suburbs where transit hub design that connects
people to the place practically does not exist. It
causes workers to have to drive for work, and women
do not have access to it [20].
METHODSThis research uses a quantitative approach toidentify spatial entrapment of women worker
mobility in a household. We present descriptive
analyses by cross tabulation method, and inferential
analysis by multinomial logistic regression method.
This study uses the micro data from National Labor
Force Survey (SAKERNAS) 2018, Central Bureau of
Statistics. We research in three metropolitan areas in
Sumatera Island, Indonesia. They are Mebidangro(Medan, Binjai, Deliserdang, Karo); Palapa (Padang,
Lubuk Alung, Pariaman);, and Patung Raya Agung(Palembang, Banyuasin, Ogan Ilir, Ogan KomeringIlir). The number of samples represented was 3.637
households. Dependent variables and independent
variables category are below.

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 The Category of Dependent Variables andIndependent Variables

p1= pr (Y=1 | x) = [𝑒 𝑧𝑖
1+ 𝑒 𝑧𝑖 + 𝑒 𝑧2

] ; probability of

man’s distance and time are more than women
Dependent
Variables
Distance(dist)
Time(time)
Independent
Variables
Income(inc)
Education(educ)
Age(age)
Women work status1.
(workstat)
2.
Women Mode oftransportation(trasnp)

Category
1.
2.
3.
1.
2.
3.

Men > Women
Men < Women
Men = Women
Men > Women
Men < Women
Men = Women
Category

1.
2.
3.
1.
2.
3.
1.
2.
3.
1.
2.
1.
3.
4.

Men > Women
Men < Women
Men = Women
Men > Women
Men < Women
Men = Women
Men > Women
Men < Women
Men = Women
FormalInformalPublic transportation
Private transportation
Othersp2= pr (Y=2 | x) = [p3= pr (Y=3 | x) = [] ; probability of

1
1+ 𝑒 𝑧𝑖 + 𝑒 𝑧2

] ; probabilityof men’s and women’s are equal

RESULTSIn the model significance test, the value of the
final intercept is 0.00, meaning that at least one
independent variable influences the dependent
variable significantly. Partial test results show that
income, women work status, and mode oftransportation significantly influence the distance andtime of work of women. In the R2 coefficient,
Nagelkerke's value is 0.638 for distance and 0.625 fortime, meaning that the variability of the independent
variable explains 63.8% of the distance variable, and62,5% of the time variable.
Table 2 in below shows the descriptive analysis
results of relationship between independent variableand dependent variable (distances). We can see thatmen’s wage more than women’s trigger men’s work
trip distance longer 66,1%. While, when women’s
wage more than men’s wage only 34,9% cause
women’s work trip distance longer. Then, when there
is an equality wages between women and men
conclude an equality distance about 76,4%.

Multinomial logistic regression analysis method
is an analysis method that represents the relationshipbetween qualitative or categorical response variables(more than 2 categories) with one or several
independent variables. Research using this analyticalmethod because the independent variables and thecategorical independent variables are very suitable tobe used to see the independent variables on theaccepted variables. The model used in this study is asfollows. Logit function from the differences of
distance and time work trip between men and womenconsists of:Table 2 Descriptive Analysis Results between
Independent Variables and Dependent Variable(Distances)
Independent
Variable
Incomei. Logit function for Y = 1 relative to logit function
for Y = 3
𝑃 (𝑌=1|𝑥)] = β10 + β11 inc1+ β12 inc2+ β13 age1 +𝑃 (𝑌=3|𝑥)

β14 age2 + β15 educ1 + β16 educ2 + β17 workstat1 + β18transp1 + β19 transp2 + 𝜀(1)
Education

ii. Logit function for Y = 2 relative to logit function
for Y = 3ln [1+ 𝑒 𝑧𝑖 + 𝑒 𝑧2

man’s distance and time are less than womenResearch Model

ln [

𝑒 𝑧2

𝑃 (𝑌=2|𝑥)] = β20 + β21 inc1+ β22 inc2+ β23 age1 +𝑃 (𝑌=3|𝑥)Age

β24 age2 + β25 educ1 + β26 educ2 + β27 workstat1 + β28transp1 + β29 transp2 + 𝜀(2)Category Y=3 is the comparison. The value of
probability is obtained from the logistic function of
the logit model of the dichotomy above. Thus theprobability values for each category are:

Women’swork status
Women’smodetransportation

620

Men >
Women
Men <
Women
Men =
Women
Men >
Women
Men <
Women
Men =
Women
Men >
Women
Men <
Women
Men =
Women
FormalInformalPublicPrivateOthers

Men >
Women
66,1%

Men <
Women
4,6%

Men =
Women
29,3%

23,8%

34,9%

41,3%

19,6%

4,0%

76,4%

49,2%

6,3%

44,5%

41,1%

12,6%

46,3%

46,9%

7,4%

45,8%

46,2%

8,0%

45,8%

44,9%

10,8%

44,4%

47,5%

7,6%

44,9%

22,6%
50,9%
6,7%
2,0%
2,4%

29,7%
4,0%
80,5%
21,3%
42,8%

47,7%
45,2%
12,9%
2,7%
12,1%

GEOMATE – Melbourne, Australia, 11-13 November 2020

In education we can see that the higher women’s
education will make the higher distance (12,6%) orsame distance (46,3%) of women’s work trip. On theother hand, there is not significantly different about
age for women’s work trip distance. Informal work
status for women make a gender gap for distance, it isabout 50,9%. Then, formal work status gives thelonger distance for women about 29,7%. The last isabout mode of transportation. Mostly, women
workers use public transportation for going throughlonger distance.
Table 3 Descriptive Analysis Results between
Independent Variables and Dependent Variable(Times)Independent
Variable

IncomeEducation

Age

Women’swork status
Women’smodetransportatio
n

Men >
Women
Men <
Women
Men =
Women
Men >
Women
Men <
Women
Men =
Women
Men >
Women
Men <
Women
Men =
Women
FormalInforma
l

Men >
Wome
n
65,3%

Men <
Wome
n
4,6%

Men =
Wome
n
30,0%

23,6%

33,0%

43,4%

19,4%

4,4%

76,2%

48,3%

6,6%

45,1%

40,8%

12,4%

46,8%

46,4%

7,1%

46,5%

45,7%

7,8%

46,5%

44,1%

10,5%

45,4%

work trips by 13,345 times. While other variables
with significant influence do not show too high a gap.
Table 4 Multinomial Logistic Regression Analysis(Distances)
Parameter Estimates
Distance

Men > Women
Std.
B
Error

8,0%

44,1%

22,1%
50,4%

27,9%
4,2%

50,0%
45,5%

Public

6,6%

80,3%

13,1%

Private

1,7%

20,0%

3,1%

Others

2,6%

43,5%

11,8%

Men < Women
Std.
B
Error

Exp(B)

Income
Men >
Women
men <
women

1,922

0,118

6,836***

0,808

0,219

2,244***

1,245

0,179

3,472***

1,837

0,225

6,280***

Education
Men >
Women
men <
women

0,074

0,116

1,077

-0,271

0,202

0,762

-0,109

0,119

0,897

0,07

0,183

1,073

Age
Men >
Women
men <
women

0,06

0,181

1,062

0,048

0,307

1,049

-0,066

0,223

0,936

-0,038

0,372

0,962

0,244

13,345***

Women’s work status
Formal-2,1940,135

0,111***

2,591

Women’s mode of transportation
Public

0,639

0,241

1,895**

0,634

0,614

1,885

Private

0,438

0,126

1,550***

0,097

0,424

1,102

**

) significant at 5%***) significant at 1%

Table 5 Multinomial Logistic Regression Analysis(Times)Parameter Estimates
Men > Women
Men < Women

Times

47,9%

Exp(B)Std.
Error

Exp(B)

B

Std.
Error

Exp(B)

0,716

0,214

2,047***

1,694

0,221

5,443***

0,119

0,195

0,887

0,154

0,181

1,167

0,073
0,222
0,860
0,124
Women’s worker status
0,135
0,109***
2,310

0,295

0,929

0,361

0,884

0,229

10,070***

Women’s mode of transportation
0,238
1,666**
0,201
0,619

1,223

B

Based on table 3 we can see how independent
variables influence dependent variables (times).
Income, work status, and mode of transportation
present significantly value. Women’s work trip times
will be longer than men are caused by getting moreincome (33%), formal work status (27,9%), andpublic transportation (80,3%). Although the higher
education women have able to increase the times ofworking (12,4%), but men’s time still longer (40,8%).
Likewise, the age shows less effect for women times.
Table 4 in below shows the results ofmultinomial logistic regression analysis for the
distances. The tendency of women with higher
incomes to travel farther is 6,280 times greater. Thatis, when men's income is higher in the household,women are 3.473 times more likely to be spatiallytrapped in distance. Then, formal work is also able toprovide encouragement to women to make further

men >
women

1,851

0,116

Income6,368***

men <
women

1,202

0,178

3,326***

men >
women

0,040

0,115

Education
1,041

men <
women

-0,092

0,118

0,912

men >
women
men <
women

-0,009

0,181

Age
0,991

FormalPublicPrivate**-0,151-2,220

0,511
0,351

) significant at 5%

0,126
***

1,421**

0,199

0,402

0,819

) significant at 1%

Table 5 in above shows the results of multinomial
logistic regression analysis for the time. The resultshows the similarity to the previous one. Income orwage influence women’s work trip in time. The
higher income, the longer time of women's work trip.
621

GEOMATE – Melbourne, Australia, 11-13 November 2020

Even so, women with higher incomes will still be
trapped in travel time. Because, when a man's income
rises, he has 6,368 times the opportunity to increase
his travel time, and women is 5,443 times. Likewise,with formal employment status can increase women's
travel time 10,070 times longer. While other variables
do not provide a significant difference in numbers.

experience inequality in mobility, because they preferto get wages below the average, rather than choosing
to travel further [6].
According to the results in this study, independent
variables such as income or wages, women’s work
status, and mode of transportation influence thepattern of women’s mobility significantly. The higher
wages, formal work status, and using public
transportation increase women’s work trip in distanceand time. However, women workers are not used tousing private transportation because of limitation ofability in having own cars and drive it. In Indonesia,
car have a similarity price with house. This researchsupport previous research that state women more
trapped spatially than men in distance and also timeof working trip. Others independent variables, age
and education does not influence significantly for
women’s worker mobility.
It shows that metropolitan areas in Sumatera
Island describe a gender gap, inequality forparticipate in labor market. Gap of wages between
men and women has a domino effect on the economy
indirectly. women with low wages are unable tocompete with male workers. even though they areable to increase company productivity. dependence
on public transportation makes women trapped inwork that only has access to the transit hub. theycannot work for long distances and long periods oftime if there is no access to public transportation.
therefore, transportation facilities in metropolitan
cities need to be improved, such as the use of LRT.
However, Palembang Cities has LRT transportation
in in the city, but the transportation network has notbeen connected to the suburbs. Mostly, workers use
traditional transportation such as tugboat to down theriver, or buses to get to where they work.

DISCUSSION
The city center becomes a magnet for people inthe surrounding area to come to cities with the aim of
improving the economy, because in the city center
there are various industries, offices, businesses andtrade. The location theory pioneered by Von Thunen
states that the main factor that determines location
selection (land use) is the land rent / purchase price.
This urban transformation will form a pattern of theexistence of a core city as a center of urban activity inthe economic, administrative, political, and theexistence of satellite cities as a buffer in providing
space for abundance of activities in the core city, suchas more affordable housing [21].
Movement of people from villages to cities or
between cities is common today. Progress in theconstruction of facilities and infrastructure that
support the need for transportation causes the patternof human movement to be faster and integrated.
Mobility or human movement that occurs
continuously is a modern phenomenon that occurs inurban areas, especially in metropolitan cities,
industrial sites, offices, and businesses areconcentrated there. The large number of industries
and businesses makes the demand in the labor market
increase, thus attracting individuals to do work bycommuting, not only from villages to cities but also
to travel long distances between cities [22].
Various theories regarding the development ofurban structures which state that the city center orcentral business district (CBD) will experiencedevelopment until it reaches the periphery thatconverts agricultural land into residential land forworkers. The CBD area offers broader employment
opportunities with higher incomes, and the sub-urban
area offers the availability of settlements at affordable
prices. The existence of cohesiveness between urban
areas will certainly affect the pattern of movement oftravel (mobility) of the community, because there will
be workers from suburban areas who come to do job
mobility. Demographic, social and economic
characteristics have an influence on a person's
mobility patterns. The demographic characteristics ofthe sexes show that compared to men, women tend to

CONCLUSIONS
Research findings show spatial entrapment in
women workers are caused by income, women work
status, and mode of transportation. Men’s wage morethan women’s trigger men’s work trip distance longer
66,1%. While, when women’s wage more than men’s
wage only 34,9% cause women’s work trip distancelonger. Then, when there is an equality wages
between women and men conclude an equality
distance about 76,4%. Women’s informal work statuscauses 50,9% men’s distance work trip be longer.
And 80,5% using for public transportation cause
women’s work trip in distance longer. Same as thelength of work trip, wages, work status, and modetransportation influence how far women’s go to work.

622

GEOMATE – Melbourne, Australia, 11-13 November 2020[10] White, M.J. (1977), “A model of residential
location choice and commuting by men and women
workers”, Journal of Regional Science, Vol. 17, pp.
41–52.
[11] Hanson,
S.
and
G.
Pratt.
1988.
"Reconceptualizing the Link Between Home andWork in Urban Geography", Economic Geography,
4: 299-321.
[12] Jonung, C. (1998). Women's work and wages :A selection of papers from the 15th arne ryde
symposium on economics of gender and family inhonor of anna bugge and knut wicksell.
[13] Warsida, R. Y., Adioetomo, S. M., & Pardede,
E. L. (2013). The Effect of Socio-Demographic
Variables on Commuting in Jabodetabek. Jpournal of
Economics and Development Indonesia, 13, 159-176.
[14] Craig, L., & van Tienoven, T. P. (2019). Gender,
mobility and parental shares of daily travel with andfor children: a cross-national time use comparison.
Journal of Transport Geography, 76, 93-102.
[15] Del Bono, E., & Vuri, D. (2011). Job mobility
and the gender wage gap in Italy. Labour Economics,
18(1), 130-142.
[16] Kimbrough, G. (2016). What Drives GenderDifferences in Commuting. Unpublished Manuscript[17] England, K. V. L. (1993). Suburban Pink Collar
Ghettos: The Spatial Entrapment of Women? Annalsof the Association of American Geographers, 83,
225-242.
[18] Kwan, M.-P. (1999). Gender and Individual
Access to Urban Opportunities: A Study Using
Space–Time
Measures.
The
Professional
Geographer, 51(2), 211-227.
[19] Ng, W.-S., & Acker, A. (2018). Understanding
Urban Travel Behaviour by Gender for Efficient andEquitable Transport Policies. International Transport
Forum.
[20] Preston, V., & McLafferty, S. (1993). GenderDifferences in Commuting at Suburban and Central
Locations. Canadian Journal of Regional Science, 16,237-259.
[21] Lee, Everett S. A Theory of Migration.
Demography, Vol. 3, No. 1. (1966), pp. 47-57.
Published by Population Association of America[22] Sharma, A., Chandrasekhar, S., 2014. Growth of
the urban shadow, spatial distribution of economic
activities, and commuting by workers in rural andurban India. World Dev. 61, 154–166.

To overcome spatial entrapment inmetropolitan areas in Sumatera Island, it needs
cooperation from various sectors, such as
governments, companies, and other stakeholders indetermining wage eligibility for women workers,
increasing transit hubs for public transportation
networks, and a labor market that is wide open andfair for women.

ACKNOWLEDMENTS
This paper is part of University of Indonesia Research
Grant 2020: Social-Humanities International Indexed
Publications (Q4).

REFERENCES[1] Lewis, J. (2001) The decline of the malebreadwinner model: the implications for work andcare. Social Politics, 8,2,152–70.
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Development Quarterly 8: 171-185.

623

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

DAMAGE PREDICTION OF THE STEEL GIRDER ARCH BRIDGEMODEL BASED ON ARTIFICIAL NEURAL NETWORK METHOD

1,4

Widya Apriani1, Reni Suryanita2*, Yohannes Firzal2, and Fadrizal Lubis1
Departement of Civil Engineering, Lancang Kuning University, Indonesia; 2,3Departement of Civil
Engineering, University of Riau 2,3, Indonesia

ABSTRACTDisasters due to the collapse of bridge structures that cannot be predicted in advance require enormous
rehabilitation costs. This is caused by a decrease in ability or damage that cannot be identified earlier. The difficultyof directly predicting the conditions of the bridge structure is caused by the limitations of the test equipment testing
conditions. Therefore, the study aims to analyse the damage location and severity identification which are criticalstructural components on the steel girder arch bridge. This research used parametric of steel girder arch bridge fortraining and testing data in Artificial Neural Networks. Simply supported girder is selected as the numerical model.
The 146 data required for the ANNs, as the stiffness of element 10th is assumed to drop 10%, 20%, 30%,and 40%of whole the elements. The ANNs parameters used are epoch 500 value. Learning rate value 0.01-0.09, architecture
3 neuron input layer, 5 hidden layer neurons and 1 output. Simulation of the amount of training data and test data
used are 90:10, 80:20, 70:30; and 50:50. ANNs are capable of producing outputs in terms of damage severity using
the stiffness reduction cases with damage index methods. Therefore, the results show that ANNs trained to have areliable potential 98% for structural damage identification. The results confirm the feasibility of the method andits application in preventing structural failure.

Keywords: Artificial Neural Network, Damage Assessment, Damage Index, Reduction Stiffness

detail. In recent times, Artificial Neural Network(ANN) method is one way that has been ascertained
to be able to monitor bridge behavior directly.
ANN is an excellent tool for recognizing
patterns in data[3]. This method can predict andevaluate the condition of the bridge structure due tovarious factors. Damage can be expressed by

INTRODUCTION
Deterioration of the bridge structure duringservice will cause damage and even collapse. This canbe caused by vehicle load overloading, fatigue onthese structural elements, earthquake occurrences
outside of planned predictions, and large wind loads.
In Riau province, several bridges have deteriorated.
Based on the assessment carried out by the Bridge
Management System (BMS) method on several
bridges that have critical values, the Merangin Bridge,
S. Jangkang Bridge, Parak Suak Buaya Bridge, andthe Darauf Parit Bridge are most critical in the upper
structure. Meanwhile, the Siak II Bridge condition isseverely damaged, consisting of severely damagedbuilding, heavily damaged floor, slightly damaged
lower part of the building, and the watershed isslightly damaged. Of the 114 bridges that exist, thisbridge requires handling in the form of rehabilitation
and repair [1].
To prevent deterioration, a method ofmonitoring a bridge is needed that can predict thecondition of a bridge at any time so that maintenancecan be done early and prevent sudden collapse. There
are several methods developed for bridge conditionassessments such as BMS and Fracture Critical
Member (FCM)[2]. However, the method has notshown satisfactory results because the real conditionsof bridge structure behavior cannot be monitored inseveral parameters and by several aspects such astenacity, lost hysteretic energy, stiffness [4].
Research shows good results where neuralnetworks with one hidden layer can help predictstability and health conditions at certain
times[4],[5]and[6]. Research on the application of
ANN on the box girder concrete bridge has been
carried out[7]. The level of success of the research
conducted on the various bridge models above, it isnecessary to develop the use of this ANN method onother bridges such as the type of curved steel frame inthis study. So that the proposed health monitoring ofcurved steel structure bridges in a disaster early
warning system attempt to be able to reduce
casualties.
This paper develops a damage index procedure
for predicting the location and the severity of the steel
beam by making a scenario that is a failure made forseveralpointsalong
theblock.
So that the maximum degradation scenario can bereflected at all. In this study, stiffness reduction,

624

GEOMATE – Melbourne, Australia, 11-13 November 2020

displacement and location is considered as theparameter that is influenced the most.

Architectural neural network layer that uses
many layers with backpropagation type. Information
processing consists of interconnected elements(neurons) that work simultaneously (fig 1). These
neurons consist of input layers, hidden layers, output
layers. The pattern of neo-neurons in the network,
connecting weights such as training algorithms andactivation functions is a determinant of the reliabilityof an ANN[11].
The training process is used to improve
memorization and generalization capabilities, namely
the ability of an ANN architecture design to absorb
the amount of input data and take the patterns thathave been learned. While the ability to generalize isthe ability of ANN to make similar patterns that havebeen learned.
A good model of artificial neural networks will
be shown by the results of a small error rate. To findout the accuracy of damage predictions, it uses the
Mean Suare Error (MSE), which shows a value close
to zero is an accurate prediction. MSE can becalculated refer to Eq. (3) [12]:LITERATURE REVIEW
Damage Index
There are various DI methods proposed bymany researchers. There have been many Damage
Index methods used by researchers. Based on theavailable literature, it was found that the global
damage index method uses multiple response
parameters that are suitable for overall evaluation[8].
The type of damage depends on various factors,
including tension strength, reduction of stiffness
reduction. However, damage assessment withmultiples factor, associates in estimation of reliabledamage. The type of damage depends on different
factors such as displacement, tensile stress. In thisstudy, the expression of damage has been proposedby considering the top parameters in the bridge
structure, namely displacement.
Structural damage reduces stiffness and alters
the structure's modal strain energy[9]. The damage
index βji for the ‘j’th element and ‘i’th mode of thebeam is provided in Eq. (1):𝛽𝛽𝑗𝑗𝑗𝑗 =

2

𝐿𝐿

𝐿𝐿

2

2

2

𝐿𝐿

𝐿𝐿

2

�∫𝑗𝑗 �∅"𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑+∫0 �∅"𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑� ∫0 �∅"∗𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑

Or can be written as eq (2):𝛽𝛽𝑗𝑗𝑗𝑗 =2"∗"�∫𝑗𝑗 �∅"∗𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑+∫0 �∅𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑� ∫0 �∅𝑖𝑖 (𝑥𝑥)� 𝑑𝑑𝑑𝑑
2

2

"∗
"
��∅"∗
𝑗𝑗𝑗𝑗 � +∑�∅𝑗𝑗𝑗𝑗 � ��∑�∅𝑗𝑗𝑗𝑗 ��22��∅"𝑗𝑗𝑗𝑗� +∑�∅"𝑗𝑗𝑗𝑗� ��∑�∅"∗
𝑗𝑗𝑗𝑗��2

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = �

(1)

𝑅𝑅 =

Artificial Neural Network
Artificial neural networks (ANN) are derivativesof the biological nervous system. This method is one
that has reliability if used to complement Structural
Health Monitoring System (SHMS)[10]. With ANN,predictions of structural health conditions can be donequickly, because in the process ANN the workings ofthe human brain. The image below shows a typical
network of 3 perceptron layers:

HiddenLayer

Output
Layer

Stiffness Reduction(3)

Correlation Coefficient (R) is a comparison of
actual values with predictive values. The value of R
can be calculated refer to “Eq. (4) [12]:where ϕ″ is the mode shape curvature, superscript *refers to the damaged state and L is the beam length.

Input
Layer

𝑛𝑛

With:
N = Amount of data
X = Observation Value
Y = Prediction Value(2)

2∑(𝑋𝑋−𝑌𝑌)2∑ 𝑥𝑥𝑥𝑥�∑ 𝑥𝑥 2 ∑ 𝑦𝑦 2

(4)

By:
X = X-X 'Y = Y-Y 'X = Value of observation
Y = Prediction Value
X ’= Average X value
Y ’= Average Y value
Methods

Displacement

Location

METHODOLOGY

F(x)

F(x)

F(x)

F(x)

This research consists of the stages of diagnosis,
analysis, and verification. The diagnosis and analysis
phase is done by entering input data (modeling ofstructure) into the finite element program to then be
used as input data on artificial neural networks withthe help of a computer program called Matlab andLusas. The model used is Siak III bridge in Pekanbaru,
Indonesia wich a steel arch bridge with a length of
168 m. Bridge structure elements consist of arch rib

F(x)Structural damage Identification

Fig 1. Architectural Layer of Artificial Neural
Network625

GEOMATE – Melbourne, Australia, 11-13 November 2020

frame (1200x800tw24mm), hanger (100 mm, fy =490MPa), tie beam (600x600 mm, tw = 24mm), floor
beam (400x400 mm, concrete).

LOAD

LVDT 1

LVDT 211 m
0 1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2132.85 mNodal number of bridge
99.71mBOX GIRDER
35.44 mSUPPORT 1

SUPPORT 1

Fig 2. Longitudinal Cross Section of the Bridge
The design has been carried out utilizing theanalytical method (NLTHA) has been carried out in
SAP2000. Then the data is propagated forward
towards the training target through an artificial neuralnetwork architecture that was previously designed.
The earthquake load used is a record of the Pekanbaru
data Modified with Kobe earthquake respons fromSeismosoft Earthquake Engineering Software[13].
Design use mechanical properties of the steel girderarch bridge (Table 1).

Fig 3 . Schematic diagram of the laboratory girder ofarch bridge model.

Table 1. Mechanical properties of the Steel Girder
Arch Bridge

Damage assessment

Type of structural
Stress yield (fy)Ultimate strength (fu)Modulus of elasticity(existing condition)
Specific gravity

Girder behavior analyzed through LUSAS is basedon the theory of similitude laws. This finite element
model simulation is useful for determining load tests.
the type of element used is C3D8R (8-node linear
brick, reduced integration). This is done to obtain thevalue of stiffness reduction which is the limit in theanalysis of the collapse with ANN.

The procedure for detecting the level of damage insteel beams is by considering stiffness reduction
every 10% of stiffness deficiency to yielding limits.
The damage scenario by simulating a proportional
stiffness reduction of up to 40% in results and as
shown in Table 3. The ANNs parameters used are
epoch 500 values. Learning rate value 0.01-0.09,
architecture 3 neuron input layer, 5 hidden layer
neurons and 1 output. Simulation of the amount oftraining data and test data used are 90:10, 80:20,
70:30; and 50:50. Greater damage (or greater stiffness
reduction) will provide greater structural deformation.
Validated FE models from simple beams wereused and 21 partitions were simulated in the beams at
a distance of 4.73 m apart, not including support,
damage locations were 4.72 m, 9.45 m, 14.18 m,
18.91 m, 23 , 64 m, 28.37 m, 33.1 m, 37.83 m, 42.56
and 47.29 m which coincide with the center of the 11
mm partition. Because the beams are symmetrical,
four different severities of damage (10%, 20%, 30%
and 40%) are introduced in the 21 left partitions to
produce a single damage scenario. For some damage
scenarios, only the smallest damage severity (10%)
and greatest damage severity (40%) were introduced
at 2 different locations.
All possible combinations were considered and105 samples were produced for analysis using ANN.
The input matrix consists of 21 rows (indicating thelocation of damage) and 102 columns (showing β ateach location for the 105 samples produced) while theoutput matrix consists of 21 rows (showing damage
locations) and 102 columns (showing the severity ofdamage at each location for 102 sample). The

JIS G 3106 SM YB
295 MPa
490 MPa
200,000 MPa
78.5 kN/m3

Experimental testing is needed to determine thebehavior of the bridge girder if the Girder load ismodeled into a simple supported I beam miniscaledwith a ratio of 1: 5. Span length of 1100 mm, depth of
150 mm. The yield load of the steel beam wasrecorded at 60 kN in the experimental testing form.
The experimental model is used to estimate lateralforce displacement relationships (stiffness) at
different loads using the LVDT tool. Girder issubjected to pushover loads based on FEMA 356.
Tests of experimental work at the Structure
Laboratory in Riau. The load is applied through theloading plate as pressure on the beam as shown in
Figure. 3626

GEOMATE – Melbourne, Australia, 11-13 November 2020

Levenberg-Marquardt backpropagation method
available from the NN tool in MATLAB R2015b isused to train the network. Sixty four of the 105
samples were randomly selected as training data sets
while the samples were used in the testing phase andthe remaining 41 samples were used in the validation
phase. Training samples are used in the network fortraining purposes and the network is adjusted
according to their mistakes; Validation samples areused to measure network generalizations and to stop
training when generalizations do not show
improvement anymore; Test samples are used to test
network performance during and after training.

first time it yields, when it is lowered by 40%. The
other damage scenarios are simulated withproportional stiffness reductions with respect to the
40% stiffness reduction at yield and are as shown in
Table 3.
Table 3 Damage scenarios of steel Hollow Beam
Damage Case

1
2
3
4

RESULT AND DISCUSSIONS
Displacements and Experimentally Measured
Deflection.

0.16
0.15

Table 2 Comparison of FE Displacements andexperimentally measured deflection.

Load
(kN)

Displacement
0,25 L(mm)(LVDT 2)
Lusas
Measur
(FEM)
ed mm
mm

Differ
ence

1,015

1,780

1,528

2,260

-0,377

2,031

3,320

3,056

4,300

-0,339

3,046

4,820

4,585

6,460

-0,329

4,061

6,480

6,113

8,880

-0,342

5,076

8,480

7,642

11,920

6,493

11,56

9,913

16,660-0,3800,400

YoungModulus(MPa)
180000160000140000120000Damage expression in the form of dislplacement,
velocity, and acceleration is obtained at each planned
damage point. Using the Park _ Ang equation, theDamage index value of the simulated damage point isshown in Figure 4.

Load variations compared to the displacement
results with finite element analysis and experimental
are shown in table 1. During the middle span testingie point 11 damaged at 39 kN load and theexperimental results stated a greater value comparedto FEM as shown in table 2.

Displacement
0,25 L(mm)(LVDT 1)
Lusas
Meas
(FEM)
ured
mm
mm

Stiffness
reduction, SR
(%)
10
20
30
40

0.14
SR 40%

0.13

SR 20%

0.12
SR 30%

0.11

SR 10%

0.1

10
20
30
40
50
60

1

3

5

7

9

11 13 15 17 19 21

distance along beam

Fig 4. Plot of β vs distance along beam
The level of damage index (β) along the block isshown in fig. 4 for the 21 locations of the gusset
bridge grder points. The highest level (β) indicates thelocation that experienced the greatest failure that isfound at the location of point 20 for all severity
reduction (SR).
After the location of the damage is first
determined from the plot β versus the distance along
the beam (fig 4). If several damage scenarios areobserved from the β plot, the β values calculated
along the beam can be input to trained ANNs to obtainthe severity of the damage in terms of reducing
stiffness at different damage locations along the beam.
In this research, the input layer-output layer andthe curve were obtained from MATLAB R2015 with
the ANN training method. The input layer consist of
β at 21 locations (excluding supports) a total of 105
samples were fed into the input layer. Target layer
consist of stiffness reduction. During the training ofDuring the test, the beam was damaged in the
middle span when the load was 11.92 kN and theresulting experimental test value was higher than the
FEM results as shown in the table above. Loads range
from 10 to 60 kN. The average percentage difference
between experimental and FEM is 0.365%. When theload value is 40 kN, the percentage differencechanges to + 0,400%. This shows that at 40 kN, thebeam has already been yielded.
When the beam is loaded in the range of 10 kN to
60 kN, the average percentage difference between theexperimental measured deflection and the deflection
produced by FEM is 0.365%. Observation of
reduction of stiffness is based on testing where the627

GEOMATE – Melbourne, Australia, 11-13 November 2020

ANN, each input vector would generate an output
vector. The difference between the target vector andthe output vector is an error and will propagate
through the network in a backward manner. In thisway, the mean square error (MSE) can be reduced sothat the output vector can be as close as possible tothe target vector. Simulation of the amount of training
data and test data used are 90:10, 80:20, 70:30; and50:50

R value measures the correlation between output andtarget, this R value varies from 0, which means
random relationship to 1, which means close
relationship. Neurons were used in the hidden layer
for this study because they gave high R values asshown in Figure 6. Trained ANNs predict the severityof damage in terms of stiffness reduction with very
low errors of 2.10e-6 for training sets, 9.05e-6 forvalidation sets and 3.85e-5 for testing sets.
CONCLUSIONSThis paper has presented techniques forpredicting damage to steel beams. This techniqueuses damage capital based on the damage index β andArtificial Neural Network (ANN) Variable β is used
to measure the severity of the damage. The procedurefor using ANN is practical because only the modeshape of the first bending mode is required and thiscan be easily obtained from measurements. The finite
element model can then be easily validated to create
databases for different damage scenarios. Therefore,
the results show that ANNs are trained to have areliable potential of 98% for structural damage
identification. The results confirm the feasibility ofthe method and its application in preventing structural
failure.

Fig 5. Error value: Best Validation
Performance vs Mean Square Error (MSE)ACKNOWLEDGEMENTS

From the figure 5, it is known that the results oftraining and testing data using ANN have produced
the best error value (Mean Squared Error, MSE) of
2.1043×10-6. ANN will be of good value if the MSE
value is close to 1.

Gratitude to the Ministry of Research, Technology
and Higher Education of Indonesia Grant for Higher
Education Cooperation Year 2019–2020, Ministry of
Public Works and Public Housing Indonesia and Civil
Engineering Study Program at Lancang Kuning
University.
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

OPTIMUM LOCATION OF LAST-MINED STOPE IN SILL PILLARRECOVERY WITH INFLUENCE OF BACKFILLING: A CASE STUDY
Huawei Xu, Derek B. Apel
Faculty of Engineering, University of Alberta, Canada
ABSTRACT
In order to extract steeply dipping orebodies, many mines in Canada have adopted sublevel a stoping method
or one of its variations, such as blasthole stoping (BHS) and sublevel longhole retreat (SLR). In such methods, sill
pillars are initially kept in place to support the great weight of the overburden in underground mining. After themining is complete, the stope voids will be backfilled with cemented rockfill (CRF). The strength of the CRF
affects the stability of the adjacent stopes in the sill pillar recovery excavation scheme. As the basic excavation
element, stope plays a significant role not only in sill pillar recovery but also in the entire mining process. Sillpillar recovery may result in prolonged failure, fatality, and equipment loss. Choosing a rational location of thelast mined stope in sill pillar recovery can effectively eliminate the instability of the recovery process. This paper
uses the Finite Element Method (FEM), to present a comparison of sidewall swellings, floor heaves and the roof
subsidence of the crossing cuts in each stope of different last mined stope location scenarios. It also presents the
optimum location of the last mined stope in the sill pillar recovery process.
Keywords: Sill pillar recovery, Crossing cuts, Cemented rockfill(CRF), Stope location

area during pillar recovery. Valley [11] optimized themining sequence, and suggested new directions forthe different methods and the potential shortcomings.
Townend [12] proposed five mitigation strategies tomitigate the high stress concentration while mining
sill pillars.
Sill pillar recovery in the blasthole stoping (BHS)
mining method, has not been widely discussed. BHS
mining includes two sublevels. The one at the top of
the stope is for drilling (overcut), and the other at thebottom is for production (undercut). For most hardrock mines in Canada, orebody blocks are usuallysteeply inclined and the BHS method is widely used.

INTRODUCTION
Pillar recovery is the practice of forming a seriesof pillars and then partially or totally extracting someor all of the pillars [1]. Pillar recovery is considered
the most hazardous form of underground mining andis thought to be an art as much as a science [2]. Sillpillars are initially left in place to support the great
weight of the overburden in underground mining.
Pillar recovery is an important step in mining
operations, specifically in the area of maximizing
resource recovery. During the process of pillar
recovery, it is possible to induce risks, such as
overlaying rock subsidence, stope failure and pillar
failure. Researchers initiated and proposed empirical,
analytical theories and numerical technical methods
to assess the stability and guarantee the safety ofminers and mining equipment. Hudyma [3][4] studied
microseismics, conventional ground control
instruments, numerical modelling and visualobservations to understand the mechanisms of pillar
failures. Mark [1] and Iannacchione [5] analyzed thestrengths and weaknesses of MHRA techniques, andassessed the major hazard risks to evaluate sill pillar
recovery in two room-and-pillar mines. Zhukova [6]used monitored underground seismic registrations
and mathematical models to improve the safety
operations in pillar recovery. Langston [7] designed
the stope layout, extraction procedure and groundsupport, which were applied to successively recovera pillar. Ghasemi [8][9] assessed the risk of pillar
recovery operations and classified that risk into four
categories by using indicators. Beruar [10] developed
proper stope sequencing to avoid the highly stressed

BACKGROUND
The strength of the backfilled cemented rockfill(CRF) affects the displacement/subsidence of theopen pit benches which determines the stability of thewhole mine site, especially for the undergroundmines transferred from open pit to undergroundmining. For the recovery of sill pillars, most current
research focuses on the room-and-pillar mining
method. In these studies, the stress movement was
mainly triggered by the layers above the mining level.
Most of the studies did not take the backfilling into
account. The relationship between the influence ofthe backfilled CRF and the stress redistribution andground displacement in the process of sill pillar
recovery has not been widely discussed. Sill pillars
play a key role in maintaining the safety of the mining
area above their location and below it. They can
prevent mining-induced stresses from moving fromone block to another. Recovering the sill pillar safely
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GEOMATE – Melbourne, Australia, 11-13 November 2020

can make the most use of the minerals. However, it
can also trigger catastrophic problems. Then the stope
sequences in the sill pillar recovery become
significant. When the backfilled CRF is involved, therational location of the last mined stope in therecovery process also becomes more significant.

pillar is located between level N1504 and level
N1505.

N1507

N1507

N1506

N1506

N1505

Establishing the Numerical Modelling

N1505

Sill

The mine in this study was initially operated as an
open-pit mine. Once the open-pit mining wascomplete, the operation shifted to undergroundmining. Under the open pit are two mineral zones:
MZ#1 and MZ#2. First, we created a 3D numericalmodel. Next, the mining-induced redistributed stressfield of the researched sill pillar was extracted andapplied to the 2D sill pillar model. As shown in Fig.
1, there are seven levels. The sill pillar is located in
the middle of the mining pipe in MZ#2. The sill pillar
is 25m high and contains 21 stopes. The heights of thestope in each level and sill pillar are the same, 25 m,
and the width is 7 m. As for the overcuts andundercuts in the stope in the sill pillar, the height is
5m and the width is same as that of the stope. The sill

Sill

Sill

N1504

N1504

N1503

N1503

N1502

N1502

N1501

N1501

Fig. 1 Modelled pipe and sill pillar
Mechanical Properties of Modelled Rock andBackfilling CRF
Table-1 shows the rock mechanical properties
applied in the model. Here, E is the elastic Young's
modulus, ν is the Poisson's ratio, γ is the unit weight,
ϕ is the angle of friction, C is the cohesive strength,
σc is the maximum uniaxial compressive strength andσt is the tensile strength.

Table-1 Material properties

Rock Mass
MZ#1
MZ#2
Granite
CRF

γ

E
(GPa)

ν

18.7
19.6
24
2

0.26
0.24
0.3
0.3

3

(MN/m )
0.024
0.024
0.026
0.022

Fig. 2 shows three examples of stope locations inthe sill pillar. SCN#1 means that the last mined stopein the sill pillar is at the very left side of the sill pillar.
SCN#21 means the last mined stope is at the veryright side of the sill pillar. During the simulation
process, the location of the last mined stope in the sill
pillar changes from SCN#1 to SCN#21. The void
areas in the figure are the overcuts and undercuts, thedark green is the sill pillar and the white is the
backfilled CRF.

ϕ
(°)

C
(MPa)

σc
(MPa)

σt
(MPa)

26.4
28.1
45
35

4.2
4.7
9.3
1.2

66
79
130
1.5

3.4
3.7
0
0

locations are numbered in the undercuts and overcuts,
as shown in Fig.3. Node 1 and Node 3 are the two top
corners, while Node 2 and Node 6 lie in the middle of
the roof and floor respectively. Node 4 and Node 8
are in the middle of sidewalls, and Node 5 and Node
7 are the two floor corners. The stability of theselocations is an external indicator of the overcuts andundercuts.
1

2

8

SCN #1SCN #11

3
4

7

6

1

2

5

SCN #21

Fig. 2 Example stope location layout of sill pillar

8

RESULTS AND DISCUSSION

7

In order to compare the displacements of different
locations in different stope location scenarios, eight

3
4

6

5

Fig. 3 Numbered location for comparison

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Node-3Node-1
Magnitude displacement(cm)Magnitude displacement(cm)Node-1
35
30
25
20
15
10
5
0
1

3 5 7 9 11 13 15 17 19 21
Stope location scenario(Undercut)

Node-3

35
30
25
20
15
10
5
0
1

3

5 7 9 11 13 15 17 19 21
Stope location scenario(Overcut)

Fig. 4 Displacement at Node-1, Node-3 in both undercut and overcut

Node-2Node-6

60
50
40
30
20
10

0
1

3 5 7 9 11 13 15 17 19 21
Stope location scenario(Undercut)

overcut is about two times that of the undercut. At
each location scenario, the two roof corners havealmost the same displacement. There is a significant
possibility that the roof corners in the overcut will failat the two edges of the sill pillar if the last stope islocated at the two edges of the sill pillar.

Magnitude displacement(cm)Magnitude displacement(c)As shown in Fig. 4, compared with thedisplacement of the roof corners in the overcut, thedisplacement in the undercut is much smaller. For theundercut, except for the locations far from the sill
pillar center, there is almost no difference between
different stope location scenarios. For most of thedisplacements, at the same location number, theNode-260

Node-6

50
40
30
20
10
0
1

3

5 7 9 11 13 15 17 19 21
Stope location scenario(Overcut)

Fig. 5 Displacement at Node-2, Node-6 in both undercut and overcut
Figure 5 shows the roof displacement and floor
heave in both the undercut and overcut. Unlike theroof corners displacement in Fig. 4, the roof
displacement in both the undercut and overcut islarger than the floor heave. Similar to the roof corners,
the roof displacement and floor heave in the overcut
are much larger than those in the undercut. In theundercut, there is almost no floor heave, and the roof
Node-8

20
15
10
5
0
1

3 5 7 9 11 13 15 17 19 21
Stope location scenario(Undercut)

Node-4Magnitude displacement(cm)Magnitude displacement(cm)Node-4

displacement is negligible. The roof displacement in
the overcut shows a slight difference except for thetwo edge location scenarios, while for the floor heavein the overcut, the edge location scenarios are almost
the same at various locations. For the sake of safety,
more supports should be installed in the roof and floor
in the overcut.
Node-8

20
15
10
5
0
1

3

5 7 9 11 13 15 17 19 21
Stope location scenario(Overcut)

Fig. 6 Displacement at Node-4, Node-8 in both undercut and overcut632

GEOMATE – Melbourne, Australia, 11-13 November 2020

Figure 6 shows the configuration of the sidewall
swellings. Similar to the roof corners displacement,
the swellings show a gradual changing trend from theedge of the sill pillar to the center of the sill pillar. In
the undercut, the sidewall swellings are almost thesame at same stope location scenarios, and theNode-7Node-5Magnitude displacement(cm)Magnitude displacement(cm)Node-5swellings are considerably lower, while the sidewall
swellings in the overcut are almost twice the size of
those in the undercut at the same stope location
scenarios. More attention should be paid on the safetyof sidewalls in the overcut.

14
12
10
8
6
4
2
0
1

3

5 7 9 11 13 15 17 19 21
Stope location scenario(Undercut)

Node-7

14
12
10
8
6
4
2
0
1

3

5 7 9 11 13 15 17 19 21
Stope location scenario(Overcut)

Fig. 7 Displacement at Node-5, Node-7 in both undercut and overcut
Floor heave is an important indicator of roadway
safety in mining engineering. Figure 7 shows theheave of the two floor corners. The heave of the twofloor corners at the same stope location in the sill
pillar is almost the same. Most of the heaves are lessthan 10 cm for the undercut. For the overcut, the floor
corners are in the granite; and for the undercut, thefloor corners are in the backfilled CRF. The floor
corner heave in the overcut is much larger than that in
the undercut, which means the sill pillar takes most of
the redistributed stresses and plays a key role inmaintaining the safety in the mining process bothabove and underneath the blocks.

The floor heave in the undercut is tiny, and thevalues of the floor heave in the overcuts are more than
7 times of that in the undercuts, which means the floor
in the undercut is more stable than the floor in theovercut. Also the roof displacements in the overcuts
are more than 2 times of that in the undercuts. The
sidewall swellings in the undercuts are about half ofthat in the overcuts at the same location scenario.
Roofs in the overcuts and floors in the undercuts
are the backfilled CRF. The strength of the CRF islower than the sill pillar hard rock, then the roof
displacement in overcuts is much larger than that inundercuts. In both undercuts and overcuts, more
attention should be paid to the supports on the roof
than on the floor.
Based on the above conclusions, for the optimum
location of the last mined stope in sill pillar recovery,
the last mined stope can be any location between No.5
to No.17 in the sill pillar.

CONCLUSIONS
The following conclusions can be reachedregarding the comparison of roof displacements, floor
heaves and sidewall swellings of different locationsin the undercuts and overcuts in various stope
location scenarios.
From the roof displacements, floor heaves andsidewall swellings in both undercuts and overcuts, thesill pillar in this hard rock mine plays a key role inmaintaining the safety of the mine throughout themining and backfilling processes.
In the same stope location scenario, comparedwith overcut, the displacement of the roof corners inundercut is smaller, and most of the roof cornerdisplacements in undercuts are less than 10 cm, whilein the overcuts, all the displacements in roof corners
are over 10 cm, which means the roof in the undercut
is more stable than that in the overcut, and more
protective measures should be taken in the roof of theovercut.

REFERENCE[1] C. Mark, F. Chase, and D. Pappas, “Reducing the
Risk of Ground Falls During Pillar Recovery,”Trans Soc Min Eng, pp. 153–160, 2003.
[2] M. Christopher and G. Michael, “Preventing Roof
Fall Fatalities During Pillar Recovery : A Ground
Control Success Story,” Int. J. Min. Sci. Technol.,vol. 27, no. 1, pp. 107–113, 2017.
[3] M. R. Hudyma and B. Mining, “Geomechanics ofSill Pillar Mining,” Proceeding 1st North Am.
Rock Mech. Symp., pp. 969–976, 1994.
[4] Y. Potvin and M. Hudyma, “Open Stope Mining
in Canada,” Proc. MassMin2000, Brisbane., no.
January 2000, 2000.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

[9] E. Ghasemi, M. Ataei, K. Shahriar, F. Sereshki,and S. Esmaeil, “Assessment of Roof Fall Risk
During Retreat Mining in Room and Pillar Coal
Mines,” Int. J. Rock Mech. Min. Sci., vol. 54, pp.
80–89, 2012.

[5] A. T. Iannacchione, “Major Hazard Risk
Assessment Applied to Pillar Recovery
Operations,” Proc. 28th Int. Conf. Gr. Control
mining. Morgantown, West Virginia Univ. USA;2009, pp. 261–270, 2009.
[6] S. Zhukova, P. Korchak, A. Streshnev, and I.
Salnikov, “Geodynamic Rock Condition , Mine
Workings Stabilization During Pillar Recovery at
the Level + 320 m of the Yukspor Deposit of the
Khibiny Massif,” VII Int. Sci. Conf. “Problems
Complex Dev. Georesources,” vol. 02022, 2018.
[7] R. B. Langston, H. A. D. Kirsten, E. A. Niemi, andJ. A. Marjerison, “Extraction of a Remnant Sill
Pillar Under Challenging Ground Conditions at
the Stillwater Mine , Nye , Mt .,” ARMA, 2006.
[8] E. Ghasemi, K. Shahriar, and M. Sharifzadeh, “A
New Method for Risk Assessment of Pillar
Recovery Operation,” Saf. Sci., vol. 48, no. 10, pp.
1304–1312, 2010.

[10] O. Beruar, A. S. Forsythe, and M. Yao, “SillPillar Mining in the Main Orebody at Vale ’ s
Coleman Mine,” ARMA, vol. 9300, no. 3830 L,
pp. 1–7, 2011.
[11] B. Valley, W. Pun, D. J. Hutchinson, and B.
Madjdabadi, “Rock Mass Change Monitoring in
a Sill Pillar at Vale ’ s Coleman Mine ( Sudbury, Canada ),” Can. RM Conf., no. January, 2012.
[12] S. Townend and A. Sampson-Forsythe,“Mitigation Strategies for Mining in High Stress
Sill Pillars at Coleman Mine – A Case Study,”
Aust. Cent. Geomech., pp. 65–78, 2014.

634

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

MEASURING ACCESS TO SUSTAINABLE TRANSPORT OPTIONSFOR THE ELDERLY
Kaniz Fatima1, Sara Moridpour1, Chris De Gruyter2 and Tayebeh Saghapour2
1
Civil and Infrastructure Engineering Discipline, School of Engineering
RMIT University, Australia, 2 School of Global, Urban and Social Studies, RMIT University, Australia
ABSTRACTThe elderly population is a major part of the total population all over the world. The public transport access forthe elderly is not easy. A connected and accessible public transport system is a key requirement for a better qualityof everyday life. Meanwhile, walking is a sustainable mode for short distances to reach desired destinations. Many
researchers have focused on elderly transport accessibility. Accessibility index is one of the popular methods tomeasure the transport accessibility. This paper identifies public transport as well as walking accessibility indicesfor elderly considering the travel from the centre of one specific Statistical Area level one (SA1) to various
destinations. According to the Victorian Integrated Survey of Travel and Activity (VISTA) data, the greater
Melbourne region is classified into three home sub-regions including Melbourne Central Business District (CBD),
inner Melbourne and outer Melbourne. The previous studies have mainly considered the distance as a measure oftransport accessibility. However, total travel time and population have not been considered in accessibility analysis.
This paper also analyses the elderly public transport and walking access in Melbourne metropolitan area using
time-based accessibility indices to identify the elderly access level. The paper also compares the elderly public
transport and walking access among the three different home sub-regions. The results from this research can beused by the planners to identify inaccessible areas for the elderly in an urban region.
Keywords: Public transport; Walking; Elderly; Accessibility

INTRODUCTION

research conducted elderly time-based measures. The
main contribution of this study uses time-based
accessibility indices to calculate the elderly (65 andover) accessibility for three home sub-region ofgreater Melbourne (inner, middle and outer). This
paper identifies and compare the elderly public
transport and walking accessibility level for thesethree homes sub-region.
The next heading of the paper discusses theprevious research related to elderly transport
accessibility. This is followed by the data collection,index calculation procedures and the methodology.
Then the results and discussion are highlighted. The
key recommendation and conclusion remarks in thelast section.

The elderly population is increasing all over theworld. The growth of the older population varies upon
the country’s characteristics such as size and growth.
According to the [1], the projection shows by 2050
one in six people in the world will be aged 65 years
or over. Reduced birth rate and increased expectation
for a better lifestyle is another reason for the rapid
growth of the elderly population.
A proper urban region should have easy public
transport and walking access to all major necessity.
Specifically, the group of the increased elderly
population needs special attention when comes tomobility. Mobility is a major part of everyday life. It
is a bridge between social welfare, mental health,physical health and daily needs. For the elderly groupof the population need more time for planning toreach destinations, more walk time and more physicalsupport. Public transport (PT) and walking are
considered a sustainable mode of transport. But for
the elderly, it is always not accessible. The mostpopular destinations may not be within PT andwalking accessibility range. If reaching to thedestination place need transport mode change, the PT
system can be more complicated for the elderly. Also,
for a walking trip, most of the elderly take longer time
to reach a destination.
Many researchers focused on public transport andwalking accessibility. However, a few types of

BACKGROUND
Mobility difficulties increase with age. According
to [2], mobility difficulties increases to 17% for those
aged 60–69 and to 39% for those aged 70 and over.
Many elderlies prefer to stay in the same place/home
after retirement. The living style is known as “Agingin place”. To support “Ageing in place” easy accessto the destinations are necessary. Several researchstudies are related to elderly health, social andaccessibility issues. Analyse the walking, private andpublic transport access is very important tounderstand elderly travel difficulties. Accessibility
measures indicate elderly travel quality.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

measures walking access from the individual’s placeof origin to destinations. The proportion of the elderly
physical activity is contrariwise with the increase of
the elderly population growth [15]. However, similar
to PT accessibility index very limited research
considers the elderly walk time. Table 2 represents an
overview of previous studies related to walking
accessibility. From table 2 summary, it is observedthat spatial distribution and closest walking
time/distance are mostly used to calculate walking
accessibility index.

Public Transport (PT) Accessibility Study
Public transport trip can be included as a walk to
the PT stop, boarding on the bus/tram/train, finding aseat, coping with interchanges as necessary andcompleting the journey to the destination. The
traveller needs to be self-assured to make this journey.
For the elderly maintaining, this information can becomplicated sometimes. Using PT is more hassle ifthey have not habituated in it a regular basis. Table 1
represents the summary of previous time-based
studies related to public transport accessibility. From
Table 1 summary it is identified that travel time is oneof the main elements for calculating public transport
accessibility.

Table 2 Walking Accessibility Study

Table 1 Public Transport Accessibility Index Study(Time-Based)
Index & References

Definition

Time-Based Transit
Service Area Tool
(TTSAT)[3]
Person-based
measures[4]
Local index oftransit availability(LITA)[5]
Gravity-based
measures[6], [7], [8]This measure considers
transit service area basedon users’ travel time.

The elderly
population andtime-based index[9], [10]Index & References

Definition

Gravity-based
accessibility[13]This measure based on thespatial distribution ofresidence & travels
time/cost between zones.
It considers an analysis of
network connectivity
and/or the characteristicsof the walking
infrastructures.
This measure considers
build-in-environment andaccessibility from an origin
to a destination
Walk Score considers the
distance to the closest
destination in each land
use category. It is based on
the gravity-based model
The WI is calculated fromthe sum of the z-scores ofthe four mentioned urban
form measures.

Topological or
Infrastructure
based
[13]
Walkability/walkscore-type
measures[13]

Person-based measures
each person within agiven time frame.
A study of LITA used
three main time variables
for calculation.

Walk Score[16]

The gravity model isfollowed by Newton's
theory of gravity. Thismodel considers that thetrips produced at an origin
and attracted to a
destination are directly
proportional to the total
trip productions at theorigin and the total
attractions at thedestination.
A recent study introduced
the elderly time-based
accessibility.

Walkability Index(WI)[17], [18]STUDY AREA AND DATASETSThe Greater Melbourne geographical area isconsidered as a case study to analyse access for theelderly.
Study area
According to the Victorian Integrated Survey ofTravel and Activity (VISTA), the greater Melbourne
region is divided into three home-region as inner,middle and outer Melbourne [19]. Figure 1 illustrates
the three home sub-region for Greater Melbourne.
The home-regions are also divided into statistical arealevel one (SA1). SA1 is known as the second smallest
geographic area after mesh block. In Melbourne,
there are around 10,290 SA1s. Most of the census
datasets are collected based on SA1s. In this study,
both public transport index and the walkability index

Walking Accessibility Index Study
Walking is encouraged by many healthprofessional and researchers [11], [12], [13]. Physicalactivity decreases the chance of getting a chronic
disease [14]. Walking is one of the mostrecommended physical activity. Walkability study
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GEOMATE – Melbourne, Australia, 11-13 November 2020

is calculated based on the SA1s. Table 3 summarized
the number of SA1s, PT coverage and elderly
population for each home sub-region. The datasets
are analysed and extracted using VISTA data and
ArcMap. From table 3 greater Melbourne outerregion contains around 50% of the total SA1s. Public
transport coverage is higher in inner and middle
Melbourne than outer Melbourne region.
Furthermore, the outer region elderly population isgreater compare to the middle and inner region.

travel mode are not documented.
Point of Interest
A detailed point of interest (POI) datasets for
greater Melbourne is extracted from the AustralianUrban Research Infrastructure Network (AURIN)[20]. These points of interests are considered as thedestination. According to the VISTA data, four majorelderly popular destinations are considered to develop
elderly PT and walkability indices [9].
Population
The population density is a major indicator of PT
and walking accessibility measure [21]. Most of theelderly accessibility index did not consider
population.
Travel Time
Melbourne public transport system is acombination of bus, train and train. Greater
Melbourne timetable for these three modes iscollected from different open datasets such as Public
Transport Victoria (PTV) and Yarra tram.
Road Network
The road network datasets are analysed using
ArcMap Network Analyst Extension. Arc Map
network analysis tools provide spatial analysis usingroad network data, travel directions data, closest
destination/PT stop analysis data and service area
analysis data.

Figure 1: Greater Melbourne Home Sub-region.
Table 3 Home Sub-Region SA1 SummaryHome Sub
RegionMelbourne
Inner
Melbourne
MiddleMelbourne
Outer% of total
SA1s% of PT
Coverage% ofElderly
Population

21.10

41.19

36.66

30.74

32.50

39.78

48.16

26.31

23.56

Street connectivity
The street network is used to calculate the numberof intersections act as a proxy to show the potential
mobility of pedestrians within the neighbourhood.
Street connectivity datasets are analysed by using
AURIN walkability tools.
SafetyPedestrians are at greater risk of traffic-related
injury than motor vehicle users. The elderly
experience higher severity of pedestrian injuries than
younger adults [22]. Accident rates datasets are
extracted and analysed from VicRoads’ crashdatabase.

Datasets
The datasets are collected from various available
sources. All the datasets are analysed using astatistical analysis software package SPSS. The
datasets analysed for this study are presented asfollows.
VISTA Data
VISTA data is a detailed household data (2016)
related to single-day travel [19]. This travel datacollected throughout across greater Melbourne for ayear. This dataset helps to identify the travel pattern
and travel behaviour. Around 46,563 travel responses
for weekdays and weekend are documented. Amongthese responses, 6,975 responses are from the elderly
population[9], [10]. Most of the elderly details for

METHODOLOGY
The study contains two main parts. The first partis the public transport accessibility index calculation.
The last part is related to walking accessibility index
calculation. Both indices are measured based on SA1s

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centroid towards specific destinations.

score calculated separately for all SA1s. The elderly
walking accessibility index is presented in equation 2.

Methodology Calculation Procedure

𝑬𝑾𝑨𝑰𝑺𝑨𝟏 = (𝒁𝑺𝒄𝒐𝒓𝒆𝑾𝒂𝒍𝒌𝑻𝒊𝒎𝒆 ) + (𝒁𝒔𝒄𝒐𝒓𝒆𝑳𝑼𝑴𝑰𝑿 ) +(𝒁𝑺𝒄𝒐𝒓𝒆 𝑺𝒕𝒓𝒆𝒆𝒕 𝑪𝒐𝒏𝒏𝒆𝒄𝒕𝒊𝒗𝒊𝒕𝒚 ) + (𝒁𝑺𝒄𝒐𝒓𝒆 𝑷𝒓𝒂𝒕𝒊𝒐𝒏 ) +(𝒁𝑺𝒄𝒐𝒓𝒆 𝑺𝒂𝒇𝒆𝒕𝒚 )(2)

The closest facility from each SA1 centroid is
identified. For both indices’ calculation, ArcMap
network analyst tools are used to identify the closest
facility and the closest public transport stop/station.
The Public transport accessibility index and walking
index classified into five different categories startingfrom very poor, poor, moderate, good and excellent.
The Natural Breaks (Jenks) classification method isused to categorize the indices [23].
The methods for public transport and walking
accessibility measure can be explained as follows:• Calculate and identify the number of SA1 for eachhome-region for greater Melbourne.
• Calculate the public transport stop coverage (bus,tram and train) for all the SA1s and separate them as
per home sub-region.
• Calculate and analyse the POIs coverage (four mosttravelled POI used by elderly) for each home subregion area.
• Calculate and analyse the index classification fromvery poor to excellent for all SA1s.
• Calculate and analyse the accessibility percentage ofPT and walking accessibility for all three home subregion.

𝒏

𝑳𝑼𝑴𝑰𝑿 = −𝟏 ∑𝒋=𝟏(𝑷𝒊 ∗ 𝐥𝐧(𝑷𝒊)/𝐥𝐧 (𝒏)(3)Where, LUMIX = land use mix score, Pi = theproportion of the area covered by land use j, n = thetotal number of land-use types.
RESULTS AND DISCUSSION
This study compares the public transport andwalking accessibility for the elderly among theMelbourne inner, middle and outer home sub-region.
Elderly
PublicComparison

Transport

Accessibility

The PT accessibility is measured for shopping
centres, health care centres, medical centres andrecreation centres. Table 4 represents the summary of
PT accessibility comparison. The POIs coverage isanalysed using ArcMap.
From table 4 it can be observed, more than 70%
of SA1s are categorized as a good and excellent
category considering shopping centre PT access in
Melbourne inner region. Only around 11% SA1s areobserved as the very poor and poor category for theelderly shopping centre access in Melbourne inner
region. On the other side, the Melbourne outer region
shows complete opposite results. Around 65% of
Melbourne outer SA1s are identified as a very poorand poor category for the elderly shopping centre PT
access. But only around 17% SA1s are observed as
good to the excellent category for the elderly PT
access. The reason can be overall shopping centres
are established a bit far away from the centroid of
SA1s. Major shopping centres generally covers
several SA1s. From some SA1s’ shopping centres canbe reached easily using one mode of transport. But
from other SA1s’ reaching to a shopping centre,
requires mode change. Also, some shopping centres
situated close to public transport stops. Furthermore,some shopping centres are not within the elderly
walking limit which takes a longer time to reach using
the PT mode. A typical shopping centre travelincludes walk time to a nearby public transport stop,
wait time, in-vehicle travel time and again walk timeafter reaching the destination (shopping centre nearbystop).
Likewise, effects can be observed for health care
centres, education centres and recreation centres. The
outer region is covered with less public transport
accessibility for the elderly than the inner and middle
region.

Elderly Public Transport Accessibility Index(EPTAI) Calculation
The time-based elderly PT accessibility index ispresented in equation 1. The total travel time forpublic transport considered as the summation of walktime, average waiting time and in-vehicle time for amode of transport.
𝐄𝐏𝐓𝐀𝐈𝐒𝐀𝟏 = ∑𝟒𝐣=𝟏(𝐏𝐫𝐚𝐭𝐢𝐨 ∗ 𝟏𝟎 ∗ 𝐓𝐓𝐓𝐫𝐚𝐭𝐢𝐨𝐏𝐎𝐈 ) (1)Where, Pratio = Population ratio, TTTratioPOI is thetotal travel time ratio for a specific POI.
TTT=Walk time (WT) to nearest PT stop/station+ Invehicle time (In VT) + Average waiting time
(AvWT)+WT to nearest stop/station of POI+WT to

POI,
TTT=WT to nearest PT stop/station+ In VT+
AvWT+ WT to POI (if WT to nearest stop/station of
POI=0)
Elderly Walking accessibility Index (EWAI)
Calculation
The elderly walking accessibility index considers
the walk time to the destination, population, street
connectivity, pedestrian safety/accident rates andland mix use. Each component for both indices iscalculated using the statistical Z-score method. The Z
638

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 4 Summary of Elderly PT Accessibility
Comparison
Melbourne Elderly PT Access (%)
Very Low
Low
Moderate
Good
Excellent
Shopping Centre (Shopping centre coverage: Inner 26.58%, Middle 34.17%, Outer 39.25%)
Melbourne Inner
5.60
5.70
16.00
46.00
26.70
Melbourne Middle

9.80

10.56

23.00

33.00

23.64

Melbourne Outer

41.16

23.33

18.75

11.70

5.06

Health Care Centre (Health care centre coverage: Inner 30.07%, Middle 28.91%, Outer 41.02%)
Melbourne Inner
4.33
6.65
13.66
37.88
37.48
Melbourne Middle

8.66

9.78

5.77

44.04

31.75

Melbourne Outer

31.93

28.55

17.00

11.67

10.85

Education Centre (Education centre coverage: Inner 21.25%, Middle 32.22%, Outer 46.53%)
Melbourne Inner
4.46
5.66
15.66
35.77
38.45
Melbourne Middle

3.45

3.85

19.56

31.56

41.58

Melbourne Outer

24.56

17.73

16.78

21.65

19.28

Recreation Centre (Recreation centre coverage: Inner 26.74%, Middle 28.83%, Outer 44.43%)
Melbourne Inner
4.36
8.64
16.74
25.83
44.43
Melbourne Middle

13.36

8.71

10.87

Melbourne Outer

28.19

24.95

18.86
16.77
11.23
excellent level of elderly walking access. Although
the outer region identifies the opposite results.
Around 45% of outer region SA1s assessed as very
poor and poor level walking access for the elderly.

Around 40% of health care centres are covered by the
Melbourne outer region while the inner region covers
around 30% and the middle region 29%. But theelderly PT accessibility is poor and very poor ontomost of the outer region. Sometime to reach theclosest facilities elderly required to change bus
services twice. However, for education centres andrecreation centres, the access level is slightly better in
Melbourne outer region. The reason is the number ofthese two POIs coverage higher than the shopping
centres and health care centres.

29.34

Table 5 Summary
Comparison

47.72

of

Walking

Accessibility

Melbourne Elderly Walking Accessibility (%)
Access Level
Inner
MiddleOuter
Very Poor
3.29
4.56
23.27

Elderly Walking Accessibility
Walking accessibility for the elderly is calculatedusing equation 2 for all three home-sub regions. For
the elderly, education centres and recreation arewithin the walkable distance for some SA1s. But mostof the shopping centres and health care centres are notaccessible through walking for elderly. Table 5summarised the elderly walking accessibility
comparison. From table 5 it can be observed walking
accessibility is higher in Melbourne inner region than
Melbourne middle and outer region. Inner Melbourne
region has more walking access to the destinationcompared to the other two home sub-regions. Morethan 76% of SA1s are categorized as a good and

Poor

8.64

5.71

21.57

Moderate

11.74

9.87

15.86

Good
Excellent

31.93
44.40

29.34
50.52

16.77
22.53

CONCLUSIONS AND FUTURE RESEARCHDIRECTIONSThis study compares the level of the elderly public
transport and walking accessibility within the greater
Melbourne home sub-regions. The study conducted a
network analysis using a time-based accessibility
indices calculation. This study provides elderly
accessibility level measure overview for three greater
Melbourne home sub-region considering four
specific destinations. Moreover, the study specifies
639

GEOMATE – Melbourne, Australia, 11-13 November 2020

that public transport and walking accessibility level ismore frequent in the greater Melbourne inner subregion than the outer and middle region.
The technical approach of this index can beapplied to other cities or larger geographic regions.
The analysis results can be used to identify the mostinaccessible region for the elderly group of people.
The EPTAI and EWAI can be calculated for other
POIs or destinations. Future research can consider
this for elderly travel. These results can be used to
implement further development of elderly sustainabletransport access. The time-based accessibility
measures can lead future planners to identify/modify
various accessibility aspects. For instance, the PT
route /stop and POIs distribution considering elderly
travel specifically newly built outer regions in future.

on Transportation and Traffic Engineering, Pp. 253257.
[11] Maghelal, P. and Capp, C.J., Walkability: A
Review of Existing Pedestrian Indices, Journal of
URISA, 2011, Vol. 23, Issue 2, Pp. 5-19.
[12] Notthoff, N. and Carstensen, L. L., Promoting
walking in older adults: Perceived neighborhood
walkability influences the effectiveness ofmotivational messages, Journal of Health Psychology
2015, Vol. 22, Issue 7, Pp. 834-843.
[13] Vale, D.S., Saraiva, M. and Pereira, M., Active
accessibility: a review of operational measures ofwalking and cycling accessibility, Journal ofTransport and Land Use, 2016 Vol. 9, Issue 1, Pp 127.
[14] Physical Activity Guidelines Advisory
Committee Scientific Report, 2008. Washington, DC:
U.S. Department of Health and Human Services.
[15] Groot, LC. DE., Verheijden, MW., Henauw, St.
De. Schroll, M. and Staveren, WA. VAN., Lifestyle,
Nutritional status, health, and mortality in elderly
people across Europe: A review of the longitudinal
results of the SENECA study, Journals Of
Gerontology Series A-Biological Sciences And
Medical Sciences, 2004, Vol. 59, Issue 12, Pp. 12771284[16] Koschinsky, J., Talen, E., Alfonzo, M. and Lee,
S., How walkable is Walker’s paradise?,
Environment and Planning B: Urban Analytics andCity Science, Vol 44, Issue 2, 2017.
[17] Sundquist, K., Eriksson, U., Kawakami, N.,
Skog, L., Ohlsson, H. and Arvidsson, D.,
Neighborhood Walkability, Physical Activity, andWalking Behavior: The Swedish Neighborhood andPhysical Activity (SNAP) Study, Social Science andMedicine, 2011, Vol. 72, Issue 8, Pp. 1266–1273.
[18] Giles-Corti, B., Macaulay, G., Middleton, N.,
Boruff, V., Bull, F., Butterworth, I., Badland, H.,
Mavoa, S., Roberts, R. and Christian, H., Developinga Research and Practice Tool to Measure Walkability:
A Demonstration Project, Health Promotion Journalof Australia, 2014, Vol. 25, Issue 3, Pp. 160–166.
[19] Victorian Integrated Survey of Travel andActivity (VISTA), Department of Transport,
Melbourne, Victoria, Australia, 2016. http://www5transport.vic.gov.au/.
[20] Australian Urban Research Infrastructure
Network (AURIN) (https://aurin.org.au/).
[21] Habibian, M. and Hosseinzadeh, A., Walkability
index across trip purposes. Sustainable Cities andSociety, 2018, Vol. 42, pp216-225.
[22] Garrard, J., Senior Victorians and walking:Obstacles and opportunities. Final Report, 2013.
[23] Brewer, AC. and Pickle, L, Evaluation of
Methods for Classifying Epidemiological Data on
Choropleth Maps in Series. Annals of the Associationof American Geographers, 2002, Vol. 92, Pp. 662681.

REFERENCES[1] World Population Prospects, United Nations,
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Population Division, 2019.
[2] Mackett, R., Improving accessibility for older
people – Investing in a valuable asset, Journal ofTransport & Health, 2015, Vol. 2, Pp. 5-13.
[3] Cheng, CL. and Agrawal, AW., TTSAT: A New
Approach to Mapping Transit Accessibility, Journalof Public Transportation, 2010, Vol. 13, Pp. 55-72.
[4] Kim HM. and Kwan MP., Space-timeaccessibility measures: A geo-computationalalgorithm with a focus on the feasible opportunity set
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Approach to Mapping Transit Accessibility. Journalof Public Transportation, 2010, Vol. 13, Pp. 55-72.
[6] Hanson, WG., How Accessibility Shapes Land
Use, Journal of the American Institute of Planners,
1959, Vol. 35, Pp. 73-76.
[7] Kwan, P., Spacetime and integral measures ofindividual accessibility: A comparative analysis
using a point-based framework, Geographicalanalysis, 1998, Vol. 30, Pp. 191- 216.
[8] Papa, E. and Coppola, P., Gravity-Based
Accessibility measures for Integrated Transport-land
Use Planning (GraBAM), Dipartimento di
Pianificazione e Scienza del Territorio DiPiST,
Università degli Studi di Napoli“Federico II”, 2013..
[9] Fatima, K. and Moridpour, S., Measuring Public
transport Accessibility for Elderly, The 6thInternational Conference on Traffic and LogisticEngineering (ICTLE), Bangkok, Thailand, Pp. 1-6,
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Transport Accessibility, APCIM & ICTTE 2018
Proceedings of the Asia-Pacific Conference onIntelligent Medical 2018 & International Conference64010th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

QUANTIFICATION OF SLOPE DEFORMATIONUSING LASER POINT CLOUD DATABY MOBILE MAPPING SYSTEM
Naoto Samori1, Satoshi Nishiyama2, Koki Sakita3, Michinari Fuziki4 and Naoya Ono5
1,2,3

Graduate school of environmental and life science, Okayama University, Japan,
4,5
Kokusai Kogyo Co.,Ltd, Japan,
ABSTRACT

There exist many slopes in Japan, most of which were created during the period of high economic growth. It
has been 50 years since they were constructed, and they are now aging. To judge the degree of deterioration, it isnecessary to understand their deformation. The examination is currently performed by close-up visual inspection.
However, the problem is that the results may vary depending on the inspector, and thus, uniform conclusions
cannot be obtained. Therefore, in order to detect the slope deformation quantitatively, we proposed a survey
method using a mobile measurement vehicle (mobile mapping system or MMS) and a differential analysis (method
applying iterative closest point) approach considered to be capable of detecting the slope deformation using a point
cloud. Point cloud is a set of points data that have position information of three-dimensional coordinates, namely
X, Y, Z(H), and color information. We carried out three separate verification steps corresponding to MMS point
cloud acquisition accuracy, deformation discriminability, and deformation extraction accuracy, and verified thedetection of bulging, which is a sign of a landslide, on slopes that were assumed to cause visual landslide. It was
confirmed that the target slope, which was assumed not to be deformed at the total station, was determined as notdeformed even by our proposed detection method.
Keywords: Road slope, MMS, ICP, Difference analysis

BACKGROUND AND PURPOSE

and segmenting out only the region of interest byenlarging/reducing, the height and width of the object
and the point cloud obtained by the laser scanner canbe used to evaluate the degree of reflection. Since
these points are all three-dimensional coordinates,
RGB values, and quantitative data, it is possible toexpress the deformation quantitatively. A surveying
method known as mobile mapping system (MMS)
uses a mobile measurement vehicle equipped with alaser scanner that can acquire such point cloud and asensor that acquires self-position information.
In this study, the slope is measured by an MMS
survey, and the detection accuracy of deformation
appearing on the slope using the evaluation of themeasured data is verified. In addition, to detect the
deformation, we introduced a difference analysis
method by superimposing two separate point clouds.
We performed three different verifications
corresponding to the point cloud acquisition accuracyof the MMS survey, the deformation discriminabilityof the acquired point cloud, and the extraction
accuracy of the deformation obtained by thedifference analysis method. Based on these results,
we forecast that a landslide would occur. The abilityof the present method to detect the protrusion of
deformation, which is a sign of landslide against the
known slope, is also confirmed. Moreover, weinvestigated whether the point cloud obtained by
MMS can extract the deformation through thedifference analysis method.

Japan occupied about 70% of its land area inmountains and hills [1]. Slopes were constructedduring the period of high economic growth, which
was attained about 50 years ago. These mountains
were cut, and slope protection was incorporated todevelop the road transportation network. However, as
years past, the slope protection was weathered by rain
and wind, and due to aging effects, deformation of theslope, such as peeling of mortar and breakage of thelegal frame, has occurred. Therefore, it is necessary
to understand the deformations to prevent the slope
from collapsing and causing damage to thesurrounding area. However, the slope inspection
method uses nondestructive examination approaches,
including palpation and tapping, mainly for close-up
visual inspection, and the results largely depend on
the experience and skill of the engineer. Therefore, as
the inspection outcome varies depending on theindividual inspector, obtaining uniform results is
impossible.
In recent years, surveying using a laser scanner,which can obtain point cloud data capable ofquantitatively evaluating the shape of a structure, hasreceived extensive attention. Point cloud data (Fig. 1)
is a set of points that have position information of
three-dimensional coordinates, namely X, Y, Z(H),and color information (RGB) obtained from camera
image data. By rotating the point cloud of the object
641

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 1 Point cloud data on slope(left: target in grating crib, right: side of slope)the situation of the point cloud at the end of iterative
closest point (ICP) is applied, and the detection of
deformation amount of the points is considered to be
by a vector through comparing the coordinate valuesof the points associated with the two point clouds.
The target slope to be measured had
approximately a length of 70 m and a height of 10 m
along the National Highway 9 and was suspected tohave caused a landslide by visual inspection in Fig. 4.
This slope was cut in 1987 during road widening, andits cross section was sloped by the ground anchor
method with a frame. Then, on May 1, 2016, theanchor head protection concrete fell and an accident
occurred where the concrete pieces had fallen on the
roadway. Subsequently, various investigations and

VERIFICATION METHOD
In this chapter, we describe the verification
approach to be used, MMS survey and difference
analysis method, the slope to be verified, and theoutline of the target installed on the slope.
MMS surveying is a method that can efficiently
and two-dimensionally acquire three-dimensional
position information about the terrain and features
around the road during normal driving of a vehicle
equipped with the necessary equipment in Fig. 2.
When the road slope is measured three-dimensionally,
the point cloud is usually obstructed by the influenceof two-way traffic and pedestrians. On the other hand,the point cloud can be lost because the MMS
performs its mobile acquisition while moving.
However, it is possible to measure few places without
such data loss. The vehicle used in this study isequipped with a laser scanner that scans 1 million
points per second, a global navigation satellite system(GNSS) receiver, an inertial measurement device, anodometer, a camera, and other sensors that acquireself-position and orientation. MMS surveys areactively used in the management assistance of roads[2] and rivers [3] in effective.
The ICP associates the closest points in the twodifferent point clouds and optimizes the position(rotational and parallel movements) for the entire
point cloud. The matching and optimization are
repeated, and the algorithm is terminated once thesepoint clouds are the closest in the terms of distance.
In the difference analysis method, as shown in Fig. 3,

Fig. 2 MMS surveying on the slope(Devices are marked by the red rectangle)642

GEOMATE – Melbourne, Australia, 11-13 November 2020

: first period: second period

Repetition (first)

1.

Correspondence ofpoints of 2 periods

2.

Rotation andtranslation

3.

Repetition
Fig. 4 Target slope(red rectangle indicates the region where anchor
head protection concrete fell, blue rectangles arereference points for verification)

application

Repetition (middle)

Red arrow can be
regarded as adeformation vector
Repetition (final)

Fig. 3Fig. 5 Measurement conditions(when measuring the slope, a distance ofapproximately 12 m vertically,
when measuring the fence, a distance ofapproximately 15 m at 60 degrees)

As a deformationin slope

Difference analysis algorithm

observations were carried out, and the slopes aroundthe anchor rupture site were assumed to have avariation mode, causing them to be displaced toward
the starting point side, although no visuallysignificant deformation was observed. Therefore, inthis study, the required deformation is defined asbulging, and this has been verified. In addition,
targets, 0.7 m square, was installed on the slope as areference point for adjustment, 8 points surrounding
the entire slope and 6 points on the side of the starting
point where fluctuations are expected. The firstmeasurement period was January 13, 2018, that is, thesecond period was May 28, 2018, and themeasurement was performed over a period of about 4
months. The MMS vehicle traveled in lane2 with adistance of approximately 12 m from the target slope
at 50 km/h and acquired the point cloud of the slope
in Fig. 5.

the acquired point cloud, and the extraction accuracyof deformation obtained through the difference
analysis method.
MMS Point Cloud Acquisition Accuracy
In this chapter, the verification of the point cloudacquisition accuracy of the MMS is presented.
The point closest to the center of the target in the
point cloud acquired by the MMS was set as thecenter coordinate of the target acquired by the MMS
survey. In addition, its true value was assumed to bea second-class A total station (TS) and was measured.
The comparison results of the points acquired by the
MMS and TS are expressed as the mean difference
and RMS error (root mean squared error). The
compared targets are the targets that surround theslope where fluctuations are expected. Note that
CloudCompare [4], which is a software for point
cloud analysis, was used to acquire the center
coordinates of the target. The mean differenceindicates the difference between the coordinatesobtained by TS and MMS, and the RMS error
indicates the variation with the difference in each of

VERIFICATION
We describe verifications on three different
parameters: the point cloud acquisition accuracy of
the MMS survey, the deformation discriminability of643

GEOMATE – Melbourne, Australia, 11-13 November 2020

the X, Y, and Z directions of the point group as themost probable value. In this study, we compared the
XY plane, which is a horizontal plane with the ground,and the Z direction, which is the height direction of
the slope. The mean difference of the MMS and RMS
error (root mean squared error) are expressed by thefollowing equations:
DIS = OBS − TV

(1)(∑ 𝐷𝐼𝑆)
̅̅̅̅̅
𝐷𝐼𝑆 =

(2)

RMS = √(∑ 𝐷𝐼𝑆 2 )/𝑛(3)𝑛from the results of 4 to 8 that the simulated step canbe detected if the deformation is 10 mm or more.

Fig. 6

where, DIS is the difference, OBS is the observed
value, TV is the true value, ̅̅̅̅̅
DIS is the average valueof the difference, n is the verification score, RMS is
the root mean squared error of the MMS survey.
It was observed that the average range was lessthan 20 mm for ΔXY and less than 30 mm for ΔZ in
Fig. 6, while the errors were less than 20 mm and 25mm, respectively. The average differences of the
MMS data calculated using the adjustment reference
points were 25 mm for ΔXY and less than 30 mm for
ΔZ in Fig. 7, while the errors were less than 20 mm
and 25 mm, respectively. The adjustment calculation
data is obtained by superimposing the coordinates of
the adjustment reference points acquired by the MMS
surveying on the data acquired by the TS. The point
cloud data acquired by the MMS survey satisfy themap information level of 0.70 m horizontally and lessthan 0.33 m vertically, both with and without
adjustment calculation, and therefore, exhibit good
accuracy. It can be said that the data of was obtained.

Fig. 7

Average difference result (unit: m)

RMS Error result (unit: m)Distinguishability of the MMS
A simulated deformed specimen is placed on theslope to verify the scale of the deformed points
acquired by the MMS.
The goal is to verify whether the data acquired by
the MMS survey is reproduced as a deformation byusing a simulated deformation specimen. The test
specimen was installed near the toe of the slope, andfaced the laser scanner. As shown in Fig. 8, twosimulated specimens were installed (5, 10, 20, 30, 50,
and 100 mm), and three simulated steps, a total of five.
As a result, the point density of the data was measured
as 967 (points/m2) on the target with a side of 0.7 m.
Next, the acquired point cloud near the specimen is
cut into a strip with a width of about 0.5 m and thecross section is displayed. It can be easily observed
from the cross-sectional view in Fig. 9 that theschematic shape of the specimen is clearly expressed.
From the results of 1 and 2 shown below, a simulated
crack in the horizontal direction of approximately 20
mm and in the vertical direction of approximately 10
mm can be visually detected. It was also confirmed

Fig. 8Fig. 9644

Simulated deformation specimen

Point cloud cross section of simulated
deformed specimen

GEOMATE – Melbourne, Australia, 11-13 November 2020

Deformation Accuracy of Difference Analysis

whether the analysis results shown in Fig. 11 could beobtained using this simulated transformation. For that,no deformation was applied to 1, so that deformation
was absent. As a result, 2 was hanging down about 40
mm, so that the deformation was indented in the depth
direction of the slope, while 3 and 4 were protrusions.
With these parameters, we verified if the deformation
was of 10 mm or 20 mm. The result is a crosssectional view of the slope, where the numericalvalues shown in Fig. 12 represent the deformation
quantity, while the arrows represent the direction of
it. All the results from 1 to 4 were as expected, where
1 shows no deformation, 2 shows the displacementthat moves towards the back of the slope, and 3 and 4
show the amount of deformation to be obtained
clearly. Although it was not considered possible, wewere able to detect a deformation that would extend
to the front of the slope. Thus, it was found that the
protrusion greater than 10 mm and lack ofapproximately 40 mm could be confirmed as
deformation.

In this section, we investigate the detection
accuracy of the deformation of the difference analysis
method. We installed a sheet that could introduce acertain amount of deformation to the four law frames
and could be scraped off (simulated deformation).
The deformation is applied not in the first period,
however, in the second period, followed by thecalculation of the deformation between the two
periods. As shown in Fig. 10, in the second period,
law frame 1 did not exhibit any deformation, while inlaw frame 2, part of the concrete was shaved and amaximum of approximately 40 mm was scraped off.
Sheets of 10 and 20 mm were installed in the legal
frames 3 and 4, respectively. We also examined

VERIFICATION THE ENTIRE SLOPE
Based on the previously obtained results,
verification using the proposed method for the target
slope is described in this section. The point cloud was
acquired by MMS for the target slope, and thechanges that occurred during the 2 o'clock period
after about 4 months were extracted using the

Fig. 11
Expected analysis result(1: no deformation, 2: deformation to the back of theslope, 3, 4: deformation to the front of the slope)Fig. 10

Two stages of simulated deformation
given to the slope(1: no deformation, 2: drop off (40 mm),
3, 4: Simulated deformation (10 mm, 20 mm))Fig. 12645

Analysis result of slope with simulated
deformation

GEOMATE – Melbourne, Australia, 11-13 November 2020

difference analysis method. As a result of measuring
the center coordinate of the reference point in thisrange by the TS, the average differences between thetwo periods are 2 mm and -1 mm in the plane andelevation directions, respectively, while the RMS
error is 1 mm. Because the error of was about themechanical error, there was no change in this slope.
Therefore, it is confirmed that the deformation(outflow) is not extracted on the target slope throughMMS and the difference analysis method. Fig. 13
shows the result of analyzing the point cloud data of
two periods. The deformation direction is indicatedby the arrow, while its size is expressed by anumerical value. When this slope actually causes
protrusion, the deformation will be expected toappear in a wide area as shown in Fig. 14. The
analysis results of the slope reveal that the errors of
the entire point cloud and MMS survey are mainly
due to the absence of vegetation and icicles in the first
period, but not in the second period. However, it canbe confirmed that there exists no deformation in awide area. Therefore, it can be stated that a
deformation such as a protrusion can be obtainedusing this method.

between two time periods through the MMS anddifference analysis method. The error of theadjustment calculation data in the MMS survey wasless than 25 mm and 30 mm in the horizontal andvertical directions, respectively. If the error was about
10 mm, it was possible to obtain a point cloud that
could identify a deformation such as a protrusion. In
addition, cracks in the horizontal direction can be
identified by approximately 20 mm, and those in thevertical direction by approximately 10 mm. The
detection accuracy of the deformation obtained by thedifference analysis method was approximately 10
mm, while the deformation imitating the protrusionand deformation imitating the lack of about 40 mm
could be detected. From these verification results,
together with the fact that target slope did not change,
it can be deduced that similar results can be obtained
through the difference analysis method for the point
cloud acquired by MMS. In other words, it was
confirmed that this method can detect the protrusion,
which is a sign of landslide.
Meanwhile, although it was found that thepresence or absence of the slope deformation can beobtained, the accuracy of the deformation amount
was unclear. Confirming the deformation state willcontribute to the prevention of the slope fromcollapsing. In the future, there is room to verify that
it is not affected by noise such as vegetation and thatthe error of MMS survey and the correction byadjustment calculation are improved. Future work
may include verification of noise effect on the
deformation, such as vegetation, as well as improving
the MMS survey error and the correction byadjustment calculation. Therefore, these noises must
be removed, and verification must be performed.

CONCLUSION
In this study, three different types of verifications
were performed for detecting the change in the slope

ACKNOWLEDGMENTS
We
would
like
to
thank
Editage(www.editage.com) for English language editing.
Fig. 13

Fig. 14Analysis results of slopes suspected tobe landslides

REFERENCES[1] Japan Institute of Country-ology and Engineering,
JICE, 2020
[2] Koichi Sakai, Takeshi Oishi, Shintaro Ono,
Yasuhide Okamoto and Takayuki Hirasawa,
Study of support for road maintenance work using
omnidirectional video, product research, Vol.70,
Issue 2, 2018, pp. 51-56.
[3] Kikuo Tachibana, Koji Mano, Hideki Shimanura
and Satoshi Nishiyama, The application of Mobile
Mapping System to river embankment
measurement, Photogrammetry and remotesensing, Vol.54, Issue 4, 2015, pp.166-177.
[4] CloudCompare project, 2020

If a landslide actually occurs, theexpected deformation646

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

MEASURING THE ACCESSIBILITY TO TRANSIT STATION:
A CASE STUDY OF TANJUNG KARANG, LAMPUNG INDONESIA
Aleksander Purba1, Diti Artanti Utami Putri2, Rahayu Sulistyorini3, Shahnaz Nabila Fuady4, and Lusmeilia
Afriani5
1,3,5Faculty of Engineering, Lampung University, Indonesia; 2,3,4Urban and Regional Planning Study
Program, Institut Teknologi Sumatera, Indonesia

ABSTRACT
Tanjung Karang station in the center of the capital city of Lampung province is the largest transit station within
the city and has been in operation for decades. In addition to serving long-distance passengers between Lampung
and South Sumatera provinces, the transit station is also connected to a number of districts in the northern part ofthe province where most of the users are commuter passengers. Analysis is conducted by using the scoring method
against a number of variables that are considered the most decisive to indicate the level of accessibility. They are
travel distance, travel time, travel cost, road network conditions, and public transport, respectively. The score ofeach variable is 2.55; 2.63; 2.74; 1.53; and 0.96, respectively, and the final score results is 2.08 meaning that theaccessibility level of the study area to Tanjung Karang station is categorized as moderate. The lowest scoreregarding public transport indicates more effort is needed to make the city bus more attractive to citizens to use
the bus to reach railway transit station.

Keywords: accessibility, transit station, public transport, Tanjung Karang, Indonesia

illustrated for the city of Galway, Ireland [4].
Otherwise, it is often claimed that a modal shift fromprivate to public transport (particularly rail) would
generate positive feedback effects, including
reductions in car travel and CO2 emissions, as well as
increases in walking and cycling. It would also create
opportunities for urban development, especially nearrailway stations. Conversely, mixed land-use
developments near railway stations would improveaccessibility for people to live, work and play close tohome with access to transit station. Analysis isconducted by using the scoring method against anumber of variables that are considered the mostdecisive to indicate the level of accessibility.

INTRODUCTION
Accessibility refers to people’s ability to reachgoods, services and activities, which is the ultimate
goal of most transport activity. Many factors affectaccessibility,
includingmobility(physical
movement), the quality and affordability of transport
options, transport system connectivity, mobility
substitutes, and land use patterns. Morecomprehensive analysis of accessibility in planning
expands the scope of potential solutions to transport
problems[1].
Otherresearcher
defining
accessibility is the measure of the capacity of alocation to be reached by, or to reach different
locations. Therefore, the capacity and thearrangement of transport infrastructure are key
elements in the determination of accessibility [2]. In
other words, people who are in locations that are moreaccessible will be able to reach activities anddestinations faster than those in inaccessible locations.
The latter will be unable to reach the same amount oflocations in a certain period of time. Accessibility
determines equal access and opportunity. The public
transport accessibility level (PTAL) in the United
Kingdom, for example, is a method of transport
planning that determines the access level ofgeographical locations in regards to public
transportation [3]. In other European part, in order torank certain place is conducted by measuring thelevels of sustainable accessibility by travel mode ateach geographical location in the study area as

CASE STUDY AND DATA
Bandar Lampung is the capital city of Lampung
province in the south of Sumatra island, Indonesia.
This city is the main gate to enter Sumatra, whichbecomes the main route for land transportation andlogistics distribution activities from Java to Sumatra,
and vice versa. Bandar Lampung has a strong role in
the growing economy of Sumatra, also becomes thecenter of economy activities in Lampung region. The
city's area is about 169.21 km², with an estimatedpopulation of 1,015,910 as of 2017. Tanjung Karang
Station in Bandar Lampung is the terminus of therailway service from Palembang, Baturaja,
Blambangan Umpu and Kotabumi. Figure 1 shows
the transit station and surrounding areas.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 1

Bandar Lampung city map

No. of passenger (depart)Passenger Statistics
The passenger traffic departing from Tanjung
Karang railway station during fiscal year 2017-2018presents in following Fig. 2. Number of the highest
departure passenger in 2017 was recorded as 71,074
in October and as many as 92,820 in 2018 occurredon December.

100,000
90,00080,00070,00060,00050,00040,00030,00020,00010,000
0

2017
2018

Fig. 2 Monthly depart passenger, 2017-2018

Fig. 3 Land use within study area
The average number of passengers per month during
2017 is 59,610 and an average of 72,281 people
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GEOMATE – Melbourne, Australia, 11-13 November 2020

during 2018 or there was a 21% increase in thenumber of departing passenger.

the result of the travel distance variable score is 2.55
of 3.0.

Characteristics of the Study Area

Travel Time

According to such literatures the most frequently
used buffer sizes in the literature (700 and 800 m)
were adopted, together with a larger buffer of 3000 m
in line with the increasing body of TOD literature
focusing on (electric) bicycles as feeder modes torailway stations.) [6], while other researchersemphasize the classification of railway stations. The
structures of a station influence the functions it canfulfil – a common topic in e.g. ecology, where
stability and reactions to disturbances are dealt with.
Spatial planning and transport policy then discuss
where which functions should be fulfilled, andrailway stations generally form part of the context ofother systems. The systematic description of theseinterrelations also illustrates why the interests of somany actors must be integrated in railway station
operation and development [7]. The scope of thisstudy is a residential area within ± 1.5 m radius of thestation with a wide coverage of ± 7 km² and covers
seven administrative districts as shown in Fig.3.
According to Fig 3, most of the land are designated
for residential (yellow), commercial (white), support
facilities (orange) and open space (green).

Almost the same as the travel distance variable,
the travel time records the time taken by the
respondent from the residence to the railway station.
The fastest time is recorded in 2 minutes and thelongest time is 30 minutes. Graphically, the travel
time required towards railway station based on threegrades is presented in Fig. 5.
0%

grade-1 (TT≤10min.);score-3

36%

grade-2(10min<TT≤20min.);score-2

64%
grade-3 (TT>20min.);score-1

Fig. 5 Travel time heading to railway station
Nearly 64% of respondents take time about 10
minutes to reach railway station from home and therest are needed the time between 10 minutes to 20
minutes. Since most of the respondents take timenearly to 10 minutes to reach railway station, a finalscore of 2.63 of 3.0 is obtained in terms of travel time
variable.

RESULT AND DISCUSSION
A 160 respondents in the study area weresurveyed and their responses related to the variable oftravel distance, travel time, and travel cost aredescribed below.

Travel Cost
Travel Distance
Travel costs vary from Rp0 to Rp15,000,
indicating there are a number of respondents on footor non-motorized transportation to reach the railway
station and some of them have changed modes several
times. Figure 6 shows the travel cost of respondents
according to each grade.

The distance from the residence to railway station
varies between 0.5 km to 4 km, with an average
distance of 1.64 km. The graph related to travel
distance based on grade level is shown in Fig. 4 below.
grade-1 (dist ≤1.5km);score-3

3%

grade-2(1.5km<dist≤3km);score-2

40%
57%

grade-1 (Rp≤5,000);
score-3

2%
22%

grade-3 (dist >3km);score-1

grade-2(5,000<Rp≤10,000);
score-2

76%

Fig. 4 Travel distance heading railway station
As many as 57% of 160 respondents reside lessthan 1.5 km from the station and 40% of them live
between 1.5 km and 3 km. Referring to these finding,

grade-3(Rp>10,000); score1

Fig, 6 Travel cost needed to reach station
As shown in Fig. 6, as many as 76% of 160
respondents paid Rp5,000 from the house to therailway station indicating the group used only one trip
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GEOMATE – Melbourne, Australia, 11-13 November 2020and one mode of transportation since Rp5,000 was theaverage fare for a single trip by bus or para-transit
mode. Based on the overall calculation result, thefinal score of travel cost variable is 2.74 of 3.0.

Scores related to road network conditions arecalculated after field observations are made. Some
decisive assessment components are the road surface,road markings, availability of sidewalks, and side
friction.

Road network Conditions

Fig. 7 Road network within study area
In this case, there are 14 road sections that wereobserved with several levels of function hierarchy,
namely secondary arterials, primary collectors andsecondary collectors referring to Indonesian urban
road classification. Road network within study area
and its surrounding presents in Fig. 7 and Table 1.

Table 1 shows the variables that contributed thehighest score to the assessment were road surfaces.
This indicates that almost all of the 14 road sections
observed were in excellent condition. Whereas theside friction variable contributes to the lowest value
expressed by on street parking, entry-exit vehicles toland-use and street vendor activities withinsurrounding areas.

Table 1 Road network assessment score
Road
section
Raden
Intan
RA
Kartini
Teuku
Umar
Kota Raja
Imam
BonjolGajah
Mada
PemudaHayam
Wuruk
Antasari
Putri
Balau
Agus
Salim
Tamin
Cut NyakDin
Sam
Ratulangi

Surface3

Assessment score
ShoulderMarking
Side
walk
2
1
2

Side
friction
1

1.80

3

1

3

2

1

2.00

3

1

3

3

1

2.20

3
3

2
1

1
2

3
1

0
0

1.80
1.40

3

1

1

3

0

1.60

2
3

0
2

0
2

0
1

0
0

0.40
1.60

3
3

1
1

1
2

1
0

1
2

1.40
1.60

3

1

2

0

1

1.40

3
3

1
0

2
1

0
0

3
2

1.80
1.20

1

1

0

1

1.20

3

Final score

Public Transport Availability

Ave.
score

In this section, the assessment is carried out
related to the availability of public transport services(city buses and para-transit) on the fourteen road
networks within the study area and the availability ofbus stops along the network. Detailed assessment
results are shown in Table 2. Based on observations,most of the road networks within the study area areserviced by para-transit, and only the main roads withthe function of secondary arterial are served by city
buses. City bus and para-transit services are fully
carried out by private companies and almost without
control by the city government regarding service
quality. Resolving this issue has been stated as for thevast majority of station areas, the transportation
supply is not enough to match the potential demand
created by the existent land uses around the stations
[8].

1.53

Note: 3: excellent; 2: good; 1: moderate; 0: poor

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GEOMATE – Melbourne, Australia, 11-13 November 2020

3.0

The classification for determining the level ofaccessibility of the study area is determined
qualitatively as follows: poor accessibility (score:
0.00~1.20), moderate accessibility (score: 1.20~2.40),
and good accessibility (score: 2.41~3.00). Hence, thefinal results of the assessment of accessibility levelwithin the study area can be seen in Table 3.
Table 3 Final score of assessment

Table 2 PT and bus stop assessment score
Road
section

Function

Raden
Intan
RA
Kartini
Teuku
Umar
Kota Raja

Secondaryarterial
Secondaryarterial
Secondaryarterial
Secondaryarterial
Primary
collector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Secondarycollector
Final score

Imam
BonjolGajah
Mada
PemudaHayam
Wuruk
Antasari
Putri
Balau
Agus
Salim
Tamin
Cut NyakDin
Sam
Ratulangi

Assessment score
PTBus
availability stop
3
3

Ave.
score

2

1

1.5

2

0

1.0

3

0

1.5

1

0

0.5

1

0

0.5

3

0

1.5

1

0

0.5

1

0

0.5

9

0

0.0

1

0

0.5

2

0

1.0

2

0

1.0

1

0

0.5

No
1
2
3
4
5

Variable
Travel distance
Travel time
Travel cost
Road network
Public transport
Final score

Score
2.55
2.63
2.74
1.53
0.96
2.08

Accessibility level
good
good
good
moderatepoor
moderate

Variables related to travel cost seem to give thehighest value to the final score, while the public
transport availability variable contributes to thesmallest score. The final results of 2.08 meaning thatthe accessibility level of the study area to Tanjung
Karang station is categorized as moderate
CONCLUSION
This paper develops a relatively simple way todetermine the level of accessibility to reach a railway
station and the end result is not so surprising since thebest practices in developing cities within developing
countries are still very limited in the practical order.
The involvement of the main actors in the context ofland-use transport interaction still requires more
efforts, especially from the central and local
government, railway companies, land developers andtransport operators in order to create sustainable
development. Experience clearly demonstrated thatknowledge sharing is an essential element for
integrated land use and transport planning to takeplace, but just bringing together under the same roof
practitioners from the two fields of expertise will notmake this form of integrated planning to occur (on thecontrary, it might aggravate personal differences)[11]. In a dense urbanized area, where the dwellingmarket is saturated, it is necessary to take explicitly
into consideration the interactions among thedifferent urban agents, and the effects of suchinteractions on the dwelling price, in order tocorrectly forecast the evolution of the land use pattern,
as stated by Coppola and Nuzzolo [12]. Moreadvanced, a resilient city can generally besummarized as the dimensions of economic resilience,social resilience, ecological resilience, andinfrastructure resilience. The results demonstrate the

0.96

Note: 3: service & bus stops available; 2: unregularly service &rare bus stops; 1: sometimes service & no bus stops available; 0:both no available

Based on Japan experiences, typically, two actors
are involved in the development of railway corridors
in Tokyo. They are the local governments and theprivate railway operators. Local governments inJapan are two-tiered consisting of prefectures serving
wider areas and municipalities serving local areas. In
particular, the prefecture, in Tokyo called the Tokyo
Metropolitan Government (TMG), plays an importantrole in the development of railway corridors. The roleof the TMG mainly concerns conditioning andfacilitating land use developments [9]. Station area
projects in Europe suggested Both technologicalchange (development of high-speed and urbanregional railway networks, transfer of distribution andmanufacturing activities away from station areas) andinstitutional change (privatization of railway
companies) play a role, and are compounded by two
diff erent strands of public policies: promoting theattractiveness of urban neighbourhoods and cities andpromoting sustainable development [10].
Final Results of Accessibility Level

651

GEOMATE – Melbourne, Australia, 11-13 November 2020

[5] Duffhues, J., Mayer, I. S., Nefs, M., and van der
Vliet, M., Breaking Barriers to Transit-Oriented
Development: Insights from the Serious GameSPRINTCITY, J of Environment and Planning
B: Planning and Design, Vol. 41, No. 5, 2014,
pp. 770–791.
[6] Caset, F., Vale, D. S., and Viana, C. M.,
Measuring the accessibility of railway stationsin the Brussels regional express network: anode-place modeling approach, J. of
NETWORKS & SPATIAL ECONOMICS, Vol.
18, No. 3, 2018, pp. 495–530.
[7] Zemp, S., Stauffacher, M., Lang, D. J., and Scholz,
R. W., Classifying railway stations for strategictransport and land use planning: Context
matters! J. of Transport Geography, Vol. 19, No.
4, 2011, pp. 670–679.
[8] Vale, D. S., Transit-oriented development,
integration of land use and transport, andpedestrian accessibility: Combining node-placemodel with pedestrian shed ratio to evaluate andclassify station areas in Lisbon, J. of Transport
Geography, Vol. 45, 2015, pp. 70–80.
[9] Chorus, P., and Bertolini, L., Developing transitoriented corridors: Insights from Tokyo,
International J. of Sustainable Transportation,
Vol. 10, No. 2, 2014, pp. 86–95.
[10] Bertolini, L., Curtis, C., and Renne, J., Station
Area projects in Europe and Beyond: Towards
Transit Oriented Development? J. of Built
Environment, Vol. 38, No. 1, 2012, pp. 31–50.
[11] Papa, E., Coppola, P., Angiello, G., andCarpentieri, G., The learning process ofaccessibility instrument developers: Testing thetools in planning practice, J. of Transportation
Research Part A: Policy and Practice, Vol. 104,
2017, pp. 108–120.
[12] Coppola, P., and Nuzzolo, A., Changing
accessibility, dwelling price and the spatial
distribution of socio-economic activities, J. of
Research in Transportation Economics, Vol. 31,
No. 1, 2011, pp. 63–71.
[13] Yan, Zheng., Xin-Lu, Xie., Chen-Zhen, Lin.,
Mou, Wang., & Xiao-Jia, H., Development as
adaptation: Framing and measuring urban
resilience in Beijing, J. of Advances in Climate
Change Research, Vol. 9, 2018, pp. 234–242.
[14] Park, S., Choi, K., and Lee, J. S., To Walk or Not
to Walk: Testing the Effect of Path Walkability
on Transit Users’ Access Mode Choices to theStation, International J. of Sustainable
Transportation, Vol. 9, No. 8, 2014, pp. 529–
541.

cross-linkage between development and urban
resilience, which is in nature a resilience indevelopment. However, improving urban resilience
to climate change requires a systematic, long-term,
and local based approach. Urban development cannot
autonomously lead to a more resilient city, it's often
on the opposite [13]. Variables related to public
transport availability which have the lowest score on
the assessment of accessibility level is one of the mostdifficult big challenges since the city mayor ofBandar Lampung more pay attention to build flyovers
and widening roads to overcome increasing travel
demand. The steps to deal with rapid motorization
through transport demand management manners andstrategies to develop the non-motorized transport
have not been seen at all. Several cities have jumped
several steps ahead related to the issue of sustainabletransport, and this issue is closely related to pathwalkability. As stated by Park et. al. [14], the pathwalkability is defined as the quality of physicalwalking environment that can be measured
objectively based on the micro-level physicalcharacteristics of a street and its adjacentintermediary space between the outer edge of the
sidewalk and the façade of nearby buildings. Basedon finding results the score of each variable i.e. travel
distance, travel time, travel cost, road network
conditions, and public transport is 2.55; 2.63; 2.74;
1.53; and 0.96, respectively, and the final scoreresults is 2.08 meaning that the accessibility level iscategorized as moderate. More efforts are needed tomake Bandar Lampung to become more accessible
for their citizens particularly the public transport
performances.
REFERENCES[1] Litman, T., Evaluating Accessibility for Transport
Planning Measuring People’s Ability to Reach
Desired Goods and Activities, Victoria
Transport Policy Institute, 2020, pp. 1-64.
[2] Rodrigue, J. P., The Geography of Transport
Systems, Routledge, New York, 2020, pp.1-456.
[3] Rosenberg, M., Defining Accessibility andMobility in Transportation and Geography,
2020, thoughtco.com/accessibility-definitiongeography-1434629.
[4] Vega, A., Using Place Rank to measuresustainable accessibility, J. of Transport
Geography, Vol. 24, 2012, pp. 411–418.

652

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SEISMIC RESPONSE OF ABUTMENTS IN A REAL BRIDGE MODEL
Desy Setyowulan1, Toshitaka Yamao2, Eko Andi Suryo3, Eva Arifi4, and Devi Nuralinah5
1,3,4,5Engineering, Universitas Brawijaya, Indonesia;
2
Center for Water Cycle, Marine Environment and Disaster Management, Kumamoto University, Japan

ABSTRACT
Abutment performance suggestively impacts the seismic response of bridge constructions. This research aims
to observe the seismic performance of abutments in the actual bridge construction with the consequence of thewing wall and movement constraint. Analytical model of concrete box girder bridge was implemented from theactual bridge in Japan, with the numerical model was performed by three-dimensional finite element model in
ABAQUS software. Four distinct abutment modelling approaches were carried out including Type 1, Type 2(ordinary form in Japan), Type 3, and Type 4 (the proposed design). The results confirmed that all types ofabutments have a capability in resisting the shear stress. Nevertheless, the overall effects of seismic groundacceleration without liquefaction effect were insignificant on the displacement and the shear stress at the surface
of abutments. Furthermore, further research is needed to determine the effect of liquefaction on the seismic
behavior of abutment.
Keywords: ABAQUS, Abutment, Seismic Response, Real Bridge Model

research, it could be confirmed that the proposed
seismic reinforcement method is very effective to becompared with the current method of reinforcement.

INTRODUCTION
Abutment has an important function on theperformance of bridge structure, because its behavior
has been found to significantly impact the response ofwhole bridge due to high intensity of seismic
excitation [1]. According to the previous experiences,many abutments collapsed in Japan due to the large
stress on the surface of abutment and pounding
between superstructure and substructure.
After 1995 Hyogo-ken Nanbu Earthquake, theconsideration of 10 cm gap which has been used in
the actual bridge in Japan was revised due tonumerous damages on bridges suffered by collisionphenomenon. Furthermore, necessary gap between
two adjacent girder or among girder and abutments
have to be taken into design of the superstructure foravoiding any damage of the bridge triggered bycollision.
The substantial effect of parapet-unified wing
walls of abutment subjected to horizontal loads
through unseating prevention structure of bridge wasstudied [2]. According to this analysis, it was foundthat bending moment at base of parapet affected by
the wing walls. In order to prove the result of
numerical analysis, an experimental model ofreinforced concrete parapet wall of abutment without
wing wall subjected to incremental horizontal load
was inspected previously [3]. From this model, it was
found that crack developed on the bottom of parapet
wall and propagate along this wall.
Recent study on cost analysis of proposed
reinforcement method for abutment due to girder’scollision was developed, which was modeled by 3DFinite Element Method [4]. According to thisMETHODS
The modelling simulation of reinforced concrete
abutments were developed by using three
dimensional solid elements, which was performed
with ABAQUS software [5]. The discretization ofabutment’s geometry, material properties for concreteand steel, applied load, and boundary condition forthe structure were addressed.
Finite Element Modeling
Analytical model of concrete box girder bridge
was assumed from the real bridge in Japan, which was
conducted previously [6, 7] shown in Fig. 1. The total
length of two span superstructures was 80 m with pieras its center and abutment in its edges. Four different
abutment modelling approaches shown in Fig. 2 wereused as the main parameter of this research. These
approaches including Type 1 as abutment without
wing wall, Type 2 as ordinary model in Japan, Type
3 as abutment with full wing wall, and Type 4 as a
new type of abutment which is proposed in thisresearch.
The dimensional configurations of the total height,
width, and thickness of the wing wall and parapet
wall were 8.0 m, 6.0 m and 0.5 m, respectively, withlength of the wing wall was 4.0 m. The gap between
superstructures were set to be 10 cm and 20 cm. The
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GEOMATE – Melbourne, Australia, 11-13 November 2020

was idealized by 3D truss elements, identified as
T3D2. In addition, the embedded method was applied
to constraint the two-node truss element (reinforcingbar) into solid element (concrete) with the purpose ofcreating a proper bond action [8].

bearing supports were assumed as fixed and roller
bearings for pier and abutment, respectively.
Additionally, the friction coefficient was set to be 0.1.
In this non-linear simulation, the material
properties for concrete are shown in Fig. 3(a), withthe ultimate strength of 29.4 MPa and 50% of thelinear elastic range. The Poisson’s ratio and modulus
of elasticity were 0.2 and 20.6 GPa, respectively. The
compression and tension damaged variables, dc anddt, were defined and plotted in Figs. 3(b) 3(c),respectively. In addition, the reinforcing bars was
assumed as steel with Young’s modulus, Poisson’s
ratio, density, and tensile strength of 206 GPa, 0.3,7850 kg/m3, and 294 MPa.

(a) Type 1(b) Type 2 (standard)

80 m(a) Side view of the bridge(c) Type 3(d) Type 4
Fig. 2 Theoretical models of abutments in ABAQUS(a) Dimensions of the pier (unit: mm)(a) Stress vs strain curve(c) Cross section of the superstructure(unit: mm)
Fig. 1 Three-dimensional finite element models ofconcrete girder bridges in ABAQUS with theview of pier and cross section ofsuperstructure [6, 7](b) Compression damage

The method of Concrete Damaged Plasticity was
selected due to its superiority in damage simulation.
The concrete material of abutment was formed with
C3D8R element, which is known as eight-node solid(brick) elements. The concrete materials for column
and box girder were idealized by beam element andshell element, respectively. While the reinforcing bar(c) Tension damage
Fig.3 Material properties of the concrete654

GEOMATE – Melbourne, Australia, 11-13 November 2020

elastic limit and allowable compressive stresses with
flexural types were defined as 1.9 MPa and 10.0 MPa.
Table 1. Allowable stresses of concrete [9]Input Seismic Wave
Level 2 Type 2 with ground Type I earthquake
ground motion with accelerations type were appliedhorizontally at the lowest part of substructures. It is
known as an inland direct strike type earthquake withlow probability of occurrence, which is representswith strong accelerations and shorter duration. Three
input seismic waves shown in Fig. 4. L2T2G1
depicted as Level 2, Type 2 and ground (soil) type 1.
Furthermore, it was assumed that no liquefaction
occurred.

Design strengths ofconcrete (MPa)Type of stressCompresFlexural
sive
compressive
stressesstresses
(MPa)
Axial
compressive
stresses
When only
concrete
carries shear
forces (τ o1 )Shearstresses(MPa)

L2T2G1-1

21

24

27

30

7.0

8.0

9.0

10.0

5.5

6.5

7.5

8.5

0.22

0.23

0.24

0.25

When
concrete anddiagonal
tensilereinforceme
nt jointly
carry (τ o 2 )

1.6

1.7

1.8

1.9

Punching
shear
stresses

0.85

0.90

0.95

1.00

(τ o3 )

Fig.4 Input seismic wave (L2T2G1-1) from JSHB
seismic waves [9].
Loading

RESULTS AND DISCUSSIONSThe loading condition for abutment underearthquake should be designed with the combinationof dead loads, earth pressure and seismic effect [9].
Calculation of the earth pressure was assumed as a
distributed load acting on the backfill of abutment.
The soil properties were determined previously by
Yamao et al., 2012. Monobe-Okabe Method was used
to calculate an active pressure strength during anearthquake, as determined in Eqs. (1) and (2). PEA
and KEA were determined as strength of an active
earth pressure (kN/m2) at depth x (m) and coefficientof active earth pressure, respectively.

Previous studies have reported that the areas
subjected to high stress is frequently observed during
the investigation of abutment’s failure. Furthermore,evaluating the response stress, shear stress anddisplacement at abutments are useful in order tocontrol the damage of abutments.

PEA = r K EA + q ' K EA
K EA =

Eigenvalue Analysis(1)
cos 2 (φ − θ o − θ )sin (φ + δ E )sin (φ − α − θ o ) cos θ o cos 2 θ cos(θ + θ o + δ E )1 +(θ + θ o + δ E )cos(θ − α ) 
cos


2

Proposal of the Damage Assessment

The dynamic characteristics for box-girder bridge
structure in this study were explicitly portrayedthrough modal analysis procedures by eigenvalue
analysis in previous research [7]. From this analysis,
it was found the predominant mode was in equal
position for bridge with different gap of 10 cm and 20
cm. Otherwise; installation of four different abutment
modelling
approaches had a significant effect on its(2)
predominant mode, due to effect of the wing wall.
The possibility of bridge with abutment Type 4 tovibrate sympathetically was at the 9th mode in Xdirection, 10th mode in Y and Z-direction.
Shear Stresses of Abutments

The determination of damage in abutment was
examined by using an allowable stress of concrete [9],as shown in Table 1. An allowable stress for four
different design strength of concrete are specified.
Therefore, allowable shear stresses as the maximum

In this numerical simulation, the maximum shear
stress occurred in all abutments with different gap
were analyzed, as shown in Figs. 5(a) through Figure5(f). A1 and A2 denote the position of abutments in
655

GEOMATE – Melbourne, Australia, 11-13 November 2020the left and right side, respectively. According tothese figures, it can be seen that installation ofabutment Type 4 in concrete girder bridge generally
increases the shear stress on vertical wall, when it iscompared to Type 2. However, the maximum resultis less than the maximum elastic limit of 1.9 MPa. It
corresponds that abutments have a capability inresisting the shear load occurs in bridge due to the
input seismic motion.

(b) A2 (L2T2G1-1)
Fig. 6 Maximum horizontal displacement at top ofabutments
The small and large displacements are determinedby the ratio of 0.009 and 0.025, respectively [10].
Displacement to the left and right side of abutments
are figured out by positive and negative values. From
those simulations, it can be clarified that effect of
input seismic motion is small on its horizontal
displacement. This condition is possibly due to theground motion which is applied at all support of thebridge, including of pier and abutments.
CONCLUSIONSThe seismic responses of abutments in a real
bridge model were investigated. The conclusions of
this research are given below.
1. Installation of different abutment modelling
approaches have a significant effect on its
predominant mode.
2. All types of abutments have a capability inresisting the shear stress occurs in bridge dueto the input seismic motion.
3. Effect of input seismic motion is small on its
horizontal displacement and the shear stress atthe surface of abutments. It is possibly due tothe position of the applied ground motion at all
supports without any consideration onliquefaction.

(a) A1 (L2T2G1-1)(b) A2 (L2T2G1-1)
Fig.5 Maximum shear stress of abutments
Horizontal displacement of abutments

ACKNOWLEDGMENTS

Figs. 7(a) through Figure 7(d) show the maximum
horizontal displacement at the top of parapet wallwith the ratio between displacements at the top ofabutment (Δ) to the height of abutment (H), Δ/H.

The authors greatly indebt to “DIKTI”(Directorate General of Higher Education) for
providing financial support through this research.
Special thanks to the Civil Engineering Department,
Universitas Brawijaya for supporting this opportunity.
REFERENCE[1]. Aviram, A.; Mackie, K.R.; and Stojadinovic,
B. (2008). Effect of abutment modeling on theseismic response of bridge structures. Earth
Eng & Eng Vib, 7(4), 395-402.
[2]. Yamao, T.; Kawachi, A.; and Tsutsui, M.
(2012). Static and dynamic behaviour of aparapet wall of the abutment. Proceeding of
the 11th International Conference on Steel,Space and Composite Structures. Qigdao,
China.
[3]. Susanti, L.; Setyowulan, D.; and Wijaya, M. N.
(2018). An experimental investigation on
behavior of RC parapet wall of abutment
under collision. International Journal of Civil
Engineering and Technology (IJCIET), 9(9),1831-1838.

(a) A1 (L2T2G1-1)

656

GEOMATE – Melbourne, Australia, 11-13 November 2020of displacement restriction and wing wall
types. Procedia-Social and Behavioral
Sciences, 218, 104-117.
[8]. Mokhatar, S.N.; and Abdullah, R. (2012).
Computational analysis of reinforced concreteslabs subjected to impact loads. International
Journal of Integrated Engineering, 4(2), 7076.
[9]. Japan
Road
Association.
(2002).
Specifications for Highway Bridges Part V:
Seismic design.
[10]. Ahmed, A. (2014). Modeling of a reinforced
concrete beam subjected to impact vibration
using ABAQUS. International Journal of
Civil and Structural Engineering, 4(3), 227236.

[4]. Hamamoto, T., Yamao, T., Setyowulan, D.
(2020). Analytical Study on Seismic Response
Reduction for PC Bridge: Effects of Cost on a
Proposed Seismic Reinforcement Method Dueto Collision. Journal of Civil, Construction
and Environmental Engineering (JCEE), 5(3),57-66.
[5]. Dassault Systems Simulia Corp. (2011).
ABAQUS/CAE User’s Manual 6.11.
[6]. Setyowulan, D.; Hamamoto, T.; and Yamao,
T. (2014). Elasto-plastic behavior of 3dimensional reinforced concrete abutments
considering the effect of the wing wall.
International Journal of Civil Engineeringand Technology (IJCIET), 5(11), 97-113.
[7]. Setyowulan, D.; Yamao, T.; Yamamoto, K.;
and Hamamoto, T. (2016). Investigation ofseismic response on girder bridges: the effect657

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

PREDICTION OF AERODYNAMIC COEFFICIENTS FOR OFFSHORETURBINE BLADE UNDER THE VARIATION OF WIND VELOCITIES
Eaindra Soe1, Yasothorn Sapsathiarn1 and Jetsadaporn Priyadumkol2*
1

Department of Civil and Environmental Engineering, Faculty of Engineering, Mahidol University, Thailand
2
Department of Mechanical Engineering, Faculty of Engineering, Mahidol University, Thailand*Corresponding author: jetsadaporn.pri@mahidol.edu

ABSTRACT
Offshore wind turbine is an environmentally friendly and sustainable technology for generating electricalenergy from wind energy at sea. Research in aerodynamics is crucial for the development of offshore wind energy
technology to improve wind turbine blade design for maximum efficiency. In this study, numerical studies for theS809 airfoil aerodynamic performances under the wind speed of 7, 17.5, and 29 m/s are presented using thecomputational fluid dynamics ANSYS Fluent program. A two-dimensional model is developed in the presented
study based on k-ω SST turbulence model. The turbulence model is validated with experimental results presented
in the literature. The simulation results including lift coefficient (CL) and drag coefficient, (CD) obtained from thenumerical study with the AOA range from -10˚ to 15˚ are presented. In addition, the effects of aerodynamic
coefficients are also presented and discussed. Numerical results show that the sliding ratio is highest for the caseof 6˚ AOA, in which the sliding ratios are 33.23, 41.90 and 45.87 at Reynolds numbers of 479211, 1198027 and
1882614, respectively.
Keywords: Offshore Wind Turbine Blade, Computational Fluid Dynamics (CFD), Aerodynamics Coefficients,
Sliding Ratio, Angle of Attack (AOA)

Many researchers have investigated theaerodynamics performances of various airfoils by
employing CFD approach [6]-[10]. The blade
profiles S809 and S816 were carried out by using
two-dimensional CFD simulations to study lift anddrag forces over the angle of attack (AOA) range of±180˚with wind velocity 11m/s at low Reynolds
numbers [6]. Five different popular airfoil sections
which were S8xx, FFA and AH series were analyzed
by using CFD method to present the best airfoil
profile for the optimum aerodynamic performance [7].
The flow and aerodynamic features of sharp
trailing edged flat plate airfoil was systematicallycompared with NACA 0012 airfoil at three various
Reynolds numbers by using CFD simulations [8].
The influences of rain on flow separation andaerodynamic performance of the single element andslatted NACA0012 airfoils through a mathematicalmodel which was developed by the commercial CFD
solver ANSYS FLUENT was investigated in [9]. The
analysis of the 2D sub-sonic flow over NACA0012
airfoil at various AOAs and operating at a Reynolds
number of 3x106 was presented in [10] to validate
through the comparison of the predictions and the freefield experimental measurements for the selectedairfoil.
In this paper, S809 airfoil profile, developed by
the NREL, is considered for analysis of wind turbine
blade at different angles of attack from -10˚ to 15˚.

INTRODUCTION
Energy demands have been significantly
increasing day by day because of not only the
expansion of populations but also the development ofindustrial sectors. Wind power becomes one of themain sustainable facility in comparison with otherenergy technologies like solar and hydropower
because it can be found everywhere [1]. In order tomake power from the wind, wind turbines are
essential technology because it can operate with air
and capture piece of its thermal energy, thendischarge it into electricity [2].
Wind power has shifted from onshore to offshore
locations in an attempt to exploit the accelerated andmore stable wind speed. While shifting offshore wind,
the tendency is to improve the sizes of the windturbine blades so that more of this consistent wind canbe taken, conclusively reducing the expense of energy[3]. At the present time, manufactures have been
discussed the state of the art of offshore wind turbines
massive of 7.5-12MW with rotor diameters of up to
200m [4].
The study of aerodynamic behavior of airfoils isessential for the design of practical wind turbine
blades. The computational fluid dynamics, CFD is thefurther suitable way to analyze the aerodynamic loads
of wind turbines with the rising computational
capacity [5].
658GEOMATE – Melbourne, Australia, 11-13 November 2020

Numerical results of simulations using Commercial
Computational Fluid Dynamics (CFD) program arepresented. Parameters including lift and dragcoefficient are carried out by considering sliding ratio
compared with three different velocities which are 7,
17.5 and 29 m/s (with Reynolds numbers of 479211,
1198027 and 1882614, respectively). The effects of
aerodynamic coefficients are discussed.

as depicted in Fig. 1. This profile is a 21% thick witha sharp trailing edge and especially designed for
HAWT blade. NREL simulated this profile by windtunnel test with special quality. Then, theaerodynamic data of this airfoil could be attained in
the technical report in [13]. The chord length of S809
airfoil is normalized equal to 1m in this simulation.

HORIZONTAL AXIS WIND TURBINE (HAWT)
Modern HAWT blades have been consistently
designed using well tested airfoils.
National
Renewable Energy Laboratory (NREL) and otheragencies have developed several airfoil families
especially for HAWT blades [6]. Before wind turbine
energy production are designed, aerodynamic related
to airfoils need to be determined.
In order to measure the aerodynamic loads, liftand drag coefficients are needed to investigate first
because these coefficients could vary with AOA, αand the shape of the airfoil. The AOA plays a vitalrole in the wind turbine performance because the lift
coefficient decreases when the AOA increases
beyond a certain value which is call a stall point [11].
The lift and drag force on airfoil have been
averaged and used for calculating lift coefficient anddrag coefficient. Lift is defined as the force on the
body in a direction normal to the flow direction. The
drag on a body in an oncoming flow is defined as theforce on the body in a direction parallel to the flow
direction [11]. Moreover, sliding ratio is one of the
predominant aerodynamic parameters of the airfoil aswell. As a consequence of this, power output would
be reduced when the sliding ratio is small. Similarly,
the airfoil is designed, the sliding ratio should be
maximized [12]. They can be defined as follows:
𝐂𝐂𝐋𝐋 =𝐂𝐂𝐃𝐃 =
𝛆𝛆 =

𝐋𝐋
𝟏𝟏 𝟐𝟐
𝟐𝟐 𝛒𝛒𝐕𝐕 𝐜𝐜

𝐃𝐃
𝟏𝟏 𝟐𝟐
𝟐𝟐 𝛒𝛒𝐕𝐕 𝐜𝐜

𝐂𝐂𝐋𝐋
𝐋𝐋
=
𝐂𝐂𝐃𝐃
𝐃𝐃

Fig. 1

S809 Airfoil Profile.

The dimension of domain and boundary condition
are mainly influenced to forecast the aerodynamic
flow field of airfoil.
In this simulation, thecomputational domain is a C-type grid topology and
it is extended to 12.5 chord lengths of airfoil in all
direction as shown in Fig. 2. Hence, the boundary
condition is applied as inlets, outlets and wall to allareas of domain as presented in Fig. 2. Thus, the inlet
turbulence is considered with three variable windvelocity which are 7, 17.5 and 29m/s according to theBeaufort scales [14] at a medium range intensity 5%.
At the outlet boundary, the pressure condition is set
to the 0 Pa and the airfoil surface is considered as noslip wall boundary condition. The convergenceaccuracy is set 10-5 for the absolute residuals variables
equation in order to get highest precisions.

(1)

(2)
Inlet
(3)

12.5c

where, ρ is air density (kg/m3), c is chord length (m),L is lift force (N/m), D is drag force (N/m), V is windvelocity (m/s), CL is lift coefficient, CD is dragcoefficient and ɛ is sliding ratio.

Inlet

Airfoil

Outlet

12.5c

NUMERICAL SIMULATIONInlet

The study and investigation of the S809 airfoil
developed by NREL are conducted using acommercial computational fluid dynamics (CFD)
package, ANSYS Fluent. The S809 airfoil is analyzed

Fig. 2659

The C-type domain for S809 airfoil
simulation.

GEOMATE – Melbourne, Australia, 11-13 November 2020

1.02˚ and 5.13˚ respectively. This study focused on
six cases of grid independent which are 17944, 42761,
110001, 185521, 319305 and 400302. The results
reveal that the trends start constant at number of
elements of 185521. Thus, this element number could
be established as a good grid independent solution.

In this simulation, Fluent solver based a finite
volume method is applied to solve the ReynoldsAveraged Navier-Stokes (RANS) equations. Thus,
pressure-based solver SIMPLE is assigned as thepressure-velocity coupling algorithm. The air flow is
set to be steady, incompressible and turbulent. In theprevious studied [5], [15], [16] they suggested that KOmega SST model was suitable for airfoil simulation.
For this turbulence model, second order upwind isused in order to control and reduce the numericalsolution errors. All these parameters are given in table
1.
Table 1 Input Parameters
Turbulence model
Fluid
Density, ρDynamic viscosity, µChord length, cDiscretization
scheme

CFD algorithm

K-Omega SST
Air, incompressible
1.225 kg/m3
1.7894 x 10-5
1m
Pressure (second order
upwind)Momentum (second order
upwind)SimpleFig. 4The drag coefficient regarding on variable
numbers of cells.

VALIDATIONThe numerical precision especially relies on thecomputational grid. Basically, there are many kindsof mesh refinement patterns in CFD simulations.
Figure 3 presents that the domain is discretized withthe unstructured quadrilateral mesh. In addition,
inflation is applied on the airfoil surface in order tocapture the effect of the boundary layer as illustrated
in Fig. 3.

The simulation results are compared with thoseobtained from experimental results [13] at threevariable AOAs which are 0˚, 1.02˚and 5.13˚ forvalidation. Table 2 shows the lift coefficients of S809
airfoil at Reynolds number 2 million. It is found thatthe errors are less than 10% of all AOAs. Therefore,
using this model can predict a phenomenon of
turbulence flows in S809 airfoil correctly.

Table 2 Comparison of lift coefficients between
numerical and experimental results

AOA

Numerical
results

0
1.02
5.13

0.13490.25120.7088Experimen
tal results[13]
0.14690.27160.7609Error (%)
8.17
7.51
6.85

RESULTS AND DISCUSSION
Fig. 3

The grid generated for the flow domain.

The results of the predicted CFD simulation at
various conditions are reported in Figs 5, 6 and 7.
Drag coefficient, lift coefficient and sliding ratio withthe range of AOAs between -10˚ and 15˚ for threedifferent wind speeds are discussed.

However, generating extra nodes and elements
can cause the required computer memory andcomputational time. Therefore, the suitable numberof elements should be determined until the further
mesh refinement could not change the results. Figure
4 shows the convergent drag coefficient determinedby relative graphs between number of element anddrag coefficient at three different AOAs which are 0˚,Drag Coefficient660

GEOMATE – Melbourne, Australia, 11-13 November 2020

The plot of drag coefficients and AOAs is
obtained as shown in Fig. 5. The plot compares thepredicted effects for various AOAs. The results showthat the AOA at the minimum drag coefficients are 0
degrees which are 0.017, 0.014 and 0.012 at 7, 17.5
and 29m/s, respectively. At AOAs greater than 0
degree, the drag coefficients increase. In addition,
there are notable rising trends from 5˚ to 15˚.

Fig. 5

to 15˚ for three different wind velocities.
Sliding Ratio/ Lift to Drag Ratio
The sliding ratio for the simulation results arereported by graphs in Fig. 7. Figure 7 shows that thelowest sliding ratio is -17.32 at AOA of -5˚ for 7m/s
wind velocity. While, at AOA -6˚, the fewest sliding
ratios are -24.26 and -27.91 at the velocities of 17.5
and 29 m/s, respectively. In contrast, the AOA at the
maximum sliding ratios are equally at 6˚ which are
33.23, 41.90 and 45.87 at 7, 17.5 and 29 m/s,
respectively. It is obvious that the airfoil has amaximum sliding ratio of 45.87 at AOA of 6˚, whichcorresponds to a wind velocity of 29 m/s. Therefore,
the higher wind velocity yields the best sliding ratio.
In addition, it is claimed that AOA of 6 degrees is thebest condition to operate a HAWT with S809 airfoil
profile with all three velocities.

Drag Coefficient Distribution between -10˚
to 15˚ for three different wind velocities.

Lift Coefficient
Lift coefficients at various velocities obtained
from using the proposed model are plotted in Figs. 6.
It is found that all results are similarly raising with avery significant slope in the AOA range from -5˚ to
10˚ for three velocities. At the AOA 6˚, lift
coefficients are 0.739, 0.783, 0.798 at 7, 17.5 and 29
m/s, respectively. While, it is found that the
maximum lift coefficient raises to hit the critical
AOA which is called stall point before reaching AOA
15˚. In this study, the critical or stall angle of attack
is around 9° - 15° for three velocities. After the lift
coefficient has peaked at the critical AOA, the trendof this decreases noticeably again. The result showsa peak of 0.920, 0.990, 1.026 at AOAs of 13, 9, 10
degrees which corresponds to a wind speed of 7, 17.5
and29 m/s, respectively.

Fig. 6

Fig. 7

Sliding Ratio Distribution between -10˚ to
15˚ for three different wind velocities.

CFD Simulation
The predictions of pressure contours and velocity
streamlines in the flow domain at a velocity of 17.5
m/s are shown in Fig.8. Figure 8 shows the
distribution of the pressure contours and velocity
streamlines at the AOA of 0˚, 6˚ and 12˚.
From Fig.8a, the results demonstrate clearly thatthe pressure on the lower surface of the geometry islarger than that of the upper part increasing AOAs.
Therefore, the under part of airfoil pressure at AOA
12˚ is greater than that side of its at AOA 0˚. It isshown that the geometry is pushed to the upward
movement as a result of airflow direction.
The velocity streamlines show significantly
different among three AOAs as shown in Fig.8b. The
simulation results illustrated the streamlines areformed to be a vortex on the trailing edge at the AOA
of 12 degrees. There is a noticeable vortex effects oftrailing part of profile at AOA of 12˚ comparing other
two AOAs 0˚ and 6˚. The vortex os expanded andLift Coefficient Distribution between -10˚
661

GEOMATE – Melbourne, Australia, 11-13 November 2020strongly separated flow above the stall angle of attack.
As a result of this, the lift coefficient would decrease.

AOA of 0 degree

AOA of 6 degree

AOA of 12 degree(a)(b)
Fig. 8 Pressure contours (a) and velocity streamlines (b) in three different AOAs at 17.5 m/s
coefficients of the critical AOA at 13, 9 and 10
degrees corresponds to a wind speed of 7, 17.5 and 29
m/s, respectively. The simulation results show small
different stall angle of attack because many airfoils[17] are typically around 15° to 20°. While, numerical
study of the S809 shows that the maximum lift-todrag ratio is achieved at AOA of 6˚ for three velocities
at 7, 17.5 and 29 m/s respectively. Aerodynamic

Conclusion
In this paper, S809 airfoil profile is studied withdifferent angles of attack between -10˚ and 15˚ fordifferent wind velocities. A two-dimensional model
is developed in the presented study based on the k-ω
SST turbulence model. Various parameters including
lift and drag coefficients and lift-to-drag ratio. The lift662

GEOMATE – Melbourne, Australia, 11-13 November 2020[8] Jha S. K., Gautam U., Pawar P., Narayanan S., andKumaraswamidhas L. A., Investigations of Flow
Phenomena Over a Flat Plate and NACA0012

analysis of wind turbine blades for a suitable angle ofattack is crucial to improve the design of wind turbine
blades for maximum efficiency.

Airfoil at High Angles of Attack. Iranian Journal of
Science and Technology, Transactions of MechanicalEngineering. 2019, pp. 1-2.

ACKNOWLEDGMENTS[9] Fatahian H., Salarian H., Nimvari ME., andKhaleghinia J., CFD Simulation of Aerodynamic
Performance and Flow Separation of Single
Element and Slatted Airfoils under Rain
Condition. Applied Mathematical Modelling,
2020.

This work was supported by Faculty ofEngineering, Mahidol University, EU Marie Curie
RISE project RESET (H2020-MSCA-RISE-2016,reference no. 730888) and Graduate Studies of
Mahidol University Alumni Association, Mahidol
University.

[10] Douvi E., and Margaris D. P., Evaluation of the
Turbulence Models for the Simulation of the Flow overa National Advisory Committee for Aeronautics(NACA) 0012 airfoil. Journal of MechanicalEngineering Research, Vol. 4, Issue 3, 2012, pp. 100111.
[11] Hansen M. O., Aerodynamics of Wind Turbines. 2015,
pp. 8-10.
[12] Hau E., Wind Turbines: Fundamentals, Technologies,
Application, Economics. Springer Science andBusiness Media, 2013, pp. 120-134

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for the S809 Airfoil. National Renewable Energy
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663

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

NUMERICAL ANALYSIS OF INVASION PATTERNS DURING
DRAINAGE PROCESS IN A SIMPLIFIED PORE NETWORK MODEL
Yuto Takeuchi1, Junichiro Takeuchi2 and Masayuki Fujihara3
1,2,3
Graduate School of Agriculture, Kyoto University, Japan
ABSTRACT
Immiscible two-phase flows in porous media concern various problems such as underground water pollution
by non-aqueous phase liquid and enhanced oil recovery. It is well known that the drainage process of two-phaseflows within porous media takes several patterns depending on the conditions, namely from a stable displacement,
viscous fingering flow to capillary fingering flow. However, the physical mechanism of infiltration and criticalconditions for varied invasion patterns have not been evident. This study employed numerical two-phase flow
simulation using the Color Gradient Lattice Boltzmann Method (CG-LBM) in a simplified pore network modelwith different capillary number and viscosity ratio between the two fluids. The CG-LBM is superior in
implementation of boundary conditions and specification of fluid parameters such as viscosity and interfacial
force, and therefore suitable for two-phase flow simulation in porous media. Simulation results confirm that low
capillary number flows produce the preferential flow for pore throats with large width, which is correspondent tothe capillary fingering flow. In addition, the retreat of the invading fluid caused by Haines jump is seen. Whencapillary number is higher, these two phenomena did not apparently take place. Higher capillary number flows
lead the invading fluid to simultaneously displacing different throats when its viscosity is higher than that of theinvaded fluid (stable displacement), and the viscous fingering flow happens otherwise. These findings suggest thatthe conditions of capillary number and viscosity ratio, and occurrence of the preferential flow and Haines jump
are key factors for different invasion patterns.
Keywords: Fingering flow, Lattice Boltzmann Method, Drainage process, Capillary number

INTRODUCTION

analyses of detailed process during displacement
since it assumes simple capillary network fluid
dynamics.
This paper numerically simulates the drainage
process within a simplified pore network model using
the Color Gradient Lattice Boltzmann Method (CGLBM) with different conditions of Ca and M . The
numerical results confirmed the three invasion
patterns depending on the conditions, and the two
notable fluid dynamic phenomena of a preferential
flow and Haines jump are seen, which are concluded
as key factors for different invasion patterns.

Immiscible two-phase flows in porous media
concern various problems such as underground waterpollution by non-aqueous phase liquid, enhanced oil
recovery, and carbon capture and storage. The
drainage process within porous media exhibits
various patterns depending on conditions namelyfrom stable displacement, a viscous fingering flow toa capillary fingering flow. Although the criticalconditions determining these invasion patterns havebeen widely researched both physically [1]–[3] andnumerically [4]–[6], there have been no universally
accepted criteria about transition among the different
invasion patterns. In addition, detailed physicalmechanism during the fluid displacement in porous
media has not been explicitly identified.
One of the most influential works about drainage
process in porous media is by [7]. They numericallysimulated displacement behavior by a capillary
network model and classified the invasion patterns
based on the saturation when breakthrough occurs.
The result is compiled as a phase diagram plotting thetypes of invasion patterns on the coordinate whose xaxis is viscosity ratio M and y-axis is capillary
number Ca . Although their work successfully
reproduced different invasion patterns and classified
them, it is reported that the threshold of the diagram
is not universally fixed [1], [3], [5]. Also, it lacks

MATERIAL AND METHODS
Numerical Scheme
The Lattice Boltzmann Method (LBM) is amesoscopic-based computational fluid method andtreats a fluid as a collection of particles which canhave finite number of lattice velocities. The
probability of particles along a certain direction at anode is described by the particle distribution function.
Fluid properties such as density, pressure and velocity
are derived by zeroth and first moment of the particle
distribution function. The computational procedure of
the standard LBM consists of two steps: a collision
step and streaming step. The simple iteration of these
two steps develops the particle distribution functions
664

GEOMATE – Melbourne, Australia, 11-13 November 2020and computation of the moments of the particle
distribution functions yields macroscopic variables at
each node in a way that it recovers the incompressible
Navier-Stokes equation within the limit of second
order accuracy and low Mach number [8].
The CG-LBM is one of the multiphase LBM
models, and good at explicit specification of fluids’
properties such as viscosity ratio and interfacial
tension. We implemented a computational method
mainly based on [9], where multiphase features ofinterfacial tension and immiscibility are incorporated
by adding a perturbation step and recoloring step tothe standard LBM procedure, respectively. On theother hand, the wettability of fluids is modelled byassigning contact angle directly without considering
contact angle hysteresis [10].
As boundary conditions, the standard bounceback condition is applied at the walls of the poremodel as a no-slip boundary condition, and the nonequilibrium bounce back method [11] is imposed at
the inlet and outlet as a Dirichlet boundary condition.
The computer code we used was verified
beforehand by simulating basic multiphase flow
problems of droplet formation and contact angle test
(not shown), and satisfying results were obtained.
Throughout the simulations in this research, thecontact angle of the wetting fluid against thenonwetting fluid is set to be 60 degrees.

higher, the viscous fingering flow takes place when
Ca is higher and M is lower, and capillaryfingering flow happens when Ca is lower. However,
the threshold of the transition among those invasion
patterns are different among different researches, andhence a universal criterion has not been available. In
addition, the physical mechanism of the different
invasion patterns has not been adequately researchedbecause of the complicatedness of porous media.
We change the two parameters of Ca and M byappropriately choosing the kinematic viscosity, fluid
density, interfacial tension, and inlet velocity fromsimulation to simulation while keeping the LBM
specific error due to large velocity and kinematic
viscosity small. Indeed, the maximum inlet velocity
allowed is set to be 0.009 and that of kinematic
viscosity to be 0.6. We realized the capillary number
ranged from Ca = 5.0 10−5 to 7.2 10−2 and theviscosity ratio M = 9.000 and 0.111 . The
simulation is stopped when the invading fluid reaches
the outlet of the medium, i.e., when breakthroughoccurs. The simulation results exhibited different
invasion patterns each of which corresponds to thestable displacement, viscous fingering flow andcapillary fingering flow. The physical mechanism of
the variant displacement processes and theirtransitional values are analyzed in the next section.

Simulation Outline
Considering the high computational cost of the
CG-LBM, a simplified pore network model is chosen
as a simulated medium. The simulated porous model
shown in Fig. 1 is composed of the four round porebodies with the same size, four rectangular throats
connecting them having different widths, and inlet
and outlet throats. The width w of the pore throatsand diameter d of the pore bodies are presented inTable 1 in a form of dimensionless lattice unit (lu).
Note that all the values are nondimensionalized using
lattice unit (lu), mass unit (mu), and time step (ts) for
length, mass, and time unit, respectively in the rest of
the paper. A constant velocity is imposed at the inlet.
The inlet boundary nodes are assumed to be
nonwetting nodes, and the outlet boundary nodes aretreated as wetting nodes. The inlet velocity drives thenonwetting fluid colored red in Fig. 1 to invade thepores and the blue wetting fluid is drained from theoutlet.
The key parameters of the simulation are capillary
number Ca = n un 
andviscosityratio
M = n w . Note that  n is the dynamic viscosityof the nonwetting fluid, un is the inlet velocity of thenonwetting fluid,  is the interfacial tension, andw is the dynamic viscosity of the wetting fluid.
Several works about drainage process in porous
media [1], [3], [5], [7] showed that the stabledisplacement occurs when both Ca and M are

Fig. 1The simulated porous model. The red region
is occupied by an invading fluid and the blue
region is occupied by an invaded fluid.

Table 1 Widths w of the pore throats and radius dof the pore bodies in the simulated porous
model
wBL
28

wTL
32

wBR
12

wTR
20

dB
50

wI
40

wO
24

Note: BL: Bottom-Left, TL: Top-Left, BR: Bottom-Right, TR:
Top-Right, B: Body, I: Inlet, O: Outlet. The unit is lattice unit (lu).

665

GEOMATE – Melbourne, Australia, 11-13 November 20202 cos[max( +  , 180 )](2),w
where cos  = −w / d and 90    180 . The
threshold pressure into the top body from the top left
throat is thus Pth = 2 cos( +  ) / 32  0.062 ,which is higher than the threshold pressure into thebottom left throat Pth = 2 cos 60 / 28  0.036 .
Hence, the invading fluid prefers the bottom left
throat to the top body (Fig. 2b). Then, the invading
fluid selects the top body rather than the bottom body,following Eq. (2) (Fig. 2c). During the invasion of the
top body, the second characteristic, or the retreat of
the invading fluid from the throat due to Haines jump
[12] occurs. Haines jump is a jump of a fluid interface
and abrupt pressure drop accompanying it [12]. Whenthe invading fluid penetrates the top body from the
top left throat, the invading fluid experiences thesudden change of the geometry and accompanying
sudden drop of the capillary pressure. This makes thepressure of the invading fluid lower than the pressure
needed to stay in the bottom left throat. Hence, theinvading fluid in the bottom left throat retreats and theredistribution of the fluid occurs. The difference
between average pressure of the invading and invaded
fluids, and the average x-velocity before and duringinvasion of the top pore body are plotted in Fig. 3 forthe same condition as Fig. 2. During the period (A),the invading fluid penetrates the bottom left throat,
keeping the constant pressure. Then, during period(B), the pressure starts to increase by growing menisci
while the interfaces stick to the critical points of the
top left throat and top body, and the bottom left throat
and bottom body. Finally it releases the pressureduring the period (C) while penetrating the top body.
Accompanying to the sharp pressure decline, theinvading fluid retreats from the bottom left throat
from (D) to (E). Also, the average x-velocity
experiences the abrupt increase and the following

RESULTS AND DISCUSSION

Pth = −

Capillary Fingering Flow
The representative flow development of capillaryfingering regime is shown in Fig. 2. This regime
assumes lower capillary number, and therefore theflow is dominated by capillary force while viscous
force is neglectable.
The first notable characteristic is the preferential
flow to larger pore throats. Referring to Fig. 2a, theinvading fluid preferentially penetrates the top left
throat without getting into the bottom left throat. Thisphenomenon is caused by variant threshold pressuresof pore throats. The threshold pressure of a throat isdefined as the pressure difference between the two
fluids needed for the invading fluid to penetrate thethroat. In drainage process, according to the Laplace
law, the threshold pressure Pth is written as
2 cos 
Pth =
,
(1)
w
where  is the contact angle. Since the contact angle
and interfacial tension are constants throughout
the domain, larger throats always hold smaller
threshold pressure, leading the invading fluid topenetrate larger throats preferentially. While the fluid
invades the pore throat with the largest throat width,
the pressure difference between the invading andinvaded fluid is fixed to the threshold pressure of thatthroat which is inevitably lower than the thresholdpressure of the other pore throat. Hence, the invading
fluid is not able to penetrate the other throat, waitingat the entrance of the throat (Fig. 2a). In addition,
another preferential pattern between a pore body andthroat is noticeable. From Fig. 2a to Fig. 2b, theinvading fluid prefers the bottom left throat rather
than the top body. The threshold pressure into a pore
body with diameter d from a throat with width w is

Fig. 2

The representative flow development in capillary fingering regime ( Ca = 3.6 10−4 , M = 9.000 ).
666

GEOMATE – Melbourne, Australia, 11-13 November 2020

abrupt decrease during the retreat. This jump andretreat happen in a short period compared to the initial
invasion of the bottom left throat: the invasion tookaround 160,000 time steps while the retreat occurredin 40,000 time steps. The change of average xvelocity shown in Fig. 3 also identifies the rapidnessof the jump and retreat. This whole process is typicalin the Haines jump phenomenon: accumulation ofpressure, abrupt exchange of pressure into velocity,and the fluid redistribution.
After refilling the bottom left throat according to
Eq. (1), the invading fluid penetrates the top rightthroat in the manner of comparison between Eq. (1)for the top right throat and Eq. (2) for the bottom body(Fig. 2d). Then, the bottom body is invaded while theHaines jump occurs (Fig. 2e) after which the top rightthroat is refilled (Fig. 2f). The threshold pressure into
the right body Pth = −2 cos(180 ) / 20 =  /10 ishigher than that into the bottom right throat
Pth = 2 cos 60 /12 =  /12 . However, Fig. 2gmanifests the opposite selection, i.e., the right body isinvaded. This discrepancy may be caused by aninertial effect. Indeed, the local Reynolds number at
the fluid interface, ReTR = UwTR /  n , where U is the

Stable Displacement
The representative flow development of stabledisplacement regime is shown in Fig. 4. In thisregime, the capillary number is higher, and theviscosity ratio is larger. Hence, viscous force of theinvading fluid is dominant. In this regime thepreferential flow and Haines jump referred to in thecapillary fingering regime do not apparently occur.
On the contrary, the invading fluid simultaneously
penetrates the bottom left throat and the top left throat(Fig. 4a). In this case, the dominant viscous force of
the invading fluid virtually neglects the local capillaryeffect due to interfacial tension, i.e., the capillarypressure of a throat does not affect the propagation of
the meniscus lying in the other throat. The retreat of
the invading fluid due to the Haines jump does nottake place for the same reason: during thedisplacement of a large pore body, the invading fluid
is still be able to displace the invaded fluid, propelledby the viscous force (Fig. 4b, c, d, e).
Viscous Fingering Flow
The representative flow development of viscous
fingering flow regime is shown in Fig. 5. This regime
represents higher capillary number and smaller
viscosity ratio. Hence, the flow is dominated by theviscous force of the invaded fluid. In this flow type,the finger-like interface is confirmed. In the same way
as the stable displacement pattern, the flow is driven
by bulk pressure difference between the inlet andoutlet, and therefore capillarity of throats does notaffect the fluid dynamics. Observing the state atbreakthrough (Fig. 5d), the seemingly preferential
path to the upper pores is evident, which is not by thecapillarity but rather by the channeling effect [14],[15]. In the viscous fingering pattern, since bulk
pressure difference is much larger than the local
capillary pressure difference, the selection of aninvaded pore does not occur pore by pore unlike thecapillary fingering pattern. Instead, the overall
viscous resistance through the series of pores from themagnitude of velocity and  n is the kinematic
viscosity of the invading fluid, during the invasion of
the border between the top right throat and the rightbody reached O (1) , which can be large enough toexert an inertial effect. The local velocity of theinterface was one or two order larger than the inlet
velocity, which caused the local increase in Reynolds
number compared to bulk Reynolds number. Since“inertia can influence the selection of the next pore to
be invaded” [13], this apparently unreasonable
preference can be caused by an inertial effect. In fact,
detailed observation of the results revealed that theinvading fluid once invaded the bottom right throat
but retreated immediately. This suggests that
although capillarity prefers the bottom right throat,
inertia overcomes it and makes the invading fluid
relax into the right body.

4
(A)

2.5

(B)

(C)

3

(D)

2.0

2

1.5

1

1.0

0

0.5

Pressure difference(left axis)

0.0
-0.5

-1

(E)

-2

Average of x-velocity(right axis)

-1.0

-3

-1.5

-4
500

550
600
time step (×2000)

650

667

Fig. 3
Average x-velocity (×10-4)

Pressure difference ( 10-3)

3.0

Pressure difference andaverage x-velocity about
invasion of top body.
(A) Penetrate bottom left
throat(B) Accumulate pressure by
growing menisci while
sticking to critical points(C) Penetrate top body while
releasing pressure(D) Start to retreat frombottom left throat(E) Retreat completely toreach left body

GEOMATE – Melbourne, Australia, 11-13 November 2020

Fig. 4 The representative flow development in stable displacement regime ( Ca = 7.2 10−2 , M = 9.000 ).

Fig. 5 The representative flow development in viscous fingering regime ( Ca = 7.2 10−2 , M = 0.111 ).
inlet to the outlet determines the flow path. In thismanner, the upper series of pores is preferable for theinvading fluid compared to the lower one. In otherwords, the upper channel is more permeable than thelower channel.

CONCLUSIONSIn this paper, the drainage process in a simplified
pore network model was numerically simulated using
the CG-LBM, and the three different invasion
patterns were obtained. These patterns correspond toa capillary fingering flow, stable displacement, and a
viscous fingering flow, respectively. The two notablecharacteristics, namely the preferential flow to wider
pore throats and the retreat of the invading fluid dueto the Haines jump were observed for the low
capillary number flows. During the Haines jump,
the sudden release of pressure and abrupt change inmean velocity were confirmed. On the other hand, thepreferential flow and Haines jump did not apparently
happen for higher capillary number flows. WhenCa  O(10−2 ) and n / w = 9.000 , the pore throats
weresimultaneously
displaced,whichiscorrespondent to the stable displacement. WhenCa  O(10−3 ) and n / w = 0.111 , the viscous
fingering flow developed, where the finger-likeinterface was observed. These findings suggest thatthe conditions of capillary number and viscosity ratio,
and occurrence of the preferential flow and Haines
jump are key factors for different invasion patterns.

Critical Values For Transition
The critical values for the transition among thethree invasion patterns are of interest from anengineering viewpoint since the different invasion
patterns significantly alter efficiency of the operation.
Table 2 and 3 show the presence and absence of thenoted phenomena in capillary fingering regime, thepreferential flow and retreat of the invading fluid due
to Haines jump, for different viscosity ratios. From
Table 2, the capillary fingering flow would transition
to the stable displacement in Ca = O(10−2 ) , whichroughly corresponds to [3] and [7]. Table 3 suggeststhat the transition from the capillary fingering to theviscous fingering would occur in Ca = O(10−3 ) ,which is considerably higher than some of theliterature [1], [3]. Since the domain size influences
invasion patterns significantly, simulation on larger
realistic porous media is needed instead of thesimulated minimal porous model for further analyses.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 2 Presence and absence of preferential flow and retreat by Haines jump when M = 9.000
Ca
logCa
Preferential flow
Retreat by
Hainesjump

5.0×10-5-4.30

1.0×10-4-4.00

3.6×10-4-3.44

3.6×10-3-2.44

Present

Present

Present

Present

Present

Present

Present

Present

7.2×10-3-2.14Intermediate

1.0×10-2-2.00
Intermediate

Present

Present

2.0×10-2-1.70

3.6×10-2-1.44

7.2×10-2-1.14

AbsentAbsentAbsentIntermediate

AbsentAbsentTable 3 Presence and absence of preferential flow and retreat by Haines jump when M = 0.111
Ca
logCa
Preferential flow
Retreat by
Hainesjump

5.0×10-5-4.30

1.0×10-4-4.00

Present

Present

Present

Present

3.6×10-4-3.44Intermediate

7.2×10-4-3.14

3.6×10-3-2.44

7.2×10-3-2.14

1.0×10-2-2.00

3.6×10-2-1.44

7.2×10-2-1.14

AbsentAbsentAbsentAbsentAbsentAbsentPresent

Present

Intermediate

AbsentAbsentAbsentAbsentREFERENCES

Numerical models and experiments on immiscible
displacements in porous media. Journal of Fluid
Mechanics, Vol. 189, 1988, pp.165-187.
[8] Krüger T., Kusumaatmaja H., Kuzmin A., ShardtO., Silva G., and Viggen E. M.. The lattice
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[9] Leclaire S., Pellerin N., Reggio M., and Trépanier
J. Y., Enhanced equilibrium distribution functions
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Boltzmann models. International Journal ofMultiphase Flow, Vol. 57, 2013, pp.159-168.
[10] Xu Z., Liu H., and Valocchi A. J., Lattice
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[13] Ferrari A., and Lunati I., Inertial effects during
irreversible meniscus reconfiguration in angularpores. Advances in Water Resources, Vol. 74,
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[14] Tsang C. F., and Neretnieks I., Flow channelingin heterogeneous fractured rocks. Reviews ofGeophysics, Vol. 36, Issue 2, 1998, pp.275-298.
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Viscous Fingering and Preferential Flow Paths in
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10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A US$6.07 BILLION JAKARTA - BANDUNG HIGH - SPEED RAIL:
CHALLENGES AMID COVID-19 PANDEMIC
Aleksander Purba
Engineering Faculty, University of Lampung, Indonesia 35145
ABSTRACTThe Jakarta-Bandung high-speed railway project is one of the government's national strategic projects in thetransportation sector and is expected to require a total investment of US$6.07 billion. The railway project willserve four main stations: Halim in Jakarta, Karawang in West Java and Walini and Tegaluar in Bandung regency.
A total of 80 km of the railway will be elevated, while a further 16.9 km will be in tunnels. The trains will travelat 350 km per hour, cutting travel time between Jakarta and Bandung to 45 minutes, faster than the approximately
three hours and a half on the Parahyangan existing train. Railway developer PT Kereta Cepat Indonesia China(KCIC), a joint venture between Indonesian and Chinese railway companies, initially planned to finish the projectand start operations in December 2021. Unfortunately, development of the project would depend on the situationof the coronavirus outbreak in the country. In order to curb the outbreak, the Jakarta government called on the
public in mid-March to implement social distancing measures by studying, working and praying at home. Hence
Chinese workers wanting to return to work on the Jakarta-Bandung high-speed railway project should abide by
the health and safety protocols. In late March a regulation offering an exemption for foreigners working on national
strategic projects from the temporary ban on foreign arrivals and transits. The ban is set to last until the government
declares an end to the outbreak. In order to speed–up the progress, the consortium have made a number ofbreakthroughs in building tunnels, railway bridges and stations to pursue the remaining progress of 56 percent
over the next 18 months.

Keywords: High-speed railway, Jakarta-Bandung, National strategic project, COVID-19

has begun discussions on possible Japaneseparticipation in a planned high-speed railway between
Jakarta and Bandung, hoping to spur progress on the
delayed Chinese-led project as costs mount.
Meanwhile, the latest information indicates that the
new proposal would combine that rail link with aJapanese-Indonesian project upgrading an existing
750 km connection between Jakarta and Surabaya.
Discussions have begun on extending the high-speed
Jakarta-Bandung railway to Surabaya and whether it
would be possible to include Japan in the consortium.
Many in the Indonesian government have argued thata single railway running through Bandung to
Surabaya would be more efficient than separate
routes going east and southeast from the capital. Cost
overruns on the Bandung-Jakarta HSR project havegiven a boost to this view. The project was originallyscheduled to be completed in 2019 at an estimatedcost of US$5.5 billion. However, the project expected
to be completed in December 2021 at a cost of over
US$6.07 billion. In mid-May 2020, construction
progress reaches 48.3% and has worked again andfollow physical distancing although the Covid-19
pandemic is not yet over. This paper identifies thechallenges to complete the HSR project successfully.

INTRODUCTIONPT KCIC is established based on deed No. 86 dated
October 16, 2015 and project Jakarta-Bandung highspeed railways (HSR) designated as one of thegovernment's National Strategic Projects in
Presidential Regulation No. 3 of 2016 concerning the
Acceleration of National Strategic Projects. Having alength of 142.3 km stretching from Jakarta to
Bandung, the HSR line has four halting stations,
Halim, Karawang, Walini, and Tegalluar with one
depot located in Tegalluar. The construction was
carried out on a massive scale to pursue operationaltargets in 2021. Of the total length of the fast train
track, more than 80 km of them have elevatedstructures while the remainder are 13 tunnels and subgrades. Otherwise, the project construction has been
facing challenges regarding land procurement as themain problem that needed to be solved after theissuance of the construction permit. And since thecoronavirus is going global, Chinese workers wanting
to return to work on the Jakarta-Bandung high-speed
railway project should abide by the health and safety
protocols. Hence, these situations cause and effect ofdelay of project delivery. The Indonesian government
670

GEOMATE – Melbourne, Australia, 11-13 November 2020

The previous paper related to the Jakarta-Bandung
HSR can be found in Purba et. al. [1], and Purba [2].
PROJECT HIGHLIGHTS
Originally approved in May 2017 following thesigning of a US$4.5 billion loan agreement between
the two countries, the project initially experienced anumber of lengthy delays, largely on account of
protracted land‑rights negotiations. KCIC as HSRdeveloper such optimism, however, went to the wall
as soon as the unprecedented extent of the Covid‑19
outbreak – and its wider implications – became
apparent, with countries across Asia and beyond
locking down flights and barring port entry. As inmany other countries, a number of Chinese project
directors, managers, engineers and consultants for the
HSR project, who had returned home for the spring
festival period, were unable to resume their roles. The
map and brief data of Jakarta-Bandung HSR line areshown in Fig. 1 and Table 1, respectively. HSR
Jakarta-Bandung comes with the latest type,
CR400AF, which is equipped with modern andreliable technology. Combined with the concept oftransit oriented development (TOD), HSR station
ensures an integrated system with a wide choice ofintegrated transportation modes, a variety of culinary
delights, facilities and other privileges [3].
As of September 2019, in line with the progress on
physical infrastructure development, the JakartaBandung HSR project in parallel is also preparing an
operating system and maintenance of facilities andinfrastructure. The contractor is preparing the needfor human resources ranging from the organizational
structure to the number of personnel for operational
after the project completion. KCIC is targeting to
involve 1,700 personnel for the train operation. In thisregard, KCIC has been partnering with PT MRTJakarta, a pioneer of modern railroad facilities inIndonesia operating the mass rapid train in Jakarta.
The two companies agreed to transfer knowledge toeach other in connection with the maintenance andoperation, which include human resources
development as well as innovation and strategies fordeveloping Transit Oriented Development (TOD) andthe other line of non-train businesses. JakartaBandung HSR is an advanced-technology but riskymega project. The risk largely is sourced from the realdemand of the train connecting the two big cities.
Indeed, a lot of people commuting from Jakarta to
Bandung, and in another way around.

Fig. 1 The proposed of HSR line
Table 1. Brief data of Jakarta-Bandung HSR line [4]Element
TerminiIntermediate station
Route length
Schedule train frequency
Job opportunity

Payback period of investment
Ticket price
Speed
Estimated journey time
Revenue from ticket sales
Commencement date
Estimated completion datePassenger flow volume (2020)
Project costs
Terms of loan (40 years loans
period)Concession period

Data
Halim (Jakarta) ~ Tegalluar(Bandung)
Karawang and Walini (West Java)
Approximately 143 km
Every 35 min (first year operation)
39,000 jobs-HSR construction
20,000 jobs-TOD construction
28,000-HSR and TOD operation
40.2 years-without TOD
23.74 years-with TOD
IDR 225,000 (US$16)
Maximum operating speed 350
km/h
Between Halim and Tegalluar:45min
US$ 168 million (2020)
2020
2020
About 29,000 passengers per day
US$ 5.135 billion-without TOD
US$ 5.294 billion-with TOD
60% in US$ with interest 2%/year
40% in RMB with interest
3.46%/year
50 years (31 May 2019 ~ 31 May
2069)

FACTORS RELATED TO RIDERSHIP
Still, it does not guarantee that the commuters areinterested to regularly take the HSR as a preferable
transportation mode. While, the train can significantly
cut the travel time more than half, the ticket pricewould also be taken into consideration. The more
expensive the ticket price, the less appetite of people
to take the HSR as a regular transportation mode.
Therefore, TOD in areas surrounding the railway ofbullet train is very important to pursue to enable
smaller towns grow as the places of living. Shorter
trip using HSR connecting the towns in affordable
costs could increase the number of passengers [5].
Issue related to the number of passenger to be
attracted by the HRS line has also been discussed in
one of the most reputable colleges. Private car is still
the main choice of transportation for Jakarta Bandung road trip. At least 127,133 trips are made per
day by private car, followed by public transportations
which carry 13,000-14,000 passengers per day, a
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Accumulated traffic (bill pass-km)

large bus carries fewer than 1,000 passengers per day,and Argo Parahyangan train carries 2,000-2,500
passengers daily. As to meet the investment value
which is Rp72,5 trillion, the minimum number ofpassengers per day should reach at least 21,134
people. The figure is considered unrealistic to beachieved. In addition, the ticket price is rather
expensive which is Rp200,000 [6]. Otherwise, railridership in Indonesia has risen substantially in recent
years, making decades of underinvestment andgrowing urban congestion important considerations
for transport stakeholders as they upgrade andconstruct new lines. Statistics Indonesia (BPS)
reports that total rail passengers rose from 199 million
in 2011 to 202 million in 2012, 216 million in 2013,
277 million in 2014 and 325 million in 2015. The
average length of a passenger journey has

simultaneously fallen from 95 km to 68 km, while thecountry’s rail network remains limited to Java andSumatra, with 22,296 km of total line operational in
2015. The Medium-Term Development Plan 20152019 includes an infrastructure development agendathat outlines projects such as having 3,258 km of
newly built or rehabilitated rail lines, made up of
2,159 km of intercity railways and 1,099 km of urban
railway, and boosting rail cargo volumes to 1.5
million twenty-foot equivalent units annually. Urban
rail lines, including a planned light rail transit (LRT)system in Jakarta, are also expected to help reduce
congestion and transport costs, which have become
the highest in South-east Asia. It was forecast that the
new line would attract around 10 million passengers
per year in first year of operation, as shown in Fig. 2.

160
140
120

Asia

100
80
60
40

Europe

20
0
1965

1970

1975

1980

1985

1990

1995

2000

2005

Fig. 2 Accumulated HSR traffic [7]reached 99.96 percent. Meanwhile, construction on
the HSR project is expected to be delayed amid the
COVID-19 pandemic. Hence, the target must be
rescheduled because the construction is suspended.
To curb the outbreak, the central government calledon the public to implement physical distancing
measures by studying, working and praying at home.
The provincial administration in Jakarta, which has
become the national epicenter of the outbreak,recently introduced large-scale social restrictions thathave suspended school, office, religious and public
activities for 14 days. About 300 out of the 2000strong Chinese workforce engaged on the project
including senior managers travelled to China in
February for the Lunar new year but were unable toreturn due to travel restrictions to prevent the spreadof the COVID-19. The supply of materials from
China has also been interrupted, although this should

For twenty years, Shinkansen services in Japan
enjoyed a sustained traffic, it gained 100 billion
passengers-kilometer. From 1994 to 2004, within thenext 20-years interval, the demand halved, because
only 50 billion additional passengers-kilometer used
HSR. When compared with most European HSR
projects which are still in their first 20-year period, it
is natural to expect high growth rates as expressed by
Fig. 2. Based on passenger traffic data obtained from
Europe and Asia, the first HSR line constructed inIndonesia is expected to gain considerable amounts ofdemand.
COVID-19 CHALLENGEKCIC initially aimed to finish the project andbegin operation by December 2021, a two-year delay
from its original target. Currently KCIC is working on
13 tunnels, while the progress of land acquisition has
672

GEOMATE – Melbourne, Australia, 11-13 November 2020

were elevated projects, including bridges in Bekasi,
west Java. Currently, 42 points were underconstruction, including 7 tunnels and a train depot in
Tegalluar, Bandung. To date, the progress of the HSR
project has reached 44% and as for land acquisition
alone, it has reached 99.96% [8]. The central
government through the Transport Ministry wasmaintaining that the project had only actually been
shut down for two weeks, with a December 2021
completion date still a possibility.

resume as Chinese industry restarts after the COVID19 lock-down. More trouble is caused by the fact thatnearly 50% of materials, including pipes,
waterproofing and signaling equipment are made in
China. In late March a regulation offering an
exemption for foreigners working on national
strategic projects from the temporary ban on foreign
arrivals and transits. Chinese workers wanting toreturn to work on the project should abide by thehealth and safety protocols. Previously, abnormal
rainfall in the Jakarta area on January 2020 caused
flash floods followed by repeated heavy rainfall andflooding into February which delayed the project. The
ministry's construction safety committee foundunmanaged debris from the HSR project had clogged
the drainage system on the adjacent JakartaCikampek toll road, causing some parts of the tollroad to be flooded during heavy rains. KCIC had
identified risks surrounding the project area to takeaction against potential disruption and environment
damage. Apart from recovery measures, thecontractors under the PT KCIC consortium (Table 2),
which include China’s Sinohydro, China Railway
Engineering Corp (CREC) and state-owned builder
PT Wijaya Karya, swept through a number oflandslide and flood-prone areas, monitoring river andwater gates across project locations to ensure theyfunction normally.

Technical Challenge
At the technical level, the challenges mainly arise
from the local topography and geological conditions,such as land subsidence or sudden earthquake
disasters. The topography along the HSR line is quite
complicated, which might require special attention
and methods for solving. Firstly, the topography
along the line is diverse and complex which includes
steep slopes which may be the cause of concern forthe operation of HSR [9]. Referring to The Ministryof Economy, Trade and Industry [10] the steepest
slope can be up to 30%, and may continue to increase
because of the land subsidence, as a result of theoveruse of underground water. The land subsidence
itself can also be a problem, because it makes the rail
uneven. In order to overcome the issue, the China RailHigh-speed (CRH) is increasingly replacing therailroad by the viaduct bridge, which can be 58% of
the total length of the HSR. However, thesechallenges may potentially be mitigated by theexpertise of CRH which reportedly owns some of theworld’s longest high-speed railway networks withhigh operating speed, and low occurrence of accidents.
As for the variety of constructive and operational
environment, CRH have faced similar or more severe
problems comparing with Jakarta-Bandung HSR
project, such as the land subsidence, high temperature,and the tunnels and bridges.

Table 2 Shareholders in PT KCIC
Indonesia (60%)
PT Wijaya Karya

22.8%

PT Kereta Api Indonesia

15%

PT Perkebunan Nusantara VIII

15%

PT Jasa Marga

7.2%

China (40%)
China Railway International Co

2%

China Railway Engineering Corp

12%

Sinohydro

12%

CRRC

4.8%

China Railway Signal & Communication Co

9.2%

New Normal
The Public Works and Public Housing (PUPR)
Ministry is preparing a protocol to carry out
construction services amid the new normal. The newnormal construction protocol is designed to address
several packages delayed due to COVID-19. The
PUPR ministry suggest that the consortium will still
operate with COVID-19 protocols under the newnormal scenario, e.g. maintaining physical distancing
practices (limited workers in one area) and limiting
extra working hours for construction workers. The
PUPR ministry also believes that land acquisitions,which require direct contact with a large number ofpeople (land owners), would remain slower thannormal. Undeniable, infrastructure development is
one of the keys to boost economic recovery postCOVID-19 as it absorbs a large number of workers.
According to Ministry of PUPR’s rule of thumb,

All such optimism, however, went to the wall assoon as the unprecedented extent of the COVID‑19
outbreak. Recently, in order to speed up progress,
some measures are being taken by the consortium toprotect employees working on the project from the
COVID-19, and communications between all parties
has been stepped up. The construction of the HSR
project is currently focusing on its tunnels, including
the main tunnel in Halim, east Jakarta which is 1,885
meters long. The project will have 13 tunnels, 7 ofwhich have started their excavation phase. Walini inwest Java was another critical spot for the project
because the developer had to construct three tunnels
with a total length of 7.44 km. The project had been
divided into 115 development points, and 32 of which
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GEOMATE – Melbourne, Australia, 11-13 November 2020

every Rp1trillion worth of construction work could
employ 14,000 workers. Overall, the government
estimates that its strategic national project
developments, included HSR project, could employ
some 4 million workers per year, which in aggregate
could employ 19 million workers in 2020-2024.
Meanwhile, the most common indicator is thenumber of passengers handled, but as stated by
Shatkin [11], the Indonesian government has sought
out investment aimed at integrating Indonesia more
tightly into global flows of investment and trade, andpart of that strategy has included the improvement oflogistics. Referring to Chinese experience as study
case, with the development of economic and social,many migrant workers began to accept high-speed
rail which the price is relative high in traffic tools. In
these migrant workers, mostly are under the age of 35.
Along with the modernization of city, population flow
more frequently. For the younger generation of office
workers, the speed, punctuality and higher service

level of high-speed rail have a great attraction to them.
Under normal circumstances, a business travel isurgent, and need to arrive on time. In previously ofhigh-speed rail appearing, air travel is likely to attract
most of this group. Because of the high-speed,
punctuality, and less sensitive to weather conditions,high speed railway can compete with civil aviation[12]. In the wider context, Mittal et. al. [13] stated thatthe factors like population, income, travel speed andland-use development patterns would have asignificant influence on the passenger transportdemand. Along Java island itself, railway passenger
services experienced a renaissance in the 1995-1999
period, with the introduction of many new passenger
express services (Fig. 3). With the advent of cheap
airplane tickets, operator PT Kereta Api has
experienced a downturn in the number of passengers
carried, though the number has stabilized and mosttrains remain at more than 50% occupancy rate.

Fig. 3 Conventional rail services along Java island

CONCLUSION
The Jakarta-Bandung high-speed railway project is
one of the government's national strategic projects inthe transportation sector and is expected to require a
total investment of US$6.07 billion. Three years after
the ground breaking, the construction of the highspeed railway has faltered because of various
technical and financial challenges, exacerbated by the
COVID-19 pandemic. In mid-February 2020,
construction progress reaches 44 percent and landacquisitions reach 99.96 percent. Due to the COVID19 pandemic, work of the project has been halted
temporarily, resulting in the delay of the targetedfinish of construction and start of the operation. In
mid-May 2020, construction progress reaches 48.3
percent and the construction has worked again andfollow social/physical distancing, although the
COVID-19 pandemic is not yet over. In order tospeed–up the progress, the consortium have made anumber of breakthroughs in building tunnels, railway
bridges and stations to pursue the remaining progressof 56 percent over the next 18 months throughconsolidation of manpower, equipment and advanced
technology. Otherwise, referring to the new normal
protocol’s, the project's management team should to
set a goal of zero infection for all employees and theconsortium raised disinfection level and frequency,
and tightened disease prevention in working areas
such as offices and construction sites. The consortium
should be conduct strict measures and madeemergency response plans to prevent cases infectedfrom outside of HSR project.
ACKNOWLEDGEMENT
I would like to express my special thanks of gratitude
to my colleagues at PS PPI of Engineering Faculty of
Lampung University as well as the Sustainable674

GEOMATE – Melbourne, Australia, 11-13 November 2020

by CENIT (Center for Innovation in Transport,
Universitat Politecnica de Catalunya), 2005.

Development Goals (SDGs) center who gave me theopportunity to do this project and provide funds forattending GEOMATE Conference in Melbourne,
Australia, 11-13 November 2020.
REFERENCES[8] Retrieved from: https://setkab.go.id/en/jakartabandung-high-speed-railway-project-to-becompleted-by-2021-transport-minister/[1] Purba, A., Nakamura, F., Niken, C., Jafri, M., andPratomo, P., A current review of high speed
railways experiences in Asia and Europe, AIP
Conference Proceedings, 1903, 2017, pp. 0600041-060004-8.

[9] Siddhartha, N., and Gusti, R., An Assessment of
Economic and Financial Impacts of JakartaBandung High-Speed Railway Project, Journalof Business and Political Economy, Vol.2, No.1,
2020, pp. 45-55.

[2][10] The Ministry of Economy, Trade and Industry,Feasibility Study for Promotion of International
Infrastructure Projects in FY2011, Study on theHigh Speed Railway Project (Jakarta-Bandung
Section), Republic of Indonesia, Prepared by
Yachiyo Engineering Co., Ltd and Japan
International Consultants for Transportation Co.,
Ltd, 2012, pp. 1-20.

Purba, A., THE CHALLENGE OF
DEVELOPING
HIGH-SPEEDRAIL
PROJECTS: RECENT EVIDENCE FROMDEVELOPING COUNTRIES, International
Journal of GEOMATE, Vol.18, Issue 70, 2020, pp.
99-105.

[3] Retrieved from: https://kcic.co.id/en/[11] Shatkin, G., Financial sector actors, the state, andthe rescaling of Jakarta’s extended urban region,
Land Use Policy, 2019, 104159.

[4] PT Kereta Cepat Indonesia China. High Speed
Railway (HSR) Jakarta - Bandung, theAcceleration of Infrastructure in West Java. Rapat
Kerja Kementerian Perhubungan (Ministry ofTransportation), 2016, Jakarta.
[5]

Retrievedfrom:https://www.tradeoff.id/2019/09/12/construction-for-jakartabandung-bullet-train-is-in-progress/[6]

UGM Students Win Potential Analysis
Competition of High Speed Rail. Available athttps://ugm.ac.id/en/newsPdf/12339-ugmstudents-win-potential-analysis-competition-ofhigh-speed-rail[12] Huang, Y., Song, H., and Jia, C. Y., Analysis on
the Main Factors Influencing the High-Speed
Railway Passenger Flow, Advanced Materials
Research, 919-921, 2014, pp. 1085–1090.
[13] Mittal, S., Dai, H., Fujimori, S., Hanaoka, T., andZhang, R., Key factors influencing the global
passenger transport dynamics using the
AIM/transport model, Transportation ResearchPart D: Transport and Environment, Vol.55,
2017, pp. 373–388.

[7] International Union of Railways (UIC).
Estimation des resources et des activitéséconomiques liées a la grande vitesse. Prepared675

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

APPLYING DECENTRALIZED CONSENSUS: A CASE STUDY OF
CARBON TRADING
Pasu Poonpakdee1 and Jarotwan Koiwanit1
1Department of Industrial Engineering, Faculty of Engineering, King Mongkut’s Institute of Technology
Ladkrabang, Bangkok 10520, Thailand
ABSTRACT

There has been a growing pressure in limiting global warming potential (GWP) because of human activities
causing increased concentrations of greenhouse gas (GHG) emissions. The increase of average global temperaturescauses impact on the environment, societies, and economies. As a result, early action is necessary to reduce GHG
emissions at reasonable levels to mitigate the future global warming. Carbon markets are one of the earlyexchanging solutions aiming at the achievement of an environmental objective for low carbon business developers.
However, all trading protocols are still having some rooms for improvement regarding how verification should be
conducted and who should be responsible for this verification. Epidemic Protocol (Gossip Protocol) is focused on
this study to offer new transaction platforms allowing parties to do carbon credit transactions without approval
from any third parties by applying the transaction exchanging concept of Blockchain technology to the algorithm.
The results show that the protocol can provide a consensus mechanism without global communication which
supports carbon trading in the decentralized network.
Keywords: Epidemic Algorithms, Distributed Consensus, Carbon Trading

INTRODUSTIONRELATED WORKS

Environmental changes influence all global
communities. Each tonne of GHG emitted mostly is
the carbon dioxide (CO2) contributing to further
climate change [1]. To cope with the reduction of the
GHG emission in an attempt to mitigate the futureclimate change, one of the options directly controls
the emissions permitted is carbon trading throughcarbon markets [2]. Carbon permit trading works bya cap fixed by the regulator in which emitters
included in the system will be able to buy or sell
carbon credits from or to other participants,
respectively in order to cover their emissions by aquantitative limit on emissions produced [3]. To
transfer carbon credit with trust and without any third
parties, the concept of Blockchain technology has
been introduced to transform transactional processes[4]–[6]. In addition, epidemic protocols have also
been applied in this study in order to present the
preliminary carbon trading work to show on how to
improve the efficiency of a distributed consensus.
The paper is organized as follows. Section 2
provides works related to this study. Section 3
proposes the algorithm used in this study. Section 4
presents the simulation configuration while Section 5
provides experimental results. Finally, theconclusions and future work are presented in Section
6.

Carbon Trading
As early action is necessary for reducing GHG
emissions, a carbon markets has now become a “capand-trade” regime allowing for an efficient GHG
emission sharing among emitters to limits theemissions below an established standard [7].
Companies are making strategies to buy or sell carbon
credits with other companies having less pollution to
avoid legislative restrictions. However, the thirdparty organizations to verify carbon trading is one of
the major issues in carbon trading. Even though acentralized verification in the regulator allows forstandardization and consistency, it requires resources,time, and a heavy monetary burden [8].
Blockchain Technology
Blockchain is a type of block structure holding acomplete list of transaction records without any banks
or any intermediaries [9]. In public blockchain, alltransactions are visible to the public in the consensus
process while private blockchain only allows nodes
coming from one specific organization to join theconsensus processes [10].
One of the greatest examples that shows how
Blockchain is implemented in the real world, is
Bitcoin. Bitcoin introduces the decentralized network
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GEOMATE – Melbourne, Australia, 11-13 November 2020

the cache in the message (line 15), then the pullmessages will also be sent to the sender (line 16).
After that, the senders will merge its transaction
caches with the caches from the pull messages (line19).
In order to detect that the information is fully
distributed to the network, the local convergencedetection mechanism is adopted to the algorithm.
After the sender receives every pull message, thesender will compare the transaction caches fromevery message including its transaction cache. If all
the transactions in the caches are the same, the sender
will start counting the round until it confirms. The
counting round is to avoid the situation that the node
misjudges the consensus status. If the number ofrounds has reached the acceptance, the sender willconsider that the information is fully distributed to the
network system and it will stop sending the pushmessages to exchange the information in transaction
cache (line 2 - 8).

by announcing the transactions. Then the transactions
need to be agreed by the member in the system [7].
Epidemic Protocols
An epidemic protocol is a distributed
communication and computation paradigm tending to
be used in an extreme-scale network system [11][12]. In this model, decentralized system has been
adopted as a basis in which new information received
can be randomly chosen peers. The protocols arescalable, easy to robust, deploy, and resilient to
failure [13].
Local Convergence Detection
In practical carbon trading applications, a local
convergence detection will be beneficial and helpful
in which this can be used to terminate the protocol
iterations [14]. Though epidemic aggregation
protocols have some weaknesses, especially on its
execution time, locally detection can help tominimize the execution time [14]. In addition, local
convergence detection can be used to locally detect
the time the information can be fully spread over thesystem. The study results showed the approval of thelocal convergence detection which can be represented
the information fully distributed to the system.
THE PROPOSED ALGORITHM
In this work, the proposed algorithm was
implemented based on gossip-based communication
paradigm. The algorithm is designed based on theconcepts of Blockchain, epidemic protocols and local
convergence detection mechanism to develop a
distributed network of institutions and regulators to
execute the trading process successfully. The related
functions to technologies can be organized as follows:- Blockchain has a role in transaction
exchanging and distributed ledger tomaintain the carbon credits.
- Epidemic protocol has a role incommunication pattern.
- Local convergence detection mechanism has
a role in consensus mechanism.
Each node has the list of transactions contained in
the transaction cache. At each cycle, the node
(sender) will send its transaction cache to a group of
randomly selected nodes (receivers) via pushmessages (line 11-12) in order to exchange the local
information. Once the receiver receives the pushmessages, receiver merges its transaction cache with

The scenario of the proposed algorithm ispresented in Fig. 1.

Fig. 1 The scenario of information distribution in
the proposed algorithm

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GEOMATE – Melbourne, Australia, 11-13 November 2020

There are 3 objectives of the simulation in thisstudy. The first objective is to capture the state thatthe node can detect that every node has received theinformation which is detected by the number of nodes
that the consensus status is true. The second objective
is to evaluate the correctness of the local information,and the last objective is to monitor the total numberof messages overhead in the system.

Table 1 describes the notation adopted in the
proposed algorithms. Algorithm 1 shows thepseudocode of the proposed algorithm.
Table 1 Notation Adopted in the Pseudocode.
Parameters
i
p
h
ci
Qi
m

m

Description
a node in the network, i ϵ V ,where V is the set of nodes
number of sending messages ineach cycle
number of cycles required toconsensus
consensus status of node i
local cache at node ipush message:- s, node originating the push
- Qs, main cache at spull message:- d, node originating the pull
- Qd, main cache at node d

EXPERIMENTAL RESULTS & DISCUSSION
The experimental results can be categorized in 3
sets. The first is the number of consensus nodes. The
second is the correctness of the local information.
Finally, it is the total number of messages overheadin the system.
Fig. 2 shows how the node determines the local
consensus status. Y-axis presents the number ofconsensus nodes, while X-axis present the number ofcycles. At the first cycle, every node will distribute
their local information on how much carbon credits
they trade with others. After several cycles, all nodes
are set the status to consensus. According to thefigures, the speed of consensus mechanism is directly
related to parameter h. At cycle 50th, a node transfers
some of its carbon credits to another. As a result, the
new transactions are created, and the information is
distributed.

SIMULATION CONFIGULATIONThis section presents the configuration ofsimulation used in this study. In terms of the technicalconfiguration, the initial topology is a regular randomgraph in which each node has 5 links to other nodes.
The graph rewiring mechanism is performed by the
epidemic membership protocol, called EMP+ [15].
The network size is assumed to be 100 nodes whichrefer as companies and each of them has the carbon
credits which are randomly selected between -100 to
200. A negative number represents carbon credits acompany emits less than its capped limit and thecredits can be sold to another company exceeding its
limit. In terms of a positive number, this represents
carbon credits a company has above the permitted
limit. Any companies can buy carbon credits to lower
their GHG emissions on a voluntary basis.
The simulation was run for 100 cycles. The cycle
length of the simulation is set to 40,200 and the
network latency is randomized between 0 to 10,000.
In the first cycle, every node will distribute theinformation in its local cache to other nodes. After 50
cycles, the simulation emulates the situation when aselected node transfers the carbon credits to anothernode.
The algorithm setting is shown as follows.
- the number of sending messages (p): 3- the number of cycles for consensus (h): 4, 5, 6,
7

Fig. 2 Number of consensus nodes
Fig. 3 shows the correctness of the local
information. Y-axis presents the standard deviation of
the information cache size in the network, while Xaxis presents the number of cycles. The result shows
the accuracy of the algorithm when the parameter ischanged. At the first cycle, every node starts todistribute the information and the information is fully
distributed after several cycles. The effect of theparameter h can be noticed at cycle 50th when the678

GEOMATE – Melbourne, Australia, 11-13 November 2020

generated address in which users do not need to reveal
their real identity. In addition, regulators who areapproving or controlling the carbon trading are nolonger needed in blockchain.
In general, it can be concluded that blockchain
technology together with epidemic protocols provide
the innovative solution for the carbon trading over thepresent forms of “cap-and-trade” system. The
protocol with different network conditions should beconsidered and recommended for future work. The
evaluation of the proposed algorithm with otherconsensus algorithms is one of the interesting
challenges in this work.

carbon credit is transferred. When parameter h is 4,
the result shows that some nodes are not being able toreceive the information because more effort isrequired to switch the consensus status and distributethe information. So the parameter h has to be set withthe condition to provide the enough time for the
information distribution.

REFERENCES[1] Moghal A. A. B., Dafalla M. A., Elkady T. Y., and
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Fig. 3 Correctness of the local information
Table 2 shows the number of messages overheadin the network. The results show that parameter h has
a strong relation with messages overhead. The higher
number of h, the higher messages overhead.

Table 2 Message Overhead
Setting of parameter h
h=4
h=5
h=6
h=7

Message overhead
4512
5784
6960
8190

CONCLUSION & FUTURE WORKS
Though the use of carbon trading to trim out GHG
emissions, understanding the maximization of the
profitability of the carbon trading project was an
objective of this study. This study demonstrates theblockchain technology together with epidemic
protocols as a smart carbon transaction platform.
Blockchain technology has recently gained
popularity as a potentially effective solution for
financial transactions. In an epidemic algorithm, thereliability of carbon trading information delivery willthen be proved by participants included in the carbon
market system without any third parties. In otherwords, each participant can interact or transfer carbon
credit through the blockchain technology with a
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Bitcoin, innovation, financial instruments, and bigdata” Academic Press, 2015.
[11] Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang,“An overview of blockchain technology:
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[14] P. T. Eugster, R. Guerraoui, A. Kermarrec, andL.
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[15] P. Poonpakdee, N. G. Orhon, , and G. Di Fatta,“Convergence
DetectioninEpidemic
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[16] P. Poonpakdee, and G. Di Fatta, “Robust andefficient membership management in large-scale
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vol. 75, pp. 85 - 93, 2017.

680

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

EVALUATING EFFECTS OF MIXING PROPORTION ON WATERRETENTION CURVE AND PORE SIZE DISTRIBUTION OFRECYCLED CONCRETE AGGREGATES BLENDED WITH
AUTOCLAVED AERATED CONCRETE GRAINSPHAM Van Nam1, Akira KATO1, NGUYEN Hoang Giang2, NGUYEN Van Tuan2, PHAN Quang Minh2,
and Ken KAWAMOTO1,2
1

Graduate School of Science and Engineering, Saitama University, Japan.
2
National University of Civil Engineering, Vietnam.

ABSTRACTDue to rapid urbanization in developing countries, the generation of construction and demolition waste (CDW)
and industrial by-products (IBPs) are increasing. In this study, we aimed to utilize the recycled materials from
CDW and IBPs as the road base materials for improving the water retention capacity of the pavement structure
especially for the water retentive pavement system, which can be expected to contribute to the mitigation of urban
heat island (UHI), reducing the road surface temperature due to subsequent water evaporation. In order to improve
the water retention capacity, testing samples graded recycled concrete aggregates (RC) from CDW blended with
autoclaved aerated concrete (AAC) grains (size: 0.106-2 mm) made from AAC scrap at different proportions, i.e.,
RC 100%, RC 80%+AAC 20%, RC 60%+AAC 40%, and AAC 100%, were prepared. The water retention curves(WRCs), which represent the relationship between volumetric water content and matric potential were measured
using a combination of hanging water, pressure chamber, and potentiometer methods in the laboratory. The
measured WRCs were fitted by either sigmoidal or bimodal water retention model and the equivalent pore size
distribution (PSDs) of tested samples were compared. The results showed a significant improvement of water
retention capacity was found by blending AAC grains and the saturated volumetric water contents for 20 and 40%
AAC blended RC samples increased approximately 1.8 to 2.2 times compared to that of RC 100%. Based on the
results from estimated PSDs, the blending of AAC grains increased significantly fine pores ranging from 0.03 to
1 µm.
Keywords: Construction and Demolition Waste, Autoclaved Aerated Concrete, Water Retention, Pore Size
Distribution

materials made from graded recycled concrete
aggregates (RC) blended with autoclaved aerated
concrete (AAC) grains to improve the water retentive
property. They reported that AAC grains themselves
had high water retention capacity because of meso
and micro-pores of AAC, and improved water
retentive property of RC. However, it is required tocollect more data for applying the developedmaterials to the road construction works.
On the background of recycling of IBPs andconstruction and demolition waste (CDW), thegeneration of IBPs and CDW have been increasing
worldwide, especially in developing countries due torapid urbanization and population explosion, a few
researchers attempt to utilize the recycled materials
from IBPs and CDW to develop road pavement andbase materials [5][6][7][11]. However, most of thestudies are mainly examining the hydraulic andmechanical properties of testing materials and very
limited studies have been done to improve the water
retention capacity of testing materials. Compared to

INTRODUCTION
It is well known that the urban heat island (UHI)
effect is one of crucial issues in urban areas becauseof the generation of a large amount of heat frombuilding environments and solar reflectance of paved
surfaces. Water retentive pavements have beendeveloped to mitigate UHI effect and reduce thetemperature of the paved surface using water
evaporation due to the heat energy from the sun[1][2][3][4] and its application in the road
construction is becoming popular these days andindustrial by-products (IBPs) have been recycled aswater retentive materials [5][6][7]. Kinoshita et al [8]and Misaka et al [9] pointed out that the surface
temperature of water retentive material decreases
with increasing of evaporation efficiency and water
retention. Therefore, if a long-term drought event
occurs by continuous sunny weather especiallyduring the summer period, water retention capacity isneeded much more. Kato et al [10] used road base681

GEOMATE – Melbourne, Australia, 11-13 November 2020

developed countries, the recycling rate of CDW and
IBPs is very small in most of developing countries
including Vietnam [12][13]. In order to promote therecycling of CDW and effective use of IBPs,
therefore, we aimed to develop the road basematerials utilizing both recycled materials from CDW
and IBPs, especially targeting Vietnam in this study.
A series of laboratory measurements to determine
water retention curves for graded RC blended with
AAC grains at different mixing proportions werecarried out using the materials taken from Vietnam.
Then, the effects of blending of AAC grains on thewater retention capacity and pore size distribution
were examined based on the measured water retention
curves.

(a)(b)

MATERIALS AND METHODS
Materials
Types of materials collected in Hanoi, Vietnam
were used in this study. Graded recycled concrete
aggregates made from CDW masonry which wasrecovered from the CDW dumping site in Hanoi (20°59' 21.6" N, 105° 53' 58.1" E), Vietnam and crushed
in Japan (Jaw crusher machine), and the graded RC(size: 0.075-25 mm) were blended with AAC grains(size: 0.106-2 mm), which was from Viglacera Joint
Stock Company in Bac Ninh province (21°11'50.8"N,
106°00'42.8"E), Vietnam as shown in Figs. 1a and 1b.
The basic physical and chemical properties of RC and
AAC were measured in the laboratory as outlined inTable 1. As shown in Table 1 AAC grains have not
only a higher water content at air-dried condition but
also a higher water absorption than those of RC,
indicating that AAC grains are beneficial to water
retention capacity. RC was first sieved with a 25-mm
sieve according to Vietnamese standard TCVN 88592011 [14], while AAC was sieved with a 2-mm sieve.
Particle size distributions of tested materials areshown in Fig. 2. RC was used as base material, andAAC was mixed with RC as water retentive material
to be 40% and 20% of mixing proportion. There weretotal of 4 tested samples: RC 100%, RC 60% +AAC
40%, RC 80% + AAC 20%, and AAC 100% in thisstudy.

Fig. 1 Tested materials (a) Graded recycledconcrete aggregate (RC), and (b) Autoclaved
aerated concrete (AAC) grains.
Compaction test (ASTM D1557-2012 [15], TCN
333-06 [16]), California bearing ratio test (CBR) (JISA 1211, 2009 [17], TCN 332-06 [18]), and constanthead permeability test (ASTM D2434 – 68
(Reapproved 2006) [19], JIS A1211-2009 [20]) werecarried out to assess maximum dry density (MDD),
CBR value, and saturated hydraulic conductivity (Ks).
The tested results were summarized in Table 2. CBRvalues increased with increasing of MDD. Inversely,
the measure Ks decreased with increasing of MDD as
discussed in [21].
Table 2 Mechanical and hydraulic properties
Test samples

MDD CBR (K98)
Ks
g.cm3
%
cm/s
AAC 100%
0.73
31
1.14×10-3
RC60%+ AAC 40% 1.2155
1.85×10-4
RC 80%+AAC 20% 1.52
113
8.78×10-5
RC 100%1.96
173
2.80×10-6abReference
>1.78
>100
> 3×10-3 c

Table 1 Basic physical and chemical properties of RC
and AAC samples used in this study
TestG
samples s

WAD

Wabs (%)

pHEC<4.75 >4.75
1MKCl mS/cm
mm mm
RC 2.63 0.9
8.5 5.2
11.0
4.8
AAC 2.51 2.1 61.4
9.0
0.9

CBR(K98): California bearing ratio value at dry density of 98%
degree of compaction.
References: [22]a, [18]b, and [23]c.

%

Gs: Specific gravity, WAD: Water content at air-dried condition,
Wabs: Water absorption, and EC: Electric conductivity.

682

GEOMATE – Melbourne, Australia, 11-13 November 2020

100
90

Percent of passing (%)

80
70
60
50
40

𝒅𝒅𝒅𝒅(𝒓𝒓)

AAC
100%
RC 60% +AAC 40%RC 80% +AAC 20%
RC 100%𝒅𝒅 𝒍𝒍𝒍𝒍𝒍𝒍 𝒓𝒓Upperlimitation
Lower
limitation

0.1
1
10
Particle size (mm)100

Fig. 2 Particle size distributions of tested materials
Measurement of the water retention curve andpore size distribution
First, the water content of the tested sample wasadjusted to become at optimum water content mixed
with distilled water. Then, the water adjusted sample
was compacted in a stainless mold (120 mm height,
150 mm inner diameter based on [15][16]. The
compacted sample was soaked subsequently in waterfor 1 week to saturate before draining to the desired
water matrix potential (ψ, -cm H2O) values using bya hanging-water column method (0 to - 60 cm H2O),
a pressure chamber method (-100 to -10000 cm H2O).
The ψ value for the air-dried sample was measure by
a dewpoint potential meter (WP4-T, Decagon
Devices Inc., WA, USA).
The measured water retention curves (WRCs)
were fitted by the Durner model [24] based on the van
Genuchten model [25] as the following equation:s

1= [𝐥𝐥𝐥𝐥( 𝟏𝟏𝟏𝟏)]|𝝍𝝍|𝑪𝑪∗ (2)The measured WRCs and estimated PSDs are shownin Figs. 4a and 4b. The fitted parameters for WRCs
were summarized in Table 3.
The WRCs showed clearly that measured θs
increased with increasing of mixing proportion of
AAC grains. The θs values for 20% and 40% AAC
blended samples became approximately 1.8 to 2.2
times higher than that for RC 100%, indicating thewater retention capacity of RC was improved byblending AAC grains. The θ decreased unimodallywith increasing water matric potential for RC 100%,on the other hand, the θ of AAC 100% and AAC
blended samples decreased bimodally with two
different fields of water retention. Especially, a sharp
drop in θ (water drainage) at around ψ = -1.0×104 cm
H2O was observed in WRCs for AAC 100% and AAC
blended samples.
As shown in Fig 4b, the blending of AAC grains
had a great effect on PSDs of tested samples. By
blending AAC grains, not only fine pores (micro tomeso-pores) ranging from 0.03 to 1 µm but also large
pores (macro-pore) ranging from 5 to 300 µm
increased compared to RC 100%. The observed
results in this study fairly related to the reported studyin Lam et al. (2018) [27]. They characterized thePSDs of AAC samples using a mercury intrusion
porosimetry and reported that the main peak of pores
for AAC samples ranged from 0.02 to 0.08 µm andthe second peak ranged around 40 µm.

10= ∑ki=1 𝑤𝑤i �𝜃𝜃−𝜃𝜃

𝒅𝒅𝒅𝒅(𝝍𝝍)RESULTS AND DISCUSSION20𝜃𝜃−𝜃𝜃r𝒅𝒅𝒅𝒅

𝒅𝒅 𝒍𝒍𝒍𝒍𝒍𝒍 |𝝍𝝍| 𝒅𝒅|𝝍𝝍|

where r is equivalent pore radius (µm), C*is specificmoisture capacity (r = 1500/|ψ| [26]). The equivalentpore radius can be calculated from the water retained
capacity (θs – θr) in this study.

30

0
0.01

=

𝑛𝑛i �

1+(𝛼𝛼i |𝜓𝜓|)

𝑚𝑚i

Table 3 Fitting parameters for WRCs
TestedAAC
materials 100%
θs
0.70
θr
0.15
α1
0.07
n1
1.20
w1
0.70
α2
9.0×10-5
n2
4.01
w2
0.30
R2
0.98

(1)

where θ (cm3/cm3), θr (cm3/cm3), θs (cm3/cm3) are thevolumetric water content, residual volumetric watercontent, and the saturated volumetric water content, k
is the number of the peak of pore-size density or
subsystems which form the total pore size distribution(in this study is 2), wi are the subcurves weighting
factors, αi, ni, and mi are the parameters of the
subcurves (mi = 1 – 1/ni ) indicate fitted parameters.
The equivalent pore size distributions (PSDs) oftested samples were obtained by using the following
equation, which was defined by683

RC 60% +AAC 40%RC 80% +AAC 20%

RC
100%

0.52
0.14
0.11
1.33
0.61
1.3×10-4
3.22
0.39
0.97

0.42
0.11
0.36
1.23
0.57
3.1×10-4
2.41
0.48
0.99

0.24
0.09
0.06
1.20

0.93

GEOMATE – Melbourne, Australia, 11-13 November 2020

Volumetric water content, θ(cm3/cm3)

0.80

ACKNOWLEDGMENTS

AAC 100%(a)

RC 60%+AAC 40%

0.70This research was supported by JST-JICA
Science and Technology Research Partnership for
Sustainable Development (SATREPS) project (No.
JPMJSA1701).

RC 80%+AAC 20%
0.60

RC 100%0.50

REFERENCES

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[3] H. Yamagata., M. Nasu., M. Yoshizawa., A.
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[5] K. Takahashi and K. Yabuta., Road temperature
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[6] C. F. Lin., C. H. Wu., and H. M. Ho., Recovery ofmunicipal waste incineration bottom ash andwater treatment sludge to water permeable
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[7] J. Bin Park., S. Lee, C. S. Gee., and H. B. Pyun.,
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bottom ash, Mater. Sci. Forum, Vol. 620–622,
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[8] S. Kinoshita., A. Yoshida., and N. Okuno.,
Evaporation Performance Analysis for WaterRetentive Material , Based on Outdoor HeatBudget and Transport Properties, J. Heat Isl. Inst.
Int., Vol. 7, No. 2, 2012, pp. 222-230.
[9] I. Misaka., K. Narita., and H. Yokoyama.,
Evaluation of Evaporation Ability of the System
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Urban Clim., 2009, pp. 2-5.
[10]A. Kato., R. Ito., A. Matsuno., T. Uchimura., N.V.
Tuan., N.H. Giang., and K. Kawamoto., Water
Retention and Gas Transport Characteristics ofRecycled Graded Roadbed Materials Blended
With AAC Grains, 17th International waste
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Proceedings SARDINIA 2019.
[11]A.
Arulrajah.,A.
Mohammadinia.,
S.
Horpibulsuk., and W. Samingthong., Influence ofclass F fly ash and curing temperature on strength

0.30
0.20
0.10
0.00
1.E+00

1.E+02

1.E+04

1.E+06

Pore size density

Matric potential, ψ (–cm H2O)
0.6
0.55
(b)
0.5
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
0.001 0.1

AAC 100%
RC 60%+AAC 40%
RC 80%+AAC 20%
RC 100%101000 100000

Equivalent pore radius, r (μm)
Fig. 4 (a) WRCs as a function of ψ, and (b) pore size
density as a function of equivalent pore radius, r.

CONCLUSIONSThe laboratory experiments were carried out toevaluate water retention curves and pore size
distributions for RC blended with AAC grains. The
blending of AAC grains improved clearly the water
retention capacity of RC and corresponding pore size
distributions. Especially, it was observed that theblending of AAC grains gave a significant increase inboth fine (ranging from 0.03 to 1µm) and large pores(ranging from 5 to 300 µm), implying that the AAC
grains have a high potential for developing water
retentive pavements. However, the blending of AAC
grains simultaneously causes a reduction in CBR(typically, AAC blending with ≥ 40%). Therefore,
further studies are needed to improve both water
retention and mechanical property to satisfy technicalrequirements for road base materials.

684

GEOMATE – Melbourne, Australia, 11-13 November 2020

[20]JIS A1218-2009., Test Methods For Permeability
Of Saturated Soils. Japanese Standards
Association ,2009.
[21]R. D. Rafaela Cardoso., Rui Vasco Silva., andJorge de Brito., Use of recycled aggregates fromconstruction and demolition waste in geotechnicalapplications: A literature review, Waste Manag.,Vol. 49, 2016, pp. 131-145.
[22]A. Arulrajah., M. M. Disfani., S. Horpibulsuk., C.
Suksiripattanapong., and N. Prongmanee.,
Physical properties and shear strength responsesof recycled construction and demolition materials
in unbound pavement base/subbase applications,
Constr. Build. Mater., Vol. 58, 2014, pp. 245-257.
[23]Method of on-site test for water permeability ofporous pavement. Manual of Pavement
Unvestigation and Testing Methods, Part 1 (inJapanesee)., pp. 122-126, 2007.
[24]W. Durner., Hydraulic conductivity estimation forsoils with heterogeneous pore structure, Water
Resour. Res., Vol. 30, No. 2, 1994, pp. 211-223.
[25]van Genuchten., A closed-form equation forpredicting the hydraulic conductivity ofunsaturated soils, Soil Sci. Soc. Am. J., Vol. 44,
No. 5, 1980, pp. 892-898.
[26]Brutsaert., Probability Laws For Pore-SizeDistributions, Soil Sci., Vol. 101, No. 2, 1966, pp.
85-92.
[27]N. T. Lam., S. Asamoto., and K. Matsui.,Microstructure and shrinkage behavior of
Autoclaved Aerated Concrete (AAC) Comparison of Vietnamese and Japanese AACs-,
J. Adv. Concr. Technol., Vol. 16, 2018, pp. 333342.

development of fly ash-recycled concrete
aggregate blends, Constr. Build. Mater., Vol. 127,
2016, pp. 743-750.
[12]N. V. Tuan., T. T. Kien., D. T. T. Huyen., T. T. V.
Nga., N. H. Giang., N. T. Dung., Y. Isobe., T.
Ishigaki., and K. Kawamoto., Current status of
construction and demolition waste management in
Vietnam: Challenges and opportunities, Int. J.
GEOMATE, Vol. 15, No. 52, 2018, pp. 23-29.
[13]T. T. Kien., L. T. Thanh., and P. V. Lu., Recycling
construction demolition waste in the world and in
Vietnam, Int. Conf. Sustain. Built Environ. Now
Futur., 2013 pp. 247-256.
[14]TCVN 8859-2011., Graded Aggregate Bases andSubbases Pavement - Specification forConstruction and Acceptance. Mistry ofTechnology and Science of Viet Nam., 2011.
[15]ASTM, D1557-12., Standard Test Methods forLaboratory Compaction Characteristics of Soil
Using Modified Effort. ASTM International,West Conshohocken, 2012.
[16]22TCN 333-06., The standard method used
related compaction of the material in a series oflayers in a fixed mould (in Vietnamese). Ministryof Transport of Vietnam, 2006.
[17]JIS A 1211-2009., Test Methods for the California
Bearing Ratio of Soils in Laboratory. JapaneseStandards Association, 2009.
[18]TCN 332-06., Process to determine CBR value ofsoil, gravel in laboratory (in Vietnamese).
Ministry of Transport of Vietnam, 2006.
[19]ASTM D2434-68., Standard Test Method for
Permeability of Granular Soils (Constant Head),
Reapproved, 2006.

685

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

A COMPARATIVE STUDY OF CONVENTIONAL EXTENDED AERATION AND
MODERN MEMBRANE BIOREACTOR METHODS OF SEWAGE TREATMENTFROM THE ENVIRONMENTAL AND FINANCIAL PERSPECTIVES
Mohamed Fetouh1, Rabee Rustum2 and Adebayo J. Adeloye3
1,2

Department of Civil Engineering, Heriot-Watt University, United Arab Emirates; 3Department of Civil
Engineering, Heriot-Watt University, United Kingdom

ABSTRACTThe conventional extended aeration (EA) method and its modifications have been considered as a routine, userfriendly method in wastewater treatment plants around the world. The membrane bioreactor (MBR) method has beenpromoted by membrane manufacturers over the past decade as the most efficient treatment method in the industry.
Research has shown the higher quality of MBR effluent compared with that of EA, but the comparison has seldom
included the financial aspects. If the quality of the effluent from both methods is acceptable for a given reuseapplication, then a major consideration in adopting either method must be financial (i.e., the capital expenditure(CAPEX) and operating expenses (OPEX) involved in their deployment). Simulation of both methods is conducted
for domestic wastewater treatment plants using the biological and financial models of CAPDETWorksTM. The CAPEXand OPEX findings from the simulation are interpreted to their present unit values by employing global market priceindices as of October 2019. Hydraulic loads from 500 to 5,000 m3/d were considered for three influent strengths:
weak, medium and strong. It is found that the EA method is less costly in meeting the effluent-quality requirementsof certain reuse applications, while the MBR is recommended for the extra strong-strength influent especially for units
smaller than 500 m3/d, due to both effluent quality and lifetime cost.
Keywords: Wastewater treatment, Extended aeration, Membrane bioreactor, Capital expenditure (CAPEX),
Operating expenses (OPEX).

of 500 to 2700 and 500 to 5000 mg/L, respectively.
The results showed that in the addition to the better
effluent quality in terms of COD, BOD5, and TSS of
the MBR method than that of the EA, despite thehigher organic load, the BOD5/COD ratio of theeffluent was 0.708 ±0.18 and 0.537 ±0.106,
respectively. This means that more stabilized effluent
could also be a benefit of the MBR method oftreatment. Meanwhile, the study recommended thatspecial attention be given to cleaning the periodicalscaling and biofouling on the MBR membrane due tothe high influent strength.
Hatami, Nadali, Roshanaei, and Shokoohi [6]evaluated the possibility of reuse of the treatedeffluent from the EA wastewater treatment plant of
Bojnoord City, Iran, for the agricultural applications.
The results showed that the removal efficiency of
BOD5 and COD was 88% and 89%, respectively,
with an average residual concentration of 27.0 and
61.0mg/L, respectively; while Chloride and Sodium
Adsorption Ratio (SAR) residual values were 3.89
milliequivalents per liter (meq/L) and 221 milligrams
per liter (mg/L), respectively. It was concluded thatthe said effluent could be valuable for agricultural
applications, but due to its high chloride
concentration, it has been recommended for semisensitive plants only.

INTRODUCTION
In recent decades, engineered wastewater
treatment methods have been progressively
developed for various environmental aspects of reuse,such as agricultural, industrial, urban, groundwaterrecharge, etc. [1], [2]. These treatment methods could
be tailored, modelled and simulated to achieve therequired level of quality for both effluent-water andsolid-waste for a given influent with the help of
proven, reliable Computer Aided Design programs.
The process of selecting a method to be implemented
and the adaptations required in order to comply witha certain environmental criterion of reuse has acommercial impact from the CAPEX and OPEX
perspectives that needs to be considered, as well,which is the aim of this paper.
Environmental Aspect
Current advances in membrane manufacturing
technology make the membrane-based methods
increasingly attractive solutions for wastewater reuseapplications [3], [4]. Mohammadi, Sabzali, Gholami,
Dehghanifard, and Mirzaei [5] compared theperformance of the EA and MBR methods for thetreatment of high-strength wastewater under similar
influent conditions, as COD was adjusted in the range686

GEOMATE – Melbourne, Australia, 11-13 November 2020

organic loads: weak, medium and strong, to cover the
potential loads from both developed and developing
communities.
The method was selected to be of a proven,commercially available nature for the sake of ease
replication of the study. Specialized softwaredevelopers, such as Hydromantis Environmental
Software Solutions Inc. (Hydromantis), EnviroSim
Associated LTD (EnviroSim), AQUIFAS, etc. haveproduced powerful mathematical engines capable ofrunning the most sophisticated, implicit mathematicalmodels of wastewater treatment with minimal
discernible error. The same have been presented in
the form of software packages (e.g. GPSXTM and
BioWinTM of Hydromantis and EnviroSim,
respectively) that come with various features such as
steady state and dynamic simulations, estimation ofchemical and power consumption, etc. Hydromantis
has further produced CAPDETWorksTM, a software
package for the simulation of wastewater treatment
plant performance while predicting the life-cycle costof the same. CAPDETWorksTM version 04 2018 isused as a methodological, experimental tool for the
subject study. The mathematical models used for both
performance simulation and cost estimation are
elaborated under ‘METHOD’.

Commercial Aspect
As it could be captured from the cited literature,in addition to other published papers, the relevant
CAPEX and OPEX of the same were have not beenaddressed. Some of the literature has addressed thepower consumption per unit flow rate of treatedinfluent, but only very limited commercial
comparative studies between the MBR wastewater
treatment method and other conventional activatedsludge methods, including EA, have been conducted.
Iglesias, Simón, Moragas, Arce, and RodriguezRoda [7] compiled the documented data from thepublic wastewater management institutions in
Catalonia and Murcia (ACA and ESAMUR), Spainfor fourteen full scale MBR units and compared themwith those installed conventional activated sludge
plants. Iglesias, Simón, Moragas, Arce, andRodriguez-Roda [7] reported that Spain has one of thehighest numbers of MBR-based municipal
wastewater treatment plants (WWTPs) in Europe.
The results showed that the CAPEX for the treatment
plants of a conventional secondary treatment
followed by a tertiary treatment of physical, chemical,
sand-filtration and disinfection is approximately 30%
less than those plants adapted MBR method; but, if
the said tertiary treatment is replaced with anadvanced membrane filtration (e.g. an ultra-filtration
membrane followed by a disinfection unit) to achieve
the same quality of the MBR effluent, the CAPEX isapproximately 10% higher than that of the MBR
plants for capacities less than 10,000 m3/d. In termsof the OPEX, the study showed that OPEX are similar
for both systems, within the limitations of the study.
There are some limitations of the citedcommercial comparative study. It did not consider thegeographical difference in the cost of the equipment
and construction material, nor the monetary value ofthe land, which represents a significant weight of the
CAPEX of both methods, as MBR always requires asmaller foot-print than EA; while the OPEX did notconsider the membrane replacement.

METHOD
Performance Modelling
Henze, Gujer, Mino, and Loosdrecht [8] havedeveloped the Activated Sludge Model No. 1 (ASM1)
which is a dynamic wastewater treatement model.
ASM1 is adopted by most of the simulation programs(e.g. GPS-XTM & BioWinTM) in their library ofmodels.
AccordingtoHydromantis
CAPDETWorksTM version 04–User’s Guide [9], therelevant adapted mode is based on the University ofCape Town (UCT) algorithm and is consistent with
ASM1. For proper replication, the Solids Retention
Time (SRT) shall be substituted with a sufficient timeto allow for both biological oxygen removal andnitrification, considering the recommended operating
SRT range for both EA and MBR methods, as well as
the actual winter temperature and the recommended
operating range of the Mixed Liquor Suspended
Solids (MLSS). The said model is an accurate
preliminary design tool, while for a more detailed
design and consideration of dynamic simulation overan extended period of time, GPS-XTM, BioWinTM,
AQUIFASTM etc., should be used. The 2018
CAPDETWorksTM version 04 is used as a computerbased simulation tool for the performance modelling.
Details of the algorithm for the influent speciation,
nitrifier growth/ decay rates, minimum aerobic SRT,
reactor solid mass, effluent BOD, etc. are elaborated
at the Hydromantis CAPDETWorksTM version 04–
Supplemental Technical Reference [10].

Aim and Method of the Study
On the trail to address the said limitations and help
select the best option from the subject methods,
especially for decentralized wastewater treatment
plants of 500 m3/d to 5,000 m3/d hydraulic load, thefollowing pages reveal an environmental andfinancial comparative study for the same. The
environmental aspect is presented by the efficiencyof-treatment for each method in terms ofcharacteristics of the treated effluent, while thefinancial aspect is presented by the method
economics in terms of the equivalent CAPEX andOPEX for the same in USD, considering the updated
global market price as of October 2019. The said
study is conducted sequentially for three types of687

GEOMATE – Melbourne, Australia, 11-13 November 2020

CAPEX and OPEX Modelling

Table 1 Financial modelling assumptions

In 1973, the Environmental Engineering Divisionof the U.S. Army prepared a cost estimation model
for wastewater treatment systems for benefit of the
U.S. Environmental Protection Agency (EPA). The
algorithm was called CAPDET, which stands for‘process design and estimating algorithms for theComputer Assisted Procedure for Design and
Evaluation of wastewater Treatment systems’. It wasrevised in 1982 to overcome the original version’s
limitation of not reflecting the regional costdifferences or accommodating site-specific design
requirements [11], [12]. Hydromantis has sincedeveloped the model further and presented it to themarket in a user-friendly software package called
CAPDETWorksTM. The 2018 version 04 is used as acomputer-based cost estimation tool for life-cycle
modelling.
The default unit rate cost of the program’s library
can be overridden with available local prices, or
updated in consideration of globally recognized costindices that are regularly published in the relevant
trade publications, such as Marshall And Swift(MAS), Engineering News Record (ENR), or
Chemical Engineering Magazine (CEM). Further,
Hydromantis has developed a compilation of costindices called HECI, HCCI and HPCI (Hydromantis
Equipment Cost Index, Hydromantis Construction
Cost Index and Hydromantis Pipe Cost Index,
respectively). For an easy global replication of thestudy, the local Dubai market prices are notconsidered, and the compiled cost indices ofHydromantis are considered instead. The same is
updated to October 2019 and kept in USD. Table 1
shows the considered financial modellingassumptions that shall be revisited on a case-by-casebasis. The OPEX over the plant lifetime is converted
to its present value and added to the relevant CAPEX,
the sum is divided by the plant capacity to calculate
the unit lifetime cost in ($/m3).

Items

Assumed Value

Interest rate
Construction period
Operating life of the plant
Electricity
Land cost
Design and engineering fees
Technical fees
Administration/legal fees
Inspection fees
Miscellaneous
Contingency reserve
Profit and overhead

8.0%
3 years
40 years
0.1 USD/kWh
20,000 USD/acre
15%
2%
2%
2%
5%
10%
15%

Simulation Procedures
Extended aeration method
On the trail to simulate the actual operation of an
EA bioreactor, the reactor is placed in a complete
plant that treats solid-waste as well, as illustrated in
Fig.1. The process flow diagram is summarized asfollows:
1- Preliminary Treatement: coarse mechanical bar
screen followed by a grit and FOG (Fat, Oil &Grease) removal chamber.
2- Primary Treatement: a primary clarifier.
3- Secondary Treatment: an EA bioreactor followed
by a secondary clarifier with Return ActivatedSludge (RAS) and Waste Activated Sludge (WAS)
streams to adjust the operating SRT and MLSS.
4- Tertiary Treatment: a multilayer sand filter.
5- Solids Treatment: a gravity thickener followed byan aerobic digester, then a belt-filter press.

Fig. 1 Process flow diagram for EA plant
688

GEOMATE – Melbourne, Australia, 11-13 November 2020

Membrane Bioreactor (MBR) method

and the dramatic increase in the difference in the
cubic meter cost as the plant capacity increases.
Table 3 Lifetime cost comparison in terms of ($/m3)
for weak-strength influent (Inf.)

In case of the MBR, both the aerated bioreactor
and the secondary clarifier of the secondary treatment
stage are replaced with an aerated membranebioreactor. For the sake of equivalent comparison, thepreliminary, tertiary, and solids treatments
considered for EA plant are kept without change in
the MBR plant; while the primary treatment was
modified to an equalization tank followed by a finescreen to comply with the recommendations ofmembrane manufacturers.
Design Factors and Operating Parameters
For the sake of a traceable replication, the default,
design factors and operating parameters at
CAPDETWorksTM database library are used withoutchange. The same can be overridden as needed.
RESULTS

Influent
Flow(m3/d)

EA
Lifetime
Cost ($/m3)

MBR
Lifetime
Cost ($/m3)

Differencein Lifetime
Cost (%)

500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000

24,60013,500
9,933
8,050
6,800
5,933
5,371
4,925
4,556
4,260

28,00016,20011,933
9,850
8,480
7,567
6,943
6,625
6,178
5,920

14%
20%
20%
22%
25%
28%
29%
35%
36%
39%

Weak-Strength Influent Case
Attention should be given to the process design
itself, as various installed EA plants have reported
acceptable performance without primary clarifier,while both the tertiary and solids treatments varyfrom one plant to another to maintain the required
level of compliance with local regulations.

An influent of 500 m3/d is considered as a typicalhydrological load for both plants. Table 2 showshigher removal efficiency of the EA method for the
biological load in the form of BOD5 and COD, in
addition to better nitrification of the ammonianitrogen (NH3-N) than the MBR method, and viceversa for the influent total suspended solids (TSS).
Both reactors demonstrated high concentrations of
nitrate-nitrogen (NO3-N), as the process design did
not address denitrification. It worth saying that for
proper replication of the study, the actual local
environmental conditions, such relative humidity,
temperature, etc. shall be revisited.

Like the weak-strength influent case, Table 4shows that the EA method has a higher reduction in
the biological load along with better nitrification,
while the MBR method shows better removal of the
TSS.

Table 2 Performance comparison in terms of effluent
quality (Eff.) for weak-strength influent (Inf.)

Table 4 Performance comparison in terms of effluent
quality (Eff.) for medium-strength influent (Inf.)

Items

Inf.
(mg/L)EA Eff.
(mg/L)MBR Eff.
(mg/L)

BOD5
COD
TSS
TKN
NH3-N
NO3-N

110
250
100
20
12
0

0.979
1.47
8
0.095
0.095
15.5

2.2
4.13
1
1.21
1.13
9.1

Medium-Strength Influent CaseA sensitivity analysis was conducted for a change
in hydraulic-load from 500 m3/d to 5000 m3/d for thesame weak-strength influent; two main findings wereobserved, as shown in Table 3: the dramatic decreasein the cubic meter cost as the plant capacity increases,

Items

Inf.
(mg/L)EA Eff.
(mg/L)MBR Eff.
(mg/L)

BOD5
COD
TSS
TKN
NH3-N
NO3-N

220
500
220
40
25
0

0.979
1.47
8
0.095
0.095
30.7

2.2
4.13
1
1.21
1.13
19.2

The same findings for the weak-strength influent
case are noticed with the medium-strength influent
case, but with smaller differences in the unit lifetime
cost between the two methods, as shown in Table 5.

689

GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 5 Lifetime cost comparison in terms of ($/m3)
for medium-strength influent (Inf.).
EA
Lifetime
Cost ($/m3)

MBR
Lifetime
Cost ($/m3)

Differencein Lifetime
Cost (%)

500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000

24,80014,30010,400
8,550
7,360
6,467
6,029
5,575
5,156
4,860

28,40016,20011,933
10,150
8,960
7,967
7,286
6,900
6,444
6,140

15%
13%
15%
19%
22%
23%
21%
24%
25%
26%

6,600
6,178
5,860

7,525
7,000
6,780

14%
13%
16%

The results of sensitivity analysis for difference inlifetime cost (%) between the EA and MBR of the threecases are represented in Fig. 2.
45%

Weak-strength
Medium-strength
Strong-strength40%
Increase in unit cost

Influent
Flow(m3/d)

4,000
4,500
5,000

35%
30%
25%
20%
15%
10%

Strong-Strength Influent Case

5%

Results were like the weak and medium-strength
influent, while the sensitivity analysis shows a muchsmaller difference in lifetime cost between the two
methods, as illustrated in Tables 6 and 7 respectively.

0%
0

2000
4000
Influent [m3/d]

6000

Fig. 2 Difference in unit lifetime cost for all strengths

Table 6 Performance comparison in terms of effluent
quality (Eff.) for strong-strength influent (Inf.)DISCUSSION
Environmental Findings

Items

Inf.
(mg/L)EA Eff.
(mg/L)MBR Eff.
(mg/L)

BOD5
COD
TSS
TKN
NH3-N
NO3-N

400
1000
350
85
50
0

0.979
1.47
8
0.095
0.095
68

2.2
4.13
1
1.21
1.13
43.3

The results show that the MBR method yields
a much better effluent quality in terms of suspended
solids (1.0 mg/L) compared to 8.0 mg/L from the EA
method, due to the inherent filtration nature of its
structure, a key merit introduced the MBR method toseveral areas of reuse-applications, as highlighted in[3]. Meanwhile, the EA method demonstrates a
relatively higher removal efficiency for the BOD5 and
COD, and better nitrification than the MBR method,
due to the higher SRT of 25.0 days compared to 10.0
days, respectively. The results that make the study in[6] appreciated in the use of EA effluent for theirrigation application for semi-sensitive plants. It isreasonable to find that the results agree with the study
in [5] from the point of efficient removal of bothmethods for the biological load being treated, while
not from the point of percentage of removal, as thesaid study was dedicated to the extra strong-strength
influent that encountered in special applications
where the COD ranges from 500 mg/L to 5000 mg/L
with a corresponding SRT of 13 to 34 days, while thesubject study considered the usual municipal influent
ranges from 250 to 1000 mg/L with a corresponding
SRT of only 10 days. Although we can benefit fromthe inherent advantage of the MBR method of
maintaining a high operational MLSS in thebioreactor with minor limitation from the relevant

Table 7 Lifetime cost comparison in terms of ($/m3)
for strong-strength influent (Inf.)
Influent
Flow(m3/d)

EA
Lifetime
Cost ($/m3)

MBR
Lifetime
Cost ($/m3)

Differencein Lifetime
Cost (%)

500
1,000
1,500
2,000
2,500
3,000
3,500

26,60015,40011,467
9,800
8,520
7,600
6,943

28,40017,00012,933
10,900
9,520
8,633
7,829

7%
10%
13%
11%
12%
14%
13%

690

GEOMATE – Melbourne, Australia, 11-13 November 2020

Hydraulic Retention Time (HRT), attention should begiven to the disadvantage of the associated productionof non-filterable inorganic compounds that could
harm the microbial population and/or the membrane
structure, as illustrated in [13].

REFERENCES[1] Metcalf & Eddy, Inc., Tchobanoglous G.,
Burton F. L., and Stensel H. D., Wastewater
Engineering: Treatment and Reuse, 4th ed.,
McGraw-Hill, Inc., ed 2004.
[2] Asano T., Burton F. L., Leverenz H. L.,
Tsuchihashi R., and Tchobanoglous G., Water
Reuse: Issues, Technologies, and Applications,
Metcalf & Eddy, Inc./ McGraw-Hill, Inc, 2007.
[3] Judd S., and Judd C., The MBR Book: Principles
and Applications of Membrane Bioreactors for
Water and Wastewater Treatement, 2nd ed.,Elsevier/Butterworth Heinemann, ed 2011.
[4] US
Environmental
Protection
Agency,Guidelines for Water Reuse, EPA, 2012.
[5] Mohammadi H., Sabzali A., Gholami M.,
Dehghanifard E., and Mirzaei R., Comparative
Study of SMBR and Extended Aeration
Activated Sludge Processes in Treatement ofHigh-Strength Wastewater. Desalination Journal,
Vol. 287, 2012, pp. 109-115.
[6] Hatami T., Nadali A., Roshanaei Gh., andShokoohi R., Feasibility of Reuse of Effluent
from the Extended Aeration Process ofWastewater Treatment Plant in the Bojnoord
City for Agricultural and Irrigation Uses.
Pajouhan Scientific Journal, Vol. 16, Issue 3,
2018, pp. 20-28.
[7] Iglesias R., Simón P., Moragas L., Arce A., andRodriguez-Roda I., Cost Comparison of FullScale Water Reclamation Technologies with an
Emphasis on Membrane Bioreactors. Water
Science & Technology Journal, Vol. 75, Issue 11,2017, pp. 2562-2570.
[8] Henze M., Gujer W., Mino T., and Loosdrecht M.
V., Activated Sludge Models: ASM1, ASM2,
ASM2d and ASM3, IWA, ed 2002.
[9] Hydromantis CAPDETWorksTM ver. 04, User’s
Guide, 2018.
[10] Hydromantis CAPDETWorksTM ver. 04
Supplemental Technical Reference, 2018.
[11] Harris R. W., Cullinane M. J., and Sun P. T.,
Process Design and Estimating Algorithms for
the Computer Assisted Procedure for Design and
Evaluation of Wastewater Treatment Systems(CAPDET), EPA, 1982.
[12] McGhee T., Mojgani P., and Vicidomina F., Useof EPA CAPDET Program for the Evaluation ofWastewater Treatment Alternatives. Water
Pollution Control Federation Journal, Vol. 55,
Issue 1, 1983, pp. 35-43.
[13] Mohammed T A.., Birima A. H., Noor M. J. M.
M., Muyibi S., Muyibi S. A., and Idris A.,
Evaluation of Using Membrane Bioreactor for
Treating Municipal Wastewater at Different
Operating Conditions. Desalination Journal,
Vol.221, Issue 1, 2008, pp. 502-510.

Financial Findings
The results could be financially interpreted asfollows:- A dramatic decrease in the unit cost as the plant
capacity increases for both methods, as expected.
- A dramatic increase in the difference in the unit
cost as the plant capacity increases as in [7].
- The said observed difference in the lifetime unit
cost decreases as the influent strength increases,
shown in Fig.2, which makes the MBR method
more attractive for strong-strength influent,especially for units smaller than 500 m3/d, leading
to the conclusion that the MBR method is not only
recommended for the extra-strong influent asconcluded in [5], but it is also more costcompetitive than other conventional methods of
biological treatment, including the EA method.
CONCLUSION AND RECOMMENDATIONSThe MBR method of municipal wastewater
treatment should not generally be specified for any
development project, as the EA method is still an
optimum solution economically, which complies withthe effluent quality required for some reuseapplications, such as irrigation of non-sensitive plants
and landscapes. If MBR effluent quality is required
for a specific area of application, the financial impactof the same should be addressed from the perspectiveof both CAPEX and OPEX. The MBR method isfound to be more attractive for the strong-strength
influent, especially for units smaller than 500m3/d,
and is recommended for the extra strong-strength
influent due to both effluent quality and lifetime cost.
For replication of the study, it is recommended
that the following points be addressed:- The local environmental conditions (relative
humidity, temperature, etc.) shall be considered.
- If a pilot plant or lab model can be furnished, theactual values of the biological reaction(s) shall beverified accordingly.
- High consideration should be given to the process
design (train of treatment) that varies from one
plant to another to maintain the required level ofcompliance with local regulations.
- Globally updated unit prices are considered towiden the scope of benefit from the study; however,for the sake of accurate local monetary values, thelocal unit prices shall be considered instead.
- The financial modelling assumption should be
revisited against the market and project conditions.

691

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

RESEARCH ON HOUSING RESTORATION COSTS AND DISASTERRISK MANAGEMENT FOR URBAN EARTHQUAKES
Norikazu Sakaba1, Harumi Yashiro2
1

Tokio Marine & Nichido Risk Consulting Co., Ltd., Japan, 2National Defense Academy, Japan
ABSTRACT

A damage estimation by the Cabinet Office of the Japanese Government examines an earthquake directly
beneath the Tokyo Metropolis as an earthquake disaster that will cause enormous damage. The economic losses
are made clear in this damage estimation; however, the effects of financial support systems such as housing
restoration funds, support grants for reconstructing the livelihoods of disaster victims, and earthquake insurance
payments, which are insufficient throughout the region, are not clarified. In an urban structure such as Tokyo,
where there are many apartment buildings, various financial support systems that were established according tolessons learned from previous disasters may not be effective. Therefore, this research performed a stochastic
seismic analysis of the Tokyo Metropolis, and clarified trends in necessary restoration costs and various financial
support funds for each category of damage (from partial destruction to complete destruction) according to the
housing category. The AAL and VaR for the necessary restoration costs after a disaster were also estimated. To
this end, individual building and households were modeled and a damage simulation was performed in order tounderstand the restoration costs required in the entire disaster area and to examine the effects of various financial
support systems for procuring restoration costs. By showing AAL and VaR respectively, the costs required
routinely and the costs needed when a major earthquake occurs were clarified. This research also showed
quantitatively the extent to which these costs and support funds can be reduced by the ex-ante measure of making
buildings earthquake proof.
Keywords: Disaster risk management, Housing restoration costs, Earthquakes in Japan, Stochastic seismic
analysis, Ex-post compensation

other cases buildings judged to be partially destroyed
were reconstructed. Therefore, the cost of the damage
is not necessarily consistent with the restoration cost.
Housing restoration is an important component
that supports livelihoods, and thus greatly influences
the recovery and reconstruction of a disaster area. To
plan rapid post-disaster recovery and reconstruction,
it is important to understand the housing restoration
costs in the entire disaster area and to implement
measures on how these costs should be procured.
Among previous research on restoration costs forrebuilding housing in disaster areas, Okada (2012) [1]stated that the funds provided through systems of
support for reconstructing the livelihoods of disastervictims are insufficient compared to the costs of
newly constructing or repairing housing. Okada(2012) conducted an earthquake damage estimationin the city of Sapporo and showed that, if the Sapporo
City Government were to cover the shortfall in theamount of money required for housing reconstruction,a considerable reserve fund far exceeding the financesof the local government would be required. In
addition, Inui (2012) [2] compiled housing damage
and rebuilding support in Ibaraki Prefecture after theGreat East Japan Earthquake. The ratio of the support
funds relative to the housing restoration costs was lessthan 20% for 41.2% of households, showing that thebenefits under the current system are extremely

INTRODUCTION
In recent years, numerous earthquakes caused byinland active faults have occurred in Japan, including
the Kumamoto Earthquakes in April 2016, theNorthern Osaka Prefecture Earthquake in June 2018,
and the Hokkaido Eastern Iburi Earthquake in
September 2018. These earthquakes caused serious
damage to buildings, and many victims were forced
to live as evacuees because they were unable toprocure housing restoration costs. Even morehouseholds are expected to suffer damage as a resultof an earthquake predicted to occur directly beneath
the Tokyo Metropolis. For rapid restoration, it isnecessary to understand the housing restoration costs
and implement measures in advance. To address thisissue, the national and local governments in Japan
have conducted damage estimations to understand thebuilding property damage and economic damage andtaken disaster prevention/mitigation measuresaccordingly. However, there is also an economic loss
due to the physical damage of completely or partiallydestroyed buildings that is not included in the costrequired to restore them. These damage estimations
are not considered to fully comprehend the restoration
costs required in a disaster area. Considering pastearthquake damage, in some cases, buildings judged
to be completely destroyed were repaired, while in692

GEOMATE – Melbourne, Australia, 11-13 November 2020

inadequate for rebuilding homes. This study
suggested that the support funds provided by thevarious financial support measures are inadequate
relative to the necessary restoration costs. However,
the targets of this study were the city of Sapporo andone area of Ibaraki Prefecture, as well as that the
numbers of households that suffered damage were nothigh. More households will suffer damage as aconsequence of the predicted earthquake directly
beneath the Tokyo Metropolis because it is a densely
populated area. Accordingly, the necessary
restoration costs and financial support systems are
expected to be different.
Regarding previous research on restoration costs
and measures, Okada et al. (2016) [3] and Nakashima
et al. (2018) [4] identified as an issue the fact that thecurrent framework of housing reconstruction support
is uniform throughout the country. By performing
damage estimations taking account of regional
differences in home rebuilding costs and annual
incomes, they showed quantitatively that thedifficulty of reconstruction and the economic burden
on households differ greatly across regions. They
stated that a system of economic assistance thatconsiders regional inequalities will reduce household
debt after an earthquake disaster and improve thespeed and quality of reconstruction. Nagamatsu et al.
(2003) [5] presented the problem that all of the
housing restoration costs cannot be procured under
existing ex-post compensation systems. They
proposed linking ex-ante damage mitigation
measures and ex-post compensation systems andsuggested that reducing housing restoration costs willlead to improved support. These findings are usefulas examinations of financial support systems relative
to necessary restoration costs. However, in an urban
structure such as that of Tokyo, where there are many
apartment buildings, existing financial support
systems may not be sufficiently effective. Future
measures must be implemented after clarifying trends
in restoration costs by housing category (apartment,
detached house, among others) and damage category.
In Tokyo, seismic retrofitting plans have beendrawn up as efforts towards earthquake disasterprevention/mitigation [5]. As of 2003, the earthquake
proofing rate was approximately 75%, while as of2018 this had increased to approximately 80%3).
However, the question of how to cover the cost,which ranges between several hundred thousand andseveral million Japanese yen per house, is a major
issue. Considering the life of a housing unit ofbetween 30 and 50 years, it is not inevitable that alarge earthquake will occur during that time.
Therefore, earthquake proofing is not likely to be
profitable for many households. Earthquake proofingthat is left to the autonomy of households may not
progress. In contrast, the US National Flood
Insurance Program (NFIP) can be cited as an example
of promotion of aggressive ex-ante measures. A

characteristic of this program is that it integrates aid
for the victims of flood damage and mitigation offlood damage. In addition, by taking ex-ante
measures, it reduces destabilization of insurance
when a major disaster happens (large insurance
payouts leading to destabilization/collapse of theinsurance system). Promoting ex-ante measures also
makes it possible to reduce compensation amounts
and allows a portion of the surplus funds that arise in
the ex-post compensation to be invested in ex-ante
measures. To implement this type of system linking
ex-ante measures and ex-post measures, it isnecessary to understand quantitatively the effects ofthe ex-ante measures. In particular, a quantitativeevaluation of the extent to which promoting
earthquake proofing will reduce restoration costs andsupport funds or stabilize support systems is desired.
To address this issue, this study conducts astochastic seismic analysis of the Tokyo Metropolis
and estimates expected values and Value at Risk
(VaR) for the necessary restoration costs after adisaster. Trends in necessary restoration costs andvarious financial support funds are clarified for eachcategory of damage (from partial destruction tocomplete destruction) according to the housing
category (apartment, detached house, among others).
By showing the Average Annual Loss (AAL) and
VaR, respectively, this study clarifies the costs
required routinely and the costs needed when a major
earthquake occurs. This study also shows
quantitatively the extent to which these costs andsupport funds can be reduced by the ex-ante measureof making buildings earthquake proof.
METHODS: DISASTER RISK MANAGEMENTWith the aim of integrating ex-ante measures andex-post compensation, this study evaluates the extent
to which ex-post compensation can be reduced whenthe ex-ante measure has been taken. Assuming that
housing units are made earthquake proof to a
performance equivalent to the standard since 1981,
the analysis is performed with the earthquake
proofing rate at the current level (apartments 82%,
detached houses 71%), 85%, and 95%. AAL and VaR
are compared for the necessary restoration costs andvarious financial support systems at each earthquake
proofing rate. The funds required per year can beunderstood from AAL, and the funds required after asmall to medium earthquake and a major earthquake
can be understood from VaR. The surplus funds thatcan be used for ex-ante measures are determined fromthe extent to which each fund has been reduced
compared to the current situation. The following
equation is used as an indicator of stabilization of thefinancial support system.
ROR=

693

AAL
AAL
=
Risk Amount 99%VaR-AAL(1)

GEOMATE – Melbourne, Australia, 11-13 November 2020

Here, ROR represents “return on risk,” and it is anindicator showing the extent to which an annual
reserve fund has been maintained relative to the risk
amount. The risk amount shows the amount in excessof the annual reserve fund. A higher ROR indicates amore stable system.

Unit: household
1 50 100 200 300 400 600 800 1000 1200 1400 1600 2000 -METHODS: EVALUATION METHOD FOR
RECOVERY COST ANALYSIS

50
100
200
300
400
600
800
1000
1200
1400
1600
1800

(a) Apartments

To understand the restoration costs required in
the entire disaster area and to examine the effects ofvarious financial support systems for procuring
restoration costs, individual building and households
are modeled and a damage simulation is performed.
By accumulating the restoration costs of eachhousehold and various financial support funds, trends
in restoration costs and various financial support
funds are evaluated quantitatively for each categoryof damage (from partial destruction to complete
destruction) and each housing category (apartment,
detached house, among others).
The damage simulation used in this study is
divided into the following evaluation processes:
exposure model evaluation, seismic hazard
evaluation, damage function evaluation, and building
asset evaluation. The results of the estimation ofrestoration costs in the entire disaster area areuncertain because there are evaluation errors in eachprocess. However, this study verified the method withproperty damage caused by the Kumamoto
earthquakes [6] and confirmed that the model’s
predicted values and the actual damage values show
good correspondence. The Kumamoto earthquakes
differ from the earthquake directly beneath Tokyo
that is considered in this study, and the target regions
also differ. However, the accuracy is consideredsufficient for examining future measures. The
following paragraphs describe each of the evaluation
methods in the damage simulation.

Unit: household
1 50 100 200 300 400 600 800 1000 1200 1400 1600 2000 -

50
100
200
300
400
600
800
1000
1200
1400
1600
1800

(b) Detached houses

Fig. 1 Distribution of Households
5%
1%
2%

9%

4%

0%
11%

38%

0%

3%
13%

15%
68%

(a) Apartments
Non-wooden 1970 or earlier
Non-wooden 1971~1980
Non-wooden 1981 or later

31%(b) Detached houses
Wooden 1970 or earlier
Wooden 1971~1980
Wooden 1981~2000
Wooden 2001 or later

Fig. 2 Proportion of households by building age,and construction material
detached houses are distributed evenly across Tokyo.
Next, Fig. 2 shows the proportions of households bystructure and construction period according tohousing type that were created. Sixty-eight percent of
the apartments are non-wooden structures built from1981 onwards, while 69% of the detached houses arewooden structures built between 1981–2000 and from2001 onwards. Apartments are typified by nonwooden structures and detached houses by wooden
structures, and, in both cases, many households residein buildings with strengths equivalent to newearthquake proofing standards.

Exposure Model Evaluation
An exposure model is created taking the Tokyo
Metropolis as the target evaluation area [6]. Here, theexposure model shows households and buildings
exposed to the seismic hazard. First, using census
mesh data, data on the number of households byhousing type and structure are created in 500-m meshunits. Next, the data on the number of households are
divided proportionally based on Japan’s Housing andLand Survey data on the number of housing units byhousing type and construction period according toprefecture or municipal district, in order to create dataon the number of households by housing type,structure, and age in 500-m mesh units.
Fig. 1 show the distributions of households byhousing type that were created. Apartments areconcentrated in the eastern part of Tokyo, while

Seismic Hazard Evaluation
The Source model was based on published datafrom the HARP 2014 [7]. In this study, only inland
earthquakes are included in the map. The earthquake
prediction equation is adopted from Morikawa andFujiwara (2013) [8]. The shallow and deep
geotechnical structures are based on the data available
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GEOMATE – Melbourne, Australia, 11-13 November 2020

in J-SHIS [9]). The distribution of seismic intensity
by return period is shown in Fig. 3.
Damage Function
Restoration

Evaluationfor

EP=0.01

Housing
Unit: JMA Seismic intensity
3.5 4.5 5.0 5.5 6.0 6.5 -Since this study aims to estimate building
restoration costs rather than the economic damage tobuildings, the necessary housing restoration costs arecalculated using the damage function proposed by theauthors [10]. This damage function is defined asseismic intensity on the horizontal axis and necessary
restoration cost ratio (ratio of restoration cost to
replacement cost) on the vertical axis. Fig. 4 shows
the damage function used in the analysis. Thisdamage function is categorized into apartments anddetached houses, and into wooden and non-wooden
structures. The construction period is divided into
1970 or earlier, 1971–1980, and 1981 or later.
Wooden structures built in 1981 or later are further
divided into 1981–2001 and 2002 or later.

4.5
5.0
5.5
6.0
6.5

EP=0.002

Unit: JMA Seismic intensity
3.5 4.5 5.0 5.5 6.0 6.5 -

4.5
5.0
5.5
6.0
6.5

EP=0.002

Unit: JMA Seismic intensity

Building Asset Evaluation
To calculate the cost of restoration, building assets
are set using new values rather than market values.
The building assets of each household are calculated
following Japan’s National Tax Agency’s“Reasonable Method of Calculating Losses” [11] bymultiplying the construction cost per floor areaaccording to structure by the total area of exclusive
space per household [12]. However, apartmentbuilding assets include both common and exclusive
space. Since there is no data available on the total areaof common space per household, this is calculated
based on the proportion of assets in exclusive andcommon spaces. Generally, fire insurance for
condominium management associations takes theassessed value of common spaces as between 40%
and 60% of the building’s total assessed value. In thisstudy, the standard that uses the inner (painted)
surface of walls, ceilings, and similar construction
elements to determine boundaries between exclusiveand common spaces is adopted, and asset value isestimated taking exclusive space as 40% and common
space as 60%. On this basis, the total area of common
space per household is calculated by multiplying theratio of assets in exclusive and common spaces by thearea of exclusive space. Table 1 shows the value ofbuilding assets for each household, and Table 2 shows
the total value of building assets in Tokyo. The tablereveals that the apartments account for the majorityof building assets in Tokyo.

3.5 4.5 5.0 5.5 6.0 6.5 -

4.5
5.0
5.5
6.0
6.5

Fig. 3 Distribution of Seismic intensity by EP

Reconstruction costs ratio

Non-wooden 1970 or earlier
Non-wooden 1971~1980
Non-wooden 1981 or later

Wooden 1970 or earlier
Wooden 1971~1980
Wooden 1981~2000
Wooden 2001 or later

1.0
Apartments
0.8
0.6
0.4
0.2
0.0
0

50

100
PGV (cm/s)

150

200

150

200

Reconstruction costs ratio

1.0
Detached houses

0.8
0.6
0.4
0.2
0.0
0

50

100
PGV (cm/s)

Fig. 4 Damage function of restoration cost

Evaluation of the financial support system forhousing rehabilitation

Housing reconstruction support programs provide
funds to those whose livelihoods have been severely
damaged by a natural disaster to help them rebuild
their lives. They use funds contributed by the national

Housing Reconstruction Assistance Program

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 1 Building assets per household
Category
Non-wooden
WoodenDetached houses
Non-wooden
WoodenApartments

Construction area
51+77m2
39+59m2
126m2
107m2

Construction cost
220K JPY / m2
174K JPY / m2
220K JPY / m2
174K JPY / m2

Building assets
28,050K JPY
16,965K JPY
27,720K JPY
18,618K JPY

Table 2 Building assets in Tokyo
Unit: 1M JPY

Category
Apartments
Pre 1980
Post 1981
Detached houses
Pre 1980
Post 1981
Total

Non-Wooden
100,727,746
100,727,746
1,837,420
1,837,420
102,565,167

Wooden12,855,517
12,855,517
34,418,929
34,418,929
47,274,446

Total
113,583,264
113,583,264
36,256,349
36,256,349
149,839,613

Table 3 Settings disaster victims' life reconstruction funds
Unit: 1K JPY

Damage State
Completely destroyed
Largely destroyed
Half destroyed

Reconstruction
Rebuilding
Rehabilitation
Rebuilding
Rehabilitation
Rebuilding

Base support
Additional support
1,000
2,000
1,000
1,000
1,000
2,000
500
1,000
1,000
2,000

Total
3,000
2,000
3,000
1,500
3,000

Table 4 Settings earthquake insurance
Damage State
Completely destroyed
Largely destroyed
Half destroyed
Partial destroyed

Insurance amount
50% ofbuilding assets

and prefectural governments from the perspective of
mutual aid. The funds set in this study are shown in
Table 3.

Payment rate
100%
60%
30%
5%

Total
50%
30%
15%
2.5%

insurance take-up rate for detached houses andexclusive space in apartments is set at 36.7% [13].
Since no data is available for the earthquake
insurance take-up rate for common space inapartments, this is set using the attachment rate shownin [14]. Here, the attachment rate represents thepercentage of households with earthquake insurance
attached to their fire insurance contracted during therelevant financial year. By multiplying the ratio of theattachment rate for exclusive space (71.9%) to theattachment rate for common space (38.1%) in 2015
by the earthquake insurance take-up rate for exclusive
space, the earthquake insurance take-up rate forcommon space is set at 18.2%.

Earthquake insurance system
Earthquake insurance is a type of non-life
insurance that compensates for losses caused bydisasters, such as earthquakes, volcanic eruptions,and tsunamis. It is operated jointly by the government
and private non-life insurance companies. Earthquake
insurance is purchased together with fire insurance,
and the insurance amount is specified as between
30% and 50% of the amount of fire insurance. In 2014,
some changes were made to the system, and partial
destruction was divided into the two categories ofsmall-scale partial destruction and large-scale partial
destruction. The earthquake insurance money set inthis study is shown in Table 4. Next, the earthquake
insurance take-up rate for each household is set. Here,the take-up rate is the proportion of all households
that have earthquake insurance. The earthquake

RESULTS AND DISCUSSION
Analysis of the Subject of Evaluation
First, the left-hand side of Fig. 5 shows thenecessary restoration costs for each earthquake
proofing rate and each return period. The respective
696

GEOMATE – Melbourne, Australia, 11-13 November 2020

Earthquake Proofing Rate 80%
Earthquake Proofing Rate 85%
Earthquake Proofing Rate 95%

Exceedance Probability

0.10000.01000.0010PartialHalf

Largely
Completely

Earthquake Proofing Rate 85%

Earthquake Proofing Rate 95%

100%

100%

80%

80%

60%

60%

40%

40%

20%

20%

0%

0%

0.000104,000,000
8,000,000
Restoration Costs (1M JPY)(a-1) EP Curve(a-2) Proportion of restoration costs by Damage State(a) Apartments
PartialHalf

Earthquake Proofing Rate 80%
Earthquake Proofing Rate 85%
Earthquake Proofing Rate 95%

Exceedance Probability

0.10000.01000.0010Largely
Completely

Earthquake Proofing Rate 85%

Earthquake Proofing Rate 95%

100%

100%

80%

80%

60%

60%

40%

40%

20%

20%

0%

0%

0.000104,000,000
8,000,000
Restoration Costs (1M JPY)(b-1) EP Curve(b-2) Proportion of restoration costs by Damage State(b) Detached houses

Fig. 5 Necessary restoration costs
graphs show the results for apartments and detachedhouses. The figure reveals that the necessary
restoration costs were reduced at all return periods asa result of promoting earthquake proofing. By
promoting the earthquake proofing rate from 80% to95%, the necessary restoration costs for apartments at
a return period of 2,000 years were reduced byapproximately 1,019,997 million Japanese yen (from7,273,176 million to 6,253,180 million Japanese yen).
For detached houses, the costs were reduced byapproximately 1,350,551 million Japanese yen (from4,532,159 million to 3,181,608 million Japanese yen).
The current earthquake proofing rate for detachedhouses is low, and therefore there was a large
reduction in the necessary restoration costs whenearthquake proofing was promoted. Next, the centerand right-hand side of Fig. 5 show breakdowns of theextent of damage by return period. The figure reveals
that the restoration costs differ for each return period
and each damage extent. The shorter the return period,
the greater the percentage of partial destruction andthe smaller the percentage of total loss. The fact thatthe damaged households requiring restoration funds
differ greatly with each return period suggests that theform of the financial support system will also differ
depending on the focused return period.
Effectivenessofearthquake-resistancenecessary restoration costs: the financial support
system
In the next section, we evaluate the extent towhich the ex-post compensation could be reduced byproceeding with the ex-ante measures. Table 5 shows
the results of AAL, 99% VaR, and ROR of various
financial support programs, as well as the cost of
recovery from the earthquake disaster. It can be seenthat the amount of financial assistance provided by
the disaster relief and reconstruction assistance andearthquake insurance both decreased as a result ofseismic retrofitting. AAL decreases by about 19-24%
for both multifamily and single-family dwellings
when seismic retrofitting is increased from 85% to95%. 99% VaR decreases by about 29-36% formultifamily dwellings and 21-27% for single-familydwellings. The impact of seismic retrofitting isgreater for VaR than for AAL than for the reduction
in aid contributed in the event of a disaster. The effectof seismic retrofitting is to reduce the amount of aid
paid out in the event of severe damage. In particular,
the Livelihood Reconstruction Assistance Program
will reduce the amount of aid paid out in comparison
to the earthquake insurance program. The Life
Reconstruction Assistance Program for disastervictims includes payments to households that are atleast half destroyed, so the number of households

for
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Table 5 AAL, 99%VaR, ROR by Earthquake Proofing rate
Unit: 1M JPY

Category
Apartments

Necessary restoration
costs
Support money fromvictims' life
reconstruction costs
Support money fromearthquake insurance

Earthquake
Proofing rate
82%
85%
95%
82%
85%
95%
82%
85%
95%

AAL
13,235
12,686
10,638
831
784
605
1,293
1,234
1,015

99%VaR
136,214
127,117
90,676
6,741
6,157
3,944
12,27911,412
8,076

Risk Value
122,979
114,432
80,037
5,910
5,373
3,339
10,987
10,1787,060

ROR
10.76%
11.09%
13.29%
14.06%
14.60%
18.12%
11.77%
12.12%
14.38%
Unit: 1M JPY

Category
Detachedhouses Necessary restoration
costs
Support money fromvictims' life
reconstruction costs
Support money fromearthquake insurance

Earthquake
Proofing rate
71%
85%
95%
71%
85%
95%
71%
85%
95%

whose houses are at least half destroyed is expected
to decrease as a result of seismic retrofitting.
Next, promoting earthquake proofing from 85%
to 95% increases ROR by between approximately
19% and 24% for apartments and between
approximately 2% and 5% for detached houses. ROR
is an indicator showing the extent to which an annual
reserve fund has been maintained relative to the risk
amount, and a higher ROR indicates a more stablesupport system. In this discussion, earthquake
proofing results in a large decrease in 99% VaR. Thismeans that the expenditure after a major disaster isstable relative to the reserve fund. The ROR showsthat the system of support for reconstructing the
livelihoods of disaster victims and the earthquake
insurance system became more sustainable.

AAL

99%VaR

8,044
6,360
5,129
656
512
389
1,476
1,167
941

90,97968,01453,970
3,359
2,486
1,814
16,695
12,481
9,903

Risk Value
82,93561,654
48,841
2,703
1,974
1,425
15,21911,313
8,962

ROR
9.70%
10.32%
10.50%
24.26%
25.92%
27.29%
9.70%
10.32%
10.50%

be used to in developing a social system for disasterprevention that combines disaster prevention
measures such as seismic retrofitting, systems of
support for rebuilding the lives of disaster victims,
and financial support measures such as earthquake
insurance.
REFERENCES[1] Shigeyuki Okada, Estimation of earthquake risk
financing of local governments necessary forreconstruction of victims' lives, Summary ofArchitectural Institute of Japan, 2012[2] Yasuyo Inui, The Feature of Housing Damage
and the Supporting Subject of Housing
Rebuilding in Ibaraki pref. after the Great
Eastern Japan Earthquake, Journal of the City
Planning Institute of Japan, 2012[3] Shigeyuki Okada, Tadayoshi Nakashima,
Reginal Disparities of Self-Payment Cost for
Seismic Destroyed House Restoration, The
Seismological Society of Japan, 2106, Fall
Meeting[4] Tadayoshi Nakashima, Shigeyuki Okada,
Financial Imbalances in Regional Disaster
Recovery Following Earthquakes - Case Study
Concerning Housing- Cost Expenditures inJapan, Sustainability, 10(9), 3225, 1-23
[5] Tokyo earthquake-resitant Portal website

CONCLUSIONSWith the aim of integrating ex-ante measures andex-post compensation, this study evaluated the extent
to which restoration costs and financial support
system funds can be reduced when the ex-ante
measure of earthquake proofing has been taken. The
study revealed that restoration costs and financial
support system funds can be reduced by promoting
earthquake proofing. The fund reducing effect wasparticularly noticeable for major earthquakes, such as
99% VaR, and was shown to contribute significantly
to improving ROR. The results of this research will

698

GEOMATE – Melbourne, Australia, 11-13 November 2020

https://www.taishin.metro.tokyo.jp/tokyo/topic
01.html[6] Norikazu SAKABA, Harumi YASHIRO et al.,Verification of earthquake damage estimation
method of local government for 2016
Kumamoto earthquake[7] Headquartersfor
Earthquake
Research
Promotion, National Seismic Hazard Maps 2014[8] Morikawa and Fujiwara, A new ground motion
prediction equation for Japan applicable up to
M9 mega-earthquake, Journal of Disaster
Research, 2013[9] National Research Institute for Earth Scienceand Disaster Resilience, J-SHIS Earthquake
Hazard Station (2020.1.10)
http://www.j-shis.bosai.go.jp/[10] Norikazu SAKABA, Harumi YASHIRO,
Economic Damage Analysis on Housing
Reconstruction by The Tokyo Near-Field

Earthquake, The Ninth Japan Conference on
Structural Safety and Reliability, 2019[11] National Tax Agency "Reasonable calculation
method of loss amount" in miscellaneous loss
deduction (2020.1.10)
https://www.nta.go.jp/about/organization/tokyo/topics/sonshitsu/index.htm[12] Statistics Bureau, Ministry of Internal Affairs
and Communications, Housing and Land
Statistics Survey, 2018[13] Non-Life Insurance Rating Organization ofJapan, Changes in the participation rate ofearthquake insurance by prefecture, 2018
http://www.sonpo.or.jp/news/statistics/syumok
u/pdf/kanyu_jishin.pdf[14] Non-Life Insurance Rating Organization ofJapan, Changes in the participation rate ofearthquake insurance by prefecture, 2018699

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

INVESTIGATION OF THE INFLUENCE OF RECYCLED RUBBERPARTICLES ON CEMENT PASTE PROPERTIES
Mohamed Atef1, NahedA.Azab1 Mohamed Hazem Abdellatif1 and Ghada Bassioni2*
1

Mechanical Engineering Department, Faculty of Engineering, Ain Shams University, Egypt, 2 Chemistry
Division, Faculty of Engineering, Ain Shams University
ABSTRACT

Egypt is one of the most consuming markets for tires all over the world. There is an urgent need to get rid ofthem by an economic and environmental way since it is considered as a harmful waste if burnt. The worldwide
trend is directed towards development of new techniques to transform the rubber into useful products to maximize
the benefit of recycling. This research deals with development of novel techniques for rubber powder recycling
for various purposes in robust construction. Recycled rubber particles (RRP) treated by two different types ofchemical treatment and mixed with cement pastes (CP) using different water to binder ratio (w/b) with water tocement ratio (w/c) kept constant at 0.3. The Fourier transform – infrared (FT-IR) spectra measured for untreated
and treated rubberized cement pastes. The compressive strength investigated for the treated and untreated samples
for 1, 3, 7, 14 and 28 days. The results aimed to enhance the compressive strength of rubberized cement pastes
through improving the bonding strength between RRP and CP. Maintaining the compressive strength in the highest
levels with different RRP percentages in addition to vibrational damping property of RRP give rubberized cement
pastes the advantage to replace ordinary concrete in most of construction applications.

Keywords: rubber particles, Waste management, cement paste, mechanical Properties
and Morocco[4].The possible increase by 2030 in the
Egyptian tires market for different kinds of vehicles
is illustrated in Figure 1. Egypt produces 20 million
waste tires/ year and barely 10% of those are recycled[5].

INTRODUCTION
At the end of the 20th century, sustainable
development and environmental protection became
key goals of modern society. Civil engineering,
especially the construction materials industry, has animportant role in the sustainable development ofbuildings, in the reduction of pollution, and inconservation of natural resources and energy savings.
In this context, the main problems that industry of
construction materials faces are mainly related to highconsumption of Portland cement and the associatedhigh emission of carbon dioxide. With reference tothe first point, concrete has been for decades the mostused construction material in the world, and theglobal production of concrete has reached a value ofmore than 1 ton of concrete per person on the planet.
Overall, cement companies are producing nearly 3
billion tons/year [1] . This led to the emission ofalmost 2 billion tons of CO2 (6-7% of global CO2
emissions) in the process [2, 3]. In order to reduce thegreenhouse effect of cement production, several
studies have identified the improvement of process
efficiency and the increase in the use of various wastematerials as a cement replacement.
According to a recent report, tire marketplace in
Egypt is anticipated to cross USD 1 billion through2020. Egypt automotive marketplace is one of theconspicuous markets in African mainland because of
its huge market size and its geological area which isspanned to Asia. In 2013, Egypt was the third biggest
vehicle creating market in Africa, after South Africa

Figure 1 Egypt Tire Market Size, By vehicle type,
By Volume [ 5 ]Utilization and disposal of used tires represent anenormous challenge to industrialized countries
worldwide because of their three dimensionalcrosslinked structure which renders themnonbiodegradable, raising serious disposal concerns[6, 7]. The most obvious hazard associated with theuncontrolled disposal and accumulation of a large
number of tires is the potential for large fires which
are extremely detrimental to the environment. The
environmental issues from the global disposal ofthese tires have led to increasing interest in economic
recycling of tire rubber [8-10]. The rubber in tires isvulcanized and cannot be melted or dissolved, whichmakes recycling challenging [11, 12]. As a result, a700

GEOMATE – Melbourne, Australia, 11-13 November 2020

compressive strength by over 5 %. The splitting
tensile strength decreased with an increase in thequantity of crumb rubber, and the modulus of rupture
was decreased by an average of 12 %. However,increased strain at failure, good energy absorption,
improved modulus of toughness, and ductility wereobserved in rubberized concrete[23]. It is studied thatthe partial effect of particle size of crumb tire as a fineaggregate replacement on compressive strength andtime dependent deformations of structural concrete.
Based on the compressive and preliminary timedependent results, rubberized concrete had lower
compressive strength than the control concrete mix
for all crumb rubber sizes. Crumb rubber size has aneffect on the strength of concrete, as the crumb rubber
became smaller compressive strength decreased [24].
As mentioned before most of researches used rubber
particles in form of fine or coarse aggregates In somecases it was used as an additive in concrete
application but was limited to be used as replacementof cement percentage in a mix design. The novelty ofthis work is studying the effect of using fine rubber
particles as partial replacement of cement oncompressive strength of different hardened cement
pastes at different water to binder ratio (W/B ratio =0.3 and 0.4). This was coupled by an extensive ESEM
micrographic study

large number of used, worn out tires are ground forthe benefits of expanding their applications [13, 14].
The application related to the ground or powdered tirerubber includes outdoor flooring and pavements,
sports tracks, road construction, etc., which fall into
the sectors with limited demanding and added value.
Various construction industries have used rubber
elements as a fundamental part of structure. This
material has been used in the construction of concert
halls as an acoustic absorber, in the construction ofbridges (buffer), among others as waterproofing andfilling in roads [15]. Research to date on the so-called
Rubcrete has provided contradictory findings. There
are some properties that are enhanced in Rubcrete
compared with conventional concrete, including
damping ratio, ductility, energy dissipation, impact
resistance and toughness [16-18]. Conversely, the key
mechanical properties of compressive strength,
modulus of elasticity, and tensile strength are reduced[19, 20]. While there are many ways of recycling tires,in the structural materials area, a significant amountof research has recently focused on using crumbrubber from recycled tires as partial replacement ofcoarse aggregates. It is determined that a wastematerial like worn out tires may enhance the basic
properties of concrete. The data presented in thisresearch showed that there is great potential for theutilization of tires as aggregates. It was consideredthat used tires would provide much greater
opportunities for value adding and cost recovery, as it
could be used as a replacement for more expensive
material such as rock aggregates. Rubber had great
capability of becoming a permanent member of theconcrete family because of its wide variety of decent
properties like better flexibility, light weight and easy
availability. Using rubber aggregates decreased theworkability of the resultant mix, but this problem canbe dealt with the use of the certain plasticizers[21].The effect of partial replacement of coarse
aggregates in concrete is studied by untreated tirerubber aggregates. It was observed that the specificgravity and bulk density of rubber aggregates are lessas compared to natural coarse aggregates. The
concrete density decreases when utilization of rubber
aggregates in concrete increased. Due to this thelightweight concrete was acquired which assists todecrease the weight of structure. Using rubber
aggregates in concreteresulted in decreasing thecompressive strength but increasing toughness ofconcrete. It was found that optimum percentage of
replacement of rubber aggregates can be up to 15%.
It was observed that such type of concrete cannot be
used in structural elements where high strength isrequired [22]. When the properties of concrete are
evaluated with crumb rubber replacing fineaggregates, the results showed that up to 15 % of fineaggregates can be replaced with an equal volume of
crumb rubber with a slight improvement of theconcrete workability. The crumb rubber improved the

EXPERIMENTAL WORK
MATERIALS AND ITS
CHARACTERIZATIONS
The materials used in sample preparation were:(i) Ordinary Portland Cement (OPC) (Type 42.5N)
was provided by Torah Co., Helwan, Egypt and (ii)the untreated recycled rubber particles (RRP) which
was delivered from Egypt local market and extracted
from car tires by grinding then sieving. The chemicalcomposition of OPC was determined by elemental
analysis using wavelength dispersive X-Ray
Spectrometry (standard less) as shown in Table 1 .
Particle Size Distribution tests (PSD) for both OPC
and RRP were performed by laser diffraction
technique using volume-based results of Malvern
Mastersizer 2000 (Dry method). As shown in Figure
3, the volume weighted Mean of the OPC and RRP
are 55.9 and 944.5 µm respectively.
FTIR test was performed for RRP by
PerkinElmer instruments spectrum one. As Figure 2
indicates, the various transmittance peaks observed in
the spectrum are related to specific functionalgroups; stretching vibration band of C-H bond at
2820 – 2970 cm-1 and C = C at 1540 – 1690 cm-1 .
Also, at 960- 1190 cm-1, the peak is assigned to C-Sbond resulted from presence of Sulfur which is usedin the vulcanization process of rubber.

701

GEOMATE – Melbourne, Australia, 11-13 November 2020

ConstituentsConstituents(wt %)

SiO2

Table 1 Chemical composition of Ordinary Portland Cement (OPC)
TiO2 Al2O3 Fe2O3tot. MgO CaO Na2O K2O P2O5 MnO

Cl

LOI

13.4

0.46

0.202

5.01

2.95

4.08

1.57

64.2

0.574

0.275

0.191

0.075

level to get the optimum properties. Each paste wasstirred continuously for three minutes by electric
mixer and assuring the homogeneity of the mixture
by visual inspection. The dosage of recycled rubber
particles (RRP) were 0 %, 5%, 15%, 25% by theweight of cement.
The resulting mixes were designated as shown in
Table 2. The control samples are C-0.3 and C-0.4 forthe cement pastes free from the RRP mixed at
different w/b ratio 0.3 and 0.4 respectively. The
rubberized cement mixtures are defined as “R5-0.3,
R15-0.3 and R25-0.3” for the cement pastes
containing 5, 15 and 25% by weight of cement andw/b = 0.3 and also in the same way for w/b =0.4.

Figure 2 FTIR spectra for RRP

Table 2 Mix design of the prepared rubberized
cement mixtures
OPC
RRP
Water
w/b
Sample
Wt.
Wt.
Wt.
ratio
(gm)
(gm)
(gm)
C-0.3
0.3
3250
0
975
R5-0.30.33087.5162.5
975
R15-0.3
0.3
2762.5487.5
975
R25-0.3
0.3
2437.5
812.5
975
C-0.4
0.4
3250
0
1300
R5-0.40.43087.5162.5
1300
R25-0.4
0.42437.5
812.5
1300

a

b

Molding
The pastes were molded immediately after the mixing
was finished in 2 ̏ inch or 50 mm cubic steel moldaccording to ASTM Designation: C-109/C 109M-07)[25]. Each cube was filled gradually then compactedusing a mechanically vibrating machine for two
minutes to remove any air bubbles in order to achieve
better impaction of the paste.
CuringAccording to BS EN 4551-1 [26] immediately after
molding, the mold was cured in a humid chamber
during the first 24 hours at room temperature and thendemolded. The demolded cubes were immersed in acontainer filled with tap water for the required curing
periods of 1, 3, 7, 14 and 28 days.

Figure 3 Particle size distribution (PSD) for a) OPCb) RRP
Methods of investigation
Dry mixing
Firstly, OPC and RRP at different weight ratios(100/0, 95/5, 85/15, and 75/25 OPC/RRP) were
carefully dry blended using a mechanical stirring for
30 min, to assure complete homogeneity,

Mechanical properties
Compressive strength measurements were carried out
on the hardened cement paste using 30 Toncompressive machine manufactured by Lloyd
Instruments Ltd, United Kingdom.
Three cubes were tested for compressive strength
measurements at the various ages of hydration 1, 3, 7,

Preparation of cement paste
The different cement pastes were made by mixing therubberized cement with tap water. The waterconsistency of each paste should be in a reasonable702

GEOMATE – Melbourne, Australia, 11-13 November 2020

ratio increases, the initial porosity increases and thenthe compressive strength decreases.
Interestingly, in case of R25-0.3 and R25-0.4 thespecimen gains about 83% of its full strength at thefirst stage of hydration up to 7 days rather thangaining only about 17% at the later stage. This meansthat most of unreacted cement is already hydrated in
the first stage due to further dilution as illustrated
above.
The strength results seem to be dependent on thenature and the physical state of the hydration products
formed within the pore system of the hardened
cement pastes; this achievement will receive further
support from the results of the microstructure of theformed hydrates as will be discussed later in thisinvestigation.

14 and 28 days and for different w/b ratio at 0.3 and0.4.
Stopping of hydration
The stopping of hydration of all the pastes was
performed on the crushed paste cubes after thecompressive strength determination.
This process was performed by rinsing about 20
grams of the grounded cement pastes from the core of
the specimen with about 150 ml of a mixture ofacetone and methanol (1:1 by volume) using amagnetic stirrer for at least one hour. The solvent
mixture was then removed by decantation andfiltration. Finally, the sample was dried at 80ºC in thedrier overnight to remove the moisture.
ESEM Study
The morphology and microstructure of hardened
cement pastes (0% and 25% RRP by weight of
cement) prepared by two different w/b ratios (0.3 and0.4) at 28 days curing time were investigated using
environmental scanning electron microscopy(ESEM). Surface images were recorded using Quanta
FEG 250 scanning electron microscope (FEI
Company, USA) available at EDRC, DRC, Cairo.
Samples were mounted onto SEM stubs. Applied
SEM conditions were a 10.1 mm working distance,with in-lens detector with an excitation voltage of 20
kV.

Table 3 Compressive strength of the control andRubberized cement mixtures samples
CuringDays

RESULTS AND DISCUSSION
Compressive behavior of Untreated Rubberized
Cement Paste
The hydration process after one day ischaracterized by two stages as shown in Figure 4. The
first stage has a sharp increase in the strength due tothe presence of pores which can accumulate theformed hydration products. This stage followed by aslight increase in the strength due to the limited poresavailable for new hydration precipitates.
Generally, it is observed that the compressive
strength increases with increasing curing time asshown in Figure 4&Figure 5 and Table 3. This ismainly due to the increase in the amount of
tobermorite like gel (calcium silicate hydrates“CSH”) and calcium aluminate hydrate (CAH) whichaccumulated in the open pores leading to a decreasein the porosity of the pastes [27, 28].
Also, it is indicated that the rate of hydration of
the Rubberized cement pastes is lower than thecontrol sample. This is attributed to dilution of
cement by RRP which resulted in decreasing theamount of CSH responsible for gaining strength.
As graphically represented in Figure 5, increasing the
w/b ratio from 0.3 to 0.4 leads to decreasing
compressive strength by 46.9% and 36.2% in case ofcontrol and 25% of RRP replacement, respectively.
This can be explained by the fact that when the w/b

Control

Rubberized

C0.3

C0.4

R50.3

R150.3

R250.3

R250.4

0

0.0

0.0

0.0

0.0

0.0

0.0

1

6.6

5.7

5.5

2.1

1.4

0.4

3

26.9

7.7

17.6

8.4

4.0

2.0

7

40.9

17.3

24.1

9.2

5.9

4.0

14

44.5

19.0

31.2

11.6

5.0

3.0

28

47.8

25.4

33.2

14.2

7.1

4.5

COMPRESSIVE STRENGTH (MPA)

60

C-0.3
R5-0.3

50
40
30
20
10
0

0

5

10

15

20

25

30

CURING TIME (DAYS)

Figure 4 Compressive strength values of thehardened cement pastes containing different
percentages of RRP at W/B = 0.3 versus curing
times

703

COMPRESSIVE STRENGTH (MPA)

GEOMATE – Melbourne, Australia, 11-13 November 2020

60

C-0.3
R25-0.3

50

C-0.4
R25-0.4

CONCLUSIONS
It is concluded from this study that the cement
replacement with RRP causes deterioration in thestrength. However the new rubberized cement
mixture (R5-0.3) can be used in different applications
instead of cement type 32.5N. As the w/b ratio
decreases, the initial porosity decreases thencompressive strength increases. Through utilizing
non-biodegradable and recycled materials like rubber
as a cement replacement a new approach is developed
g to produce a sustainable construction material.

40
30
20
10
0

0

5

10

15

20

25

30

REFERENCES

CURING TIME (DAYS)

Figure 5 Effect of w/b ratio on the compressive
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Lopez-Cuesta,"Compatibilisationofpolyethylene/ground tyre rubber blends by γirradiation," Polymer degradation and stability,
vol. 91, no. 10, pp. 2375-2379, 2006.

Environmental scanning electron microscope(ESEM)The ESEM micrographs obtained for thecontrol and rubberized cement pastes are shown in
Figure 6 (a-d) obtained after 28 days of hydration.
The micrographs for the pastes displayed theformation of both microcrystalline and fibrous
crystals of cement hydration products, mainly as
calcium silicate hydrates (CSH), calcium hydroxide
and calcium aluminate hydrate[29, 30]. These
hydrates are formed around and in-between the
cement grains and appears as clear binders between
the partly hydrated cement grains. A denser structureof the formed hydrates could be clearly distinguishedin C-0.3 than C-0.4, as a high w/b ratio leads to highporosity then lower strength.
The role of RRP as a filler appears clearly in case of
R25-0.3 and R25-0.4 micrographs. The highreduction in the strength by 25 Wt. % RRP
replacement resulted from decreasing the amount ofhydration product and the agglomeration of RRP as
illustrated in Figure 6 (b&d).
(b)

(a)

40µm
(c)

40µm
(d)

40µm

40µm

Figure 6 ESEM micrographs for hardened cement
mixtures after 28 days: a) C-0.3 b) R25-0.3 c) C-0.4d) R25-0.4704

GEOMATE – Melbourne, Australia, 11-13 November 2020[22]P. Asutkar, S. Shinde, and R. Patel, "Study on the
behaviour of rubber aggregates concrete beams
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[24]K. Mushunje, M. Otieno, and Y. Ballim, "Partial
replacement of conventional fine aggregate withcrumb tyre rubber in structural concrete–effect ofparticle size on compressive strength and timedependent deformations," in MATEC Web ofConferences, 2018, vol. 199: EDP Sciences, p.
11002.
[25]A. International, "C 109/C 109M-07. Standard
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[26]B. BS, "4551: Part 1: 1998," Methods of testing
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[27]Y. Al Wahedi, J. Awayes and G. Bassioni,"Influenceofclassical
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[29]
G. Bassioni and M. M. Ali, "The effectof counterion in lignosulfonates as oil-wellcement retarders," Advances in cement research,
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[30]C.-Y. Chen and M.-T. Lee, "Application of
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[12]M. Sienkiewicz, J. Kucinska-Lipka, H. Janik, and
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containing ground tire rubber with and without
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[14]Z. Wang, Y. Zhang, F. Du, and X. Wang,"Thermoplastic elastomer based on high impact
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concrete columns under seismic loading," inStructures, 2015, vol. 3: Elsevier, pp. 13-27.
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Engineering
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FRP-confined and unconfined crumb rubber
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[21]S. R. Tarry, "Effect of partial replacement ofcoarse aggregates in concrete by untreated andtreated tyre rubber aggregates," International
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705

10th Int. Conf. on Geotechnique, Construction Materials and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

IMPROVEMENT OF SOIL STRENGTH USING RICE HUSK ASH
Jamie Ure Ovia1, Najmun Nahar1, Alex Otieno Owino1, Noma Tamaki2 and Zakaria Hossain1
1
Dept. of Environmental Science Graduate School of Bioresources, Mie University, Japan
2
Make Integrated Technology Company Ltd., Osaka, Japan
ABSTRACT
Rice husk is considered an agricultural waste in most countries, but recently studies have shown that by burning
this waste product in a controlled environment can produce rice husk ash (RHA), which is a very useful productdue to its high SO2 percentage. Because of the high SO2 percentage, RHA has great potential to improve soil
properties. In this paper three types of RHA ratio additive mixtures is investigated, 5%, 10% and 15% incomparison with the control soil. The strength of each of the RHA mixtures is determined by conducting theCalifornia Bearing Capacity (CBR) test for all the mixtures with curing periods of 1 and 7 days. The objective of
the paper is to determine the optimum RHA ratio in which sandy soil reaches its peak bearing capacity.
Keywords: Rice husk ash; Ground improvement; Optimum moisture content; California Bearing Capacity

INTRODUCTION

This paper aims to investigate the bearing
capacity of soil when mixed only with RHA at
different percentages. The reaction of the amorphoussilica within the RHA is observed as it reacts withwater and soil both for 1 and 7 days. Ultimately, theoptimum RHA mixture that gives the highest bearing
capacity is determined.

Soil ground improvement is the alteration of soil
to enhance its physical properties. Altering theproperties can increase shear strength of a soil andimprove load bearing capacity of a subgrade tosupport structures. Various innovative techniques
have been investigated by geotechnical engineers
over the years. An example is the influence of using
lime piles on improvement of bearing capacity andfailing slope stabilization of clay soils wasinvestigated by Rogers and Glendinning [1].
Investigators Gidley and Sack proposed several
methods for utilizing some industrial wastes inengineering construction [2].
Rice husks in many countries is considered to be
agricultural waste and is unnecessarily dumped orburned in open heap areas [3]. According to recent
study in 2014 the amount of rice husk generated
globally was estimated to be 1.482 x 108 tons [4][5].
Rice husks if utilized correctly has immense potential
to create many different products such as utilization
for bioenergy as well as various industries that will
use the amorphous silica (i.e. ceramics, glass making,steel production. Pharmaceuticals, rubber, etc.).[6]The rice plant has the unique ability to take silica
content from soil and eventually the silica
accumulates into its structure as it grows [7]. The partof the plant with the highest silica content is the ricehusk. When burned at high temperatures within an
enclosed chamber, rice husk ash is produced. Ricehusk ash has high percentage of siliceous minerals
and so it has potential pozzolanic properties.
Pozzolanas are siliceous and/or aluminous materials[8]. Pozzolanas have little or no cementitious value,
but which will in finely divided form and in thepresence of moisture, chemically reacts with calcium
hydroxide liberated during the hydration of Portland
cement to produce stable, insoluble cementitious
compound which can be used to stabilize soils[9][10].

MATERIALS AND METHODS
Materials
Soil
The soil used for this paper was collected from theHanda area, Tsu City, Mie Prefecture. The physicalproperties of the soil are shown in Table 1. According
to the graph of particle size distribution curve in Fig.
1 it was determined that the coefficient of gradation(Cu) and the coefficient of curvature (Cc) are 9.275
and 0.906 respectively. Moreover, in regulation with
the United Classification of Sand soil by ASTM D2487, the soil was classified to be well-graded sandwith silt (SW-SM).
Table 1. Physical properties of soil

706

Parameters

ValuesSpecific gravity
Maximum dry density
Optimum moisture content
Liquid limit, LL
Plastic limit, PL
Plasticity index, PI

2.74 g/cm3
1.696 g/cm3
17.50%
37.52%
28.87%
8.55%

Sand (75 μm - 2 mm)
Silt (5 - 75 μm)
Clay < 5 μm

89%
9%
2%

GEOMATE – Melbourne, Australia, 11-13 November 2020

Soil particle size

Laboratory Test

RHA particle size

100

Percent Finer (%)

California Bearing Ratio (CBR) Test
80

The California Bearing Ratio (CBR) test was
conducted in accordance with Japanese Industrial
Standards (JIS A1211). The test used a standard CBR
testing machine with a 5.08cm piston that penetrated
the soil sample at a velocity of 1.27mm/min by meansof a screw jack. Recordings were then taken at
penetration depths of 0.5mm, 1mm, 1.5mm, 2mm,
2.5mm, 3mm, 4mm, 5mm, 7.5mm, 10mm and12.5mm.
After three separate recordings were taken forsample, each data was then plotted on a loadpenetration graph, but first load was needed to be
calculated by taking into account the conversion
factor of the CBR machine and multiplying it witheach of recordings to get the loads. The best graphshowing an accurate curve that runs convexly
upwards was then chosen. If
The final step is to calculate the CBR percentage
value by using Eq. (1). The load strength obtained
from 5mm penetration was divided by the standard
load strength of 5mm which is 19.9kN and thenmultiplied by 100 to get the final CBR value inpercent.

60
40
20
0
0.001

0.01

0.1

1

10

Particle diameter (mm)Figure 1. Particle size distribution curve for soil and
RHA
Rice husk ash (RHA)
The RHA used was provided by Make Integrated
Technology (MIT) Co., Ltd, Osaka Japan. By burning
rice husks, itself at high temperature within an
enclosed industrial machine, MIT company has beenable to successfully convert high amounts of ricehusks into a valuable material. The RHA used had
SO2 percentage of generally 85-90% as certified by
Japanese Agricultural Standards (JAS). The chemicalcomposition of the RHA is shown below in Table 2.
The RHA also had a loss ignition of 85% and generalcomposition of particle sizes ranging from 0.1-0.3μm,

𝐶𝐶𝐶𝐶𝐶𝐶 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 (%) =Mass percentage
91.1013CO2

4.3490K2O

2.4031CaO

0.5723

P2O5

0.5178SO3

0.3784Cl

0.1867MgO

0.1646MnO

0.1414Na2O

0.0696

Fe2O3

0.0536Al2O3

0.0350ZnO

0.0172Rb2O

0.0072

SrO

0.0016Br0.0015(1)

The soil mixture types and Optimum Moisture
Content (OMC) of these mixtures are given in Table
3. Three types of RHA mixes were used; 5%, 10%
and 15%. The control or regular soil was also tested
to have a baseline to determine whether RHA
additives change the composition of the soil mixture.
The samples were prepared first by mixing in of RHA
to the soil and water was added. The amount of water
added was 1% below the OMC of each individual
sample. Afterwards, the specimen was placed into a
CBR mold and compacted with an automatedhammer. The CBR mold was wrapped airtight andplaced to cure for 24 hours and 7 days. The CBRmolds were then tested on each of their respective
days. Note that each soil mixture was tested threetimes and the most accurate data was collected fromthe three tests.

Table 2. Chemical composition of RHA

SiO2

× 100

Rice husk ash mixtures and curing period

refer to Fig.1.

Element compound

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎ𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆ℎTable 3. All the soil mixtures tested

707

Mixture sample

RHA

OMC

SymbolControl
1
2
3

0%
5%
10%
15%

17.5%
21.0%
25.0%
29.0%

5R
10R
15R

GEOMATE – Melbourne, Australia, 11-13 November 2020

RESULTS AND DISCUSSION

increases thereby decreasing the CBR values. Also,
this approves that RHA has little to no cementitious
value and is not suitable to be a soil stabilizer alone.

Penetration Curve
The Fig.2 shows the penetration curve after 1 dayof curing. It was observed that there was significant
increase in applied load once the RHA was added tothe soil. But as the RHA ratio increased over 5%, theapplied load started to decrease.
Control
5R
10R
15R

160
Applied load (KN)

CBR Value, %

200

0 day

120

7 days

70
58.9

60
50

52.3
46.4 4741.5 41.5

34.5 36.3

40
30
20
10
0
Control

80

5R

10R

15R

Specimen Type40

Fig. 4. CBR values for each specimen type both 1-day
and 7-day

0
0

2.5

5

7.5

10

12.5

Depth of penetration (mm)CONCLUSION
Fig. 2. Penetration curve for 1-day curing
In summary, when working with RHA it is good
maintain the mixture to be around 5% which will givethe best bearing capacity for sandy soils. Additionally,
excess of RHA within a sample will only worsen its
properties and make the soil unsuitable. To conclude,
I recommend in the future further work be done
investigating the pozzolanic reaction between RHA
and Portland cement to find out a suitable mix ratiofor Geotechnical engineering purposes, and how theamount of time affects the soil mixtures properties.

From Fig.3 after 7 days of curing, the penetrationload curve increased to some degree when comparing
to 1-day curing period. The trend is of the ratio
percentages was still maintained with 5% RHA has
the highest applied load.

Applied load (KN)

250

Control
5R
10R
15R

200
150

ACKNOWLEDGMENTS
The first author would like to express gratitude to
Japan International Cooperation Agency (JICA) for
granting scholarship to study at Mie University.

100
50

REFERENCES

0
0

2.5
5
7.5
10
Depth of penetration (mm)12.5

[1] Rogers C D F, Glendinning S (1997).
Improvement of clay soils in situ using lime piles,
UK. Journal of Engineering Geology, 47(3): 243–
257
[2] Gidley JS, Sack WS. Environmental aspects ofwaste utilization in construction. J Environ Eng
ASCE 1984;110(6):1117–33.
[3] Ogbuagu N.J. (2018) Evaluation of rice husk ash
and Portland cement reinforced clay for use as
road subgrade using the CBR test, Journal of
Bioresources and Bioproducts. 2018, 3(2) 65-70[4] FAO Statistical Yearbook. (2013) World foodand agriculture. Rome: Food and Agriculture

Fig. 3. Penetration curve for 7-day curing
CBR value
The CBR values obtained for both 1 and 7 days
are shown in Fig.4. The 5% RHA showed highest
CBR value out of all the mixtures, with the lowest
being 15% RHA which was even lower CBR value
than the soil control. The initial increase of CBRvalue with 5% RHA mixture may be because the ratio
between soil and RHA gives a good plasticity index,
but as RHA is added to the soil the plasticity index

708

GEOMATE – Melbourne, Australia, 11-13 November 2020

Organization of the United Nations; ISBN 97892-5-1073964[5] FAO Rice Market Monitor (RMM). April 2015;
18(1): p. 1-14
[6] Pode R., (2016). Potential application of ricehusk ash waste from rice husk biomass power
plant, Renewable and Sustainable Energy
Reviews 53, 1468-1485.
[7] R.G. Smith and G.A. Kamwanja, (1986) The use ofrice husk for making a cementitious material, Proc.
Joint Symposium on the Use of Vegetable Plants
and their Fibers as Building Material.

[8] Sima, J. (1974). Portland-Pozzolona Cement:
Need for a Better Application. Indian Concrete J.
48: 33-34.
[9] Saad, S.A (2015). Pozzolanic Reaction
Mechanism of Rice Husk Ash in Concrete – A
Review, Applied Mechanics and Materials Vols.
773-774 (2015) pp 1143-1147[10] V.G. Papadakis, and S. Tsimas. (2002)
Supplementary cementing materials in concrete:
Part I: efficiency and design, Cement andConcrete Research, Vol. 32 (10), pp. 1525-1532.

709

10th Int. Conf. on Geotechnique, Construction Materials, and Environment,
Melbourne, Australia, 11-13 November 2020, ISBN: 978-4-909106049 C3051

SURFACE SHAPE MEASUREMENT AND EVALUATION OF
MECHANICAL STABILITY BY DEM SIMULATION FOR DAMAGEDTRADITIONAL STONEWALLSSatoshi Sugimoto1, Maho Yamaguchi1 and Minoru Yamanaka2
1

Faculty of Engineering, Nagasaki University, Japan; 2Faculty of Engineering, Kagawa University, Japan
ABSTRACT

Kumamoto Castle has constructed about 400 years ago, and now the Cultural Affairs Agency of Japan treats it
as an important historical spot. The 2016 Kumamoto Earthquake was damaged a lot of stone walls of this castle.
Authors continue to investigate the deformation of unstable stonewalls by high accuracy laser module formeasuring distance. And these stonewalls should be monitored continuously for evaluation of mechanical stability,
so we developed and established the wireless network system for measuring the changing angle of some stonewalls
surface in this castle. When installing this system in the future, the results of shape measurement of the surface of
these stone walls will be important in identifying the locations to be installed and setting priorities.
The authors also carried out making a simulation model and calculation for evaluation of stonewalls stabilityby the distinct element method under the several conditions of stonewall shape and interface of materials. The
failure area of stone walls and backfill are estimated by these results of DEM simulation, and it is discussed thatthese areas should be reinforced under the repair of damaged stonewalls.
Keywords: Damaged stone wall, Stability, Wireless sensor network, Monitoring and DEM simulation

research, we will build a model of stone walls. Then,numerical analysis of dynamic behavior was carriedout, and factors affecting the stone wall were
examined by comparing the collapse patterns undervarious conditions.

INTRODUCTION
The 2016 Kumamoto earthquake caused
enormous damage to ground structures such asbuildings and stone walls at Kumamoto Castle.
Besides, many stone walls in Kumamoto Castle
collapsed and overturned, and tensile cracks occurredin the back of the embankment. The 2016 Kumamoto
earthquake is characterized by the fact that
Kumamoto Castle was damaged by a foreshock witha seismic intensity of 5+ and the main shock of 6+.
Although the stonewalls of Kumamoto Castle
escaped deformation during the foreshock, manycases were found to have been deformed or collapsed
due to the mainshock (S.Sugimoto et al., 2018 &2017). And the stone walls that have undergone
deformation will be examined for restoration workand appropriate management in the future. However,
it is very difficult to distinguish whether the stone
wall originally had a deformation or if thedeformation was caused by the earthquake. Therefore,
when considering the priority of restoration from thedegree of deformation, it is required for theadministrator to perform simple measurements daily.
Besides, it is necessary to elucidate the mechanism of
the stone wall collapse so that similar damage will
never occur again in the future when another largescale earthquake will occur. In this study, we used a
simple method to quantitatively measure thedeformation of the stonewall and used data collection
and the distinct element method (analysis code:
UDEC) to support the selection of remote monitoring
points (Y. Katsuda et al., 2018 & 2017). Based on the

TYPE OF STONEWALL STRUCTURE
The stone walls of Kumamoto Castle can beroughly divided into three types according to theinternal structure of the stone walls. In this study, thethree types of shapes were named "stone base type","half stone base type", and "stoneless base type" byadding the distance to the literature. Figure 1 shows
the three types of internal structure, shapecharacteristics, and constituent materials. The stonebase type is a stone wall with a low height, so many
are made around the gate. The half stone base type isoften made in the stone walls around the moat or themain enclosure. Since the northwestern part is notalong the mountains, it is thought that many stone
walls on this side were made of soil embankments andrubble. Many stoneless base types are made fromsouth to southeast of Kumamoto Castle. This isprobably because it was possible to pile up stonewalls
on a steep slope. After all, the southeast side is along
Mt. Chausu.
MEASUREMENT WITH A PORTABLE LASERRANGEFINDER
The Leica Geosystem's DISTO D510 is a portable
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GEOMATE – Melbourne, Australia, 11-13 November 2020

laser rangefinder that is mainly used to measure theshape of land plots and buildings. As shown in Figure
2, it is easy to measure linear distances and elevation
angles. This instrument has an accuracy of 1.0 mm
per 10 m and is available at a measuring distance ofup to 200 m. Furthermore, it has been shown that
there is a variation of about 0.3% when the angle
between the measuring instrument and the horizontal
plane exceeds 45 degrees. Therefore, in this study, it
is necessary to fix the portable laser range finder(DISTO D510) to a tripod, measure the verticaldistance and the horizontal distance, and to measure
the shape within the range of 45 degrees. The
condition was set.
Type name

Stone base type

Half stone base typewall, the measured lateral position, height, and depth
were input into the graph drawing software to createa shape distribution map. As shown in Fig. 2, a laser
range finder fixed on a tripod is installed in front of
the target stonewall, and the distance and angle ofeach stone wall are measured in the vertical direction.
After the measurement, find the vertical andhorizontal coordinates of each stone wall based on the
distance and angle and plot the cross-section. If theground where the stone wall is built inclines, it isdifficult to create an accurate surface shape
distribution map if measured normally. Therefore,
when performing the measurement, we decided to use
the bottom of the slope as the origin of the coordinate
axis and use the two poles to measure the step. The
distance between poles can be found by using twopoles. Therefore, by adding the vertical distance
between poles to each survey line, the standard can be
unified and a surface shape distribution map can becreated. Photo 1 shows the measurement using a pole.

Stoneless base type

Style

State at thetime ofcollapse
Stone
Stone
Materials

Stone
Cobblestone

Cobblestone

Embankment
Embankment

HeightLow

A little high

High

Slope

steep

A little lenient

A little lenientFig. 1 Shape features of each stonewall.
Measuring method
Fig. 2The measurement was performed according to thefollowing procedure. Install a measure parallel to thesurface of the stone wall to be measured. At this time,either the left or right end is set as the measurement
origin. A marker is set at the point set as the origin sothat it can be easily compared in future measurements.
Determine the distance between survey lines
according to the length and size of the stone wall.
After determining the measuring line, install themeasure so that it intersects with the measureinstalled parallel to the stone wall. Install a portablelaser range finder (DISTO D510) on the tripod andmeasure the measurement point. At this time, one
point will be measured for each stone wall on thesurvey line, and the center of the stone wall will bemeasured.
There are two ways to organize the data in themeasured stone wall cross-section: top reference andbottom reference.
The top reference is the uppermost stonewall as
the origin of the X-axis and the Y-axis, and thebottom reference is the measured data with thelowermost stone wall as the X-axis and the Y-axis
origin. By setting a standard, it is easy to judge thecharacteristics of the deformation and collapse of thestone wall when the surface shape of the stone wallchanges. Moreover, based on the bottom of the stone

Measurement schematic diagram and photo.

Photo 1 State of pole installation.
SURFACE SHAPE MEASUREMENT
A portable laser range finder was installed in frontof the target stone wall, and the distance and angle ofeach stone wall were measured in the verticaldirection. After the measurement, the vertical andhorizontal coordinates of each stone wall were
calculated based on the distance and angle, and thecross-section was plotted. This time, the measured
data of 1984 and 2018 were used for comparison. The
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GEOMATE – Melbourne, Australia, 11-13 November 20201984 data is the newest data measured by Prof.
Kuwahara (1984) before the Kumamoto earthquake.
In selecting the measurement points, in addition to thepoints measured in the past, we newly measured thepoints with large deformation. Up to now, themeasurement has been performed in 8 steps, and a
total of 150 points have been measured as shown in
Fig. 3.

Fig. 4 Surface shape distribution map. (unit: m)
DYNAMICANALYSISELEMENT METHODBYDISTINCT

The stone base type among the three types ofstone walls was modeled by the distinct element
method program (analysis code: UDEC) and the
dynamic analysis was performed. In this research, thedistinct element method (analysis code: UDEC),
which is suitable for analyzing discontinuous
structures such as stone walls and stone bridges, isused to reproduce individual movements of stone
walls and stones. The stone base type consists only ofchestnut stone and stone wall stone, and the damage
was the largest of the three types, and more than halfof the total collapsed, so this paper describes theresults of this type.

Fig. 3 Overall view of measurement points.
One case of the measurement result
STONEWALL OF "CHAYAGURA" TRACE
It is a stone wall located on the south side ofKumamoto Castle. Photo 2 is a panoramic view of thewestern surface of the stone wall at "Chayagura" trace.
A total of 8 lines were measured at 1m intervals.
Figure 4 shows the surface shape distribution map of
the western surface of the stonewall. At around 5m,
the surface shape is protruding. The possible reason
for this is considered to be a phenomenon caused by
the reduction of the gap between the stones, which is
caused by the movement of the stones accumulated in
the rear part of the stone wall due to the influence of
external force such as vibration. It was confirmed at
the site that the deformed parts were the same withthis figure. This deformation seems to reflect theresult of active earth pressure in the central part of thestone wall during the earthquake.

Analysis model
Figure 5 shows a “stone base type” analyticalmodel with a stonewall slope of 70°. Since the stonebase type is built in a place where there is nobackground, stones were piled up on the left and right,
and the inside was packed with a quarry stone block,as in the previous literature. The sandy ground
represented by elasto-plastic elements was installed
under the stone wall, and in the actual stone wall, thelower stone wall was buried in the ground, so thelower three steps were fixed as the foundation.
Boundary condition
In this study, the boundary conditions shown in
Fig. 5 were set to properly reproduce the transmissionof seismic waves from deep underground. A dashpot
was provided on the boundary surface of the sandy
ground at the bottom of the individual element model
to make the bottom viscous boundary. The bottom
viscous boundary is a boundary condition that
evaluates the semi-infinite ground below the model
bottom, and it is possible to absorb the wave energy
dissipated at the bottom boundary and input only therising wave component of the seismic wave. As aresult, the transmission characteristics of seismic
waves from deep underground can be satisfied. A freerock mass region was set up on both boundary

Photo 2 Stonewall of "Chayagura" trace.
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surfaces on the side of the distinct element methodmodel as energy transfer boundaries. The energy
transfer boundaries allow wave energy to bedissipated laterally in the model and absorbed in thefree rock region. As a result, the lateral boundary canbe evaluated as a semi-infinite ground area, and theactual foundation rock area can be reproduced.

Table 1

Characteristic value of block surface.
Case 1Case 2
Normal stiffness
3000
3000
(N/mm)
Shear stiffness
3000
3000
(N/mm)

Cohesion (kN/m²)

00

Shear resistanceangle (deg)1530

Table 2
Shear resistance angle value between
different materials.

Fig. 5 Cross-section of the analysis model. (unit: m)

Stones andcobblestones(deg)Stones andsandy ground(deg)

Cobblestones
and sandy
ground (deg)

15

15

15

Input parameter
Table 1 shows the characteristic values of thesurface of each block, and Table 2 shows thecharacteristic values of each joint. In this study, we
focused on the stone base type that caused the mostdamage. To compare with the existing literature, thecharacteristic value was analyzed with the same value
as the previous research. And the density of stones
and cobblestones are applied each 2.5 kg/cm3 and 2.2
kg/cm3.

Fig. 6 Schematic diagram of the stone base typemodel creation.
Relationship between stone wall height andcollapse range

Model creation

In order to clarify the relationship between theheight of the stone wall and the collapse range, thecondition of the physical properties of each block was
not changed, and only the height was set to half thevalue of the previous studies. The height of thestonewall used in the previous research is 12.0 m, andthe height of the stone wall used in this research is 6.0
m. Figure 7 shows the analytical model of 12.0m and
6.0 m with a shear resistance angle of 15° on thesurface of each block with a stone wall slope of 70°.
Figure 8 shows the displacement vectors of the
12.0 m high model and 6.0 m high model during
deformation. As the height of the stone wall became
lower, a difference appeared in how it collapsed under
the same conditions. The slope of the slip surface
varies depending on the height of the stone wall. Thisis because when the height is 12.0 m, the earth wallpressure is relatively small and the earth coverpressure is relatively small. Therefore, the horizontal
force acting by the inertial force of the seismic motion
is larger than the earth's cover pressure. It isconsidered that the surface inclination is steep. Whenthe height is 6.0 m, the height of the stone wall is low,
so the force exerted by the overburden pressure does
not change much depending on the place, and thehorizontal force acting by the earthquake motion is

Figure 6 shows a drawing of the stone base typewith a stonewall slope of 70°. The left figure is amodel before cutting the stone. We made two blocks
inside the stonewall and one block above the stone
wall and cut the block into 40 cm square. Since it isharder to create a gap when dropping one block than
dropping it separately, we created blocks separately.
In order to get closer to the actual construction
procedure, rubble stones were made at a certain
height and allowed to fall freely and packed inside thestonewall. Based on the reflections of previous
studies, a gap was created in the corner of thestonewall just by free fall, so acceleration was appliedin any direction to the model with the stone wall andthe base fixed. From this, as shown in Fig. 8, it can be
confirmed that the stones are sufficiently clogged.
Earthquake motion input
The input ground motion is given to the bottom of
the individual element model. The input waveform
had a frequency of 10 Hz, an amplitude of 1 m/sec,
and an excitation time of 2 seconds, after which thegravity analysis was performed for 1 second.
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GEOMATE – Melbourne, Australia, 11-13 November 2020

larger than the overburden pressure. It is consideredthat the slope of the slip surface is gentle.
Figure 9 shows a comparison of the horizontal
displacement of the stone wall at the top of the crownwhen a ground motion is applied, based on the height.
When the height was 12 m, the horizontal
displacement was larger than that when the heightwas 6 m. In both cases, although the stone wall has
collapsed, the height of the stone wall is higher thanthat of the lower stone because it is taller, and it isthought that the stone wall was more susceptible tothe influence of the stone. In addition, it is consideredthat the movement of the upper area also differs
depending on how the clogs are clogged.
Height : 12.0 m

6.0 m, and the shear resistance value of each block
was The calculation was performed under theconditions of 30° and 15°. The results are shown in
Figure 10. When Case 1 and Case 2 are compared, theinclination of the slip surface is significantly different
because the shear resistance angle is different even if
the height is the same. The distinct element method
was used to express that the larger the shear resistanceangle, the more difficult the stone wall is to collapse.
Height : 12.0 m

Height : 6.0 m

=15°, 1.25 sec

=15°, 1.75 sec

=30°, 1.5 sec

=30°, 2.0 sec

Height : 6.0 m

Fig. 7 Two kinds of models of stonewalls.

=15°

Fig. 10
Slip surface compared with each shear
resistance angle.

=30°

Fig. 8 Displacement vector diagram after the start ofearthquake motion.
Fig.11 The horizontal displacement of the top of thestonewall is compared with the shear resistance angle.
Figure 11 shows a comparison of the horizontal
displacement of the stonewall at the top of the crownwhen a ground motion is applied, based on the valueof the shear resistance angle. When the shear
resistance angle is 15°, the upper stone wall isdisplaced from 0.25 seconds after the start ofvibration, whereas when the shear resistance angle is30°, the vibration is observed from 1.75 seconds after
the start of vibration. It is considered that the larger
the shear resistance angle is, the larger the force toresist when the earthquake motion is applied becomes,
and it becomes difficult to transmit the force to theupper stone wall.

Fig. 9 Horizontal displacement of the top of thestonewall compared with height.
Relationship between shear resistance angle andcollapse range
In order to clarify the effect of the size of the shear
resistance angle on the cobblestone surface on thecollapse of the stone wall, the other parameters wereconstant, the stone wall slope was 70°, the height was

CONCLUSIONSIn this study, the simple method is used toquantitatively measure the deformation of the stone714

GEOMATE – Melbourne, Australia, 11-13 November 2020

wall, data collection and stone base type stone wall
are used as the basis for the selection of the remotemonitoring target, and the distinct element method (Adynamic analysis was performed using the analysis
code: UDEC) to reproduce the collapse behavior of
the stone wall. The results and considerations
obtained in this study are described below.
1) Using a portable laser rangefinder, the surface
shape of stone walls at various places in Kumamoto
Castle was measured easily. In the stone wall with aheight of about 5 m, many deformations wereobserved, which seemed to be extruded by the stoneson the back. It is highly probable that a temporary
active earth pressure condition was caused by theearthquake motion. Although it is necessary to verify
the consistency with the 3D measurement results, it isa simple measurement method, but it is possible toquantitatively grasp the characteristics of the surface
shape.
2) The damage situation at each height of the stone
wall was collapsed in all cases when the height was
12 m. On the other hand, when the height was halved
to 6 m, no collapse occurred when the shear resistanceangle of the stonewall and the stone was 30°. From
this, it can be confirmed that not only the slope of thestone wall but also the height of the stone wall
contribute to the collapse.
3) It was found that the higher the height of the stonewall, the greater the depth of the stone, and the lower
the height, the gentler the slope of the slip surface.
4) It was found that the stone-base type collapse
factor in which the stone is composed is the active
earth pressure generated when the stone is destroyedby the earthquake. However, it was confirmed that if
the shear resistance angle of the stones exceeds acertain value, the active earth pressure is not
generated enough to collapse the stone wall.
5) Even with the same height model, if the shear
resistance angle is different, the displacement of thestone wall in the upper stage when vibration is applied
is different. It is thought that this is because the larger
the shear resistance angle, the greater the force thattries to resist when an earthquake motion is applied,
and the more difficult it is to reach the upper
stonewall.

instruments and information.
REFERENCES[1] S. Sugimoto., M. Yamanaka., H. Maeda., N.
Fukuda., Y. Katsuda.: Research of damaged
condition by the 2016 Kumamoto Earthquake
and ground investigation on stone walls and earth
structures in Kumamoto Castle, International
Journal of GEOMATE, Vol.14, Issue 45, 2018,
pp.66-72.
[2] S. Sugimoto., M. Yamanaka.: Reports ofdamaged earth structures in Kumamoto Castle,
Proceedings of 54th Natural disaster and science
symposium, 2017, pp.45-51. (in Japanese)[3] Y. Katsuda., Y.Jiang., K. Omine., S.Sugimoto.:
Research of damaged stone structures inKumamoto Castle by the 2016 Kumamoto
Earthquake, Proceedings of
72th JSCE
Conference (CD-ROM), 2017, pp.479-480. (inJapanese)[4] Y. Katsuda., S. Sugimoto., M. Yamanaka.:
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(in Japanese)[5] H. Yamamoto, T. Nishigata, S. Yao, K. Nishida,
H. Kasa, I. Wada.: Study on Friction
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behavior of shaking table test by stability
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T. Nishigata, Y. Wada: Experimental study on
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the castle wall, Proceedings of 62th JSCE
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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 investigation with several

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