Edited by
Dr. Akiyuki Kawasaki,
Dr. Kunnawee Kanipong,
Dr. Hyunmyung Kim,
Dr. Withit Pansuk6th Joint Student Seminaron Civil Infrastructure
6-7 August 2013
Bangkok, Thailand
Co-Organized by
School of Engineering and Technology,
Asian Institute of Technology (AIT), Thailand
International Center for Urban Safety Engineering (ICUS)
Institute of Industrial Science
The University of Tokyo, Japan
Chulalongkorn University
Myongji University, Korea
Ajou University, Korea
andSeoul National University of Science and Technology, Korea

Edited by
Dr. Akiyuki Kawasaki, Dr. Kunnawee Kanipong,
Dr. Hyunmyung Kim and Dr. Withit Pansuk

Organization Teams
Prof. Worsak Kanok-Nukulchai (AIT, Thailand)
Dr. Pennung Warnitchai (AIT, Thailand)
Dr. Kunnawee Kanipong (AIT, Thailand)
Dr. Masahiko Nagai (AIT, Thailand)
Dr. Pakawat Sancharoen (SIIT, Thailand)
Dr. Withit Pansuk (Chulalongkorn U, Thailand)
Assoc. Prof. Thavivongse Sriburi (Chulalongkorn U, Thailand)
Dr. Hyunmyung Kim (Myongji U, Korea)
Dr. Jeong Whon Yu (Ajou Univ., Korea)
Dr. Sung Bong Chung (Seoul National Univ. of Science andTechnology, Korea)
Prof. Kimiro Meguro (UT, Japan)
Prof. Haruo Sawada (UT, Japan)
Dr. Wataru Takeuchi (UT, Japan)
Dr. Akiyuki Kawasaki (UT, Japan)

6th JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURE
August 2013
PREFACEIn this era of rapid globalization, having an international sense and broad
human resource network is essential. In particular, building up a good
relationship with various communities during young school days will be ofadvantage in the future. To give such an opportunity to students from Asian
countries, we held the 1st, 2nd 3rd, 4th and joint student seminar in July 2008,
July 2009, July 2010, August 2011 and August 2012, respectively, andfollowing this success the “6th Joint Student Seminar on Civil Infrastructures”
was held on 6th -7th August, 2013.
The objectives of this seminar are:1) to experience the international seminar,
2) to improve the presentation skill,3) to share the research information and friendships.
The number of participants was 57, consisting of 1 faculties, 1 company
and 20 students from Myong Ji University, Seoul National University of
Science and Technology, Chulanongkorn University, The University of Tokyo
and Asian Institute of Technology.
On the first day, we had a presentation session, having 1 faculties (Ajou
University) and 1 company (Hilti Thailand Ltd) lectures and 20 students’presentation. The topics covered wide range areas of civil engineering andevery student did their best in his/her presentation. During the seminar, students
and faculties had lively exchange of views beyond their specialty, culture andnationality. At the end of the seminar, excellent presentation awards were given
to the following 3 students.
1. Mr. Tsubasa Sasaki from University of Tokyo, Japan
2. Ms. Fei Jiang from University of Tokyo, Japan
3. Mr. Seemanta Bhagabati from Asian Institute of Technology, Thai
On the second day, participants went on a field trip to visit construction
site of the new MRT Blue Line (Underground) at Hua Lampong area. The
Project is a 27 kilometers underground and elevated heavy rail transit systemwith 4 underground stations and 15 elevated stations situated along the route, 3intervention shafts, a depot with operation control center and 1 park and ride. In
the afternoon, visited Hua Lamphong Railway Station, cultural sites of
Bangkok Wat Kalayanimit, Kao Sarn Road, Wat Sraket Rajavaravihara andBowonniwet Temple.
The seminar was quite successful and fruitful: this seminar gave not only
knowledge and information but also a lot of other stimuli to the students. We
hope to continue to hold this kind of interchange activities in the coming years.
Finally, we would like to express our sincere gratitude for those who kindly
supported and contributed to the success of this seminar.

AKIYUKI KAWASAKI, KUNNAWEE KANIPONG,
HYUNMYUNG KIM AND WITHIT PANSUKICUS
Report
ICUS
Report7272

Table of Contents
Page

Opening Session
SECTION 1

SECTION 2

SECTION 3

Invited Lectures
Big Data and Transportation Information Platform in Korea
Jeong Whon Yu

1

Understanding Post-Installed Anchor Systems & Code Design Requirements
Kunakorn Pragthong

17

Student Presentations
Car Navigation Data Analysis for Transportation Planning
Sung-Hi An

45

The impact of flow velocity on microbial precipitation of calcium carbonate
among soil
Tsubasa Sasaki

63

Comparison of benefits for Political boundary and Natural boundary from
Hydropower development in the Mekong
Seemanta Bhagabati

75

Evaluating thermal comfort in city life and its relation to socio-economic
activitie
Noriko Okamura

87

Student Presentations
Development of real wind turbine blade vibration measurement techniqueby 3D motion analysis software
Ryoichiro Hoshino

97

Forest carbon estimation using the combination of LiDAR data and highresolution imagery in Ludikhola Watershed, Nepal
Dang Anh Nguyet

103

Three-dimensional simulation of flow of fresh concrete in multi-dimensional
steel bar arrangement by MPS
Shunya Kimura

117

Application of Risk Management to forecasting Transportation Demand
Baekkyu Namkung

127

Characterization of Air Quality in Global Mega-Cities by Remote Sensing
and Inventory Data
Aya Fujikawa

137

6th Joint Student Seminar on Civil Infrastructures

6th Joint Student Seminar on Civil InfrastructureAugust 2013,
August2013,Thailand
ThailandSECTION 4

SECTION 5

Structural equation models regarding to inter-city van transit in Thailand:
A case stuy of difference of travel distanceTharadol Punlop

149

Basic research for quantitative analysis of oil-contaminated soil purification
by microorganisms
Shunya Suzuki

161

Modeling of oven-dried mortar after fire exposure by Rigid Body Spring
Model (RBSM)
Onnicha Rongviriyapanich

173

Student Presentations
Current Situation about Reconstruction of Hakozaki Peninsula after theGreat East Japan Earthquake
Sae Shikita

185

Neighboring Search Process in Genetic Algorithm for Train Scheduling
Problem
Jetpan Wetwitoo

193

An investigation of road bridge maintenance system in Japan in developed
society
Mari Nakashima

199

The role of mutual assistance during disaster in the aging society
Nao Sasaki

211

The Calibration of Road Network Travel Time Function in Korea :The Issues and Solutions
Choong-Shik Lee

219

Study on Effect of Street Blockades on Transportation of Seriously-Injured
Victims to Hospitals-Case Study of Expected Tokyo Inland Earthquake
Fei Jiang

235

A study of low cost grout for Preplaced Aggregate ConcreteMichael Coo

247

A Study on Strategy for Effective Disaster Information Dissemination :Case Study in Thailand
Niwat Apichartbutra

263

Student Report
Report from Student Participants on The 6th Joint Student Seminar on Civil
Infrastructures
Sung-Hi An, Tsubasa Sasaki, Seemanta Bhagabati, Noriko Okamura, Ryoichiro
Hoshino, Dang Anh Aguyet, Shunya Kimura, Baekkyu Namkung, Aya Fujikawa,Tharadol Punlop, Shunya Suzuki, Onnicha rongviriyapanich, Sae Shikita, Jetpan
Wetwitooait, Nakashima Mari, Nao Sasaki, Choong-Shik Lee, Fei Jiang, Michael
Coo and Niwat Apichartbutra

273

6th6thJoint
Student
Joint
StudentSeminar
Seminaron
onCivil
Civil Infrastructure
Infrastructures

ICUS Report
Report 72
72
ICUS

Seminar Venue

Opening ceremony (Dr. Akiyuki Kawasaki)

Dr. Jeong Whon Yu

Mr. Kunakorn Pragthong

Ms. Sung-Hi An

Mr. Tsubasa Sasaki

Mr. Seemanta Bhagabati

Ms. Noriko Okamura

6th Joint
Infrastructure
6th
Joint Student
StudentSeminar
SeminarononCivil
Civil
InfrastructureiiAugust 2013,
August2013,Thailand
Thailandii
ii

Mr. Ryoichiro Hoshino

Ms. Dang Anh Nguyet

Mr. Shunya Kimura

Mr. Baekkyu Namkung

Ms. Aya Fujikawa

Mr. Tharadol Punlop

Mr. Shunya Suzuki

Ms. Onnicha Rongviriyapanich

6thJoint
JointStudent
Student Seminar
Seminar on
6thon Civil
Civil Infrastructures
Infrastructure

ICUS Report
Report 72
72
ICUS

Ms. Sae Shikita

Mr. Jetpan Wetwitooait

Ms. Nakashima Mari

Ms. Nao Sasaki

Mr. Choong-Shik Lee

Ms. Fei Jiang

Mr. Michael Coo

Mr. Niwat Apichartbutra)

6th Joint
Infrastructure
6th
Joint Student
StudentSeminar
SeminarononCivil
Civil
Infrastructure

iii
iii

August 2013,
August2013,Thailand
Thailand

Chairperson (Dr. Hyunmyung Kim)

Chairperson (Dr. Sung Bong Chung)Committee (Dr. Withit Pansuk)

Closing ceremony (Dr. Kunnawee Kanitpong)discussion
DuringduringMeetingsessions

Dr.Kunakorn Pragthong(left) Hilti (Thailand) Present
Ltdfrom Hilti (Thailand) Ltdiv
iv

6thJoint
JointStudent
Student Seminar
Seminar on
6thon Civil
Civil Infrastructures
Infrastructure

ICUS Report
Report 72
72
ICUS

discussion
duringcoffee break
DuringSeminarDuring
WelcomeDinner
WelcomeDinner

WelcomeDinnerDuring
WelcomeDinnerExcellence Presenter Mr. Tsubasa Sasaki

6th Joint
Infrastructure
6th
Joint Student
StudentSeminar
SeminarononCivil
Civil
Infrastructure

Excellence Presenter Mr. Seemanta Bhagabativv

August 2013,
August2013,Thailand
Thailand

Excellence Presenter Ms. Fei Jiang

Excellence
Presenters
Excellence
Presenter
2013

Briefing at Constructionof the new Metropolitan
Rapid
Transit(MRT) Blue Line
Presentationsite
at Construction
site of the new
MRTBlue Line

Processof excavating
thethemaintunnel
Processof excavating
from
main
tunnelMouth of the
andtunnel
see theand
TunnelBoring
Machine(TBM)
Gotunneldown thesee thereal TBM

vi
vi

6thJoint
JointStudent
Student Seminar
Seminar on
6thon Civil
Civil Infrastructures
Infrastructure

ICUS Report
Report 72
72
ICUS

Entrance of the tunnel
and Tunnel
Boring Machine(TBM)
Entranceof the tunnel

the tunnel
InsideInside
the tunnel
and TBMTMB Control Room

Present
for presenter
Mr.
Kanok
Duangkhae
Appreciation
for Mr.
Kanok
Duangkhae

Enjoy a trip

6th Joint
Infrastructure
6th
Joint Student
StudentSeminar
SeminarononCivil
Civil
Infrastructure

vii
vii

August 2013,
August2013,Thailand
ThailandSeminar:
at Chulalongkorn
University
GroupGroup
PhotoPhotoat Chulalongkorn
University

Photo Photoat Construction
site of site
the new
Field Group
Trip: Groupat Constructionof theMRT
newBlue
MRTLineBlue Line

viii
viii

6thJoint
JointStudent
Student Seminar
Seminar on
6thon Civil
Civil Infrastructures
Infrastructure

ICUS Report
Report 72
72
ICUS

FieldGroup
Trip: GroupPhoto
Hua Lamphong
Railway
Photo atHuaatLamphong
Railway
StationStation

Photo atBowonniwet
TempleTemple
FieldGroup
Trip: GroupPhoto
at Bowonniwet

6th Joint
Infrastructure
6th
Joint Student
StudentSeminar
SeminarononCivil
Civil
Infrastructureix
ixInvited Lectures

BIG DATA AND TRANSPORTATIONINFORMATION PLATFORM IN KOREA
Jeong Whon Yu
Division of Environmental, Civil and Transportation Engineering
jeongwhon@ajou.ac.kr

Big Data and Transportation
Information Platform in Korea
August 6, 2013

Jeong Whon Yu

Associate Professor
Department of Transportation Systems Engineering
Ajou University

Big Data• Wikipedia: Big data are a collection of data
sets so large and complex that it becomesdifficult to process using on-hand databasemanagement tools or traditional dataprocessing applications.

211August 2013, Thailand
August 2013, Thailand

Big Data

3

Big Data in Transportation
http://www.youtube.com/watch?v=r37jt-PeL8k

42

6th Joint Student Seminar on Civil Infrastructure

2

6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

Why Platform in Korea?
• Korean Government’s new
strategies and policies– Government 3.0– All information must be open to
public– Creative economy• Ministry of Land and Transportation• Ministry of Safety and Administration• Seoul City Government5

Why do we need a platform?
• New Trend: Public information must be publicized.
MOLT(Geographic Information
Open Platform)

Seoul City Government(Open Data Plaza, Open
Government 2.0)

MOSA
(Public Data Portal)

Key Words
Communication
SafetyAdvancement
Excellency

Open

Happinessof Korean
PeopleShare
Involvement
Cooperation
6

Big Data and Transportation Information Platform in Korea

3

Big Data and Transportation Information Platform in Korea

3

August 2013, Thailand
August 2013, Thailand

Why do we need a platform?
Open-Gov

E-Gov
•
•

Online service
One-way decisionmaking/service led
by Government••

Gov 3.0
Gov of People’s
happiness

Information is open
to public
Public involvementin policy-making•••

Two-way service
Create new values
Individuallycustomized service

Transition from one-way to two-way service
Creation of new values through two-way service

How to create new values?
Stimulate interaction among players in eco-system
 Platform is the key technology
7

What is the platform?
• In the vicinity of train platform, economy grows due toincrease in platform users.
• Business models create additional profits.
Additional Profits
DemandSupplyPassenger
travelRental cars

Conviniencefacilities

KTX(High Speed)
Passengers

Shops
Taxis

Regular train

Train Platform

Freight train

Goods
transport

Logistics

Vending
machines
Freight

Restaurants

Hotels/Lodging

8

4

6th Joint Student Seminar on Civil Infrastructure

4

6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

What if there is no platform?
Intermodal Transfer Facility

Train Station

SubwayCity bus

KTX

Hotel
Taxi

Restaurant

Passenger travel service

Bank

Hospital

Hotel

Parking

Restaurant

Conference
centerRentalcar

Bank

Value-added Business

Passengers

Additional
service users

Both travel and other services are notavailable at the same time/place.

Passengers

Additional
service users

Both travel and other services areavailable at the same time/place.
9

A Platform Success Story in Korea• Social Network Platform by “Kakao Talk”– Various value-added services(online games) through platform
Other service users

Passengers

Messenger service
users

Kakao platformgame users

Value-added
service

Online game
serviceConvenience facilities
Food

Hotel

Shop

Bank

RentalTaxi

Travelservice

Intermodal Transfer Facility
KTX

SubwayTrain

Bus

Messenger
serviceKakao Games

Kakao’s Social Network Platform
Friend DB

10

Big Data and Transportation Information Platform in Korea

5

Big Data and Transportation Information Platform in Korea

5

August 2013, Thailand
August 2013, Thailand

Differences from Internet Portal
Service Users

Transportation
Information
PortalWebpage search

PublicEntities

File download

GeneralPublicService Users

Developer

Transportation
Information
Platform

Transportation
통계정보
InformationAPI통계정보 APIAPI

Shortest Path
Search App

Cell phone
PC

Public공공
Entities공공기관기관Private공공Companies공공기관기관

General공공기관
Public공공기관

Webpage search

File download

11

Transportation Information Platform
Transportation
Information
Users

Public Entities

Logistics
Companies

PrivateCompanies

Individual
Developers

Standard Protocols for Traffic
Information Provision (OpenAPI)

Transportation Information Service
Real-time
Traffic
Information

Transportation
Information
Platform

Incident
Information

Traffic
Forecasting

Traffic Information Analysis
Traffic Information Processing
Various Traffic Data/Information Integration...

Traffic
Traffic DB
Data/Information
ProvidersNational Highway Agencies Korea Expressway Corp. Municipal Traffic Information Centers

126

6th Joint Student Seminar on Civil Infrastructure

6

6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

Relationship between Platform and Its Ecosystem
Ecosystem without
Platform

Platform-centered
Ecosystem
Active growth of ecosystem

Little growth of ecosystem
Loss of users

New users

Few users

Many users

Limited service tolimited users

Any requests
from any users

Transportation Information
Platform
NHA

National
Highway
Traffic
Information

KEC

Muni. Govs.

NHA

KEC

Muni. Govs.

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

National
Highway
Traffic
Information

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

13

Relationship between Platform and Its Ecosystem• From Korean Government’s viewpoint
Public request of
information

Publicizing Traffic
Information

NHA

Trans-Government
Platform

KEC

Seoul

Open Platform

Busan

….

14

Big Data and Transportation Information Platform in Korea

7

Big Data and Transportation Information Platform in Korea

7

August 2013, Thailand
August 2013, Thailand

Relationship between Platform and Its Ecosystem• From city governments and public entities’ viewpoints
After

BeforeAccept the Goverment’s open
platform policy while maintaining
its own independence

Transportation
Information Platform

Difficulties in integrating
information due to lack ofstandard interface

NHA

KEC

Muni. Govs.

National
Highway
Traffic
Information

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

NHA
Platform

KEC
Platform

Muni. Govs.
Plaform

National
Highway
Traffic
Information

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

Seamless information
communication/integration
among relevant entities 15

Relationship between Platform and Its Ecosystem• From private companies and people’s viewpoints
After

BeforeEasy,
convenient,and various

Where is theinformation I am
searching…?

Various service demand
Easy acquisition/process
A variety of quality
information

Transportation Information
Platform
NHA

KEC

Muni. Govs.

National
Highway
Traffic
Information

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

NHA

KEC

Muni. Govs.

National
Highway
Traffic
Information

Expressway
Traffic
Information

Urban
Highway
Traffic
Information

16

8

6th Joint Student Seminar on Civil Infrastructure

8

6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

Effects of Platform Construction• Provision of common interface
After

BeforeCommoninterface
access+Response

serviceRequestUsers
service

Response
RequestRequestTraffic Info. Platform

service

Response

Response+

users

service

Individual
accessRequest

NHA

KEC

Muni. Govs.

NHA

KEC

Muni. Govs.

National
Highway
Inforamtion

Expressway
Information

Urban
Highway
Informatin

National
Highway
Inforamtion

Expressway
Information

Urban
Highway
Informatin

17

Effects of Platform Construction• New job opportunities
BeforeAfter
Finding andutilization of
new data

Customized
Service

Data
Scientists

DataManagers

Data input,
query, update,and deletion

Design of new
APIs

Tranffic Information Platform
serviceserviceservice

Traffic DB

Traffic
volumeTravelspeed

Traveltime

Traffic
incidents...

Traffic DB

Traffic
volumeTravelspeed

Traveltime

Traffic
incidents...
18

Big Data and Transportation Information Platform in Korea

9

Big Data and Transportation Information Platform in Korea

9August 2013, Thailand
August 2013, Thailand

Current Ecosystem of Traffic Info.
• Collection of Traffic Information by Seoul City Government
Processing
Traffic
Information

Direct data collection

Taxis
 Vehicle ID, Date/time,
Location, Travel time

Data collection andintegrationBuses Vehicle ID, Date/time,
Location, Travel time

Integration
among relevant
agency data

Standard node-link coding

Urban highway

Collected data Police, Call taxi companies, SK M&C

 Link ID, Date/time, Traffic volume,
Urban expressway and bridges

Travel speed

 Seoul City Infrastructure Agency

Provision of
Traffic Information

19

Current Ecosystem of Traffic Info.
• Provision of Traffic Information by Seoul City Government
General Public

CCTV images,,
Incident,s/Accidents
Traffic Congestion

CCTV images,,
Incident,s/Accidents
Traffic Congestion(DB integration)

CCTV images,,
Incident,s/Accidents
Traffic Congestion(DB integration)

Private Companies

Bus Information System

Internet

VMS

Mobile App (Open-API)

Public Entities

Internet Portal

NHA

PoliceKEC

Muni. Traffic Centers

Mobile telecom company

20

10

6th Joint Student Seminar on Civil Infrastructure

10

6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

Current Ecosystem of Traffic Info.
• Collection of Traffic Information by Korea Expressway Corp.
Provision of Traffic
Information

KEC
Data Collection/Integration

Integration withexternal data

Direct data collection

Statistical
Analysis전구간교통량 통계노선 배분교통량 통계

영업소간교통량 통계

영업소간 평균
통행시간 통계

Standard link-node code수도권교통량 통계

…..

National highway
 National Highway Agencies영업소간 집계

Data
Aggregation영업소별 집계
1일•
출발/도착영업소
•일시•
교통량/통행시간
(차종별 합계)•차종별 통행시간

1시간
15분 집계5분 집계

PPP(Public-Private-Partnership) toll road•영업소 입출구 현황(영업소, 일시, 입출구 교통량)•차종별 및 합계•TCS/hi-pass분류•입출구 차로별/시간대별•교통량

TCS: 영업소 진출입 자료
•
•
•
•
•
•
•

Raw data

입출구 Toll ID수집일시차종출발/도착 영업소출발/도착 일시통행시간
TCS/hi-pass 분류

21

Current Ecosystem of Traffic Info.
• Provision of Traffic Information by KEC
CCTV images,,
Incident,s/Accidents
Traffic Congestion(Dedicated internet
network)

CCTV images,,
Incident,s/Accidents
Traffic Congestion(DB integration)

General Public
Internet

Mobile App

VMS

CCTV images,,
Incident,s/Accidents
Traffic Congestion(DB integration)

Public Entities

Private Companies
Internat Portal

Broadcasting
Companies (TPEG)

MOLT

Seoul Traffic Center

Muni. Traffic Center

Private-sector Traffic Information Provider

PPP Toll Road Operators

22

Big Data and Transportation Information Platform in Korea

11

Big Data and Transportation Information Platform in Korea

11

August 2013, Thailand
August 2013, Thailand

Current Ecosystem of Traffic Info.
• Collection of Traffic Information by National Highway Agencies
Processing traffic
information

Direct Data Collection
AVI Date/time, Vehicle
plate/type, etc.

Data Collection/Integration
NHA
Highway Traffic
Information
Centers

VDS Date/time, Volume, Speed,
Occupancy
CCTV Images

Integration withexternal data

Standard link-node code

Urban highway and expressway

Collected data Link ID, Date/time, Volume,
Speed Municipal traffic information centers
National expressway

Provision of
Traffic Information

 KEC

23

Current Ecosystem of Traffic Info.
• Provision of Traffic Information by NHACCTV images,,
Incident,s/Accidents
Traffic Congestion(Dedicated internet
network)

General Public
Internet

VMS

NHA
Highway Traffic
Information Centers

CCTV images,,
Incident,s/Accidents
Traffic Congestion(DB integration)

Public Entities

MOLT

KEC

Muni. Traffic Info. Centers

Police24

12

6th Joint Student Seminar on Civil Infrastructure

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6th Joint Student Seminar on Civil Infrastructures

ICUS Report 72
ICUS Report 72

Problem in Current Ecosystem• In case of Seoul Metropolitan area
– Disconnected Traffic Information Services• Data are collected/processed/provided, separately by each entity.
• National ITS Center is in charge of integrating data, but provides
simple traffic congestion information only (practically difficult to
use).
NH 3
NH 1

Seoul Traffic
Info. Center

Urban
Highway

PE 60
PE 130NH 43

NHA

National ITS
CenterNational
Highway

NH 46
NH 1
NE 15

KEC

Expressway

PE 171

NE 1

25

Ecosystem of Traffic Information
With Platform

Without Platform
Developer
service

Muni.
Govs.

Urban
Highway

Developer

Need to use
differentinterfaces due todisconnected
systemDeveloper

NHA

National
Highway

Users소비자

Provision ofservices throughintegrated
interface

Muni.
Govs.

Urban
Highway

NHA

National
highway

service

Traffic
Info.
Platform

Users

Developer
KEC사)
KEC

Expressway

Expressway…

26

Big Data and Transportation Information Platform in Korea

13

Big Data and Transportation Information Platform in Korea

13

August 2013, Thailand
August 2013, Thailand

Strategy for Constructing Ecosystem• Benchmarking successful cases in the past
– Kakao Talk Online Game Platform• 1st party’s killer-app(Kakao Talk Messenger)  Increase in general users
 1st Party Driven Platform
3rd party
developer

1st party
developer

Initial

Inter
medi
ate

Final
General users

General users

1st Stage
Attract general users through1st party Kakao Talk Killer App

1st party
developer

2nd Stage
Expansion of services byattracting 3rd party gamedevelopers

27

Strategy for Constructing Ecosystem
3rd party service users

1st party serviceusers

1st party serviceusers

3rd Party Service
Anypang, Dragon flight, I-luv-coffee, Hero’s war, etc.
1st Party Service
Messenger, Kakao
story, Kakao place …

Kakao Talk
Messenger

1st Party Service
Messenger, Kakao
story, Kakao place …

Kakao Social Network Platform

28

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Strategy for Traffic Info. Ecosystem• Need to provide a quality 1st party service– Provision of traffic information throughvisualization– Education of data scientists for creating newservices through advanced data analysis– Advertisement of traffic information services togeneral public• Need to provide incentives to 3rd party developers– Creation of developer communities for active
communication– Provision of convenient environments fordevelopers– Education and advertisement of developers

29

Strategy for Traffic Info. Ecosystem• Loose Interoperability– Independent operation and service provision– Minimize the conflicts with external entities
Provision of Integrated
Traffic Info. Services

Request/Response

NHA

Archive/ Platform
Analysis
Archive/
Analysis
Meta Data
Repository
Archive/
Analysis
Remote Analysis
Function

Provision of Independent
Traffic Info. Services
Remote Analysis
Function

Remote Analysis
Function

Provision of Independent
Traffic Info. Services

Request/Response

Request/Response

Municipal Governments

KEC

Traffic DB

Traffic DB
Traffic DB

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Big Data and Transportation Information Platform in Korea

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Strategy for Traffic Info. Ecosystem• United Platforms– Keep its own autonomy– Seamless connection between unit platforms

Traffic Info. Platform

Traffic Info. Platform

Traffic Info. Platform

NHA

KEC

Municipal Goverments

Traffic DB

Traffic DB

Traffic DB

31

Summary• Emergence of transportation big data
• Strong request of making public information publicized• Strategies for constructing traffic information platform– Standardized platform– Independent platform having its own autonomy– United platform with seamless connection and service• Socioeconomic effects– Creation of new job: data scientists– Enhancement of general public’s satisfaction/happinessthrough providing individually customized traffic information
service• Current issues– Legal and institutional supports– Advertisement of transportation big data and platform– Creation of developer/expert groups: communities, forum
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UNDERSTANDING POST-INSTALLEDANCHOR SYSTEMS & CODE DESIGNREQUIREMENTS
Kunakorn Pragthong
Hilti (Thailand) Ltd
moth@hilti.com

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Understanding Post-Installed
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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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August 2013, Thailand

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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August 2013, Thailand

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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August 2013, Thailand

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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August 2013, Thailand

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Understanding Post-Installed Anchor Systems & Code Design Requirements

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Student Presentations

CAR NAVIGATION DATA ANALYSISFOR TRANSPORTATION PLANNING
Sung-hi AN1, Hyun-myung KIM 2, Seung-hoon CHEON 3
and Choong-shik LEE 4
1
Researcher/Transportation Planning Lab, Dept of Transportation Eng,
College of Engineering, The MyongJi University, South Korea
jjquty@daum.com
2
Professor, Doctor, Director, The MyongJi University, South Korea
3
Doctor, Dept. of National Survey and Analysis,Center for Korea Transport Database,
The Korea Transport Institute, South Korea
4
Researcher/Transportation Planning Lab,Dept of Transportation Eng,
College of Engineering, The MyongJi University, South Korea
ABSTRACTThe purpose of this work is to analyze car navigation data as averification data about Travel Time Fuction(TTF) for transportation
planning. Navigation data are collected from Korean road network links
through navigation terminals so we decide to use these data to replace
preexistence velocity and travel time data. From this, we could obtainaverage OD travel time and variance database, average vehicle OD trip
distance & Variance between origin and destination zone, number of linkpassing OD pair, and database of generating/arriving trip ratio by each link.
And we could also get the customized data from car navigation databaselike rate of innerzonal trip for each link, average link passing vehicle travel
distance & variance DB, sample database of OD traffic’s link passing rate,and location of signal intersection node DB. So we calculate link priority.
And if the distance between links is short and innerzonal trip rate is high,that link is evaluated as a link which has a low compatibility. To use
navigation data for the future, we develop four strategy – that is 1) extend
the range of data collecting network, 2) representative verification, 3)
improve data collect system focus on passenger car, and 4) increase
quantity of collected data. And, also, theoretical research and operational
application are two considerations for applying navigation data on
transportation planning. The navigation data, so called big data, has played
an important role in transportation engineering field in the near future.
Even though using navigation data is initial step, if more concerns are given
to study how to use transportation-related big data, then we could get moreaccurate and realistic traffic data..
Keywords: navigation data, travel time function (TTF), TTF
verification, innerzonal trip analysis, link traffic.

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1. INTRODUCTION
In Korea, we use the Travel Time Function (TTF) for traffic assignment
model by modeling travelers’ route choice behavior. To do this, we have tocalibrate TTF with reality.
In a prior study, TTF is calculated by observed traffic volume andtraffic assignment model. The observed traffic contain both internal andexternal zone trip, however assigned traffic cover only external zone trip.
For example, on arterials, 70% less assigned than traffic counts on 40%
links. Because of the gap between these two data, it is impossible to analyze
internal and external trip respectively with current traffic analysis technique.
Also, it is hard to analyze reliability of TTF calculation.
Therefore, analysis of innerzonal traffic flow at a nationwide level in
Korea has to be planned with navigation data.
For TTF, traffic counts survey for all links is impossible and proved
car-based travel time survey costs huge time and budget. However, withnavigation data, it is not only easy to get samples from widespread links but
also possible to replace existing velocity and travel time data.
The important thing for analyzing is spatial data. Prior traffic datacannot define geospatial relationship between branch passage rate andtraffic volume, but with navigation data it is easy to define spatiotemporal
relationship between traffic network components. If we secure a relation
between OD pairs and link, we could verify OD estimation and OD pairs’
traffic volume. Then it is possible to apply these data for congestion
management and ramp metering system, also, we could obtain path data andlink data. Then we could know which links are overlapped on different
paths.
2. NAVIGATION DATA COLLECTION AND PROVISION
We collect a navigation data from three navigation terminals – SOFTMAN,
GINI, MAPPY – and data collecting period is August 1st 2011 to August 1st
2011. To increase a navigation data’ quality and quantity, the navigation
data is collected as node-link unit. With raw data, we produce
approximately 120 million trip data. We follow a procedure as shown in
Figure 1: to get geospatial data.

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Figure 1: Data Provision
3. NAVIGATION DATABASE CONSTRUCTION
We follow this procedure to construct database.

Figure 2: Procedure of Constructing Database
3.1 Aggregated link data
To construct database based on navigation data, we aggregate link
basic data. An average link travel speed, passing vehicle sample size, andfreeway top 5%, 10%, 15% velocity for each links are calculated.
The processed navigation data constructed by link travel time areutilized for TTF calculation and reliability verification. And, this data makeit possible to analyze greenhouse gas emission

Car Navigation
Car
Navigation Data
Data Analysis
Analysis forfor Transportation
Transportation Planning
Planning

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Figure 3:Figure
Data Aggregation
3: Data Aggregation
3.2 Aggregatednavigationdata
3.2 Aggregatednavigation
dataWith linkWith
based
linkdata,
based
we data,
makewenavigation
make navigation
data. Thedata.
elements
The elementsofofnavigationnavigation
data is as datain theis following.
1) Average
travelODtime& varianceas in the following.
1) OD
Averagetraveltime & varianceDB, 2) Average
trip distancevariance
betweenandorigin andDB, 2) vehicle
AverageOD
vehicleOD trip&
distance& DB
variance
DB origin
betweendestination
zone, 3) zone,
Numberof link passing
pair,OD
andpair,4) DB
destination
3) Numberof link OD
passingand of4) DB ofgeneratinggenerating/ arriving /trip
ratio byarriving
tripeachratiolink.
by each link.
3.2.1 OD 3.2.1
travelODtime
databasetraveltime database
We use average
trip velocity
to maketravelODtime
We useODaverage
OD tripdata
velocity
dataODto maketravel time
database. database.
Table 1: Average
travelODtime
Table 1: OD
Averagetravel time

Ave.
Ave. travel time Travel time Travel time
Ave.
Ave.travel time
Ave. travel velocity
Ave. travel(km/hr)velocity
Number(km/hr)of valid
NumberSampleof valid Sample(sec or min) (sec or min)traveltravelvariance(
variance(
O
D
O
D
Total
Total
Zone zone distance
Zone zone distance
day afternoon
morning daynightafternoon
morningnight
day afternoon
morning daynightafternoon
morningnight
day afternoon
morning daynightafternoon
morningnight
day afternoon
morning daynightafternoon night(km) morning(km)

3.2.2 Enumeration
3.2.2 Enumerationof OD pairsof OD
on apairs
link on a link
To make To
database,make database,
we calculate
we calculate
the number
theofnumber
each linkof eachpassinglinkOD
passing OD
pairs. If apairs.
link Ifhasa manylink has
ODmanypairs,OD
it ispairs,
regardit isasregard
a mainasobserving
a main observing
site.
site.
Then linksThen
are used
links forarelink
usedcriticality
for link criticality
analysis. analysis.
3.2.3 Trip3.2.3
generation
Trip generation
and attraction
and attraction
analysis analysis
We verifyWe
zone
verifycentroids’
zone centroids’
location. location.
Table 2: Generating
Table 2: Generating
and arriving
andration
arrivingof links
rationinofthe
links
zone
in the zone

3.3 Customized
navigationdata
3.3 Customized
navigation
data
Customized
data is different
with aggregated
Customized
navigationnavigation
data is different
with aggregateddata. Thisdata. Thisdata is estimatedby algorithm.
Customized
data fourcontain four
data is estimatedby algorithm.
Customized
navigationnavigation
data contain1) innerzonal
Rate of innerzonal
trip each
DB forlink, 2) Average
link
databases.databases.
1) Rate oftrip DB for
link,each2) Averagelinkpassingtravelvehicletraveland
distanceand database,variance database,
Sample ofdatabase ofpassing vehicle
distance
variance3) Sample3)database
OD linktraffic’s
linkrate,passingand 4) Location
signal intersection
node
OD traffic’s
passingand rate,
4) Locationof signal ofintersection
node
database database

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3.3.1 3.3.1
Rate Rateof innerzonalof innerzonal
trip Database
trip Database
Innerzonal
Innerzonal
traffictraffic
volumevolumeis mainis maindatabase
databaseto build
to builda navigationa navigation
data, data,
so with
so thiswithdata
this wedatadecide
we decide
innerzonal
innerzonal
reflectreflect
traffictrafficratio. ratio.
TableTable
3: Innerzonal
3: Innerzonal
traffictraffic
volumevolume3.3.2 3.3.2
Link-based
Link-based
traveltravel
distance
distance
analysisanalysis
We useWelinkusepassing
link passing
vehicles’vehicles’trip distancetrip distancefor reviewing
for reviewing
a roleaofrole
roadof roadand input
and input
data ofdata
networkof network
consistency
consistency
analysisanalysisfunction.
function.

Figure4: Link
vehicles’trip distance
Figure4: passing
Link passing
vehicles’trip distance
4. RESULT
OF CONSTRUCTEDLINKLINK
BASED
DATA
4. RESULT
OF CONSTRUCTED
BASED
DATA
4.1 Sample
data data
analysis
4.1 Sample
analysis
To analyze
KTDB
navigation
data, data,
total total
numberof vehicle
tracking
To analyze
KTDB
navigation
numberof vehicle
tracking
path datafrom from
MN-Soft
is 125is million
trips. trips.
Data Data
are collected
path collected
data collected
MN-Soft
125 million
are collectedfrom from
August,2011 2011
to July,2012.2012.
In April,
2012,2012,
most mostdata areand andAugust,to July,In April,
datacollected
are collectedin august,2011 2011least least
data are
The general
patternof navigation
in august,datacollected.
are collected.
The general
patternof navigation
data isthatisleast
data exists
in theinsummer
and numberof dataconstantly
datathat least
data exists
the summer
and number
of is
data
is constantly
increasing
throughout
the winter.
increasing
throughout
the winter.
TableTable
4: Distributionof numberof navigation
data bymonth
4: Distributionof numberof navigationdata
by monthMonthMonth

NumberNumberof dataof data

Data size
Data sizeAug. 2011
Aug. 2011

100,867,854
100,867,854

4,477.516
4,477.516
MB MB

117,622,988
117,622,988

5,221.250
5,221.250
MB MB

150,111,285
150,111,285

6,663.383
6,663.383
MB MB

177,879,,140
177,879,,140

7,895.984
7,895.984
MB MB

223,420,038
223,420,038

9,917.492MB
9,917.492MB

279,396,760
279,396,760

12,402.266
12,402.266
MB MB

Car Navigation
Data Analysis
for Transportation
Planning
Car Navigation
Data
forPlanning
Car
Navigation
Data Analysis
Analysisfor Transportation
Transportation
Planning

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Jul. 2012

353,584,875

15,695.414 MB

517,133,669

22,955.219 MB

551,465,149

24,479.172 MB

526,109,425

23,353.633 MB

347,283,353

15,415.711 MB

138,557,074

6,150.500 MB

To analyze collected navigation data spatially, we express number ofcollected link passing traffic volume on transportation digital map. Links areconcentrated in urban area.

Figure 5: Distribution of number of navigation data
4.2 Innerzonal travel analysis
The reason for using navigation data is that TTF observed data reflect
innerzonal traffic volume. With this data, we analyze innerzonal traffic
volume with over 30 samples of whole network.

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4.2.1 Innerzonal traffic ratio evaluation method
We review whole tracking path data which start at the same site andfind the trips starting and finishing at the same zone. With these two data,
we calculate each links’ proportion of whole passing track. If sever links are
passing a same EMME/3 link, that link takes a weighted average link
length.

Figure 6: methodology of calculating innerzonal traffic rate
4.2.2 Innerzonal traffic calculation
On arterial which has low VDF grade, many links are calculated asratio than on high TTF level.

Figure 7: Identifying links : High on arterials in terms of numbers
4.2.3 Nationwide travel ratio
We analyze innerzonal traffic rate by TTF grades. Freeway and urban
express are taking up to 10% and arterials take up to 45%. For all arterials,
one-lane road has higher innerzonal traffic rate than two-lane road. As
density of intersection is increasing, innerzonal traffic rate is decreasing.
And low VDF grade road and urban road have high rate.

Car Navigation
Car
Navigation Data
Data Analysis
Analysis forfor Transportation
Transportation Planning
Planning

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Figure 8: Ratio of innerzonal traffic
4.2.4 Analysis of urban/rural area innerzonal traffic respectively
Innerzonal traffic rate in urban area is lower than rural area. And
innerzonal traffic rate gap between one-lane road and two-lane road isgetting smaller when number of intersection is increasing.

Figure 7: Identifying link: High on arterials in terms of numbers
4.2.5 Spatial innerzonal traffic analysis
With innerzonal traffic rate, it is possible to give a trip spatial
distribution which is an important role in transportation planning. On thenational scale, urban area has lower innerzonal traffic rate. Innerzonal traffic
rate is higher at rural area and regional road than urban area. This is because
size of zone in urban area is smaller than rural area, so short trip isconsidered as external zone trip.
We do detail analysis to capital area. With this, we find there is a great
different between innerzonal traffic ratio in a same city. It means there is adifference of road function in a city, so we separate interregional road andinnerregional road with navigation data. In addition, innerzonal traffic rate
make it possible to define a road function for national land use.

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Figure 8: Ratio of innerzonal traffic on rural area
4.2.6 Unobserved links’ innerzonal traffic ratio
The number of links which secure minimum innerzonal traffic rate is
31,932 after passing more than 30 sample links. To modify innerzonal
traffic rate on unobserved link, we follow a step as shown in Figure 9:
Technique 1, we estimate innerzonal traffic rate about other observed
point for arterial TTF calculation. Technique 2, if at least 10 links haveexperimental traffic volume, it is possible to calculate average rate by city.
Technique 3, if link has less 10 links, we apply other external cities’ averagevalue. If links are not estimated after Technique 3, we use urban/regional
estimated average value.

Figure 9: Flow step for modifying innerzonal traffic rate on unobservedlinks

Car Navigation
Car
Navigation Data
Data Analysis
Analysis forfor Transportation
Transportation Planning
Planning

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Figure 10: Example of data creating method
4.2.7 Result of application
We apply innerzonal traffic volume rate on the national network withobserved rate (technique 1), and estimated rate (technique 2, technique 3
and technique 4).

Figure 11: Innerzonal traffic volume on the national network
4.2.8 Limitation and problem
With navigation data we get many useful data for transportation
planning, however there are limitation and problems for using navigationdata.
In principle we have to use observed data to analyze traffic volume
but we still use estimated data with technique 2,3 and 4. Second problem isthat even though some cities have a same TTF grade among every 247
cities, a gap between zones which have a same grade may exist. Because of54
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this reason,
this reason,
there there
is a limit
is a limit
to apply
to applya average
a average
valuevalue
to cities
to cities
whichwhichhave haveaasame same
grade.grade.
On arterials,
On arterials,
30~40%
30~40%of trafficof traffic
is innerzonal
is innerzonal
traffic.
traffic.
However,
However,
there thereis ishuge hugedeviation
deviationeven evenon a onsame
a sameVDF VDFroad. road.
Therefore
Therefore
we have
we haveto extend
to extend
traffictraffic
networks’
networks’spatialspatialboundsboundsto collectto collectnavigation
navigation
data and,data also
and, havealso haveto to
improve
improve
map-matching
map-matching
techniquetechniqueto increase
to increase
observed
observed
traffictraffic
rate. rate.
4.3 Analysis
4.3 Analysis
of innerzonalof innerzonal
trip generating-arriving
trip generating-arriving
on mainon mainlink link
With Withnavigation
navigation
data, data,
it is it possible
is possible
to verify
to verify
zone zone
centroid’s
centroid’s
connectivity
connectivity
analysis.
analysis.
Navigation
Navigation
data shows
data showseach eachvehicle’svehicle’s
OD trips,
OD trips,
so weso we
calculatecalculateeach eachlink’slink’s
generating-arriving
generating-arriving
traffictraffic
volume.
volume.
We evaluate
We evaluate
current
current
centroid
centroid
connector
connector
link validity
link validity
with with
innerzonal
innerzonal
trip generating-arriving
trip generating-arriving
on onmain main
links links
and itandcould
it could
be usedbe usedas a asconnector
a connector
modification
modification
normative
normativereference.
reference.

FigureFigure12: Example
12: Exampleof assessingof assessingzone zone
centroid
centroid
connector
connector
link validity
link validity
To assessTo assessvalidity
validityof suggestedof suggested
technique,technique,we evaluate
we evaluate
Gangnam-gu,
Gangnam-gu,
SeoulSeoul
centroid
centroidlink validity.
link validity.
Separating
Separating
links links
in Gangnam-gu
in Gangnam-gu
at KTDBat KTDB
network
networkfrom fromotherotherzone’szone’slinks links
is needed,
is needed,so wesocolor
we color
yellowyellow
as a centroid
as a centroid
connectors
connectors
and purple
and purple
whichwhich
is directly
is directly
connected
connected
with centroidwith centroid
connectors.
connectors.
Especially
Especiallytraffictraffic
generating
generating
rate onrateplumeon plume
coloredcoloredlinks links
explainexplainexactexact
traffictraffic
generate
generate
location
locationfrom from
centroid
centroid
connector.
connector.

Car Navigation
Data Analysis
for Transportation
Planning
Car Navigation
Data
forPlanning
Car
Navigation
Data Analysis
Analysisfor Transportation
Transportation
Planning

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Figure 13: Example: validity of zone centroid connect in Gangnam-gu

Figure 14: Trip generation rate in Gangnam-gu

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Figure 15: Trip arrival rate in Gangnam-gu
Both Figure 14: and Figure 15: is examples of validity of zone
centroid connectors in Gangnam-gu. Figure 14: shows innerzonal trip
generation rate and Figure 15: provides innerzonal trip arrival rate.
4.4 OD travel pattern analysis
The number of link OD passing data is a data to evaluate how many
links between regions to determine whether the passage is not treated. Also
it identify how largely a link give an impact on travel demand in the
network spatially. We calculate average number of link passing OD for each
TTF level. As a result there are more link pass OD pairs in the city than in
the rural area.
Average number of link passing OD on urban freeway is hight thannational expressway. On arterial road, a reado which has low TTR grade has
more links passing OD pairs.
4.4.1 Spatial analysis of number of link passing OD pairs
Number of link pass OD pairs in the city is much more than in therural area.
If OD pairs have many links pass, it could be effect on extensive area
when a road has problems like car accident, etc. So this is an important data
for further study like disaster evacuation and so on.

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4.5 Link priority analysis
Link priority takes high score if link has more links passing navigation
sample data, more passing OD pairs, and more lower inner traffic rate. Link
priority is calculated with this formula.
Link priority = 0.333*(grade of sample navigation data) + 0.333*(grade ofpassing OD pairs)+ 0.333*(grade of inner traffic rate)(1)
Link priority shows importance of each links on KTDB network and it
could be used for selecting investigation area as evidentiary material in the
future.
To recognize link priority, we mark on a map with TTF grade. Aninterest thing is sometimes link priority shows different scale amongneighbor links.

Figure 16: Example: difference of link priority
4.6 Zone network compatibility analysis
If link passing vehicles’ distance is short and innerzonal travel rate ishigh, then we evaluate that link has low compatibility. On freeway,
identification which link has low compatibility relatively is possible. In caseof TTF 14, urban fringe has low consistency links, especially Seoul innercity highway.

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Figure 17: Example of link compatibility
5. FUTURE CHALLENGE
Through this study, we find navigation data has many benefits for traffic
research. However, we still have to improve navigation data for traffic
research or other transportation parts. So, we make a strategy. Figure 18:
shows strategies what we make.

Figure 17: Example of link compatibility
First, range of data collecting network should be extended. This isbecause current navigation data collecting system focus on big cities so it is
not enough to analyze throughout the whole country. Second, representative
verification is needed. The navigation data are not verified sufficiently somore statistical analysis with classification is required. Then, we have toimprove data collect system focus on passenger car. Analysis for thecharacteristic of route choice by car is needed for multi-user traffic
assignment and verification so trucks’ tracing path data or buses’ tracking
data should be collected. Last one is we need to increase collected data
quantity. Constant data collection is needed because of lack of samples.

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ThailandThailand5.1 Theoretical
5.1 Theoreticalresearch
research
The navigation
The navigation
data ondatatransportation
on transportation
planning
planning
is applied
is applied
into two
into twoparts. parts.
First First
one isone
theoreticalis theoreticalresearch
researchpart andpart secondand second
one isone
operationis operation
application
application
part. part.
Main Main
point pointof theoreticalof theoreticalresearch
research
is route
is route
information,
information,
relationshiprelationshipbetweenbetweenOD-Link,
OD-Link,
tracking
tracking
path by
path
carby
and
cartravel
and travel
time –timevelocity.
– velocity.
1) Develop
1) Develop
traffictraffic
assignment
assignment
modelmodel
basedbasedon route
on route
To remedy
To remedy
user equilibriumuser equilibrium
traffictraffic
assignment’s
assignment’s
shortcoming,
shortcoming,
we weneed needto develop
to develop
a modela modelwhichwhich
can calculate
can calculateroute route
choicechoicebasedbasedon onobserved
observed
data. data.

FigureFigure
18: Flow
18: Flowchart chart2) OD2)estimation
OD estimation
basedbasedon navigation
on navigation
data data
OD estimation
OD estimation
is a method
is a method
to estimate
to estimate
OD trip
ODbasedtrip basedon observedon observed
link linktraffic.traffic.
Prior Prior
methods
methods
are usingare using
UE traffic
UE traffic
assignment
assignment
model.
model.
However
Howeverititcannotcannot
calculatecalculateexact exact
route route
choicechoicerate andrate link
and passing
link passing
rate whichrate whichisischangedchangedeveryevery
iterations,
iterations,
it means
it meansthat therethat there
is a limit
is a tolimit
estimate
to estimate
accurate
accurate
OD trip.
OD So
trip.
analysis
So analysisof relationshipof relationshipbetweenbetweenOD andODLinkand with
Link navigation
with navigation
data could
data could
improve
improve
OD estimation
OD estimation
accuracy.
accuracy.
3) Traffic
3) Traffic
assignment
assignment
modelmodel
basedbasedon ODonlink
ODchoice
link choiceprobability
probability
We need
We OD
needtrip
ODlinktrippassing
link passing
rate torate
determine
to determine
representative
representativeroute.route.
By using
By using
this rate
thisonrate
traffic
on traffic
assignment,
assignment,
it is possible
it is possible
to calculate
to calculate
realistic
realistic
traffictraffic
pattern.
pattern.
Also, Also,link passing
link passing
rate helps
rate helps
to analyze
to analyze
averageaverageand varianceand variancein long-termin long-term
analysis.
analysis.
It is useful
It is useful
to develop
to develop
theoreticaltheoreticaltraffictraffic
assignment
assignment
modelmodel
like stochastic
like stochastic
traffictraffic
assignment.
assignment.
Traffic
Traffic
assignment
assignment
modelmodelwith linkwith linkchoicechoiceprobability
probability
can becan
developed.
be developed.
4) Analysis
4) Analysisof reliabilityof reliabilityof roadof travelroad travel
time time
TravelTravel
time reliability
time reliability
is consideredis considereda traffic
a traffic
network
network
analysisanalysis
becausebecausethe number
the numberof dataof isdatanotis enoughnot enoughto analyze.
to analyze.
Probabilistic
Probabilistic
analysisanalysisisispossiblepossiblesince sincenavigation
navigation
data data
has iterative
has iterative
traveltravel
choicechoice
behavioral
behavioral
information.
information.
Probabilistic
Probabilistic
analysisanalysis
makesmakes
it possible
it possible
to evaluate
to evaluatereliability
reliabilityof routeof route
or ODortravel
OD travel
time. time.
5.2 Operational
5.2 Operational
application
application
To apply
To applynavigation
navigation
data fordatatransportation
for transportation
planning,
planning,
immediately
immediately
available
available
methodmethodshouldshould
be developed
be developed
to operate
to operatecurrent
current
navigation
navigation
data data
information.
information.
In network
In network
verification,verification,other otherdata cannot
data cannot
verifyverify
a traffic
a traffic

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network. Analysis of OD travel time and link travel time’s standard
deviation is possible to validate reliability of national road travel time. We
need to develop verification method with navigation OD trip data which hasa high percentage of the KTDB component.
(1) Truck-bus priority assignment multi-user vehicle model.
Some traffic assignment package supports MUV model. So,
theoretically Multi-user class assignment is suggested. However, route
choice description has less reality, so utilitarian value and solution algorithm
is inefficient and it is hard to get a unique solution. Therefore, truck andbus’ routes should be captured with navigation data.
(2) Develop network verification method with navigation data
Verifying network components is possible with navigation data.
Generating and arriving links for a zone will be useful for checking centroid
connectors’ reasonability. If sufficient navigation data is cumulated, it could
be an important data to construct physical network. \(3) Network service level evaluation index
We can analyze the reliability of network travel time for a wholenetwork. To analyze travel time variance, enough sample data should becollected. With this it is possible to analyze road grad or regional travel
time.

Figure 19: analysis of travel time standard deviation(4) KTDB OD trip verification method
We construct trip OD table with observed data but it is hard to verify
and use screen link method, cordon line method or link traffic verification
method. There is a limitation to using indirect verification method withtraffic assignment. Starting from the navigation data through the data toidentify definition and innerzonal trip ratio, it is possible to develop a direct
OD verification methodology.

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THE IMPACT OF FLOW VELOCITY ON
MICROBIAL PRECIPITATION OF CALCIUMCARBONATE AMONG SOIL
Tsubasa Sasaki1 and Reiko Kuwano2
1
Graduate student, Dept. of Civil Engineering,
The University of Tokyo, Japantsubasas@iis.u-tokyo.ac.jp
2
Professor, IIS, The University of Tokyo, Japan
ABSTRACTMicrobial precipitation of calcium carbonate has the potential tomitigate the geodisasters with minimal adverse effects on the environment.
One of the remaining challenges for its field-scale implementation isnonuniform distribution of calcium carbonate precipitation among soil. In
this research, bacterial solution and cementation solution, both of which isnecessary to make the precipitation occur, were infiltrated to sandy
specimens with varying flow velocity to see the effects of flow velocity on the
distribution of the precipitation. The uniformity of the precipitation was
examined by means of needle penetration tests. In addition, precipitation
efficiency and effluent pH were recorded. The result indicated that slower
delivery of cementation solution resulted in more uniform precipitation
when bacteria had been equally distributed over the specimen. Furthermore,
the quicker the injection of bacterial solution was, the more uniform
precipitation occurred, which suggested that fast transfer of bacterial
solution led to their uniform deployment in the specimen. Precipitation
efficiency and effluent pH were not affected by flow velocity within therange examined in this study.
Keywords: soil improvement, microbes, infiltration, flow velocity,
calcium carbonate precipitation.
1. INTRODUCTION
Microbial calcium carbonate precipitation has a great prospect of mitigatingsoil-related disasters such as liquefaction and landslides while presenting aminimal level of harsh effects on the environment, as opposed to theconventional cement improvement that are often accompanied by dangerous
side effects (Kamon et al. 2005). The aforementioned precipitation isusually mediated by bacterial species possessing urease, an enzyme which
decomposes urea into ammonia and carbon dioxide. The produced ammonia
raises pH, helping carbon dioxide to become carbonate ion, which triggers
calcium carbonate precipitation near particle-particle contacts among soil
with the presence of ample calcium ion, thereby reinforcing soil structures.
Previous research has already suggested that calcium carbonate precipitation

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can be effective in improving sand’s behavior under undrainded condition(DeJong et al. 2006). However, a successful implementation of calcium
carbonate precipitation could be hindered by the degree of precipitation
being nonuniform among the target soil, since the actual effects of the
improvement cannot be fully guaranteed without quantitatively assessing
the distribution of precipitated calcium carbonate. So far laboratory-scale
tests have been facing the nonuniformity of the precipitation. For instance alaboratory experiment, in which small sandy columns (50 mm*100 mm)
were injected with bacterial solution and cementation solution from one sideof the column at flow rates as slow as 2 ml/min, came out with the results ofcalcium carbonate precipitation being concentrated near the injection point(Mortensen et al. 2011). Other research, where 0.5m sandy columns weretreated in a similar manner, also showed an unequal distribution of calcium
carbonate (Martinez et al. 2011, Martinez et al. 2013). Larger scale
experiments revealed the same tendency. For example, 5 m column and 100
m3 box experiments resulted in the amount of precipitation peaking at
regions close to the injection points (Whiffin et al. 2007, Van Passen et al.
2009). Moreover, effectiveness of the treatment cannot be verified from theresults of triaxial experiments with nonuniformly cemented specimens.
Therefore, it is imperative to achieve uniform precipitation of calcium
carbonate among the target soil. In this research, attempts at inducing
uniform calcium carbonate cementation among sandy specimens were madeby injecting bacterial and cementation solution at different flow rates. This
study aims to create an effective way of preparing uniformly cemented
specimens so that they can be examined in triaxial experiments, in order toevaluate their improved properties appropriately. Furthermore, this researchis to provide an insight into optimum implementation of the microbial
calcium carbonate precipitation on a larger scale.
2. MATERIALS AND METHODS
2.1 Bacterial Solution
Bacterial solution was prepared by inoculating an arbitrary amount of
Sporosarcina pasteurii (ATCC 11859), which had been cultivated on culture
plates, into 0.13M Tris buffer at around pH 9.0 that also contains 20 g ofyeast extract and 10 g of ammonium sulfate per 1 l. These two ingredientswere put into separate flasks containing the Tris buffer, and they weresterilized at 121 oC for 20min before mixing. The recipe for the culture plateis identical to the chemical solution mentioned above except 20 g of agar
per liter being added to make the solution gelled. After the inoculation, thesolution was incubated for 24 to 26 hours at 30 oC in order to let the bacteria
to augment up to a stationary phase.
2.2 Cementation Solution
Cementation solution was mainly comprised of urea and a calcium
source; the former provides energy for the bacteria and the latter is essential
to induce calcium carbonate precipitation. Specifically, the chemical

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solution employed in the experiment contains 30.03 g of urea, 73.507 g ofcalcium chloride bihydrate, 3 g of nutrient broth, 10 g of ammonium
chloride and 2.12 g of sodium bicarbonate per liter of distilled water.
Nutrient broth provides necessary nutrition for the bacteria to augment
themselves, and ammonium chloride and sodium bicarbonate play a role instabilizing pH of the solution. The chemical solution was not sterilized prior
to the injection to the specimen. All the chemicals used to prepare thebacterial and cementation solution were obtained from Wako Pure Chemical
Industries, Ltd., Japan.
2.3 Specimen Preparation
The specimens were prepared by water-pluviating Toyoura sand (Gs =2.656, emax = 0.992, emin = 0.632, d50 = 180 m) into cylindrical plastic
molds (50 mm*100 mm) aiming at the relative density around 90%. A
filter paper was placed at the top and bottom of the sand column to prevent aloss of sand particles from the mold. Tow different types of preparation
were performed in the experiment. In the first series of testing (i.e. Test 1)
the specimens were created by pouring Toyoura sand into bacterial solution
in the same manner as the water pluviation method, in order to deploy thebacteria uniformly over the specimen. Cementation solution was infiltrated
through the specimen from the top afterward. In the second series of testing(i.e. Test 2) the specimens were formed in the same way, but distilled water
was used instead of bacterial solution during water pluviation. Thenbacterial solution was injected from the top of the specimen followed bycementation solution.
2.4 Testing Procedure
The water-pluviated specimens were fitted with an acrylic hollow
cylinder at the top of the mold and a plastic tube at the bottom so that the
infiltration of solution from the top to the bottom became possible. The tube
was connected to a small tank where negative pressure was applied tocontrol the flow velocity of solution (Figure 1). Various levels of negative
pressure were used, ranging from -1 kPa to -40 kPa, and the pressure was
kept constant during infiltration. Since the water level of the solution
declined as the infiltration progressed, the flow velocity could not be
maintained constant. In Test 1, four specimens were prepared with thebacteria already distributed uniformly among each specimen. 90 ml of
cementation solution was transferred to each sand column at varying flow
velocity and the infiltration was stopped while the top surface of the column
was still slightly covered by the solution. The specimen was detached fromthe tank after the infiltration, and its top and bottom end were sealed
properly in order to prevent drying of the sand. In Test 2, firstly 90 ml ofbacterial solution was injected to the specimen at different flow velocity.
Then 90 ml of cementation solution was poured by the force of gravity. In
both tests, the time needed for the solution to infiltrate the first 10 ml, andfrom that onward every 20 ml, was recorded, which was then used to
calculate flow velocity. The flow velocity, v, was derived using Equation (1).

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velocity on
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where q = flow rate, A = area of sand column’s cross section perpendicular
to its length and e = void ratio.
In addition, the specimen was retained for 20 hours after the infiltrationof cementation solution to let calcium carbonate precipitation develop. Thendistilled water was added from the top to stop the precipitation process andto wash out all remaining chemical substances in the specimen. The details
about the testing condition are shown in Table 1.
Cementation
solution

Pressure tank (negative
pressure inside)

A plastic moldpacked with
Toyoura sandEffluent
Figure 1: Infiltration of cementation solution to the specimen
The degree of cementation and its distribution was evaluated by needle
penetration tests, in which the cemented sand columns were pierced for 20
mm from the surface along the length of the column with a needle made ofbrass (Figure 2(a) and (b)). The resistance on the needle was recorded as it
moved down in the specimen at a speed of about 30 mm/min. After the first
20 mm of layer was done, that layer was removed from the mold, and thenext 20 mm of layer underwent the test. This was repeated until the entire
length of the column had been finished with the penetration test.
Specifically, the length of the sand column was 100 mm so the penetration
test was performed for five layers for a single specimen (Figure 2(c)). The
five lines, representing the relationship between penetration resistance andpenetration depth for each of five layers in a specimen, are hypothesized tobe similar to each other if the cementation is distributed uniformly over the
specimen. The results for the cemented specimens were compared withthose for an untreated specimen, in order to see the level of reinforcementby the precipitation of calcium carbonate. In addition to the needle
penetration test, the amount of precipitated calcium carbonate wascalculated in two different ways; one was based on the concentration of
calcium ion in the effluent of cementation solution ejected from thespecimen (i.e. referred to as precipitation efficiency Ⅰin Table 1), and theother is derived by comparing the weight difference of the specimen before
and after the treatment (i.e. precipitation efficiency Ⅱ). The effluent was

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further examined to assess its pH, which could affect the environment
adversely if its value becomes too high.

(a)

(b)

(c)

3 mm

Figure 2 : (a) An overview of the penetration apparatus;(b) the needle; (c) division of the sand column into five layers for the
penetration test
3. RESULTS AND DISCUSSION
The results of the penetration tests are shown in Figure 3, 4 and 5.
Specifically the results with the specimens in which the bacteria were
deployed uniformly during specimen preparation are shown in Figure 3 (i.e.
Test 1), and Figure 4 reveals those for the specimens that were infiltrated
with both bacterial and cementation solution after the preparation (i.e. Test2), while the result for the untreated specimen is illustrated in Figure 5 as abench mark. Each legend shown in the graphs (e.g. 1-A) corresponds tothose in the Table 1.
Table 1: Details of testing condition and results
Test number

1

2

Column

A

B

C

D

A

D

Bench
mark

Relative density (%)

93.1

95.1

94.7

92.6

89.3 91.1 96.9 91.5

102.5

Pore volume (ml)

78.6

78.1

78.2

78.7

79.6 79.1 77.6 79.0

76.1

Infiltrated BS volume (ml)

-

-

-

-

90

90

90

90

0

Infiltrated CS volume (ml)

90

90

90

90

90

90

90

90

0

Precipitation efficiency Ⅰ (%)

96.5

97.3

98.1

96.5

96.5 97.3 98.1 97.00Precipitation efficiency Ⅱ (%)

86.9

91.1

87.7

87.5

91.1 89.1 82.1 72.6

0

7.5

-

Effluent pH
7.5
7.3
7.3
7.3
Note: BS = bacterial solution; CS = cementation solution

B

7.3

C

7.5

7.3

Figure 3(d) shows the level of calcium carbonate cementation and its
distribution for the specimen in which cementation solution was transferredby the force of gravity, i.e. very slow delivery. It was evidenced that the
specimen was cemented fairly uniformly under that condition (i.e. uniform
distribution of bacteria prior to slow infiltration of cementation solution), as
each layer showed similar responses to each other. The penetration
resistance also increased by more than 100 per cent compared to the bench

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mark, indicating the effectiveness of calcium carbonate precipitation as away to reinforce soil. In the case of quicker delivery of cementation solution
than by gravitational force, the uniformity seemed to be disturbed (Figure3(b) and (c)). Moreover, the penetration resistance declined slightly when
the solution was transferred very quickly (Figure 3(a)), even though
precipitation efficiency was as high as the other specimens. Figure 3(e)shows the flow velocity during infiltration for each specimen. Precipitation
efficiency and effluent pH were not affected by flow velocity (Table 1).
Note that the drop in penetration resistance in the fifth layer does not meanthat this part was not cemented with calcium carbonate. This is because thebottom of the mold was drilled as the outlet for solution, which provided
much less reaction force to the needle. A similar behavior was observed forthe untreated specimen (Figure 5).
Penetration depth (mm)

(a)

0

1-A

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

50

100

Penetration resistance (N)

150

200

(b)

Penetration depth (mm)

0

1-B

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

68
68

50

100

Penetration resistance (N)

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0

Penetration depth (mm)

(c)

1-C

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

100

150

200

0

(d)
Penetration depth (mm)50

Penetration resistance (N)1-D

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

100

150

8

Flow velocity (mm/sec)

(e)

50

200

Penetration resistance (N)

1-A (CS)
1-B (CS)
1-C (CS)
1-D (CS)

7
6
5
4
3
2
1
0

0

10

20

30

40

50

60

Infiltrated volume (ml)

70

80

90

Figure 3: (a) to (d) results of penetration tests for Test 1;(e) flow velocity of cementation solution (CS) during its infiltration to
the specimen

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0

2-A

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

100

150

200

0

Penetration depth (mm)

(b)

50

Penetration resistance (N)2-B

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

Penetration depth (mm)

(c)

50

100

150

Penetration resistance (N)

200

0

2-C

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

70
70

50

100

150

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Penetration depth (mm)

(d)

0

2-D

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

50

100

150

8

(e)

2-A (BS)
2-B (BS)
2-C (BS)
2-D (BS)
2-A (CS)
2-B (CS)
2-C (CS)
2-D (CS)

7

Flow velocity (mm/sec)

200

Penetration resistance (N)

6
5
4
3
2
1
0

0

10

20

30

40

50

60

Infiltrated volume (ml)

70

80

90

Figure 4: (a) to (d) results of penetration tests for Test 2;(e) flow velocity of each solution (BS and CS) during their infiltration to
the specimen

Penetration depth (mm)0Bench mark(No treatment)

5

1st layer
2nd layer
3rd layer
4th layer
5th layer

10

15

20
0

50

100

150

Penetration resistance (N)200

Figure 5: Penetration test result for the untreated specimen

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The results for Test 2, where both bacterial and cementation solution
were infiltrated after specimen preparation, are shown in Figure 4. By andlarge, the uniformity of cementation was no better than that for Test 1. A
relatively large difference in penetration resistance for each layer in Figure4(a) and (b) indicates the slow delivery of bacterial solution led to
nonuniform distribution of bacteria, which then resulted in the concentrationof cementation around the middle of the specimen. By contrast whenbacteria were migrated at faster rates, more uniform cementation took place(Figure 4(c) and (d)). This could be due to the bacteria having been
scattered over the specimen uniformly with higher rates of injection.
Therefore, these results suggest that the key to successful
implementation of the calcium carbonate precipitation, i.e. uniform
cementation among the target soil, is to inject bacterial solution at fast rates,
followed by slow infiltration of cementation solution. Furthermore, effluent
pH was no higher than 7.5 in all the test cases, indicating almost no negative
impact on the environment in terms of pH changes.
4. CONCLUSIONSIn this study, small sandy specimens of Toyoura sand were microbiallyprecipitated with calcium carbonate by infiltration bacterial and cementation
solution from the top to the bottom of the specimen at varying flow velocity.
The quicker the bacterial solution was transferred, the more uniformly thebacteria seemed to be distributed. By contrast, the delivery of cementation
solution needed to be slow enough to induce the precipitation equally overthe specimen. Therefore, a better strategy for the uniform cementation of thetarget soil could be injections of bacterial solution at fast rates, followed byslow infiltration of cementation solution. As far as the flow velocity
employed in this research, precipitation efficiency was not affected andremained as high as just below 100 %. Effluent pH was no higher than 7.5,
indicating minimal harsh effects on the environments with regard to pHchanges.
REFERENCES
Kamon, M., Inui, T. and Shoji, Y. (2005): “Experimental study on thelong-term environmental impact caused by the cement
stabilization/solidification of soft ground”, Annuals of Disas. Prev. Res.
Inst., Kyoto Univ., No. 48 B
Dejong, J.T., Fritzges, M.B. and Nüsslein, K. (2006):”Microbiallyinduced cementation to control sand response to undrained shear”, J.
Geotech. Geoenviron. Eng., 132, pp1381-1392
Martinez, B.C., DeJong, J.T., Ginn, T.R., Montoya, B.M., Barkouki,
T.H., Hunt, C., Tanyu, B. and Major, D. (2013): “Experimental optimizationof microbial-induced carbonate precipitation for soil improvement”, J.
Geotech. Geoenviron. Eng., 139, pp587-598

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Martinez, B.C., Barkouki, T.H., DeJong, J.T. and Ginn, T.R. (2011);“Upscaling of microbial induced calcite precipitation in 0.5m columns:
experimental and modeling results”, Proc., ASCE Geo-Frontiers 2011 Conf.,
ASCE, Reston, VA., pp4049-4059
Mortensen, B.M., Haber, M.J., DeJong, J.T., Caslake, L.F. and Nelson,
D.C. (2011): “Effects of environmental factors on microbial induced
calcium carbonate precipitation”, Journal of Applied Microbiology, 111,
pp338-349
van Paassen, L.A., Harkes, M.P., van Zwieten, G.A., van der Zon,
W.H., van der Star, W.R.L. and van Loosdrecht, M.C.M. (2009): “Scale up
of BioGrout: a biological ground reinforcement method”, Proc., the 17th
International Conference on Soil Mechanics and Geotechnical Engineering,
pp2328-2333
Whiffin, V.S., van Paassen, L.A. and Harkes, P.M. (2007): “Microbialcarbonate precipitation as a soil improvement technique”, Geomicrobiology
Journal, 24, pp417-423.

The impact
The
impact ofof flow
flow velocity
velocity on
on microbial
microbial precipitation
precipitationof
ofcalcium
calciumcarbonate
carbonateamong
amongsoil
soil

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6thCOMPARISON OF BENEFITS FORPOLITICAL BOUNDARY AND NATURAL
BOUNDARY FROM HYDROPOWER
DEVELOPMENT IN THE MEKONG
Seemanta Sharma BHAGABATIa, Akiyuki KAWASAKIa,ba
Regional Network Office for Urban Safety,
Asian Institute of Technology, Thailand.
seemanta0605@gmail.combInstitute of Industrial Sciences, The University of Tokyo, Japan
ABSTRACT
Hydropower seems to be a very promising source of energy, although
it has huge economic, social and environment impacts at a local, national,
and trans-national level which may result to transboundary conflicts among
the riparian countries. Past literature has suggested that these conflicts may
be approached through benefit sharing. Selecting different sets of riparians(such as countries (Political boundary) and river sub-basins (Natural
boundary)) within the same study area may lead to different benefit sharing
system. While a lot of research has been done in the country aspect(political boundary), the natural boundary aspect has been left simply
untouched. This study shows promising results of the natural boundary
approach. The study attempts to investigate the difference in benefit sharing
in the transboundary sub-basin, the Sesan and Srepok sub basin, covering
areas of Cambodia and Vietnam in the Mekong River Basin using a gametheory approach. Two sets are defined, based on Political boundary(National boundaries) and based on Natural boundary (Sub-basinboundaries). A wide range of parameters such as energy production,
irrigational benefits, flood control socio-economic costs etc. have been
incorporated to define models and methodologies. This study then compares
these two sets using the game theory concepts, such as core stability andincentive compatibility. Here we show that depending on the type ofriparians chosen, the extent of benefit sharing changes. The results of thestudy will provide a basis for local policy decisions and regional planningin the Mekong River and beyond.
Keywords: Mekong River Basin, sub-basin, Hydropower development,
Game Theory, Natural boundary, Political boundary, Optimization
1. INTRODUCTION
Rivers are one of the main sources of fresh water. They play a dominant rolein sustaining ecosystems. Rivers have always played a vital role in theeconomic development of mankind. A healthy river is typically one with75
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protected watersheds, conserved wetlands, protected aquatic and riverine
terrestrial biodiversity and controlled water abstraction and wastewater
discharge (Sadoff et al., 2002).
There are hundreds of rivers in the world, of which 261 rivers areshared by two or more countries, hence the name International rivers (Wolfet al., 1999). Proper water resources allocation is an important issue andthere have been many critical debates and conflicts throughout the world in
the history of mankind (Falkenmark and Lundqvist, 1995; Postel, 2000;
Tarlock, 2001). Many multilateral agreements on transboundary cooperationof international rivers have been drafted in the 20th century (Beach et al,2000), yet it is unclear how the interactions among the riparian states willproceed in the near future (Ward, 2002).
Watersheds have their own natural boundaries. These boundaries havenothing to do with the political boundaries such as national boundaries andprovincial boundaries, etc. In case of international rivers; it is often foundthat that river watershed is shared between two or more countries. Eachcountry has its own policies and agreements about the watershed. A numberof multilateral agreements on such transboundary watershed cooperation has
been established. Although, a lot of research has been performed in the
country extent (political boundary), the watershed extent (natural boundary)has been simple left untouched. There exist very little to no history on suchresearch.
This paper tries to include the natural boundary extent for policy
implementations. It tries to show how natural boundary extent can also be
used in case of transboundary scenarios. The Sesan and Srepok sub-basinsof the Mekong are selected as the study area. A number of parameters wereselected to form a model to optimize the benefits from hydropower
development. Optimization is performed for both country-wise (politicalboundary) and sub-basin wise (natural boundary). The study tries to provide
a new basis for local policy makers
2. GAME THEORY
Game theory models strategic situations, or games, in which an individual’s
success in making choices depends on the choices of others (Myerson,1991). According to Langlois (1996), Game Theory can be described as thescience of strategy or that of conflict resolution; at its core, it has thecharacteristics of a mathematical construct: a set of concepts andassumptions, fundamental theorems, and applications to real world issues.
2.1 Core stability and incentive compatibility
For a scenario to be successful, it must provide a non-empty core.
Core, here, refers to the benefit arising out of cooperation among the three
countries. The larger the size of the core, the greater is the amount of benefit.

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Formally the core is defined as the set of all benefit allocation vectors
which satisfy two conditions:
Efficiency:

Coalition rationality:

where N is the grand coalition that includes all players j, and v is thecharacteristic function value, or the total benefits to the members of thecoalition. Efficiency states that the sum of benefits to each player is equal tothe value of the grand coalition. Coalitional rationality states that players are
not incentivized to leave the grand coalition for a subset coalition (i.e.
individual action or partial coalitions).
3. STUDY AREA
3.1 The Sesan and Sre Pok sub-basin
The Mekong River, the longest international river of Asia, is the ninth
largest river in the world, spanning 4,909 km and draining an area of
795,000 km2, discharging 475 km3 of water annually. The river basin is
thickly populated with over 72 million inhabitants (Campbell 2009). The
Sesan and Sre Pok sub-basins, is a large tributary system of the MekongRiver, with a total area of 56,085 km2 covering the border areas of the“Indochina Junction” in Cambodia and Vietnam. It is shared among the twocountries, Cambodia (25,975 km2; 46.3%) and Vietnam (30,110 km2;
53.7%).
3.2 Hydropower scenario of the sub-basins
Hydropower potential of these sub-basins has attracted attention for
more than 50 years. The total number of HEPs already constructed, underconstruction or in the designing or master planning stage amount to 21, as ofApril 2009 (Asian Development Bank (ADB, 2009)). These HEPs aregrouped into 4 groups, viz. Group 1 – under operation HEPs, Group 2 –HEPs under construction, Group 3 – HEPs in the design stage and Group 4– HEPs in the master plan stage. These plants are shown in the Figure 1
based on their group. The total number of HEPs in each group and the total
annual production of each country are shown in table 1(MRC, 2009).

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Figure 1: Hydropower development in the study area
Table 1: Hydropower data of the study area
Total Annual Production (Number of HEP), GWh
Land Area(km2)

Group 1
In
Operation

Group 2
Under
Construction

Group 3In
Designstage

Group 4In MasterPlan stage

Total

Total
Reservoir
Area

Cambodia

25,975

3 (1)

0 (0)

0 (0)

6,538 (6)6,541(7)106,323

Vietnam

30,1105,955 (7)4,623 (5)1,056 (1)181 (1)

11,815(14)94,335

Total

56,085

5,958 (8)4,623 (5)1,056 (1)6,719 (7)18,356(21)241,141

Source: MRC, 2009

4. METHODOLOGYThis chapter provides the research procedure and followed methods of thestudy to achieve the objectives. This study uses the concept of game theory
to optimize the hydropower scenario and minimize the conflicts anddisputes arising due to hydropower plants and show that cooperation willlead to a better outcome.

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4.1 Parameters considered
There are two types of parameters; one provides the benefits while theother causes us losses. In the first category, parameters like energy
production, irrigational benefits, benefits from fisheries and flood control
are considered; while annual cost of project, cost of transmission lines,
socio-economic costs, loss of fish, navigational loss and environmental
losses fall into the second category.
4.2 Estimation of investment funds available
To optimize the different scenarios, the investment funds of each unit
(country or sub-basin) have been used as a constraint. It has been calculated
based on two factors, viz. (a) past construction activity and (b) future
projects. In calculating the average cost of past HEPs, Group 1 and Group 2
HEPs are considered, while for future projects, Group 3 and Group 4 HEPs
are considered. The maximum of the two costs is rounded-off and fixed as
the unit’s hydropower construction investment funds. The funds for
Cambodia, Vietnam, Sesan and Srepok were assumed as US$ 1000, 300,
900 and 200 Million respectively.
4.3 Optimization model and scenarios considered
A very simple model has been in this study. Each country or sub-basin(unit) is a player having two options: to build or not to build additionaldamns. The objective is to maximize the net total annual benefits in the subbasin. The grand coalition case seeks to maximize total benefit.
Sets

I
J

Parametersgroupi

Srepokuniti

budgetjbi,jci,jVariables
xi = {0,1}
Objective Function:

HEPs identified by Project ID
Countries – Cambodia, Vietnam
Sub-basins – Sesan, Srepok
1 – in operation, 2 – under construction,3 – in design, 4 – in master plan
1 – Cambodia, 2 – Vietnam, 3 – Sesan, 4 unit hydropower investment fundbenefits of HEP i on unit jcosts of HEP i on unit j
HEP decision variable

where C is a particular coalition status among the set of all the possible
coalitions.
The constraints of this optimization models were the individual unit’s
investment funds and the benefit-cost ratio (b-c ratio) of individual HEPs,
i.e., while optimizing, the first priority was to select HEPs within the
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available fund having the highest b-c ratio. Then the second best HEP interms of b-c ratio was selected and so on.
A total of five different coalition scenarios were selected including
joint and individual funds as well as partial coalitions for present and futurescenarios. These are discussed below:
0

Status quo: The present hydropower situation. Only the existing
HEPs in operation, group 1 HEPs, are selected.
1 Complete under-construction projects: The existing HEPs in
operation (group 1) and the under construction HEPs (group 2)
are considered.
2 Individual unit maximization: Each unit maximizes its benefits
by constructing additional HEPs in group 3 and 4 within
individual fund constraints; all groups.
3 Two units’ simultaneous maximization (separate funds): Boththe countries/sub-basins maximizes simultaneously but within
their individual fund limits; all groups.
4 Two units’ simultaneous maximization (joint funds): Both thecountries/sub-basins combines their funds and then jointly
maximize; all groups.

5. RESULTS AND DISCUSSION
The optimization has been performed in two steps; first for the twocountries (political boundary) and second for the two sub-basins (natural
boundary). The results of optimization are described in detail in thefollowing sub-sections.
5.1 Optimization for two countries (political boundary)
Referring to table 2 (results of optimization – political boundary),
Cambodia at present is in loss because of the construction and operation of
HEPs in Vietnam. These HEPs are operating along the Cambodia-Vietnam
border; hence almost all of the downstream costs of these HEPs were
suffered by Cambodia. Currently, Cambodia is suffering a total of 4.1
Million US$ as downstream costs. Cambodia will also be suffering thedownstream costs of the currently under construction HEPs (an additional
3.24 Million US$). Vietnam, on the other hand, is getting high benefits of
271.89 Million US$. The benefit will further increase by 199.13 Million
US$ as the currently under construction HEPs are completed.
For individual maximization, highest benefit is achieved when
Cambodia maximizes, (Max Cambodia) (346.59 Million US$). This isbecause of the fact that, Vietnam has already exploited its hydropower bybuilding a number of HEPs and doesn’t have much scope for further
development, whereas Cambodia has just started to construct HEPs and has
a lot of HEPs in the design and master plan stage (group 3 and 4).

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When two countries maximizes together (Max Cambodia + Vietnam), ahigher total net benefit is attained (618.42 Million US$). This benefit can befurther increased by 13.1 Million US$ when they cooperate and maximize
jointly with combined funds (Max j(Cambodia + Vietnam)). The highest net
benefit of 631.52 Million US$ is reaped in this scenario (increase in net
benefit of 363.73 Million US$).
Table 2: Results of optimization based on countries (political boundary)
Scenario

Net Benefits (Million USD)

Change in Net Benefits (Million
USD)
Cambodia
Vietnam
Total

Cambodia

Vietnam

Total

Status Quo-4.1

271.89267.79---Complete current projects
Individual
Max Cambodia
sub-basin
Max Vietnam
maximization
Two sub-basin
Max (Cambodia +Maximization
Vietnam)Two sub-basin
Max j(Cambodia
Maximization+ Vietnam)(Join Funds)-7.34

471.02463.68-3.24

199.13195.89119.73494.65614.38123.83222.76346.59-7.34

475.06467.72-3.24

203.17199.93119.73498.69618.42123.83226.8

350.63132.83498.69631.52136.93226.8

363.73

5.2 Optimization for two sub-basins (natural boundary)
Referring to table 3 (results of optimization – natural boundary), it canbe seed from the results that no downstream costs were borne by Sesan subbasin when any HEP is constructed in the Sre Pok sub-basin, and vice-versa.
This is because of the fact that, downstream costs were suffered only along
the downstream area of the same river basin.
At present, Sesan sub-basin has a very high benefit of 235.45 Million
US$ compared to only 6.81 Million US$ of Srepok sub-basin. This isbecause a large number of HEPs (6 HEPs) are operating in the Sesan subbasin compared to Srepok sub-basin (only 2 HEPs).
When the under-construction HEPs are completed, the net benefit for
Sesan and Srepok sub-basins are increased by 53.41 and 119.6 Million
US$ respectively with a total increase of 173.01 Million US$.
In case of individual maximization, highest benefit is achieved when
Sesan maximizes (455.96 Million US$) (Max Sesan), a net increase of
213.69 Million US$. The reason behind this trend is that there are 7 HEPs ingroup 3 and 4 for Sesan sub-basin compared to only 1 HEP for Srepok subbasin, i.e., there is not much scope for Srepok sub-basin to construct more
HEPs.
For two sub-basin simultaneous maximization (separate funds) (Max(Sesan + Srepok), a slightly higher net benefit of 458.32 Million US$ isachieved. But when the two sub-basin maximize with joint funds (Maxj(Sesan + Srepok), a much better net benefit is achieved (551.33 Million
US$). This net benefit is the highest among all the scenarios considered(increase in net benefit of 309.07 Million US$).
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Table 3: Results of optimization based on sub-basins (natural boundary)
Scenario

Net Benefits (Million USD)

Change in Net Benefits (Million USD)

Sesan

Srepok

Total

Sesan

SrepokTotal

Status Quo

235.45

6.81

242.26---

Complete current projects
Individual
Max Sesan
sub-basin
Max Srepok
maximization
Two sub-basin
Max (Sesan +maximization
Srepok)Two sub-basin
Max j(Sesan +maximization
Srepok)(Join Funds)

288.86126.41415.27

53.41

119.6

173.01329.54126.41455.95

94.09

119.6

213.69288.86

128.78417.6453.41

121.97175.38329.54128.78458.32

94.09

121.97216.06404.83146.5

551.33

169.38139.69309.07

5.3 Core stability and incentive compatibility
Figure 2 (a) is plot of individual maximization and joint maximization
for the two countries (political boundary). The benefit of Cambodia isplotted in the x-axis and the benefit of Vietnam is plotted along the y-axis.
The values obtained in table are normalized w.r.t. the total net benefits ofthe two country maximization with joint funds.
The first line (at 19.9) shows the benefit achieved by Cambodia in
individual maximization and the second line (at 24.5) shows the benefit
Cambodia can achieve from joint cooperation (x-axis). Similarly, the firstline (at 0.0) is the benefit to Vietnam from individual maximization and thesecond line (at 21.4) shows the benefit Vietnam can achieve from joint
cooperation. The box thus formed by these intersecting lines is the core,
which is non-empty.

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Figure 2: Graphical representation of the core for (a) country optimization(normalized) and (b) sub-basin optimized (normalized)
Similarly figure 2 (b) is the plot of individual maximization and joint
maximization for the two sub-basins (natural boundary). Here again thenormalized values are used for plotting.
The benefits of Sesan sub-basin are plotted along the x-axis and thatof Srepok sub-basin are plotted in the y-axis. The first line (at 59.8) shows
the benefit achieved by Sesan sub-basin in individual maximization and thesecond line (at 77.1) shows the benefit that can be achieved from joint
cooperation (x-axis). Similarly, the first line (at 52.4) is the benefit to
Srepok sub-basin from individual maximization and the second line (at76.6) shows the benefit that can be achieved from joint cooperation. The
box thus formed by these intersecting lines is the core, which is non-empty
as well.
The existence of a core means that there exist several feasible
allocations of benefits among the units (countries/sub-basins) where there
are economic incentives for the units to cooperate.
The core in figure 2 (b) is much bigger compared to the core in figure 2 (a).
This shows that the possibility of benefit sharing is higher in the case of
sub-basins (natural boundary), which in turn means that the concept of
optimization based on natural boundary is actually feasible.
6. CONCLUSIONS
Based on the optimization results (shown in table 2 and table 3), it can beseen that cooperation can lead to increased benefits from hydropower
development. From table 2 it should be observed that Vietnam is getting thehighest benefits (498.69 Million US$) in the two sub-basin maximization
with separate funds (Max (Cambodia + Vietnam)) which is equal to thebenefit it is getting in the case of joint funds (Max j(Cambodia + Vietnam)).
Hence, if some incentives are not provided to Vietnam, it may not opt for
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joint fund cooperation. Hence, in order for the cooperation schemes to be
successful, there must be economic incentives for individual players to stayin a particular coalition. The concepts of game theory, such as core stability
and incentive compatibility, can be applied to show that there exists, in thiscase study, large benefits from full cooperation with joint budget planning.
Benefit sharing is critical to achieving a distribution that will incentivize all
players to cooperate, but the proof of the existence of a non-empty core
points to the existence of several feasible benefit allocation schemes that are
viable.
As mentioned earlier, from figure 2 (a) and figure 2 (b) it can beobserved that the core for sub-basin (natural boundary) maximization wasmuch bigger compared to the core of country (political boundary)maximization. This leads to the fact that the approach of optimizing basedon natural boundary is acceptable and is in fact quite feasible.
Also, from the results of optimization, it should be noted that by changing
the extent of optimization (country or sub-basin), the net benefits arechanged although all the input parameters were the same.
REFERENCES
Asian Development Bank (ADB) (2007) Na Bong-Udon Thani Power
Transmission Project Report: Lao People's Dem Rep
Asian Development Bank (ADB) (2009) Sesan, Sre Pok and Sekong River
Basins Development Study in the Kingdom of Cambodia, Lao People's
Democratic Republic and Socialist Republic of Viet Nam. Pilot testing
IHA-SGAP in the 3Ss Basins Inception Report
Beach, H et al (2000) Transboundary freshwater dispute resolution: Theory,
practice, and annotated references. United Nations University Press,
New York
Campbell, Ian C. (2009): ‘Introduction”. The Mekong: BiophysicalEnvironment of an International River Basin, 1-11, Academic Press
Falkenmark, M, & Lundqvist, J (1995) ‘Looming water crisis: newapproaches are inevitable. L. Ohlsson (Ed.), Hydropolitics: Conflicts
over water as a development constraint’. London: Zed Books, 178-212
Langlois, J P & Langlois, C (1996) ‘Rationality in International Relations:
A Game-Theoretic and Empirical Study of the U.S.-China Case’.
World Politics, 48(3), 358-390
Mekong River Commission (MRC) (2009) Hydropower Sector Review for
the Joint Basin Planning Process, Basin Development Plan
Programme, Phase 2
Myerson Roger, B (1991) Game theory: analysis of conflict. Harvard
University Press
Postel, S (2000) ‘Entering an era of water scarcity: the challenges ahead’.
Ecological Applications, 10(4), 941-948
Sadoff, C W & Grey, D (2002) ‘Beyond the river: the benefits ofcooperation on international rivers’. Water Policy, 4(5), 389–403
Tarlock, A (2001) ‘The changing environment of international water
management law’. Water Law, 12(6), 351-357

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Ward, D R (2002) Water wars: Drought, flood, folly, and the politics ofthirst. Riverhead Books, New York.
Wolf, A T et al (1999) ‘International River basins of the world’.
International Journal of Water Resources Development, 15(4), 387427.

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6thEVALUATING THERMAL COMFORT IN
CITY LIFE AND ITS RELATION TO
SOCIO-ECONOMIC ACTIVITIE
Noriko OKAMURA1, Wataru TAKEUCHI2,
Shin AKATSUKA3 and Kei OYOSHI4
1
Graduate Student, IIS, The University of Tokyo, Japannokamura@iis.u-tokyo.ac.jp
2
Associate Professor, IIS, The University of Tokyo, Japan
3
Yamanashi Institute of Environmental Science, Japan
4
EORC, Japan Aerospace Exploration Agency, Japan
ABSTRACT
Measuring composite temperature, indexes expressing sensible
climate, by satellite remote sensing is useful for evaluating thermal comfort
in city life in large area in real time. The purpose of this study is to develop
a method for calculating Wet Bulb Globe Temperature (WBGT), a
composite temperature used to prevent heat disorder, and Wind Chill
Temperature (WCT) from thermal infrared data of Multi-functional
Transport Satellite (MTSAT).
Firstly we prepared WBGT and WCT calculated from in-situ
measurement data in three cities and then did regression analysis with IR1
and IR2 bands of MTSAT data. From that, formulas expressing relations
between the indexes and MTSAT data was obtained. Next the formulas of
WBGT and WCT obtained by each city’s data were applied for other cities
and compared the results. The comparative analysis leads to making one
equation useful in whole area.
The derived formulas could express the tendency of WBGT and WCTchanges. However they were likely overestimated by satellite when in-situ
value was low and underestimated when in-site was high.
Besides, as one of the indexes of socio economic activity, we examined
the relationship between WBGT and GDP per capita of each country. It
showed a possibility of that this system, calculating thermal comfort with
MTSAT, would be useful in analyses about relationships between thermal
comfort in cities and socio economic activities.
Keywords: Wet Bulb Globe Temperature (WBGT), Wind Chill
Temperature (WCT), Multi-functional Transport Satellite (MTSAT)

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1. INTRODUCTION
Heat island is one of the urban problems in recent years. Satellite remotesensing is widely used for evaluating its effect. For example algorithms
have been developed for land surface temperature retrieval from Multifunctional Transport Satellite (MTSAT). However, human’s health andthermal comfort are affected by not only the temperature but also otherfactors including humidity, wind speed and solar radiation. Thus we usually
use composite temperatures like Wet Bulb Globe Temperature (WBGT),
which are indexes expressing sensible climate, for assessing environment.
Measuring sensible climate by satellite remote sensing is useful forevaluating thermal comfort in city life in large area in real time.
In this study, we developed a method for calculating WBGT, a
composite temperature used to prevent heat disorder, from thermal infrared
data of MTSAT.
WBGT is useful in hot, therefore another method for cool season wasneeded to evaluate thermal comfort all year around. We used Wind Chill
Temperature (WCT) index, a cold index used to avoid injuries from the cold,in cold season as well as WBGT in hot season. We tried to develop amethod for calculating WCT and enable evaluating thermal comfort with
MTSAT data in cold season too.
2. METHODOLOGY
2.1 Data selection
2.1.1 Surface Data
Surface data (atmospheric temperature, dew point, atmosphericpressure, wind speed) of three cities in Asian-Pacific region were
downloaded from NNDC Climate Data Online provided by NCDC(National
Climate Data Center).
StationLatitudeLongitude
Data

TOKYO
35.683139.767
hourly2011/1/1
12/31

Table 1: Station data-

SAPPORO
43.06
141.329
hourly2011/1/1
12/31-

TAIPEI
25.033121.517
3 hourly
2011/1/1
12/31

-

SEOUL
37.567126.967
3hourly
2011/1/1- 12/31

URUMQI43.8
87.65
3hourly
2011/1/1 12/31

2.1.2 MTSAT data
We used IR1 and IR2 channel of MTSAT data archived by IIS, The
University of Tokyo.

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Table 2: MTSAT technical specifications

Channel
IR1
IR2
IR3
IR4
VIS

Wavelength10.5-11.5µm
11.5-12.5µm
6.5-7.0µm
3.5-4.0µm
0.55-0.90µm

Spatial Res.
4km
4km
4km
4km
1km

2.2 WBGT
2.2.1 What is WBGT?
WBGT (wet bulb globe temperature) is a composite temperature used
to estimate the effect of temperature, humidity, and visible and infrared
radiation on humans. It is widely used to determine appropriate exposurelevels to high temperature (Table 3), and is derived from the following
equation (1).
WBGT = 0.7Tw + 0.2Tg + 0.1Td(1)
Where, Tw=wet-bulb temperature(℃) Tg=globe temperature(℃)
Td= dry-bulb temperature(℃)(C.P.Yaglou and Minard,1957)
Table 3: Risk level of heat disorder

Level*
WBGT(℃)>31
Danger
28-31
Alert
25-28
Advisory
21-25
Caution*The risk level is proposed by the Japan Amateur Sports Association(1994)

In this study, the following simple estimate formula(2) was used. The
equation(2) was derived with a relation between Tg and Td, ”Tg = 1.45Td –7.09” (Niigata Agricultural Research Institute, Horticultural Center, 2004).
WBGT = 0.7Tw + 0.4Td – 1.4(2)

2.2.2 Developing a method for calculating WBGT
Figure 1 is a flow chart of making regression equations between
WBGT and MTSAT data.
Surface data**Tokyo,Sapporo,Taipei

MTSAT data
(IR1,IR2)equation(3)(4)

Tw,Td at thestations

equation(2)

Comparative
analysis

Regression analysis
Tw = f (IR1,IR2)
Td = f (IR1,IR2)

WBGT at thestations

equation(2)

WBGT = f (IR1,IR2)

Figure 1: flow chart of making relational expression between WBGT and
IR1, 2
Evaluating thermal
Evaluating
thermal comfort
comfort inin citycity life
life and
anditsitsrelation
relationtotosocio-economic
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Calculating WBGT with surface data
WBGT were calculated in use of equation (2) with surface data. Dry-bulb
temperature(Td) and Wet-bulb temperature(Tw) were needed to use (2). We
substituted the values of atmospheric temperature for Td in the equation andcalculated Tw from the surface data by using the following formulas(3)(4).
es = 6.1078･10^(7.5T/T+237.3)
Where, es=saturation
temperature(℃)

vapor

(3)
pressure(hPa)T=air(Tetens, 1930)e = e’s - 0.000662･P(Td-Tw)(4)
Where e=vapor pressure(hPa) e’s=saturation vapor pressure at Tw(hPa)
P=atmospheric pressure(hPa)(Sprung, 1855)
Regression analysis
Regression analysis was conducted to derive relational expressions between
Tw and MTSAT data, and Td and MTSAT data. We used IR1, (IR1-IR2)
and(IR1-IR2)^2 as terms of equations. Then we got relational expressions
between WBGT and MTSAT data by substituting Tw and Td’s expressions
for equation(2).
Comparative analysis
Relational expressions of WBGT obtained by each cites’ data were applied
for other cities and compared the results.
2.3 WCT
2.3.1 What is WCT?
WCT (Wind Chill Temperature) index is used to provide a formula for
calculating the dangers from winter winds and freezing temperatures. It isbased on the rate of heat loss from exposed skin caused by the effects ofwind and cold as below.
WCT = 13.12 +0.6215･T – 11.37･V^0.16 + 0.3965･T･V^0.16
Where, T=air temperature( ℃ )speed(km/h)
WCT
0- -9
-10 - -27
-28 - -39
-40 - -47
-48 - -54

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(5)
V=windTable 4: Risk level of frostbite

Level
Low
Low
Risk: exposed skin can freeze in 10 to 30 minutes
High risk: exposed skin can freeze in 5 to 10 minutes
Very high risk: exposed skin can freeze in 2 to 5 minutes

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2.3.2 Developing a method for calculating WCT
Figure 2 shows how to make relational expression between WCT and
MTSAT data.
equation(5)**Sapporo,Urumqi,Seoul

Surface data**
MTSAT data
(IR1,IR2)

WCT at the stations

Regression analysis

WCT = f (IR1,IR2)

Figure 2: flow chart of making relational expression between WCT and
IR1,2
Calculating WCT with surface data
WCT were calculated in use of equation (5) with surface data.
Regression analysis
Regression analysis was conducted to derive relational expressions between
WCT and MTSAT data. We used IR1, (IR1-IR2) and(IR1-IR2)^2 as termsof equations.
Comparative analysis
Relational expressions of WCT obtained by each cites’ data were applied
for other cities and compared the results.
3. RESULTS
3.1 WBGTThe following relational expressions were derived as the results.
From the data of Tokyo,
Tw=0.0141･IR1 +5.12(IR1-IR2) -0.363(IR1-IR2)^2 +2.09
Td=0.0470･IR1 +5.30(IR1-IR2) -0.351(IR1-IR2)^2 -2.65
WBGT=0.0287･IR1 +5.71(IR1-IR2) -0.395(IR1-IR2)^2 -0.997
From the data of Sapporo,
Tw=0.0239･IR1 +5.83(IR1-IR2) -0.481(IR1-IR2)^2 -6.52
Td=0.0330･IR1 +6.35(IR1-IR2) -0.522(IR1-IR2)^2 -6.98
WBGT=0.0299･IR1 +6.62(IR1-IR2) -0.545(IR1-IR2)^2 -8.75
From the data of Taipei,
Tw=0.00204･IR1 +3.50(IR1-IR2) -0.248(IR1-IR2)^2 +14.5
Td=0.0294･IR1 +4.04(IR1-IR2) -0.248(IR1-IR2)^2 +9.26
WBGT=0.0132･IR1 +4.05(IR1-IR2) -0.273(IR1-IR2)^2 +12.4

(6)

(7)

(8)

Figure 3 and Figure 4 show the annual change in Tw and Td in Tokyo. “Insitu” is from surface data and “satellite” is calculated with regression

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equations. In Figure 5~Figure 7, “in-situ” shows annual change of WBGT
calculated with surface data, “satellite(Tokyo)” shows it calculated fromequation(6), “satellite(Sapporo)” from equation(7) and “satellite(Taipei)”
from equation(8). Table 5 shows RMSEs between “in-situ” and each“satellite”.

Figure 3:Annual change in Tw in
Tokyo(2011)

Figure 4:Annual change in Td inTokyo(2011)

Figure 5: WBGT annual change in Tokyo(2011)

Figure 6: WBGT annual change in Sapporo(2011)

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Figure 7: WBGT annual change in Taipei(2011)
Table 5: RMSEs between “in-situ” WBGT and each “satellite” WBGT
City

Tokyo
SapporoTaipei

Formula
Tokyo
7.27
10.6
8.65

Sapporo
9.90
8.26
14.7

Taipei
10.5
16.4
4.58

3.2 WCTThe following relational expressions were derived as the results ofregression analysis.
From the data of Sapporo,
WCT=0.0305･IR1 + 4.09(IR1-IR2) + 0.0576(IR1-IR2)^2 – 6.706
From the data of Urumqi,
WCT=0.360･IR1 + 6.96(IR1-IR2) + 0.132(IR1-IR2)^2 -93.0
From the data of Seoul,
WCT=0.0909･IR1 + 6.54(IR1-IR2) + 0.109(IR1-IR2)^2 – 20.2

(9)
(10)
(11)

In Figure 9~Figure 11, “in-situ” shows annual change of WCT
calculated with surface data, “satellite(Sapporo)” shows it calculated fromequation(9), “satellite(Urumqi)” from equation(10) and “satellite(Seoul)”from equation(11). Table 6 shows RMSEs between “in-situ” and each“satellite”.

Figure 8: WCT annual change in Sapporo(2011)

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Figure 9: WCT annual change in Urumqi(2011)

Figure 10: WCT annual change in Seoul(2011)
Table 6: RMSEs between “in-situ” WCT and each “satellite” WCT
City

SapporoUrumqi
Seoul

FormulaSapporo
9.84
15.2
12.1

Urumqi
14.4
13.2
11.5

Seoul
12.3
14.3
10.4

3.3 Relation between WBGT and WCT
Figure 11 shows WBGT and WCT annual change in Sapporo in 2011.
The graphs of both WBGT and WCT were very close. The tendency ofthese indexes by satellite data was similar.

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Figure 11: WBGT and WCT(in-situ/satellite) annual change in
Sapporo(2011)
3.4 Relation to socio economic activity
As one of the indexes of socio economic activity, we examined therelationship between WBGT and GDP per capita of each country. Figure 12
shows the relevance. It can be said that countries where WBGT is high tend
to have low GDP per capita.

aAustlaria/ｂJapan/ｃHongKong/ｄKorea/e Taiwan/
Size of circles
expresses amount of
GDP per capita

ｆRussia/ｇNorthKorea/ｈChina/iNepal/ｊPakistan/ｋBangladesh/ｌPhilippines/ｍIndia/ｎThailand/oMyanmar/ｐIndonasia/ｑVietnam/ｒSingapore

Figure12: relation between WBGT and GDP per capita
4. DISCUSSION AND CONCLUSION
This study demonstrated a method for evaluating thermal comfort bycalculating WBGT and WCT using MTSAT data. Regression analyses wereconducted and relational expressions between WBGT and MTSAT data,
WCT and MTSAT data. It enabled evaluating thermal comfort in resolutionof 4km by remote sensing. The formulas can express the tendency of
WBGT and WCT changes. However they are likely overestimated bysatellite when in-situ value is low and underestimated when in-site is high.
Further study is required to derive more accurate formulas. We try to

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express the indexes by each one equation with MTSAT data in whole area
referring to the RMSE by comparative analysis.
In addition, as one of the indexes of socio economic activity, we
examined the relationship between WBGT and GDP per capita of eachcountry. It could be said that countries where WBGT was high tended tohave low GDP per capita. It showed a possibility of that this system,
calculating thermal comfort with MTSAT, would be useful in analyses
about relationships between thermal comfort in cities and socio economic
activities.
REFERENCES
Japan Meteorological Agency 2003. JMAHRIT Mission Specific
Implementation, Ver. 1.2.
Oyoshi, K., Takeuchi, W. and Tamura, M. (2009). High- frequency
land surface temperature monitoring with MTSAT. In: proceed. annual
meeting of JSPRS
Shin Akatsuka, Kei Oyoshi and Wataru Takeuchi, 2010. Mapping of
precipitable water using MTSAT data. 31st Asian conference on remotesensing (ACRS): Hanoi, Vietnam, 2010 Nov. 2.
Wataru Takeuchi, Kei Oyoshi and Shin Akatsuka, 2012. Superresolution of MTSAT land surface temperature by blending MODIS
and AVNIR2. Asian Journal of Geoinformatics, 12 (2).

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6thDEVELOPMENT OF REAL WINDTURBINE BLADE VIBRATIONMEASUREMENT TECHNIQUE
BY 3D MOTION ANALYSIS SOFTWARE
Ryoichiro HOSHINO1 and Kichiro KIMURA2
1
Graduate Student, Dept. of Civil Eng., The University of Tokyo, Japanhoshino@iis.u-tokyo.ac.jp
2
Professor, Tokyo University of Science, Japan
ABSTRACTThe number of large-size wind turbine has been increasing at aremarkable rate. However, the characteristics of wind turbine bladevibration under operation are not fully understood. Therefore, we analyzed
the vibration of the blade tip by using a three-dimensional motion analysis
software on the data retrieved by two sets of high speed video cameras.
Since application of usual calibration method which requires to place acalibration scale near the blade on site was technically difficult, wedeveloped a devised method. It consists of two steps; we take video records
on site and do calibration independently in the university by reproducing asimilar camera setting. In analyzing the video data, we improved theexisting procedure by adopting coordinate transformation. As a result, we
could detect what we think one of the natural frequencies of the blade.
Keywords: wind turbine, blade vibration, high speed video, 3D motion
analysis, calibration, coordinate transformation.
1. INTRODUCTION
The number of large-size wind turbine has been increasing at aremarkable rate in Japan. However, the characteristics of wind turbine bladevibration under operation which cause fatigue damage are not fully
understood. Considering these circumstances, the goal of this study is set toevaluate characteristics using a three-dimensional motion analysis software.
Following the preceding relevant studies, we developed a simplified methodof vibration measurement of the wind turbine blade.
2. OUTLINE OF MEASUREMENT METHOD
We used a three-dimensional motion analysis software with a commercialname of DIPP-Motion PRO developed by DITECT corp., Japan. We took
video records of a real wind turbine under operation by two high speed
cameras HAS-L1, DITECT corp. at the rate of 500 frames per second. We

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need to conduct high accuracy calibration in order to convert the video
records into three-dimensional data, which requires to place a calibration
scale near the blade on site. Such usual calibration is, however, timeconsuming and unrealistic. Therefore we developed a devised method whichit consists of two steps; we take video records on site and do calibration
independently in the university by reproducing a similar camera setting, as
schematically shown in Figure 1. We used surveying instruments and madesome numerical programs to help the placement of cameras in a speedy andhighly accurate manner.

calibration scale

In sitetaking calibration(a) plan view
WindturbineCalibrationscale
42.5[m]Building in theuniversity

13.25[m]CamerasIndependent calibration

On site(b) elevation view
Figure 1: Schematic view on similarly relation image

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3. RESULT OF MEASUREMENT AND ANALYSIS
3.1 Result of measurement
Figure 2 shows a medium-size wind turbine, KWT300 model, which
is located at Futtsu factory of KOMAIHALTEC Inc., Chiba prefecture,
Japan. We took video records of blade vibration there and conductedcalibration independently in Noda campus of Tokyo University of Science
by reproducing a similar camera setting. In order to understand thecharacteristics of blade tip vibration, we needed to analyze the blade tip
vibration continuously for relatively long time. Therefore, we used a wideangle lens for the camera.
One example of preliminary analysis result is shown in Figure 3. We
set the origin of the time independently when we start tracking of each blade
tip. As a result, motion of the three blade tips in the x-coordinate directionof the original scale employed for the calibration, as illustrated in Figure 4a,
was similar to each other. This behavior confirms the validity of the three
dimensional motion analysis conducted in this study. However, the directionof this x-coordinate did not match the wind direction (i.e., the normal
direction of the rotor’s rotation plane). Therefore, the above motion was notcaused by the vibration of the rotor but was caused by its rotary motion.

Figure 2: Wind turbine in Futtsu, Chiba prefecture,}

Fully overlapped to each otherFigure 3: X-coordinate direction before coordinate transformation

Development of real wind turbine blade vibration measurement technique
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Coordinatetransformation

y’

z

x’
z’

a) Original scaleb) Modified scale
Figure 4: Coordinate transformation image

3.2 Coordinate transformation
In order to cope with the problem described in 3.1, we applied
coordinate transformation from the original scale to the modified scale as
illustrated in Figure 4. In the modified scale, x’-coordinate corresponds tothe wind direction, and y’- and z’-coordinates correspond, respectively, tothe vertical and horizontal directions in the rotor’s rotation plane.
The motions of the three blade tips in x’-, y’- and z’-coordinate
directions are shown in Figure 5 for the same record as shown in Figure 3.
From Figure 5, we could observe that the period of the blade motion in x’coordinate direction was much shorter than those in y’- and z’-coordinate
directions. Therefore, we regarded that we can evaluate the blade vibrationin the wind direction by conducting such coordinate transformation.
It is to be noted that the motion in the y’-coordinate direction wasalmost the same among the three blades, while the motion of the blade 1 inthe z’-coordinate direction was different from those of the other blades. We
do not know the reason for such discrepancy, but it may be linked to theaccuracy of the measurement and/or that of the coordinate transformation.

(a) X’-coordinate direction

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ICUS72Report 72}}

Fully overlapped
Fully overlappedto each otherto each other}}(b) Y’-coordinate(b) Y’-coordinate
directiondirection

Fully overlapped
Fully overlappedto each otherto each other(c) Z’-coordinate(c) Z’-coordinate
directiondirection
Figure5:Figure5:
After coordinate
After coordinatetransformationtransformation
3.3 Analysis
3.3 Analysisof the bladeof thevibration
blade vibrationin the windin thedirectionwind direction
In this measurement,
In this measurement,
we couldwerecord
could therecordmotionthe data
motionfor data
limitedfor limited
duration durationof aboutof0.4
about
second0.4 (i.e.,
secondabout(i.e.,200
about
frames200 could
framesbecould
recorded).
be recorded).
Thus, weThus,couldn’t
we couldn’t
apply spectral
apply spectral
analysis analysis
on the onrecorded
the recorded
data. data.
Alternatively,
Alternatively,in order into order
calculateto calculate
the period
the ofperiod
vibration,of vibration,
we fittedwethefitted themeasuredmeasured
motion inmotionthe windin thedirectionwind direction
with a sinusoidal
with a sinusoidal
curve ascurve
shownasinshown in
Figure 5a.
FigureAs a5a.
result,
As atheresult,periodtheofperiod
the measuredof the measured
motion was
motion0.38wassec.,
0.38 sec.,
which waswhich
commonwas common
value among
valuetheamong
threetheblades.
three blades.
It shouldIt beshouldnotedbethat
notedthisthatmotionthis data
motionwasdatarecorded
was recorded
at a blade
at a blade
rotation rotation
speed ofspeed
39.5 rpm,of 39.5whichrpm,corresponds
which corresponds
to strongtowind
strongconditions.
wind conditions.
Another Anothermotion data
motionrecordeddata recorded
under similar
understrong
similarwind
strongconditions
wind conditions
yielded yieldeda periodaofperiod
aboutof0.38
about
sec.,
0.38
suggestingsec., suggestingthat thisthat
value
thisis value
the predominantis the predominantperiod underperiodtheunderstrongthewinds.
strong winds.
Figure 6Figure
shows 6a shows
result froma result
analysisfrom analysisof the motionof the data
motionrecordeddata recordedatatlower blade
lowerrotation
blade rotation
speed ofspeed
29.7 rpm.
of 29.7
In rpm.
this case,In thisthecase,estimatedthe estimatedperiod period
was 0.43wassec.,0.43
while
sec.,
thewhile
sinusoidal
the sinusoidal
curve fitting
curvewasfitting
not wasso relevant
not so relevant
as was as was
the case thewithcase
the with
data theshown
datainshown
Figurein5a.
FigureThese
5a.different
These different
periods may
periods
be may beaffected affectedby the centrifugal
by the centrifugal
force induced
force inducedby the rotor
by therotationrotor rotation
but it is but
not it is not
Development of real wind turbine blade vibration measurement technique
Developmentof real windturbinevibrationmeasurement
techniquetechniqueby 3D motion
Developmentof realwindbladeturbineblade vibration
measurement
by 3Danalysis
motion software
analysis101
software101
101
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clear. In addition, we could observe that the amplitude of the bladevibration in the wind direction was reduced with the decrease in the blade
rotation speed.
As a whole, the estimated period of the blade vibration wasapproximately 0.4 sec., and thus one of the natural frequencies of theseblades could be evaluated to be around 2.5 Hz. This evaluation result isaffected by several factors, such as the shade of the tower which interfereswith tracking of the blade tip, the focusing condition of the cameras that
depends on the distance of the target from the cameras, and the accuracy ofthe coordinate transformation. Due possibly to these factors, some of thedata recorded when the wind direction changed could not be analyzed in areasonable manner.

Figure 6: The motion data of lower blade rotationd4. SUMMARY
In order to measure vibration of real wind turbine blade, we developed adevised method consisting of two steps; we take video records on site anddo calibration independently in the university by reproducing a similar
camera setting. In analyzing the video data, we improved the existingprocedure by adopting coordinate transformation. As a result, we could
detect what we think one of the natural frequencies of the blade.
In the above method, even under strong wind conditions, we needed to
set cameras with high accuracy both on site and in conducting thecalibration. We also needed to apply coordinate transformation to the dataobtained from three-dimensional motion analysis.

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InfrastructureFOREST CARBON ESTIMATION USINGTHE COMBINATION OF LIDAR DATA
AND HIGH RESOLUTION IMAGERY IN
LUDIKHOLA WATERSHED, NEPALDANG Anh Nguyet1, Michael WEIR2,
Yousif HUSSIN3 and Akiyuki KAWASAKI4
1
Research Assistant,
Regional Network Office for Urban Safety (RNUS),
Asian Institute of Technology (AIT), Thailand.
dang@ait.ac.th
2
Course Director Natural Resources Management, Faculty of GeoInformation Science and Earth Observation (ITC),
University of Twente, The Netherlands.
3
Associate Professor Faculty of Geo-Information Science and Earth
Observation (ITC), University of Twente, The Netherlands.
4
Associate Professor, International Center for Urban Safety Engineering(ICUS), Institute of Industrial Science (IIS),
University of Tokyo, Japan
ABSTRACT
Carbon dioxide (CO2) is believed the main anthropogenic greenhousegas which causes global climate change. In the carbon cycle, forest plays animportant role both as source and sink. Therefore, forest carbon inventories
and emission reduction programs, in particular REDD, are one of the mainefforts to combat climate change. REDD provides opportunities for Nepal
and other developing countries to take part in the international carbon
market and to promote sustainable forest management. In this research, thecombination of LiDAR data and high resolution satellite imagery was
carried on in order to assess the forest carbon sequestration in Nepal. The
application of LiDAR data and high resolution satellite imagery in carbon
estimation are mostly based on the strong relationship between tree
parameters, crown projection area (CPA) and height, with field basedcarbon stock of individual trees. The crown projection area is delineatedfrom a very high resolution Geo-Eye satellite imagery with Region growing
algorithm. Modelling the relationship between CPA and LiDAR derived
height with carbon stock of Shorea robusta and Other species results in R2
values of 0.76 and 0.68 respectively.
Keywords: above ground biomass, forest carbon estimation, very highresolution image, LiDAR, object based image analysi

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1. INTRODUCTION
Carbon dioxide (CO2) is believed to be the major greenhouse gas whichcauses global climate change. In the carbon cycle, forest plays an importantrole both as a carbon source and a carbon sink. When forest is cleared ordegraded, its stored carbon is released into the atmosphere as CO2.
Consequently, forest turns into the carbon source which enhances climatechange. Therefore, forest carbon inventories and emission reduction
programs are crucial for combating climate change.
At the Bali Climate Change Conference in 2007, REDD, which stands
for Reduced Emissions from Deforestation and Forest Degradation, wasadopted by the parties of the United Nations Framework Convention on
Climate Change (UNFCCC). It opened new opportunities for developing
countries to participate in forest carbon financing (MOFSC, 2009). Within
the concepts of REDD, countries that demonstrate forest carbon reserves
and emission reductions are able to sell carbon credits on the international
carbon market (UNFCCC, 2005). Similar to other forest carbon inventories
and emission reduction programs, estimates of forest carbon stock arerequired to generate baseline information for REDD application.
The main carbon pools in the forest are biomass (above-ground andbelow-ground biomass), dead organic matter (dead wood and litter) and soilorganic matter (IPCC, 2006). Among these, above ground biomass (AGB)
governs the potential carbon emission that could be released to theatmosphere due to deforestation (Gibbs et al., 2007). Estimation of AGB,
therefore, is the most critical step in quantifying carbon stocks from theforest (Gibbs et al., 2007). According to the IPCC definition, AGB is allliving biomass above the soil including stem, stump, branches, bark, seeds,
and foliage; the carbon makes up approximately 47% of AGB. Because ofdifficulties in collecting field data of below-ground biomass, most previous
research focused on AGB (Lu, 2006) and this present study also focuses on
the carbon stock contained in AGB.
As with most forest measurement, field methods can be employed for
AGB estimation with high accuracy but this approach is generally time
consuming, labour intensive, and difficult to implement in remote areas (Lu,2006). Meanwhile, remote sensing methods can be combined with fieldmeasurements to estimate AGB at a wide range of scales with relatively low
cost (Popescu, 2007). As a result, remote sensing methods have been
increasingly applied and become the primary source of data for AGB or
carbon estimation (García et al., 2010; Lu, 2006).
A variety of satellite data are used for AGB or forest carbon
estimation. These data are broadly classed into optical sensor data, radar
data and light detection and ranging data (LiDAR). Each type of data has its
own characteristics, both advantages and disadvantages. Hence, theintegration of different sources of remotely sensed data may enhance theinformation extraction process and overcome the drawbacks of using one
type of data alone (Sohn et al., 2007). In the light of this, the combination of

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low density LiDAR data and high resolution satellite image is also one ofpotential approaches to individual tree-based carbon estimation (Jochem etal., 2009; Kim et al., 2010).
2. STUDY AREA
The Ludhikhola watershed is in the Gorkha, the Western development
region of Nepal and has an area of 3,610 km2. The Ludhikhola watershedarea is located in the southern part of Gorkha district between 27o55’02’’ to27o59’43’’N and 84o33’23’’ to 84o40’41’’E and covers an area of 5750
ha.The Ludhikhola watershed was selected for this study based on threemain criteria. Firstly, the watershed is representative for hilly areas which inthe sub-tropical ecological zone of Nepal. The specific species for thisecological zone are Shorea robusta, Schima Wallichi and Castanopsis
indica. The interest of local people is important to any forest management
and conservation programmes. Involving local communities help to increase
the local people’s awareness of the benefits that they receive when
deforestation and forest degradation is reduced. Local people, therefore, areready to support and assist the research. Last but not least, the accessibility
is should be considered because of the limitation of time for field work as
well as specific terrain in Nepal. The study area meets this requirement
because it has good road access.

Figure 1: The Ludhikhola watershed
3. DATA AND METHODOLOGY
3.1. Data
The field data of DBH (Diameter at breast height), tree height andcanopy density were collected in 86 Circular plots of diameter 12.62 m (areaof 500m2). The Geo-Eye imagery used for this research are Geo-Eyepanchromatic 0.50 cm and Geo-Eye multispectral 2m images recorded on 2
September 2009 (Source: ICIMOD, Nepal). The Airborne LiDAR data used
for this research was recording using a Leica ALS50-II LiDAR-scannerconsisting of a laser scanner, a geodetic-quality GPS receiver and an inertial
measurement unit (IMU), which provide information about scan angle andForest carbon estimation using the combination of LiDAR data and high resolution imagery in Ludikhola
Forest carbon estimation using the combination of LiDAR data and high resolution imagery
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the aircraft coordinates. The data was collected from 16 March to 2 April
2011 in UTM WGS 84 coordinate system (Source: FRA, Nepal). The
average point density of the LiDAR data is 0.8 points per m2. The LiDAR
scanning process was at an absolute altitude of 2200m and recorded with ascan frequency of 52.9 kHz. The other data includes Topographic maps
1:25000, Digital Elevation Model (DEM) with 20 m resolution.
3.2. Methodology
3.2.1. Image pre-processing
The Geo-eye imagery was obtained in five separate images (4multispectral bands and 1 panchromatic band). The first operation done wasto stack the image before images fusion in Erdas Imagine 2011. And then, apanchromatic (PAN) image was fused with multispectral imagery, using
HPF (high pass filter) pan-sharpening method, in order to acquire the image
that has the spatial resolution of the panchromatic imagery and the spectral
resolution of the multispectral imagery. As a result, a MSS pan-sharpened
image with 0.5 resolution has 4 bands which will be useful for tree speciesclassification and identification. After fusion step, Image filter was applied
to sharpen the image objects. In this study, 5-by-5 filter was used because it
avoids removal of small peaks in the canopy (small trees) while maximizing
the smoothing function.
3.2.2. Creating the DSM, DTM and CHM from the LiDAR data
DTM and DSM were created directly using LasTools software. Digitalsurface model values (DSM) were computed for each point by gridding thepoint to desired pixel size. If a cell contained no laser reflection point, thevalue of this cell was determined by averaging the height values found in
the eight neighboring cells (St-Onge et al., 2008). The DTM is generatedusing the ground return (last returns) elevation values using an initial
triangulated irregular network (TIN). The interpolated grid cell size for both
DTM and DSM was 0.5m which is the spatial resolution of Geo-Eye
imagery. The tree height information on the field data collection was used tothreshold the laser height values in order to eliminate the effect of shrubs
and understory vegetation.The Canopy height model (CHM) is computed as
the difference between DSM and the corresponding DTM values.
3.2.3. Image- to- image co-registration
The dissimilarities between the Geo-Eye imagery and the CHM due todifferences in the acquisition process (different sensors, platforms, time ofrecording) need to be corrected by co-registration procedures. In thisresearch, manual registration with polynomial transformation was used toco-register Geo-Eye imagery with the Digital camera imagery as reference
image instead of using CHM. The reason is that it would hardly to find thecontrol points from CHM. Meanwhile, the ortho-rectified Digital camera
imagery was simultaneously acquired with LiDAR data and matches with

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the LiDAR. Root mean square error is used to validate the co-registration
result.
3.2.4. Tree crown delineation
Tree crown delineation was done in eCognition Developer 8.7 using
the hybrid method. This technique makes use of the phenomenon that inhigh spatial resolution imagery, trees appear as bright objects surrounded bya shaded area and the tree tops typically the brightest spot within the brightobject. Local maxima and minima have frequently been used to detect tree
tops and define crown boundaries, respectively. Local maxima are used asseeds for growing and local minima are used as a restriction for growing
region. Starting at a potential seed pixels, neighboring pixels are examinedsequentially and merged to growing region based on the similarity to theseed pixels, which is defined through both the spectral variance andgeometry of the object. This process continues until a significant boundary
is found and then these pixels are considered to belong to the region
corresponding to the seed pixel. One to one correspondence method was use
to validate the segmentation.
3.2.5. LiDAR derived height
The height of a tree is defined as the maximum value of CHM
corresponding to the tree crown. The delineated crowns from Geo-Eye
imagery were used as the zone to extract the local maximum value from theCHM. Within each crown, the maximum pixel value was extracted from
CHM using Zonal statistic tool in ArcGIS 10. Linear regression is
performed between ground-measured heights and LiDAR derived heights
yielded a R2 to validate CHM created.
3.2.6. Object-based classification
Once the tree crowns have been delineated, the tree species can beclassified by means of the nearest neighborhood algorithm in eCognition
Developer 8.7 software. In comparison to pixel based training, the object
based approach of nearest neighbour requires fewer training samples. The
training samples are image objects which are the result of the segmentation
process. The species information of the object was taken from the fieldsample data. The field sample data was divided into two parts, 2/3 was used
for training data and 1/3 was used for classification validation. The feature
space was created based on the training samples. The object features thatwere selected for feature space creation are layer value of each MSS bands,
panchromatic band; object geometry and the thematic layer information
related to each object. Starting from the selected sample, nearest neighborclassification looks for the closet sample in the feature space for each image
object and assigns the class for that object. The descriptive statistic as wellas the observation from the field shows that more than 70% of the trees instudy area are Shorea robusta. Therefore the image was classified in twoclasses: 1) Shorea robusta and 2) Other species. The accuracy of theclassified image is assessed by comparing it to reference (ground truth) data.
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Accuracy assessment is done using the error matrix or confusion matrix
which compares the classification result with true world in Erdas Imagine
2011.
3.2.7. Multiple regression for carbon estimation
Research of Nakai (2009) has indicated that the multiplicativeequation offered better results than the ones using tree height or crown area
alone. Therefore, multiple regression models relating field-based carbon
stock with tree height (from CHM) and crown projection area (CPA) weredeveloped. Prior to regression analysis, the detected and delineated trees areautomatically linked with the field measured trees to combine theinformation for each tree. Each tree has the information about tree height(extracted from the CHM), CPA (extracted from the tree crown delineation
result) and field-based carbon (calculated using the appropriate allometricequation). Microsoft Excel was used to develop regression analysis. The
independent variables are height and CPA, the dependent variable is carbon(C). The developed model is called the regression predictive model ofcarbon. The significance of the model was tested through an analysis ofvariance (ANOVA)
3.2.8. Carbon model validation
The model was validated using the test data set obtained from the field.
Validation of the model was done by comparing the amount of carbon
predicted by the model and amount calculated from the field data. Rootmean square error (RMSE) is calculated to check the amount of error in the
carbon stock map.

Where Cp- Carbon predicted by the model, Co-Carbon calculated
N-Number of observations
4. RESULT
4.1. Tree crown delineation
Individual tree crown was delineated using Region Growing approach, theresult of image segmentation process is shown in flowing figure:

Figure 2: Image segmentation result

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The total 369 trees were digitised as the reference to assess theaccuracy of segmentation, classification result as well as the extracted
height from the CHM. Accurate segments are the segments that have one to
one matching with the corresponding reference crown. The one to one
matching criteria were introduced in the Methodology chapter.
Out of 369 trees, there are 265 trees meet the one to one matching
criteria. Thus, the segmentation accuracy in general is 71.9 % (28.1% error.
Over segmentation yields commission errors as one tree is segmented tomore than one object for one reference tree. If there is no tree is identified
for one reference tree exist, under segmentation or omission errors are made.
4.2. Canopy height model validation
The linear regression model was used to relate the LiDAR-derived
heights with the field inventory data of the individual trees. The independent
variable is LiDAR-derived height and dependent variable is the measured
height. Of the 369 trees, one third was selected randomly to validate the
LiDAR-derived height of individual tree. The obtained coefficient ofdetermination is 0.72. The RMSE is 2.68 (m), RMSE in percentage is 17.9%.
4.3. Object-based image classification
The reference dataset of 369 trees was randomly divided into twoparts, two third was used as training data, one third was used as validation
data. The delineated crowns were used to classify into two classes: Shorea
robusta and Other species.

Figure 3: Map of tree species in the study area

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Overall classification accuracy is 81.30% (19.7% error) and kappa is0.4023. Shorea robusta was classified better compared to Other species
with Producer’s accuracy is 79.6% and Users accuracy is 96.3%.
4.4. Regression analysis
Out of a total of 369 trees, 73 Shorea robusta trees and 29 Other
species trees was used for regression analysis. Firstly linear regression
analysis was used to test whether CPA and LiDAR-derived height ofindividual crown could explain the amount of carbon stock of individualtree. The test was applied for the two classless: Shorea robusta and Other
species. Analysing the result will help us to make sure that it makes sense toinclude both independent variables (CPA and Height) in multiple regression
models. The F significant of ANOVA test examining this relationship ofShorea robusta and Other Species are really low: 2.91E-16 and 1.46E-06. It
can be concluded that both CPA and height have a significant relationshipwith the amount of carbon of each individual tree. Therefore, it is significant
to involve both CPA and height in the multiple regression analysis toestimate carbon. In the multiple regression model, the independent variables
are CPA and height and the dependent variable is carbon. The carbon model
was built for species class.
4.5. Model validation
A linear regression model was applied to validate the developed
carbon predictive model above. Measured and predicted carbon stocks wereplotted against each other and the regression co-efficient was calculated.
The coefficient of regression for Shorea robusta is 0.68 and for Other
species is 0.62. The RMSE is equal to 36% for Shorea robusta and 23.8%
for Other species
Shorea robusta
Regression Statistic
Multiple R
0.872763
R Square0.761716
Adjusted R
Square0.754908
Standard error
29.40335
Observations
73
CoefficientIntercept-139.75
Slope CPA
5.886757
Slope Height
7.133373
P-value
Intercept
1.49E-13
Slope CPA
2.82E-06
Slope Height
6.05E-09
One Way ANOVA test
F
111.8833
F significant
1.58E-22

Other Species
Regression Statistic
Multiple R
0.82393
R Square
0.678861
Adjusted R
Square0.654158
Standard error
26.45676
Observations
29
CoefficientIntercept
125.3983
Slope CPA
-3.85821
Slope Height
3.500388
P-value
Intercept
0.002566
Slope CPA
0.002087
Slope Height0.009776
One Way ANOVA test
F
27.48093
F significant
3.86E-07

Table 1: Multiple regression analysis results

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5.

DISCUSSION

5.1. Tree crown delineation
The Region growing algorithm for individual tree delineation releases
the satisfactory results in previous study (Broadbent et al., 2008; Darius etal., 2002). The accuracy of segmentation achieved in this study is almost
exactly similar to the results of research of Bunting et al (2006) who
obtained 72% well isolated trees. Tiede et al. also reported 72% accuracy ofcrown delineation of the tree in forest area Germany. Ke et al. (2011)
obtained the accuracy of 70% while using region growing to delineate thetree crown for Norway spruce trees.
However, in dense forest area, the neighbouring trees might haveshade and obscure the edges of their neighbours which results in darker
image values at tree boundaries and leads to the identification of false seeds(local minima and local maxima) (Li et al., 1992). The over-segmentation
produced multiple segments overlapping with a single reference object. On
the other hand, under-segmentation may produce larger segments whichcontain the reference (Ke et al., 2010). In our study, the number of tree oversegmentation was found more than the tree under segmentation. Among thereference crowns, Shorea robusta has 81 over-segmented crowns and only 4
under- segmented crowns; Other species class poses 16 over segmented
crowns and 3 under segmented crowns. The reason for these uncertainties
may also be explained by the complex forest structure in the study area. We
have observed the overlapping and intermingling situation in almost
sampling plot.
The shadow is also the main factor causing uncertainties in crowndelineation (Martinez Morales et al., 2008). The shadow in the image is dueto the effect of view angle, topography and sun elevation. The image for thisstudy was acquired around 10a.m with view area within ±220 off-nadir withsun angle 450. The view of ±220 off-nadir may result in casting highshadows because the trees trend to lean away from the nadir. In addition, thestudy area is on the mountainous terrain, therefore, topographic distortions
is observed negatively affect the image quality by causing more shadow
effect. The poor spectral separability between different species is also one ofreasons causing poor delineated crown. Additionally, there are a number oftrees cannot delineated because of the weak signal.
5.2. LiDAR derived height
Each crown delineated from the Geo-Eye imagery is considered an
individual tree. The height of one individual tree is the maximum pixel
value of CHM which is inside the boundary of the tree crown. In general,the height derived from LiDAR data (point density of 0.8/m2) fitted quite
well with the measured height from the field with the R2 of 0.72. Comparedto the research of Leckie et al (2003) in coniferous forest, the R2 when
fitting measured height and predicted height yields 0.84 with the LiDAR
point density of 2/m2 for . With the same point density (2/m2), St-Onge
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August 2013, Thailand(2000) archived the R2 of 0.90 for coniferous forest. Reitberger et al (2007)
results indicate that the detection rate for coniferous trees is 61 % and for
deciduous trees 44 %, respectively 7 % of the detected trees are false
positives. There are a limited number of researches on the LiDAR derived
height in the deciduous forest and tropical forest (Asner et al., 2011). Drakeet al.(2002) examined the relationship between corresponding LiDAR andfield profile metrics and achieved R2 of generally 0.83.
There are many reasons causing the error of LiDAR derived height.
Firstly, the LiDAR point does not hit the top of the tree (St-Onge et al.,2001), especially in dense forest like the study area, and leads to theinaccurate predicted height. In addition, the extracted tree height in thisstudy also depends on the delineated crown from Geo-Eye imagery. If thecrown is poor delineated, the top of tree A may be placed in the crown oftree B. The residual of DTM and DSM interpolation process is also one
source of errors.
5.3. Model Development
Multiple regression analysis was used to develop the relationshipbetween CPA, height and carbon stock of the individual tree. The regression
analysis shows that tree height of Shorea robusta has stronger relationshipwith the carbon stock (R2=0.67) compare to CPA. Meanwhile, for otherspecies, the opposite is the case, the CPA (and not the height) of Other
species is more strongly link to carbon stock.
The coefficient of determination obtained for Shorea robusta was 0.76
and 0.67 for Other species. The reason why Other species has lower
coefficient of determination is that many different species are used the same
allometric equation for this class. Different species each have their own
characteristics, for example, Schima wallichhii is short but the crown islarge mean- while Castanopsis indica is tall but the crown is medium in size.
Both CPA and height can be include in the multiple regression model
because, CPA and height is tested and has no relationship. The reason is thatthe leaves of Shorea robusta are always collected to make traditional bow
by the local people, there for many tree has the deformed crown.
5.4. Biomass and Carbon stock estimation
The results of this research show that the carbon stock of the study
area was approximately 41 Mg Cha-1. This result is comparable to thecarbon stock estimated with the range of 34.30 – 97.86 MgCha-1 in Nepal
forest of Baral et al. (2009). Rachna (2011) also chose Ludhikholawatershed as the study area. However the research of Rachna (2011)
focused on the carbon estimation Ludi-damgade community, one of fivecommunity forests in our research, with the carbon estimate of 31MgCha-1.

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6. CONCLUSION AND RECOMMENDATION
For each class (Shorea robusta and Other species), linear regression
analysis was used to relate carbon stock of individual tree with CPA andheight, one after other. The results of linear analysis show that, both CPA
and height have relatively good relationships with the carbon stock ofindividual tree. Therefore, the multiple regression analysis was applied touse both CPA and height to explain carbon stock. The developed model thenwas called the regression predictive model of carbon. The multiple
coefficient of regression obtained from regression predictive model ofcarbon for Shorea robusta is higher than the one of Other species, R2= 0.76
and 0.68 respectively.
The combination of LiDAR data and VHR Geo-Eye imagery provides
a good source of information which can be used for forest carbon stock
estimation. In order to increase the accuracy carbon estimation using the
combination of LiDAR data and VHR Geo-Eye imagery, the foreseen
uncertainties should be reduced by: (i) developing local level allometricequations for Nepal for better carbon stock estimation, (ii) finding a method
to better integrate the CHM and Geo-Eye imagery in tree crown delineation
process.
REFERENCE
Asner, G., Mascaro, J., Muller-Landau, H., Vieilledent, G., Vaudry, R.,
Rasamoelina, M., Hall, J., & van Breugel, M. (2011). A universal
airborne LiDAR approach for tropical forest carbon mapping.
Oecologia, 1-14.
Baral, S. K., R, M., & S, R. (2009). Above-ground carbon stock assessment
in different forest types of Nepal. Banko Jankari, 19(2)(10-14).
Broadbent, E. N., Asner, G. P., Keller, M., Knapp, D. E., Oliveira, P. J. C.,& Silva, J. N. (2008). Forest fragmentation and edge effects fromdeforestation and selective logging in the Brazilian Amazon.
Biological Conservation, 141(7), 1745-1757.
Bunting, P., & Lucas, R. (2006). The delineation of tree crowns inAustralian mixed species forests using hyperspectral Compact
Airborne Spectrographic Imager (CASI) data. Remote Sensing ofEnvironment, 101(2), 230-248.
Darius, S., & Culvenor. (2002). TIDA: an algorithm for the delineation oftree crowns in high spatial resolution remotely sensed imagery.
Computers &amp; Geosciences, 28(1), 33-44.
Drake, J. B., Dubayah, R. O., Knox, R. G., Clark, D. B., & Blair, J. B.
(2002). Sensitivity of large-footprint lidar to canopy structure andbiomass in a neotropical rainforest. Remote Sensing of Environment,81(2–3), 378-392.
García, M., Riaño, D., Chuvieco, E., & Danson, F. M. (2010). Estimating
biomass carbon stocks for a Mediterranean forest in central Spainusing LiDAR height and intensity data. Remote Sensing ofEnvironment, 114(4), 816-830.
Forest carbon estimation using the combination of LiDAR data and high resolution imagery in Ludikhola
Forest carbon estimation using the combination of LiDAR data and high resolution imagery
113
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113
in Ludikhola Watershed, Nepal

August 2013, Thailand

Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring andestimating tropical forest carbon stocks: making REDD a reality.
Environmental Research Letters, 2(4).
IPCC. (2006). 2006 ICPP Guidelines for National Greenhouse Gas
Inventories.
Jochem , A., Holllaus, M., Rutzinger, M., Hofle, B., Shaudeuder, K., &Maier, B. (2009). Estimation of aboveground biomass using airborne
LiDAR data
Ke, Y., & Quackenbush, L. J. (2011). A comparison of three methods forautomatic tree crown detection and delineation from high spatial
resolution imagery. International Journal of Remote Sensing, 32(13),3625-3647.
Ke, Y., Quackenbush, L. J., & Im, J. (2010). Synergistic use of QuickBird
multispectral imagery and LIDAR data for object-based forest speciesclassification. Remote Sensing of Environment, 114(6), 1141-1154.
Kim, S., Kwak, D., oLee, W., Son, Y., Bea, S., Kim, C., & Yoo, S. (2010).
Estimation of carbon storage based on individual tree detection in
Pinus densiflora stands using a fusion of aerial photography and
LiDAR data. SCIENCE CHINA Life Sciences, 53(7), 885-897.
Leckie, D., Gougeon, F., Hill, D., Quinn, R., Armstrong, L., & Shreenan,
R. . (2003). Combined high-density lidar and multispectral imagery
for individual tree crown analysis. Canadian Journal of Remote
Sensing, 29(5), 633-649.
Li, X., & Strahler, A. H. (1992). Geometric-optical bidirectional reflectancemodeling of the discrete crown vegetation canopy: effect of crownshape and mutual shadowing. Geoscience and Remote Sensing, IEEE
Transactions on, 30(2)(276-292).
Lu, D. (2006). The potential and challenge of remote sensing-based biomass
estimation. International Journal of Remote Sensing, 27(7), 12971328.
Martinez Morales, R., Miura, T., & Idol, T. (2008). An assessment of
Hawaiian dry forest condition with fine resolution remote sensing.
Forest Ecology and Management, 255(7), 2524-2532.
MOFSC. (2009). Ministry of Forests and Soil Conservation, Government of
Nelpal/ REDD- Forestry and Climate change cell. Retrieved 28 May,
2011, from http://mofsc-redd.gov.np/introduction/
Popescu, S. C. (2007). Estimating biomass of individual pine trees using
airborne lidar. Biomass and Bioenergy, 31(9), 646-655.
Rachna, S. (2011). Comparison of individual tree crown delineation method
for carbon stock estimation using very high resolution satellite images.
MSc Thesis. University of Twente Faculty of Geo-Information andEarth Observation ITC,. Enschede.
Reitberger, J., Heurich, M., Krzystek , P., & Heyder, U. (2007). single tree
detection in forest area wiht high densitt LiDAR data
Sohn, G., & Dowman, I. (2007). Data fusion of high-resolution satellite
imagery and LiDAR data for automatic building extraction. ISPRS
Journal of Photogrammetry and Remote Sensing, 62(1), 43-63.
St-Onge, B. (2000). Estimating individual tree heights of the boreal forest
using airborne laser altimetry and digital videography. Paper
presented at the Workshop of ISPRS WG III/2 & III/5 : Mapping

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surface structure and topography by airborne and spaceborne lasers,
La Jolla (Californie), pp. 179-184.
St-Onge, B., Hu, Y., & Vega, C. (2008). Mapping the height andabove‐ground biomass of a mixed forest using lidar and stereo Ikonos
images. International Journal of Remote Sensing, 29(5), 1277-1294.
UNFCCC. (2005). Kyoto Protocol.
Retrieved 26 May, 2011, fromhttp://unfccc.int/kyoto_protocol/items/2830.php
Y. Nakai, F. Hosoi, & Omasa, K. (2009). Estimating carbon stocks ofconiferous woody canopy trees using airborne LiDAR and passive
optical sensor.

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6thTHREE-DIMENSIONAL SIMULATION OF
FLOW OF FRESH CONCRETE IN
MULTI-DIMENSIONAL STEEL BAR
ARRANGEMENT BY MPS
Shunya KIMURA1 and Kohei NAGAI2
1
Master course student, Department of Civil Engineering,
The University of Tokyo, Japans-kimura@iis.u-tokyo.ac.jp
2
Associate Professor, ICUS, IIS, The University of Tokyo, Japan
ABSTRACTAs the amount of steel bars in buildings increases, it gets moredifficult to compact a fresh concrete in the congested area of steel bars. It
becomes important to establish a numerical model to predict thecompaction and flow behavior of flesh concrete. The aim of this research is
to simulate the flow behavior of fresh concrete under multidirectional bar
arrangement condition by three dimensional MPS (Moving Particle Semiimplicit) method, one of the particle methods. It is based on a fluid
mechanics in which Bi-viscosity model is applied to the particle element
where yield value and plastic viscosity are set as the yield point parameterand hence different from methods such as the Distinct Element Method(DEM) used in the previous researches in the similar field.
L-shape box flow tests were simulated where the steel bars were set
multi-directionally at the distance of 75mm and 150mm in grid andcompared with the experimental results. Considering the computationaltime and the gap between steel bars, the particle size in the simulation is set
to 10mm. From the analysis, flowability and passing behavior of concrete
through the steel bars is examined by changing material models anddistance between steel bars. Simulation of flow behavior of the viscous fresh
concrete is found to be similar to that observed in the experiment.
Keywords: MPS, flow analysis, Bingham fluid, flowability of flesh
concrete, flow behavior under obstacles.
1. INTRODUCTION
Recently, as the amount of steel bars in the building structure is increasing,it gets more difficult to compact a fresh concrete in the congested area ofsteel bars. It becomes important to establish a numerical model to predictthe compaction and flow behavior of flesh concrete in congested
arrangement of steel bars where means of compaction is self-weight ofconcrete and injection of pump only. Therefore, in case of some structures,

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flow experiment with full-scale model is needed. But many cases ofexperiments cannot be done because of high cost and time. However, if
evaluation of workability by flow analysis of concrete is established, many
parametric studies can be done, and prediction of workability in advance
becomes possible (Tanigawa and Mori, 1988).
Recently, MPS (Moving Particle Semi-implicit), one of particle
methods, is proposed as an analysis method suitable for large deformation
problem (Y. Chikazawa, S. Koshizuka and Y. Oka, 2001). MPS is one of
the analysis methods analyzing incompressible flow. It is different from
DEM (Distinct Element Model) applied in the previous research in this fieldin this point. MPS can solve large deformation problem of free boundary
without cells and elements. Based on such an advantage, 3-dimension flow
analysis of flesh concrete; which is non-Newtonian fluid, has been tried.
Constitutive model of flesh concrete is regarded as bi-viscosity model, and
it is reported that flow of flesh concrete is modeled. It is also reported thatflow stop and stuck of flesh concrete can be expressed by shear velocity ofparticles (Urano, Nemoto and Sakihara, 2012). The aim of this research is tosimulate the flow behavior of fresh concrete under multidirectional bar
arrangement condition. In this research, we examine slump flow experiment
analysis and L-shape box flow analysis on distance between steel bars andmaterial properties effect. Next, results of numerical simulation arecompared with that of experiment.
2. METHOD OF ANALYSIS
2.1. Summary of MPS
MPS, one of particle methods, is the analysis method that analyzes
incompressible flow by Lagrangian Method which calculates every particle
movement (Figure 1).
Generally, wall boundary is replaced with particles in MPS. However,in this research, surface mesh is used as wall boundary. This enables it todecrease number of particles and computational time.

Figure 1: Conceptual diagram of MPS
2.2. Establishment of distance between particles
Flesh concrete is presumed as Bingham fluid and homogeneouscontinuum model. Therefore, aggregates and mortar are not distinguished

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and movement of aggregates in concrete is not covered by the analysis. And
smaller the distance between particles, larger will be the number of particles,
and hence flow behavior can be expressed in detail. Considering thecomputational time and distance between steel bars in preliminary analysis,
the particle size is set to 10mm. Preliminary analysis on the particle size
effect is described in 4.1.
2.3. Constitutive model of flesh concrete
In Bingham fluid, if shearing stress is less than yield value, it isregarded as immobility, and shear velocity is zero. Therefore, stress cannot
be calculated and analysis does not work (Japan Society of Civil Engineers,
2003). In this research, bi-viscosity model is applied to particle element asshown in Figure 2. Bi-viscosity model behaves as highly viscous fluid until
shearing stress reach yield value. Constitutive equations are expressed asfollows (equations 1 and 2).

 ij   P ij  2( (1) ij   P ij  2( (2)where;
P = pressureη = plastic viscosity
τy = yield value
ij = shear velocity

y



)ij

  c)ij  cycδij = Kronecker deltaΠ =2εij2
Πc is index determining mobility or immobility, and expressed as follows(equation 3).
2
 2C y y  c  

(3)
where;Cy = yield point parameter

Figure 2:Bi-viscosity model

Three-dimensional simulation of flow of fresh concrete in multi-dimensional steel bar
Three-dimensional simulation of flow of fresh concrete in multi-dimensional steel bar arrangement by MPS
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3. ANALYSIS MODEL3.1. Slump Flow Experiment Model
Slump flow experiment model is as shown in Figure 3, number ofparticles in numerical model is 5277.

Figure 3: Particle model of slump flow experiment3.2. L-shape Box Flow Model
L-shape box flow model is created having 39117 numbers of particles
as shown in Figure 4. Arrangement of steel bars is as shown in Figure 5, andc/c distance between bars (=D) is 75mm and 150mm in grid. Diameter ofsteel bar is 22mm.

Figure 4: L-shape box model

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Figure 5: arrangement of steel bars
3.3. Material Model
Material properties, which are yield value and plastic viscosity, areadopted from previous analysis (Urano, Nemoto and Sakihara, 2012). They
are named as Material 1 and 2. To study the effect of variation of plastic
viscosity, Material 3, 4 and 5 are adopted. Detail of material properties areshown in Table 1.
Material
12
3
45

Table 1: Material properties

Yield value(Pa)
1000
160
500
500
500

Plastic viscosity(Pa*s)
300
300
300
500
700

3.4. Analysis cases
Analysis is done in two steps as shown in Tables 2 and 3. In
preliminary analysis, D is set to 75mm because particle size will have asignificant influence while passing through steel bars. Aim of preliminary
analysis is to examine the effect of particle size variation. And after
studying the result of preliminary analysis, particle size is set to 10mm infurther analysis cases.
Table 2: Preliminary analysis cases

case
Pre1
Pre2
Pre3

Material
1
1
1

case
1
2
3

Material
2
5
5

D(mm)
75
75
75

Particle size(mm)51020Table 3: Analysis cases
D(mm)
75
75
150

Particle size(mm)
10
10
10

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4. RESULT
4.1. Preliminary analysis
Result of preliminary analysis is as shown in Figure 6. In case of Pre3,
particles seem to be stuck when they pass through steel bars. From the result,
it is observed that larger the particle size, smaller will be flow distance.
Also computational time increases as particle size decreases. Hence infurther research, considered computational time and particle size effect,
particle size is set to 10mm.

(a) Pre1(5mm)(b) Pre2(10mm)(c) Pre3(20mm)
Figure 6: Result of preliminary analysis
4.2. Slump flow experiment analysis
Result of slump flow experiment analysis is shown in Figure 7. It isobserved that as yield value and plastic viscosity increase, slump and slump
flow decrease. From the result, it is observed that fluid property isdetermined by yield value and plastic viscosity.

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Figure 7: Result of slump flow experiment analysis
4.3. L-shape box flow analysis
Result of L-shape box flow analysis in case 1 is as shown in Figure 8.
Figure 8 (a) shows that particles pass through steel bars, and Figure 8 (b)shows that flow of particles stopped by compaction.

(a) at 3.5 seconds(b) at 180 seconds
Figure 8: Result of analysis case 1
4.4. Comparison with experiment4.4.1. Experiment
Experimental results used for comparison is taken from a study doneby Penta Ocean Construction. In this research, L-shape box model is basedon same experiment. Experimental setup is shown in Figure 9 (a). In thisexperiment, anti-washout underwater concrete having high viscosity is used.

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August 2013, Thailand(a) at 0 seconds(b) at 600 seconds
Figure 9: Experiment setup
4.4.2. Analysis
In the experiment study, it is stated that high viscosity material model
is chosen. But value of plastic viscosity of concrete is not available; hence
simulation results shown here are based on Material 5. Results ofexperiment and these analyses are shown in Figures 10, 11, 12 and 13. Incase of D=150mm, flow behavior in analysis is similar to that observed inexperiment. On the other hand, in case of D=75mm, flow distance inanalysis is larger than that in experiment. The reason is in case of small D,
flow of flesh concrete is obstructed by formation of arches of aggregates.
Our analysis model does not consider mortar and aggregates separately;
hence formation of arch of aggregates is not modeled perfectly as found inexperiment. As a result, flow behavior in analysis is found to be different
from that in experiment is case of smaller value of D (=75mm). But in caseof D=150mm, formation of arches of aggregates is not found, hence
experiment and analysis results hold a better match.

(a) at 5 seconds(b) at 40 seconds
Figure 10: Result of experiment in D=75mm

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Figure 11: Result of analysis in case 2(a) at 5 seconds(b) at 40 seconds
Figure 12: Result of experiment in D=150mm(a) at 5 seconds(b) at 40 seconds
Figure 13: Result of analysis in case 3
5. CONCLUSION
Flow analysis of flesh concrete can be done by using MPS. Further, flow
behavior in multi-dimensional steel bar arrangement is compared withexperiment with high viscosity material model. In case of large distance
between steel bars, flow behavior similar to experiment is found. But in caseof small distance, in which formation of arch of gravels can occur, andhence result of experiment does not match with the numerical simulation.
Therefore, gravels need to be considered in case of small distance.
REFERENCES
Y. Chikazawa, S. Koshizuka and Y. Oka, 2001. A particle method forelastic and visco-plastic structures and fluid-structure interactions, Vol27,
No. 2, Computational Mechanics, 97-106
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Yasuo Tanigawa, and Hiroshi Mori, 1988. Approach to establishmentof “construction and design method” of concrete, No.501, Journal of
Cement and Concrete, 11-20. (In Japanese)
Japan Society of Civil Engineers, 2003. Chapter 4, Modeling of flesh
concrete, present condition and problems of technology of compaction
analysis, present condition and problems of consistency evaluation of flesh
concrete, 69-97. (In Japanese)
Shinji Urano, Kouzi Nemoto, and Kohei Sakihara, 2012. Research
about application of flow simulation for evaluation of filling-ability of selfcompacting concrete, Vol68, No. 1, Japan Society of Civil Engineerscollected papers, 38-48. (In Japanese).

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APPLICATION OF RISK MANAGEMENTTO FORECASTING TRANSPORTATION
DEMAND
Baekkyu NAMKUNG1, Sungbong CHUNG2Doctor’s Course, Graduate of Railroad, Seoul Nat’l
University of Science & Technology, Korea
baekkyou@nate.com
2
Assistant Professor, Graduate of Railroad, Seoul Nat’l
University of Science & Technology, Korea
1

ABSTRACTTransportation SOC investment feasibility is analyzed byforecasting transportation demand. In order to reflect the change of future
trip-pattern, O/D is adjusted according to the land-use development plan.
However, social & economic indicators and land-use development plan indeveloping counties such as Korea are apt to change rapidly, thus theerrors in forecasting demand occur. For instance, the future traffic volume
analyzed in Korea’s LRT projects (Yong-in, Uijeongbu, etc.) has a marginof error of -70~-90% because they did not reflect the change of future
development plan. In most countries such as Korea, to analyze theuncertainty of future plan, a simple sensitivity analysis is performed. In
some countries such as Japan and the UK transport demand, they have theirown risk management systems to respond to the risks and prepare
countermeasures to minimize the risks of the project. However, the standard
procedure and methodology of risk management which can be applied
during forecasting traffic demand does not exist. In this study, the errors inforecasting demand system are reviewed and to deal with these errors
effectively the methodology of risk management is developed. To develop it,
general risk management techniques such as risk identification, risk
assessment, risk responses are applied.
Keywords: SOC investment feasibility, land-use development plan,
uncertainty of future plan, risk management, transportation demand error.
1. INTRODUCTION
Since the enactment of “Act of Private Investment Inducement on SOC
Facilities”, both the government and private sector have increased theirinvestment in transportation SOC. However the risk of SOC investment isincreasing in terms of the uncertainty of cost such as financial crisis andprice rise of raw materials and of transportation demand such as the
reduction of future development plans and the changes in trip patterns. For
instance, regarding Uijeongbu LRT, Uijeongbu-si has signed atransportation demand agreement for 79,000 passengers without reviewing

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the uncertainty of transportation demand in future, yet it turned out only
11,000 passengers used the service. As a result, Uijeongbu-si’s burden on
MGR (Minimum Revenue Guarantee) increased, with the actual demand of
Uijeongbu LRT being 10% of the agreed demand, which lead to an increaseof the government assistance. Eventually, KORAIL(Korea Railroad) has
acquired the shares of private capital, 2.5 years after the opening of service.
An investment evaluation system such as pre-feasibility study or
feasibility assessment conducts an analysis of sensitivity which increases ordecreases discount rate, cost or benefit in response to the changes in socialand economic environment, which in fact does not respond to the changes
systematically. Given the need of the introduction of a systematic risk
management system to a investment assessment system approving
transportation SOC investment, this study is intended to suggested step–bystep risk management in order to cope with the risks and uncertainty of
projects through continued transportation SOC projects management fromthe perspective of transportation demand forecast.
2. THE REVIEW OF PREVIOUS STUDIES
Sungbong CHUNG and Justin S. CHANG (2007) have categorized the error
factors which may occur in each step of 4-Step Demand Forecast Method
into: Measurement or data error, Model specification error and External or
exogenous error (Mackie & Preston, 1998; Flyvberg, 2005 Flyvberg et al.,2005, 2006; World Bank, 2005), typed errors and then conducted an effectanalysis in accordance with the error factors of demand forecast. The results
suggested the demand variation is affected by ‘car occupancy’ and ‘relevantplans’ the most. Taking account of the fact the social and economic
uncertainty may change the results of transportation demand forecast,
Sungbong CHUNG (2011) suggested ‘step-by-step risk management plans’such as trip generation and distribution, modal split and trip assignment byusing Delphi technique in order to apply the risk management to the processof transportation demand forecast. The results of Delphi analysis suggested‘the uncertainty of relevant plans’, ‘problems of model application’ and‘problems of major parameter calculation’ are critical risk factors in ‘trip
generation and distribution’, ‘modal split’ and ‘trip assignment’,respectively.
3. CASE STUDIES IN HOME AND ABROAD, IMPLICATION AND
IMPROVEMENTS
3.1 The state of Risk Management in Home and Abroad
In Korea, as shown in Table 1, the economic analysis of pre-feasibility
study and feasibility assessment focuses on B/C due to the variation of cost,benefit and discount rate, which has the limitation in systematically
responding to the changes of conditions in future.

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Table 1: Standards of a sensitivity analysis

Classification
Cost
Benefit
Discount rate

Scope of application
ㆍ+10%, +20%, +30%, +40%, +50%
ㆍ-30%, -20%, -10%, +10%, +20%, +30%
ㆍ 3.5%, 4.5%, 6.5%, 7.5%

In the case of private capital projects in Korea, 'quantification of risk'
item is assessed via ‘private capital eligibility review’ (Korea Development
Institute). However the countermeasures of PSC project model and PFI
project model are suggested and included in VFM assessment. (see Figure
1).

Figure 1: Private investment projects risk management process
In Japan, unlike other countries, a structured process of risk analysis
and risk quantification has not been established, yet risk-sharing is specified
in details. In the event of natural disasters, inflation, changes in demand andchanges economic conditions which were not considered when making anagreement, additional expenditure may incur. Thus the disputes arisingfrom the risk factors in future are minimized through the identification ofrisk factors and clear definition of assessment, partakers and ways ofsharing.
Table 2: Risk factors identified during the review of risk sharing (Japan)
Classification

Survey , design
Land acquisition
Construction

MaintenanceCompletion of projects

Risk factors
 Delay in design and construction, excess of expenditure
above the agreed amount, defects of products such as
designs, etc.
 Land acquisition, Land use right acquisition for a certain
period of time Excess of expenditure above the agreed amount due tothe delay in construction, damages to third parties during
construction, defects after the completion of projects
 Delays in operation, differences in public service
demand forecast, maintenance, operational interruption,
property damage, accidents relating to maintenance andoperation, defects in repaired parts, etc.
 Restoration after removal, transfer of public facilities

In UK, the Exchequer checks all risk factors and assesses the effectand probability; makes Risk Register agreed by Integrative ProjectManagement Team; and reviews the risk factors in every Gateway Process

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to check if the project can progress to the next stage in order to reduce theoverall projects risks from A to Z.
After deciding the balance between cost and risk regarding the benefit to betransmitted to the ordering body, the Exchequer focuses on risk
management and the opportunities which will increase values.
Table 3: Risk Management Structure in UK

Step① Risk check② Risk evaluation③ Risk response

Description
 Workshop methods, Check-list
 Understanding the likelihood and the impact on businessresults, and quantifying them: Estimated risk cost= Probability × Risk effect Qualitative evaluation and Quantitative evaluation: Cost exceeding basic cost (riskprovision), cost increase arising from the delay inconstruction
 Avoidance Reduction- Re-design and detailed design, site investigation- Alternative construction methods to avoid the existingrisk factors- Selecting alternative procurement path

In Canada, it is stipulated that Project Management Manual needs tobe prepared. Manual covers work management, quality management, time
management, cost management, risk management and communication
management, etc. across the entire project.
Table 4: Structural risk analysis framework in Canada

Classification① Preparation② Risk assessment③ Risk response④ Risk management⑤ Publishingassessment report

Description
 Forming Risk Review Team consisting of stake-holders andexperts and scheduling projects
 Calculating risk factors at workshop through brainstorming Analyzing the risk factors and likelihood on the basis of theassessment reports in the past
 Analyzing the size of risk effect and grouping them inaccordance with the severity of risk
 Assessing risks in accordance with risk factors in each group
 Deciding measures against major risk factors and establishingplans
 Transferring strategies and plans to Risk Review Team Applying established plans and monitoring Comparing estimated effect with actual effect and analyzingthem Writing items to be supplemented and improvement plans

3.2 Risks of Errors in Transportation Demand Forecast
In Korea, traffic demand is forecasted via traditional 4-Step Traffic
Demand Forecast. Traditional traffic demand forecast is based on theassumption- the current traffic pattern will be maintained in the future,
which may lead to wide margin of error in the event of sharp changes in

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future population, and socio-economic indicators or additional development
plans.
Table 5 shows the comparison of the total of O/D in the metropolitan
areas provided by the National Transportation DB. Traffic varies up to 30%
depending on the publication year, indicating the results of project demand
forecast may vary depending on the distribution year.
Table 5: Korea Transportation DB (KTDB) O/D in Capital area
(Based 2021)
Year
Trip
2002
GrowthTrip
2006
GrowthTrip
2007
GrowthTrip
2011
GrowthTotal
56,16658,487
4.13
53,671-8.23
48,810-9.06

Auto18,564
20,86712.41
20,9880.58
20,387-2.86

Bus
17,235
15,884-7.84
15,821-0.40
11,870-24.97(unit : thousand trip/day, %)
Rail
Taxi
Other
14,6002,918
2,850
14,301
3,420
4,015
-2.05
17.21
40.87
9,962
3,220
3,680
-30.34-5.84-8.34
10,045
3,846
5,463
0.82
19.41
48.45

Recently the errors in forecasting the demand of Incheon International
Airport Railroad (A’REX) is controversial. As shown in Table 6, an error of
about 90% in demand forecast compared to 2010 occurred. Regarding therealization of future development plans reflected in demand forecast, thesize of future land plan was reduced and the transportation network plan
was candled after the completion of project, and wide margin of error inmodal split occurred, which led to the overestimation demand. Thus it isessential to manage such risk factors systematically as uncertainty factors
during demand forecast may threaten the feasibility of project.
Table 6: Comparing the actual demand and demand agreement in A'REXClassification

Opening

Forecasted Demand
Actual Demand
Error
Error Ratio

207,421
13,329-194,092-93.57(unit : person/day, %)1 year after
2 year after
opening
opening
226,642
248,294
16,59518,063-210,047-230,231-92.68-92.73

3.3 Implications and Improvements
Korea’s investment evaluation system reviews the changes of B/C
roughly via a sensitivity analysis, and thus has the limitation in coping withthe future risk factors. To resolve these problems, studies on Risk
Management have been conducted in Korea too, yet the results are notreflected in reality. In Korea, risk management in transportation SOC
investment focuses on a total construction cost and it is stipulated thatappropriate measures by project stage against risks need to be prepared.
Risk management implementation process is in general based on 3-Step:
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Risk Aawareness->Risk Assessment->Risk Response for the purpose of
systematic risk response, indicating that it is essential Korea’s investment
evaluation system also need to introduce such systematic risk management
system as soon as possible.
In other words, it is essential to prepare measures against the demandchanges arising from the uncertainty in the future conditions during the
project implementation through more systematic risk management andfurthermore introduce overseas 3-Step Risk Management System (Risk
Aawareness->Risk Assessment->Risk Response) for the purpose of
preparing the systematic risk management measures according to thetransportation project investment stage.
4. INTRODUCTION OF RISK MANAGEMENT
4.1 Outline of risk management
Oren and Rothkopf (1975) defined Risk as the likelihood of ‘Loss’,‘Injury’, ‘Disadvantage’ and ‘Destruction’. Risk Management is a total
process which identifies/controls/removes and minimizes the risk effects on
projects, and the insurance theories in the US in the 1950s had addressed
this notion for the first time. In general, Risk Management identifies causesof risks under the certain circumstances; analyzes risk effect; measures andevaluates the consequences; and then finally establishes and takes thecountermeasures to reduce negative effects of risks or eliminate the risk
itself.
Risk Identification is a process to: (1) identify the causes of the risks
associated with certain projects and classify them systematically according
to a consistent standard, (2) determine the gravity of consequences, (3)select the critical risk factors to consider in priory during risk analysis.
A risk analysis processes all available quantitative data
mathematically and statistically and quantifies the likelihood of specificrisks and potential consequences. As for the materials that require subjective
judgment during risk analysis and risk assessment, the judgment by experts
and hands-on officers is useful. In this case the judgment by experts andhands-on officers is not just their opinions but the assessment systematicallyand consistently organized. It is critical to use a methodology that is able todisplay the judgments with objective probability after eliminating prejudicesand errors in judgments, and quantify them systematically.
The purpose of Risk Response is to eliminate the adverse effect ofrisks as much as possible and increase control over risks. Risk Response isbased on two basic directions: the establishment of counterstrategies and theallocation of the counterstrategies against specific risks.
Risk Response strategies included: Risk Avoidance, Risk Reduction, Risk
Transfer and Risk Retention. The purpose of Risk Avoidance is to avoid thepotential loss and avoid exposure to risks, yet in compensation for potential

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benefit or opportunities. The purpose of Risk Reduction is to reduce thedegree of exposure to risks by reducing the likelihood of risks by taking
every possible measure, which can be accomplished through a clear
understanding of assumptions, more concrete design and the re-design ofincomplete parts. Risk Transfer is a way to transfer or share the potential
consequences of risks to and with other groups (organization) through anagreement. Risk Retention is a strategy which is to intentionally andconsciously take risks which cannot be transferred or avoided in accordancewith the requirements, finance and capabilities of project managers.
4.1 Outline of risk management
This study has suggested the step-by-step measures against risks byapplying risk management techniques to future demand forecast andeconomic analysis in terms of total project cost. Furthermore, this study has
analyzed the uncertainty risk factors in each step of 4-Step Demand Forecast
Method (risk assessment) in order to suggest specific countermeasuresagainst risks. As for risk response methods, this study has suggested specificcountermeasures regarding 4 alternatives (Risk Avoidance, Risk Reduction,
Risk Transfer and Risk Retention) for the purpose of selecting the bestalternatives.

Figure 2: Introduction of risk management (idea)
First, as for Risk Identification, it needs to extract the risk factors
which are expected to occur through the consultation with experts or
responsible departments after extracting risk factors by step as shown intable 7. It shows examples of risk factors by demand forecast step andsuggests the risk factors-future development plan, uncertainty of traffic
network plan, uncertainty of socio-economic indicators and the variation of
Korean currency.
As for the selection of means, Table 7 suggests response to thechanges in the fare by transportation means and the risk factors regarding
the changes in modal split in accordance with the introduction of newtransportation; and as for trip distribution, suggests the change of nationaltraffic network and the uncertainty of project type. Thus it needs to suggest
all possible risk factors for seamless project implementation by calculatingrisk factors additionally.

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Table 7: Risk factors by demand forecast stage (examples)

Step

Trip generation

Trip distribution

Selection of
means

Trip
Assignment

Description
 Realization of reflected future development plans and traffic networkplans Rapid change in socio-economic indicators such as population
employees
 Changes of basic unit needed for forecasting future development plansdemand Rapid changed in trip patterns(present and future) due to the changes inland use plans and introduction of new transportation
 Reduction of long-distance trip due to the development of information
technology including Internet Rapid changes of fuel costs and vehicle operating costs
 An increase of public transportation fare
 Sharp differences between agreed fare and actual fare(private investment
project)
 Change in the use of means due to the introduction of new transportationmeans
 Changes in national traffic network due to the modification of nationalplans
 Changes in car occupancy due the change of life style An increase of toll due to a project shift from public project to private
investment project Changes in trip time and demand due the construction of intermediarystations in future(railroad)

Risk Assessment step assesses the risk level of risk factors extracted
by Risk Identification and the degree of Risk Response may vary dependingon the results of risk assessment. Risk Assessment first judges whether risk
factors are able to affect the feasibility of projects and then identify the risk
level of risk factors through sensitivity analysis used in the current
investment evaluation system. It establishes the risk factors extracted fromrough evaluation as the critical management elements and then analyze thechanges of actual demand and benefit and the effect of the risk factors on
the feasibility of projects by reflecting the above critical management
elements in to actual demand forecast step.
Step① Rough evaluation② Extracting criticalmanagement
elements③ Detailed
evaluation

Table 8: Risk assessment methods

Description
 Judging if feasibility of project is secured by applying the demandvariation rate to benefits Designating the projects which are difficult to secure feasibility
during rough evaluation as critical management elements Excluding the projects which will secure feasibility through theadjustment of total project cost even it they did not have thefeasibility during rough evaluation
 Identifying the degree of effect of project feasibility by applying
the critical management elements to demand forecast step Establishing the standards for increase/decrease of demand bycountermeasures in each project step

Risk Response step suggests the countermeasures against risks byproject step after feasibility assessment on the basis of the findings of a risk
assessment. The countermeasures in general include Risk Avoidance, Risk

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Reduction, Risk Transfer and Risk Retention. It categorizes the risk
response measures into 4 steps according to the results of risk assessment
and also suggests the project promotion directions which will be used as theguidelines when projects face difficulties during implementation.
Table 9: Risk Responses (examples)
Project Step

Risk Avoidance

Basic(Implementation) design Detouring
 Step by step
project
implementation
 Re-review ofproject feasibility

Under
construction

 Discontinuing
construction
temporarily andreviewing thefacility efficiency
plans (Change offacility use)

Aftercompletion of
construction

 Delay in opening

Risk
Reduction
 Project
reduction
 Investment
cost
reduction
 Delay inopening Reductionof project
cost byreducing thesize ofproject Opening ofsomefacilities

Risk Transfer
 Adjustment ofproject cost
sharing Increasing
project costby issuingbonds Shifting
project fromgovernment
project toprivateinvestment
project Operational
subsidies

Risk
Retention

 Continue
to proceed Continue
to proceed Complete
opening

5. CONCLUSION
With respect to transportation demand forecast, this study has suggested risk
management measures from the perspective of transportation demand
forecast on the basis of the risk management system suggested by Oren andRothkopf (1975). The results of foreign case study suggest most countries
use 3-steps (Risk Awareness->Risk Assessment->Risk Response). Thus thisstudy has reconstituted above framework in such a way as to satisfy theinvestment evaluation system in Korea and then suggested application. The
replacement of a rough sensitivity analysis conducted in the investment
evaluation system in Korea with the risk management suggested in thisstudy will enable the timely response to risks arising from the uncertainty oftransportation demand forecast through early awareness of risks that may
occur during project implementation.
This study has suggested step-by-step risk management measures withrespect to transportation demand forecast for the purpose of reducing theuncertainty factors in the investment evaluation system on the traffic
facilities in Korea. However there is a need of further studies on detailed
step-by-step countermeasures on the basis of the results of the those studies,risk assessment indicators and risk response classification standards in orderto establish a rational and objective risk management system in Korea.
If the awareness of the uncertainty of transportation demand forecast
expands and studies to reduce the uncertainty continues, the unnecessary

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financial losses due to incorrect demand forecast will be prevented.
Furthermore the further studies on cost-calculation, benefit calculation andeconomic evaluation in decision-making process will ensure more rationaland effective investment.
REFERENCES
Oren, Shmuel S., Michael H. Rothkopf (1975), Optimal bidding insequential auctions, Operations Research, 23, pp. 1080-1090
Sungbong CHUNG (2011), Application of Risk Management to
Forecasting Transportation Demand by Delphi Technique, Korea Safety
Management & Science, Vol.13, No.2, pp. 267-273
Sungbong CHUNG, Justin S. CHANG (2007), Demand Forecasting
Errors in Road Projects: Causes and Effects, The Korea Transport Institute
Korea Development Institute (2008), A Study on Standard Guidelines
for Pre-feasibility Study on Road and Railway Projects (5th Edition)
Sang-dal SIM, Ji-eun CHOI (2006), A study on risk management of
Privately Financed Infrastructure Projects, Korea Development InstituteSang-dal SIM (2005), A Research on the Risk Assessment andAnalysis of Private Financing of Infra-Structure Projects, Korea
Development Institute
Ministry of land, Transport and Maritime Affairs (2011),
Transportation Facility Investment Evaluation Guidelines (4rd)Transportation Department, City of Edmonton (2006), ProjectManagement Manual Update 2006
UK OGC, OGC Gateway Process Review.

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CHARACTERIZATION OF AIR QUALITYIN GLOBAL MEGA-CITIES BY
REMOTE SENSING AND
INVENTORY DATAAya FUJIKAWA1 and Wataru TAKEUCHI2
1
Master Student, IIS, Graduate School of Tokyo University, Japanayafuji@iis.u-tokyo.ac.jp
2
Associate professor, IIS, The University of Tokyo, Japan
ABSTRACT
According to previous study, aerosol is classified into two types –natural or artificial. However, it isn’t so clear that what rate does aerosol
contain in the air and how much two types aerosol affects. The objective of
this study is to clear the relation between air pollution and human activity,
so we use indexes as angstrom exponent (ANG), Aerosol Optical Depth(AOD) by MODIS. Firstly, aerosol condition was classified by ANG-AOD.
A scatter plot was made about 61 mega cities. Air conditions of cities wererevealed three characteristics- the environmentally friendly cities(European
cities, Tokyo, New York, etc) ・ the cities influenced artificial
aerosol(Shanghai, Lagos, etc)・the cities influenced natural aerosol (Kabul,
Baghdad, etc). Secondly, the value of ANG-AOD was transformed to thevalue of RGB color space. Not only chosen cities but also cities where are
not chosen and the other areas are categorized. Thirdly, the value of RGB
was found trend. The RGB data of monthly average were compared inTokyo, Kabul and Shanghai. The superiority of RGB was different withseasons and the combination of cross values(R&G, G&B, B&R) was
different with cities. In the future, the result of this research could be anindex of the choice to invest for the air pollution improvement.
Keywords: MODIS, Angstrom exponent, Aerosol Optical Depth,
secular.
1. INTRODUCTION
1.1 Background & Objective
Today, it is considerably revealed that what is the aerosol itself.
However, it isn’t so clear that how much range natural or artificial aerosol
affects and that how different it affects by each seasons. Especially, it isn’t
so clear how much degree aerosol in the field of transportation affects
atmosphere. So, I focus on Angstrom exponent (ANG) and optical thicknessof aerosols (AOD) to analyze the state of the atmosphere.

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The objective of this research is to find the characteristic of each citiesor the regionality by researching Angstrom exponent (ANG) and optical
thickness of aerosols (AOD) and comparing the data of each year ormonthly average.
1.2 METHODOLOGY

Figure 1:

Flow chart of this research

Figure 1 is flow chart of this research. Firstly, AOD-ANG scatter plot
is made by using the data from 2001 to 2010 given MODIS. The number ofvehicle or acuteness of air pollution is taken into account to choose target
cities. Secondly, the value of RGB is transformed the data of AOD-ANG.
Thirdly, the value of RGB is analyzed secular change.
2. RESULT
2.1 AOD-ANG data

Figure 2: Method of AOD-ANG scatter plot

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Hara et al. (2012) analyze only AOD trend. This research analyzes
AOD and ANG. The ten years’ data from 2001 to 2010 in each city are used.
The maximum value of monthly average is regarded as the AOD-ANG
value in this year. By using the ten data from 2001 to 2010, the average andstandard deviation are calculated to map the average and to graph theaverage and standard deviation. Table 1 shows 61 target cities.
Table 1: the list of 61 target cities
Country
Austria
Belgium
Czech
FranceGreeceItaly
Romania
SwedenUkraineAfghanistan
Bangladesh
China
India
Indonesia
Japan
Myanmar
Nepal
Pakistan
RussiaSouth Korea
Thailand
Argentina
BrazilChile
Peru
United States
NigeriaCity
ViennaBrussels
PragueParis
AthensRome
Bucharest
Stockholm
Kiev
Kabul
Dhaka
Chongqing
ShanghaiBangalore
MumbaiJakartaTokyo
Naypyidaw
Kathmandu
Islamabad
Khabarovsk
Novosibirsk
Busan
Bangkok
Buenos Aires
Rio Dejaneilo
Santiago
Lima
New York
Lagos

Country
Belarus
Bulgaria
EnglandGermany
Hungary
PolandSpain
Switzerland

City
Minsk
Sofia
LondonBerlinBudapest
Warsaw
MadridBern

Azerbaijan
China

Baku
BeijingHong Kong
Ulumuti
Kolkata
New Delhi
Baghdad
AlmatyYangonPyongyang
ManilaMoscow
SingaporeSoul
Ho Chi Min
SydneySao Paulo
Mexico City
Los Angeles
Washington DC
Johannesburg

India
Iraq
Kazakhstan
Myanmar
North Korea
Philippines
RussiaSingapore
South Korea
Vietnam
Australia
BrazilMexicoUnited States
South Africa

2.1.1 Mapping AOD-ANG
Figure 3 shows the average of AOD and ANG. The size of circle
means the value of AOD and the color means the value of ANG. Kitajima etal. (2002) says the bigger the value of AOD is, the worse the air is and thebigger the value of ANG is, the more artificial aerosol is in the air. In otherwords, bigger circles mean the value of AOD is bigger and the air is thedirtier. More red means the value of ANG is bigger and the rate of artificial
aerosol in air is bigger. More blue means the value of ANG is smaller andthe rate of natural aerosol is bigger.

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Figure 3: Mapping AOD and ANG

Figure 4: Legend for figure 3
2.1.2 Scatter plot
Figure 5 is AOD-ANG scatter plot. A point is average and a bar isstandard deviation. This result shows air condition is divided there
characteristics –the environmentally friendly city (European cities, Tokyo,
New York, etc.)・the city influenced artificial aerosol (Shanghai, Lagos,etc.)・the city influenced natural aerosol (Kabul, Baghdad, etc.). Figure 6
shows three atmosphere characteristics.
Around green Area is the environmentally friendly city, around red
area is the city influenced artificial aerosol and around blue area is the city
influenced natural aerosol.

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Figure 5: AOD-ANG scatter plot
2.2 RGB data
By using the result of 2.1.2, the reference point is determined. Table 2
shows the value of AOD, ANG, R, G and B. Figure 7 shows a method torefer to Yamagata et al. (1997).

Figure 6: Three atmosphere characteristics

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Table 2: Each value of each color point
Point
Red
Green
Blue

AOD
1.3
0.1
0.3

ANG
1.6
1.9
0.5

R
255
0
0

G
0
255
0

B
0
0
255

Figure 7: Legend of RGB

Figure 8: Method of RGB transformation

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Figure 9: RGB data in 2001(monthly average)

Month

Month

Figure 10 Around Kabul of monthly
average

Figure 11 Around Tokyo of monthly
averageKabul, Tokyo and Shanghai are chosen as the standard place of RGB.
Figure 10 shows the RGB data of 10 years monthly average around Kabul,
figure 11 is around Tokyo and figure 12 is around Shanghai. Figure 10~12
are found characteristic of each city. Figure 10 shows the value of B is thebiggest all year round. This means around Kabul is influenced natural act all
year round but the air is steady. Figure 11 shows in spring the value of B is
the biggest but in autumn and winter the value of G is the biggest. In
addition, the value of R is the smallest and steady. This means artificial
aerosol is steady all year round and air is clean. Figure 12 shows the valueof B is the biggest except summer and the value of R is bigger than that of
Kabul and Tokyo. This means the air around Shanghai is the dirtiest.

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MonthFigure 12: Around Shanghai of monthly average
2.3 Secular change of RGB
Secular change of RGB is analyzed around Kabul, Tokyo and Shanghai.

Figure 13: Method of secular change
Figure 13 shows method of secular change of RGB. This result is Figure
14~17 below.

Figure 14: Secular change of three cities

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Figure 15: Secular change of Kabul

Figure 16: Secular change of Tokyo

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Figure 17: Secular change of Shanghai
Figure 14 shows secular change around Kabul is less than that ofTokyo or Shanghai. Figure 15 shows each value of RGB hardly changes.
This means The air condition in Kabul has been steady from 2001 to 2010.
Figure 16 shows tha value of B is changing greatly. This means naturaleffect may be increasing year by year. Figure 17 shows the value of R is
more incresing year by year than that of G and B. This means something of
human act may increase.
3. CONCLUSION & FUTURE WORKAOD and ANG can be understood visually by mapping or scatter plot. 61
cities in the world is divided three atmosphere characteristics –theenvironmentally friendly city, the city influenced artificial aerosol, the city
influenced natural aerosol-. It is clear air condition all over the world is
close to three characteristics. Focusing on the unit of city, the superiority of
RGB is different with seasons and the combination of cross values (R&G,
G&B, and B&R) is different with cities. Secular change may becharacteristic each city or region. It is clear some value of RGB is changing
year by year.
As future work, it needs adding NO2 that comes from exhaust gas as
an index to analyze the value of red means the effect of artificial origin
including natural fire and exhaust gas. In addition, inventory data mixture
these results to research whether there is a relation between these results andreal ground activities.

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REFERENCES
Takeshi Kitajima, Hiroki Bono, Itaru Sano and Sonoyo Mukai, 2002,
Photopolarimetory of Atmospheric Aerosols
Yukari Hara, Itsushi Uno, Atsushi Shimizu, Nobuo Sugimoto, Ichiro
Matsui, Toshimasa Ohaea, Zifa Wangt, and soonchang Yoon, 2012, Recent
Inter-Annual Trend of Spherical Aerosol in East Ashia
Yoshiki Yamagata, Mikio Sugita, Yoshifumi Yasuoka, 1997,
Development of Vegetation-Soil-Water Index algorithms and applications,
Journal of the Remote Sensing Society of Japan

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Air Quality
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andInventory
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STRUCTURAL EQUATION MODELSREGARDING TO INTER-CITY VAN
TRANSIT IN THAILAND:A CASE STUDY OF DIFFERENCE OFTRAVEL DISTANCE
Tharadol PUNLOP1, Kunnawee KANITPONG2and Piyapong JIWATTANAKULPAISARN3
1
Project Researcher, TRE, SET, Asian Institute of Technology, Thailanddee.storm88@gmail.com
2
Associate Professor, Faculty of Transportation Engineering,
Asian Institute of Technology, Thailand
3
Senior Researcher, ThaiRoads Foundation, Bangkok, Thailand
ABSTRACTIn the recent years, public van transit has been grown up withdecreasing in public bus ridership. Therefore, the situation seems to be
more critical when public bus providers decided to cancel the service andinvest more on the van. However, the van service is notable in suchdangerous to get accidents. Many passengers cannot accept this risk andstill looking forward to take the public bus. Hence, understanding theattitudes of van passengers might be necessary for the next move. Most of
studies about the van completed with urban transportation, thus, the gap ofsome fields of knowledge is still appeared. To fulfill this gap, the study withlonger traveling of the public van need to be proceeded. An inter-city type of
the public van should be appropriated. Short-distance and long-distanceinter-city van transit are obvious to see the difference of individual's
attitudes. Structural equation modeling (SEM) has been selected to be themeasured tools of this study. Its advantages are helpful and suitable with all
attitudinal and behavior studies. By using a questionnaire survey applied tointer-city van transit passengers, 2 SEM models which are different by
scope of the study were conducted. One was for the travel within 100 km of
distance while the another was responded to a traveling between 100 km to
250 km. Moreover, willingness to pay (WTP) attitudes were included in the
study to extend more possible details beside the basic attitudinal variables.
The findings were what the situation be. Passengers who are careless tosafety and WTP will accept the van service. But the interested result wasthat serviceability is important to short-distance traveling whileconvenience should be managed for long-distance journey. The
countermeasures were also suggested to improve the van transit and makethe public open their mind to the better service.
Keywords: attitudinal data, inter-city transportation, public
transportation, road safety, structural equation modeling, van transit,
willingness to pay.

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1. INTRODUCTION
Thailand, the developing country of south-east Asia has been improved
economic and transportation system from time to time. We have public
transit providing by vans. Normally this mode of transportation is notfamous to carry mass passenger. However, when the van was used as the
public transit, it attracted passengers by its serviceability and flexibility.
Therefore, an idea to use vans as a transit vehicle is becoming popular forservice providers especially to the public bus providers. The result of policychange in many companies resulted in reduction of the public bus services,moreover, in some provinces have been canceled the former buses. The
trend seem to be a gradually change from the bus to the van. But the vanservice has been noticed in such dangerous to get an accident easily.
Furthermore, many sources found that public van operators use excessive
speed over the limit. While some passengers cannot accept the risk of thevan service, then canceling the bus service is inappropriate. Before making
the future decision, understanding the attitudes of van passengers might benecessary to support the shifting plan. Because of investment in public
services affects to national transportation and commuting, it should be
careful to avoid economic drawback when we have to return to invest on
buses. Thus, the objective of this research is to investigate attitudinal factorsthat influence passengers to use the van service and prioritize what the vanpassengers need. However, most of studies about the van have been finished
about urban services. Hence, the gap of some fields of knowledge is still
appeared. To react to the current situation, the study with longer traveling of
the public van is a good choice. Thus, an inter-city type of the public van
was selected. Short-distance and long-distance inter-city van transit were
picked as the scope of the study to differentiate individual's attitudes.
2. RELATED STUDIES
Because of this study has the unique characteristics (the van transit has beenused majorly only in Thailand), a few research papers are close to. However,according to attitudinal and behavioral studies, structural equation modeling(SEM) technique is becoming popular to use as the analysis tool. This study
attempted to achieve the result of inter-city van passenger's thought by using
this modeling analysis. Moreover, even though foundations of safety anddepartment of land transportation of Thailand have been propagated how
dangerous of the van transit is and also used more advance speed detectors
to reflect and enforce the service, but the van passengers are still use theservice and some van transit operators keep find the way to break the speed
limit without be able to detect. Thus, willingness to pay of van passengers
can be the one of indicators to investigate the service acceptance and also
display to the impact on service satisfaction. Thus, research papers about
structural equation models on transit services and willingness to pay (WTP)
have to be involved.

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2.1 Transit usage studies
Kunasol (2000) studied about the factors that related to the use ofurban van transit by passengers. He suggested that passengers in developing
counties require transport services which can give then the high level offlexibility. Therefore, the service also can adept itself to satisfy thepassenger's need. By use of utility theory, passengers were asked to rate thescore and weight important level on each van characteristic. He found that
only travel time and convenient factors were necessary for daily transit vansatisfaction. However, utility theory cannot investigate how correct andpossible of attitudes factors that were used in the models. Thus, SEM is analternative. This method can test a model as overall linear combination andspecify observed variables into latent variables (Thomus F. and Golob,
2001). Another thing is SEM can test causal hypotheses and can customize
its components flexibly by appropriate assumption. And because of WTP
variables may include both of revealed preference and stated preference data,
SEM can allow jointing of their error terms. Fillone et al. (2005) used thistechnique to assess the variables which affected to mode selection behavior.
By addition of demographic variables, they concluded that demographic
characteristics were greater influenced to consider traveling rather than themode characteristic. However, to encourage passengers the use the public
service, safety, security, convenience, accessibility and reliability were thethings which should be conserved and improved. Most of structural equation
models were conducted under using of questionnaire survey. Due to many
types of data that can be analyzed by the method, agreement (Likert's scale),interval scale and also integer scale are all usable. Investigation of public
transportation by Ngatia et al. (2010) was the study that aim to measure
overall satisfaction of transit services in Kenya (in summarized of both bus
and Matatus, the minibus ). Therefore, the influenced factors were found byusing structural equation technique. Latent variables of service quality,
safety, and travel cost were the major impacts of overall transit service
satisfaction. Matatus is close to the van transit in Thailand. Consequently, it
makes traffic congestion and has risk taking behavior of drivers. But it isvery popular because of its flexibility.
2.2 Willingness to pay studies
In the current study, WTP takes place as the one of latent variables of
all SEM models. The way to select the question on the questionnaire is very
important. Normally, the best solution to adjust the model that was
insignificant is editing of questionnaire until the model has passed. Thus,
careful design of the questions is the key to avoid loss of time and funds.
However, the inventory data of inter-city van transit in Thailand have been
disabled so that use of contingent valuation method to conduct WTP instated preference is impossible. Svensson (2009) said revealed preference
approach is more actual and reliable. However, it is not flexible because of
its use depends on the context of a public investment. While the reviews of
Kahneman et al. (1999) found that stated preference questions are better for
attitude expression. In case of valuation exercise, closed-end survey will bevaluable if it uses with subsequent of the same question either revealed
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preference or stated preference (Horowitz, 2005). In addition, the provisionof WTP questions should be concern. Public-related question will give theresult different from the private-related question. However, Svensson andJohnsson (2010) followed the economic theory that a selfish individual
should give the same WTP values of both public and private goods on risk
reduction. Therefore, a pure altruist should have the value of public goods
higher than private goods. But the result did not follow the assumption
condition which the private WTP were around three times more than public
WTP (by using a contingent valuation survey). Thus, both private andpublic provision questions will be used in the current study to attempt thebest solution. Nevertheless, when use for public purposes, the WTP elicited
scenario should also be in public.
3. METHODOLOGY
The data of attitudes of inter-city van passengers was collected from
November, 2012 to February, 2013. This cross-section data carries
demographic characteristics and individual's agreement on each factor thatmight affect to overall satisfaction (by assume that the service usage isunder the basic of transit mode satisfaction). By using of interviewing
questionnaire survey in total 8 representative provinces and Bangkok, 906
samples were picked which around 879 samples were completely filled in.
Therefore, the scope of the study has been anticipated to separate into 2
definitions, short-distance inter-city van traveling which allocates under 100
km and long-distance inter-city van traveling which allocate beyond 100 km
to 250 km from Bangkok. Figure 1 displays the area of each scope of thestudy. However, 444 fulfilled samples were used for short-distance analysis
while another 435samples were used in long-distance calculation.

Figure 1: The study areas and data collection locations

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In this study, the tool to develop the models of inter-city van transit usage
attitudes is SEM. Every input data should be treated as scale variable ordummy variable. Table 1 shows up all possible variable in the models bothobserved variables and latent variable followed the influent assumption in
Figure 2. However, under certain direction of the study, factor
categorization is obviously match with using of confirmatory factor analysis(CFA). Thus, CFA was used as the measurement model to indicate usablefactors which will be able to develop each SEM model.
Variable
type
Dependent
variable

Independent
variable

Table 1: The detail of variables in SEM models

Latentvariable-

Demography

Convenience

SafetyIndependent
variable
Serviceability

Environment
WTP

Observed variable
overall satisfaction
femaleage
family statusprovincial hometown
education level
workability
monthly income
privacy
accessibility
connectivity
conditioner
seat comfort
attractivenessstation comfort
flexibility
belt usage avoidance
dangerous speeding
feel about get off riskover capacity
security weakness
PREtime satisfaction
OVtime satisfaction
POStime satisfaction
PREcost satisfaction
OVcost satisfaction
POScost satisfaction
schedule information
service rate
reliabilityenergy saving
pollution reduction
congestion reduction
attitudinal WTP
ACCinsurance owing
ACCinsurance WTP

RemarkCurrently working

PRE pre-mode
OV on-vehicle
POS post-modePRE pre-mode
OV on-vehicle
POS post-mode

ACC accident
ACC accidentScale
5-scale (score)Dummy
6-scale (interval)
3-scale (level)Dummy
8-scale (level)Dummy
6-scale (interval)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
5-scale (agreement)
Dummy
5-scale (level)

Figure 2 displays the ideal SEM model under the assumption that eachservice characteristics can influence passengers to use the inter-city vantransit beside demographic characteristics. Moreover, WTP can leadpassengers to think about the service as well. Thus, WTP might relate to
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each service characteristics implicitly and individually. In addition, allobserved variables should be tested by CFA first to group the variables
under their categories, then the remaining factors after adjustment will beable to build up the SEM model. Figure 3 shown the example of CFA resultof measurement.

Figure 2: The ideal SEM model regarded to the assumption

Figure 3: The example of CFA result of measurement
About the accuracy of the model, Root Mean Square Error of
Approximation (RMSEA), Comparative Fit Index (CFI), Goodness of Fit
Index (GFI) and Adjusted Goodness of Fit Index (AGFI) will be used toindicate the model fit. Based on Shi et al. (2011), the model will be

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significant when RMSEA is less than 0.08, and CFI, GFI and AGFI are
above 0.90, also P-value is higher than 0.05, and CMIN/df is lower than 3.
4. INITIAL DATA ANALYSIS
For the overall respondents, inter-city van transit users were around 60%
female. And 40% of their age were 20-29 years old. Most of them were
single in family status (almost 60%). Therefore, passengers who have theirhometown outside Bangkok and perimeter were the main population of thevan users. Around a half of passengers got or was still studying bachelor
degree. However, their occupations were students (almost 27%) following
by merchants (or handle own business) and company staffs with the samerate (just above 20%). For relative monthly income, more than one third ofpassengers got 10,000 to 19,999 Thai baht per month while another justover one third got the income lower than 10,000 Thai baht per month.
Moreover, passengers who are rarely use the service (lower than 2 days permonth) were the main group around 38% of the service's passengers. By theway, the average satisfaction score of inter-city van transit was 3.34 out of 5,
somewhere between medium and pretty high.
5. ATTITUDINAL DATA ANALYSIS
Because observed variables on each journey phase might appear in themodels. Thus, to make clearer vision about journey characteristic of intercity van transit, Figure 4 demonstrates how passengers travel from their
origin to their destination. The attitudinal analysis features with 2 SEM
models which calculated separately. Therefore, the outcomes were as
follows.

Figure 4: The journey characteristic of inter-city van transit
5.1 Short-distance traveling model
CFA test for short-distance observed variables was fine. Only some of themwere removed to satisfy the model fit. Regarding to follow the result ofStructural equation models regarding to inter-city van transit in Thailand: A case stuy of difference of travel
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CFA, SEM model was developed by the grouping results on each category.
By cut-off basic strategy, an adjustment provided the outcome only
influenced factors under the primary assumption. Figure 5 shows the resultof SEM model which all significance measurement values were allappropriate.
-.27

e23

.08

ACCinsurance owning

1.02
e22

-.11

.31

.55

over capacity

.38
e13

e1
e3

-.13

-.25
Safety Issues

dangerous speeding.09

hometown

.29

1.01

.26

e15

genderDemography

WTP
attitudinal WTP

.04

.20

.29

.34
overall satisfaction

e25

.62
.29
.53

.31
e18

POScost satisfaction.55
Serviceability

.40
e17

OVtime satisfaction.63

Chi-square = 16.251, df = 21, p = .755, CMIN/df = .774
RMSEA = .000, CFI = 1.000, GFI = .992, AGFI = .983

Figure 5: The SEM model of short-distance inter-city van transit
From the figure, WTP of short-distance inter-city van transit
passengers accorded with other latent variables with the same rate (absolutevalues). However, riskier services and higher serviceability can force
passengers to have more WTP (anticipated to support their life and improve
the service). Therefore, female and provincial passengers may have lower
WTP. Nevertheless, WTP did not inversely relate enough to believe thathigher WTP passengers will attempt to avoid the van service (that indicate
only by overall satisfaction of the service). Demographic variables also
acted in the same way. Only serviceability and safety issues can obviously
influence passengers to think about using the van service. Firstly,
passengers will consider serviceability at the major factor to pick the intercity van transit for traveling. Then unsafe condition will be the secondary
factor to care about. Therefore, a better van service can catch higher demandof passengers while a riskier van service might make passengers to avoid
the service.
5.2 Long-distance traveling model
For long-distance inter-city van transit, CFA test of observed variables
reached the significant level of model fit requirement by factor elimination.
Thus, SEM model was developed by the grouping results on each category
following the result of CFA. After adjustment, the proper SEM model

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contented with the variables under the primary assumption and structure.
Figure 6 displays the final SEM model of this scope.
.37

e23
e22

.26
.23

.51

ACCinsurance owning

.37
WTP

attitudinal WTP

.48

e10
e9

.37
.39
.48

air conditioner
accessibility.63

privacymonthly income

1.17
.05

.26
e11

workability

Demography.61
Convenience

.35

-.03

overall satisfaction

.14

e25

.69
.25
-.12

e15
e14

.17
.98

get off risk
dangerous speeding.41
Safety Issues.99

Chi-square = 38.220, df = 29, p = .118, CMIN/df = 1.318
RMSEA = .027, CFI = .981, GFI = .983, AGFI = .967

Figure 6: The SEM model of long-distance inter-city van transit
The figure above shows the result that WTP had the direct relation
with demographic characteristics. Therefore, passenger who is workable orgain higher monthly income might respect higher WTP. While riskier
services and higher serviceability can force passengers to have more WTP(anticipated to support their life and improve the service). However, theimpacts of WTP and demographic characteristics to the service usage wereconsiderably low. For a longer distance service, first thing that passengers
look forward was convenience. The loading factor of this latent variable
0.35 that was the highest among the other. Therefore, passengers will be
more satisfied with a better convenient inter-city van. Oppositely, the minor
influence which can affect passengers to avoid the service was the unsafe
condition. This latent variable can mainly indicate by dangerous speeding
behavior (observed variable) which gained the loading factor value of 0.99
to the factor of safety issues. Moreover, bad get-off condition (which canmake passengers get the accident by stopping not close to stations orfootpath) affected to safety issues only 40% of speeding behavior.
6. CONCLUSIONS
According to the results, inter-city van transit passengers are the persons
who need the extreme advantages to deal with the time frame and comfort
problems. However, they neglect the most importance factor intransportation, the safety. Even though the traveling is short or long, they
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prioritize the factor of safety as secondary. Thus, the possible reason is they
can accept the risk that they will receive. Therefore, they can probably takethe service as long as they can accept to dangerous speeding behavior, riskof getting off from the service vehicle and carrying passengers more thanthe capacity of van. There is a attitudinal differentiation between 2 formats
of travel distance. Hence, short-distance traveling requires time control andinter-city van transit that can reach the transfer points which passenger cancontinue the journey with the cheaper transport mode. Therefore,
serviceability of the van service should be reserved or improved to attract
passengers. On the other hand, long-distance inter-city van transit should
focus on convenience deeply. Passengers should be treated as they can feellike they are sitting in their private car. Moreover, accessibility is the factor
to be considered. Passengers will be satisfied when they can reach theservice easier. Lastly, for longer traveling, passenger's endurance is limited.
Thus, air conditioner inside the service vehicles should work properly tomake passengers feel relax and comfort.
7. RECOMMENDATIONS
Passengers of inter-city van do not consider safety as a first priority until
they got an involvement of crash accident. Indeed, this group of inter-citytransit user usually concentrates to how quick and convenient they can get.
Thus, the idea of using passive countermeasure is more appropriate. The
better approach is to educate the public to wake up on safety. Passengers
should reach an effective service which they can feel it is safer than before.
By avoidance of use and resistance from the public, service providers haveto improve their service to satisfy a need of passengers. The anothercountermeasure can be implemented by using encouragement of inter-citytransit safety. Making the service providers believe that inter-city van transit
can be provided in higher quality and safety standard with subsidizationfrom the government such as taxation reduction. Alternatively, the moneyfrom charity foundation can help to improve the service as well (because
there is not any effort from passengers to pay the aid).
ACKNOWLEDGEMENTS
This study would not be possible without the supports from the author'sfamily and members of transportation faculty of Asian Institute ofTechnology. The author would like to express his thankful to his adviser,
Assoc. Prof. Dr. Kunnawee KANITPONG who has sacrificed her valuable
time to guide the author. Lastly, the study committee, Dr. Piyapong
JIWATTANAKULPAISARN and Dr. Thirayoot LIMANOND, they havegiven the guidance to the author and the author has never thought to havethe committee who are better than them.

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REFERENCES
Alexis M. FILLONE et al., 2005. Assessment urban travel: A
structural equation modeling (SEM) approach. Proceeding of the Eastern
Asia Society for Transportation Studies, Vol. 5, pp. 1050-1064
Bunruk Kunasol, 2000. Analysis of factors affecting van transit
operation and usage in Bangkok metropolitan area. Institute of engineering.
King Mongkut’s institute of technology Thonburi, Thailand.
GITHUI John Ngatia et al., 2010. The structure of user's satisfaction
on urban public transport service in developing country: the case of Nairobi.
Proceeding of the Eastern Asia Society for Transportation Studies. 2010.
Jing Shia, Yun Bai, Li Tao, &Paul Atchley, 2011. A model of Beijing
drivers’ scrambling behaviors. Accident Analysis and Prevention 43, pp.
1540–1546.
John Horowitz, 2005. The use of a real-money experiment in a statedpreference survey. Department of Agricultural and Resource Economics.
University of Maryland.
Kahneman, D., Ritov, I., Schkade, D., 1999. Economic preferences orattitude expressions? An analysis of dollar responses to public issues. J.
Risk Uncertainty 19 (1–3). pp. 203–235.
Mikael Svensson, 2009. The value of a statistical life in Sweden:
Estimates from two studies using the “Certainty Approach” calibration.
Accident Analysis and Prevention 41 (2009). pp. 430–437.
Mikael Svensson, &Maria Vredin Johansson, 2010. Willingness to pay
for private and public road safety in stated preference studies: Why thedifference?. Accident Analysis and Prevention 42 (2010). pp. 1205–1212.
Thomus F. Golob, 2001. Structural Equation Modeling for Travel
Behavior Research. Transportation Research Part B: Methodological, 37,
2003, pp.1-25.

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BASIC RESEARCH FOR QUANTITATIVEANALYSIS OF OIL-CONTAMINATED SOIL
PURIFICATION BY MICROORGANISMS
Shunya SUZUKI
Graduate Student, ICUS, IIS, The University of Tokyo, Japans-suzu@iis.u-tokyo.ac.jp
ABSTRACT
Recently, environmental damages are becoming serious in countries
all over the world. This sometimes directly affects our health. It is necessary
to estimate the environmental risk quantitatively and to establish thetechnology for purification of contaminated materials. Especially thenecessity of purification technology of oil is growing, considering the trendof excluding fossil fuel. In this research, for the quantitative evaluation of
the remediation of oil contamination, basic study was conducted.
A technique of “bioremediation” which purifies contaminated soil by
the function of microorganisms is getting attentions. Bioremediation studies
in the laboratory as well as in the field have been carried out so far, but
quantitative evaluation of decomposing process of the oil contamination isstill the least understood. A package of numerical analysis called
BioDuCOM was developed by Ishida (2003) to simulate decomposition oforganic wastes by aerobic microorganisms. In order to upgrade this
BioDuCOM, basic element tests have been conducted.
In the element tests, temperature, soil type and water content werechanged to examine a rate of oil concentration in soil. As a result, change inoil concentration was strongly influenced by volatilization rather thanbiodegradation. And also the temperature changes affected a lot.
Keywords: bioremediation, microorganisms, quantitative analysis,contaminated soil by light oil, GC-MS.
1. INTRODUCTION
Nowadays, soil pollution is becoming serious in all over the world. It isdifficult to identify and solve this problem, because underground situation
can not be directly observed.
Even when the same term of soil pollution is used, there are various
forms of it and of course the way to solve this problem is different by eachcase. Although solidification, insolubilization, chemical oxidation andphysical method like incineration and excavation are often used as the way
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microorganisms to decompose the polluted matters gathers attention.
Advantages of this bioremediation are to be low-cost, environmentallyfriendly and wide-range in application.
In Japan, the number of oil factories and gas stations are annuallyshrinking owing to decrement of dependence on fossil fuel energy and low
profitability resulted from furious price wars. These facilities cause theirunderground soil polluted and it leads the risk of health hazard.
In this study, in order to invent the tool which can analyze
quantitatively the process of purifying oil contaminated soil, the purpose is
to clarify the reaction mechanism of oil purification by obtaining the data inelemental experiments. In past research,”BioDuCOM” was invented which
can analyze quantitatively the process of composting. Therefore the final
purpose is to add the model of oil-polluted soil purification to the
BioDuCOM.
2. PAST RESARCH
2.1 Bioremediation
Bioremediation is defined as an environmental remediation techniquewhich remediates polluted soil and underground water to its original state byliving things activities and classified roughly into in-situ bioremediation
which treats on the polluted spot and ex-situ bioremediation which uses
reactors or piles up polluted soil in the place. These construction ways are
selected considering construction period, pollution range, pollution
concentration and pollution depth. Moreover, processing technology arechosen considering attribute of pollution matter, ground property in theplace and surrounding environment.
As oil is originally derived from nature, it includes some components
which are easily biodegraded. It is said that in biodegrading oil, todecompose in aerobic state is more efficient than in anaerobic state. And
heavy oil and crude oil cannot be eliminated completely by only
biodegradation. It is said because soil in Japan includes oil-degrading
microorganism, there is no need to select bioaugmentation which is theconstruction way to introduce bacteria from outside that can decompose
pollution matter when they are very few. Therefore in Japan, in-situ
bioremediation or upgraded one to attach nutrition to activate
microorganism is often applied.
2.2 BioDuCOM
Ishida et al. (1999) developed DuCOM to analyze corrosion anddeterioration of concrete by thermo-hygro physics and Nattakorn et al.
(2003) upgraded it to BioDuCOM dealing with organic matters. In past
research, optimizing system in compost processing on a large scale was
succeeded by using BioDuCOM.

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During biological decomposition of substrate, a number of processes takes
place simultaneously in the physical, chemical and biological domains
within the compost matrix. In order to model all these primary phenomena,
BioDuCOM integrates heat, moisture, oxygen and microbe system andgives optimum solution such as amount of reactant decomposition andoxygen consumption.

Figure 1: Flowchart of BioDuCOM system framework
2.3 Reaction mechanism of microorganism growth
Cell density, X, changes according to specied growth environment
determined by the mass and energy transport models in Figure 1. Growth of
microorganism is assumed to follow the batch growth scheme Michael et al.
(2003). Cell density in the exponential (growth) and death phases change
exponentially, while there is no net change in cell population during the lagand stationary phases. The rst-order rate equation for growth in the growth
scheme can be expressed by the following equation:(1)

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Figure 2: Typical growth curve for batch growth
The growth equation contains two terms: a growth term (the rst termon the right-hand side) and a death term (the second term). X represents thecell density per unit compost volume, kg/m3. μ is an environment-dependent
specic growth rate and s-1. μd, the equivalent death rate, s-1, is assumed tobe constant under whole process. Two numerical parameters, α and β, areintroduced to numerically control both terms on the right-hand side of thegrowth equation. Both parameters possess either value of unity or zero at thecorresponding growth phase. This gives the advantage of freedom indening the growth phase based on changing environmental conditions. In
lag and stationary phases, α and β are both zero; in exponential phase only ais unity; in death phase only b is unity. It is assumed that the growth phasechanges from exponential phase to stationary phase when substrate, S, iscompletely degraded.
Many important factors inuence the composting process; some ofthem have been identied as playing major roles in controlling microbial
growth: temperature, availability of water, oxygen concentration, substrateconcentration, and inhibitors. Based on these ve factors, environmentdependent growth rate can be expressed by the following equation in
multiplication form:(2)
where μ is the environment-dependent specic growth rate, s-1, μT(T) thetemperature-dependent maximum specic growth rate, s-1, f(O2) the oxygen
reduction factor (-), fH2O the water availability-reduction factor (-), f(S) the
substrate-reduction factor (-), f(I) the inhibitor reduction factor (-), T thetemperature, K, θ the volumetric water content, m3/m3, O2 the gaseous
oxygen concentration, m3/m3, S the substrate concentration, kg/m3 and I the
inhibitor concentration, kg/m3. Eq. (22) consists of multipliers, whichindicates that each parameter has a direct inuence on the growth rate. As
any parameter approaches zero, the total growth rate will be aﬀected.

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3. EXPERIMENT
3.1 Outline
Objective of the experiments was to measure how much oil in polluted
soil could be biodegraded. Soil samples polluted by light oil were prepared.
Type of soil, temperature, and water content of the samples were changed.
These samples were put on constant-temperature and constant-humidity
chamber for a month, after that those of light oil concentration were
measured by test method of total petroleum hydrocarbon (TPH) with mass
spectrograph. To know how much light oil was decomposed by
microorganism activity, sterilized samples by autoclave were also monitored.
3.2 Preparation for experiment
Soil types used were loam soil collected in Tokyo University and Seeddeveloped at collaborative company Vioce. Seed included many oildegrading microorganisms and nutrients such as nitrogen and phosphate
which were used to activate microorganisms. Samples were made bycombining these two types of soil. Sterilized and not sterilized samples wereprepared for each pattern. Sterilization was conducted in 120°C for 20
minutes by autoclave. Sample patterns are given in Table 1. Oilconcentration in all samples was adjusted to 6000mg/kg.
Table 1: Sample patterns
Sample name
LA
LAS
LAT20
LAST20LAW50LASW50
SeA
SeAS

Sorts of soil
Loam
(Seed 10%)
Loam
(Seed 10%)
Loam
(Seed 10%)
Only Seed

Temperature
Humidity
40°C
90%
20°C
60%
40°C
90%
40°C
90%

Water
content
30%
30%
50%
30%

Sterilized
No
Yes
No
Yes
No
Yes
No
Yes

3.3 Measuring oil concentration
Light oil concentration in soil sample was measured by test method of
TPH in accordance with the oil concentration countermeasure guideline
specified by Ministry of the Environment. TPH is an abbreviation of “Total
Petroleum Hydrocarbon”. After extracting oil content in soil with carbon
bisulfide, the extracted sample was separated by boiling point with nonpolar column of a mass spectrograph GC-MS. Then, of the normal paraffinin acquired chromatogram, comparing the total peak area of all components
in the boiling range between C6 and C28 to that of ASTM standard light oilin same condition, TPH concentration could be calculated.

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4. RESULT AND DISCUSSION
4.1 Experiment results
Observed decrement of light oil concentration for 32 days is shown in
Figure 3.

Figure 3: Oil concentration (ratio to 8th day) of all samples
The vertical axis is oil concentration ratio to 8th day which isnormalized. The reason which the ratio to 8th day is adapted is that in firstweek of this experiment the influence of volatilization is more exceeding
than biodegradation therefore it was considered the first week was notuseful for examining biodegradation of oil. The horizontal axis is elapsedday. The dotted lines are for sterilized samples and solid lines are forsamples without sterilization. The oil concentration decrement of sterilized
sample is because of volatilization and the difference between dotted line
and solid line can be regarded as decrement by biodegradation.
It can be found in Figure 3 that all oil components, even sterilized ones,
almost disappeared. This explains that the influence of volatilizationstrongly works. This is because light oil is the substance likely to beaffected by volatilization, in addition to that the experiment apparatus wasintended to ensure breathability therefore surface area faced to air was large
and volatilization influence became large.
4.1.1 Influence of water content (Comparison between LA and LAW50)
Compared between LAW and LAW50, it could be found that
biodegradation progress in 30% water content, although it didn’t progress in
50%. According to past research on composting, the optimum water content
is around 100%, there was a large divergence between them. It is consideredthat this is because the optimum water content of oil-degrading
microorganism is lower than that of composting microorganism. If water insoil is higher than the optimum water content, the void in soil is filled with

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water, then there is a decrease in the oxygen supply to microorganism andtheir activity is lost. However this hypothesis has not been verified on oildegrading microorganism, this is a future problem.

Figure 4: Oil concentration (ratio to 8th day) of LA and LAW50
4.1.2 Influence of soil type (Comparison between LA and SeA)
Compared between SeA and SeAS, the sterilized sample SeAS
decomposed oil component more than not sterilized sample SeA, thereaction of biodegradation couldn’t be found. It is considered that this isbecause SeA was composed of only seed composting, therefore excessive
nutrients were included in SeA soil. N atoms invested as nutrients exist as
ammonia, this ammonia easily becomes ammonia gas along increment of
temperature, soil drying and so on. Ammonia gas is known as substancepreventing reaction of microorganism and this phenomena is called gas
injury in like agriculture sector. Table 2 shows concentration of N and P
atom in LA and SeA mixed as nutrients. LA contained only 10% seed
composting, on the other hand SeA was composed of only seed composting.
Table 2: N and P atom used as nutrients in LA and SeA
LA
SeA

N (mg/kg)
1800
12800

P (mg/kg)
1400
10100

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Figure 5: Oil concentration (ratio to 8th day) of LA and SeA
4.1.3 Influence of temperature (Comparison between LA and LAT20)
In regard to LA and LAT20 whose temperature was changed from
40°C of LA to 20°C, the difference by biodegradation was clearly observed.
It can be read from Figure 6 that the amount of biodegradation of LAT20 islarger than LA. This result means that oil-degrading microorganism is more
active in 20°C than 40°C and coincides with past research which explains
the optimum temperature of oil-degrading microorganism is generally
around 20°C to 30°C.

Figure 6: Oil concentration (ratio to 8th day) of LA and LAT20

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4.2 Proposing a new model of BioDuCOM
4.2.1 Physical behavior
Based on these experiment results, a new model about purification ofoil polluted soil was proposed. Previously mentioned, BioDuCOM
expressed the process of composting by microorganisms with integration ofheat, oxygen and water system. However, oil decomposition couldn’t beanalyzed with this model as it was, because influence of volatilization which
occurred independently of biodegradation was not considered in this model.
And also, it is considered that real scale pollution has light and shade of oil
unlike this sample which was made uniform concentration, therefore
advection and diffusion owing to this uneven concentration have to beconsidered. Hence a new framework is proposed in the following fig byadding past framework to physical behavior system such as volatilization,
advection and diffusion.
Initialcondition
Physicalbehavior system
Heat system
BiomodelOxygen system

Turning / shifting

Water system

Convergenceh k

Out put

End
Figure 7: Proposed new framework of BioDuCOM considering physicalbehavior
4.2.2 Parameter of water content and temperature
Water content and temperature contribute to the function of
microorganism growth, μ. Each model is expressed as following expressions.
Water content function fH2O(θ):

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(3)

where
θmin: minimum water content
θmax: maximum water content
θopt: optimum water content
Kθ, n: parameter
Temperature function fT(T):
(4)
where
T: absolute temperature
R: gas constant
AT: exponential function item
E1: microorganism activation item
E2: microorganism unactivation item
KT: coefficient
It is proposed that the parameters in these expressions are changed tosatisfy the observed results in conducted experiments without changing theformation of expressions in themselves. Specifically, the parameters inwater content function and temperature function are modified so that
biodegradation proceed more rapidly in 50% water content and 20°C
temperature than 30% and 40°C. In future, the consistency of these model
have to be confirmed by accumulated accurate data and analyticalverifications.
5. CONCLUSION
In order to construct a new model that can analyze the process of
biodegradation in oil-contaminated soil and to accumulate enough data forconstructing it, elemental experiments were conducted. In these experiments,
type of soil, water content and temperature were changed as parameters ineach sample, and amount of biodegradation was examined. As a result, it
was found that the optimum water content and temperature of oil-degrading
microorganism was different from those of composting microorganism inpast research. Therefore the modification of past BioDuCOM model had tobe considered, the parameters of water system and temperature system in
BioDuCOM were revised and the necessity of new model related to physical

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behavior such as volatilization, advection and diffusion was recognized andproposed. In the future, the validity of these modifications and propositions
has to be verified.
REFFERENCES
Nattakorn Bongochgetsakul, and Tetsuya Ishida, 2006. Integrated
Quantitative Analysis for Designing of Composting System. Asian Journal
on Energy and Environment 7(04), 410-422
Nattakorn Bongochgetsakul, and Tetsuya Ishida, 2008. A new
analytical approach to optimizing the design of large-scale composting
systems. Bioresource Technology 99, 1630–1641
L G Whyte, L. B., 1997. Biodegradation of petroleum hydrocarbons
by psychrotrophic Pseudomonas strains possessing both alkane (alk) andnaphthalene (nah) catabolic pathways. Appl. Environ. Microbiol, 3719-23.

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6thMODELING OF OVEN-DRIED MORTAR
AFTER FIRE EXPOSURE BY RIGID BODYSPRING MODEL (RBSM)
Onnicha RONGVIRIYAPANICH1, Withit PANSUK2,
and Yasuhiko SATO3
1
Graduate Student, Chulalongkorn University, Thailand,
onnicha_r@yahoo.com
2
Assistant Professor, Faculty of Engineering,
Chulalongkorn University, Thailand
3
Associate Professor, Faculty of Engineering, Hokkaido University, Japan
ABSTRACT
Concrete is one of the most popular construction materials because it
has many benefits in construction duration; however, it also has many cons
in use at the same time especially for a severe fire deterioration. Nowadays,
fire accidents often occur easily in all over the world included Thailand andalmost of structural concrete under fire incidents were diminished theoverall performance, i.e. the mechanical properties caused by adecomposition of underlying chemical compound of material strength andchange of physical properties.
A series of experiment is conducted on macroscopic specimens inburning process conforming to ASTM E119, then, they are cut in order toinvestigate the attenuation of chemical properties on less than 0.15 mm
diameter of powder samples, physical properties on 5 mm cube andmesoscopic samples for mechanical properties. The test data of thedeteriorated oven – dried mortar in term of the material model arecompared with the numerical simulation concerned the effect of thisincident on mechanical properties due to a chemical – physical relation for
describing the damages occurred.
Based on the results of the current study, the proposed numericalsimulation can be used to assess the changes of material model as afunction of the residual chemical compound in whole samples after hightemperature treatment.
Keywords: fire exposure, deterioration, rigid body spring model,
RBSM, mortar
1. INTRODUCTION
Although a conventional concrete consisting of mortar and aggregate phase
has its own soundness behavior such as strength, permeability, fire –resistance etc. depending on designated specifications that would be

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deteriorated by various factors in severe conditions; generally, fire accident
brings the extremely serious deterioration to concrete structure. The
properties of this binder are, therefore, chemically disturbed. Since thebinding ability upon cementitious material is diminished under fire
exposure, any deterioration may easily occur throughout developed space
which was formed after fire exposure. Besides its chemical properties, thepore structure and material strength of damaged mortar is also one of thefundamental properties that should be considered for post – hightemperature treatment condition. In addition, conventional methods forclarifying the fire deterioration on mortar are inadequate and do not allow todefine the actual causes of changes in basic properties; therefore, to obtain
the better understanding, the chemo – physical and physical – mechanicalrelations are strongly needed for evaluating a post – fire material
performance.
In this study, one mix proportion of mortar conforming to JIS R5201 –Physical testing method for cement was used. At the age of 28 days old, allof dried prismatic specimens (50 x 400 x 20 and 50 x 400 x 40 mm3) werestored in a sealed box until test to keep constant moisture content andadditional damages in samples for both before and after subjecting to hightemperature. Three series of specimens consisting of control specimens, 60
and 120 minutes were exposed to fire with unsealed condition and heatingprocess is conforming to the standard temperature – time curve of ASTM
E119; maximum temperature for an hour of exposure is 927 °C and anothercase is 1010 °C. Then, the burnt samples were cut to for the evaluation of
mechanical properties. At given period, Thermal Gravimetric/Differential
Thermal Analyzer (TG/DTA) and Heavy Liquid Separation and Methanol –Salicylic acid techniques are conducted on powder sample in order to obtainthe content of calcium hydroxide and calcium silicate hydrate which has an
influence on physical characteristic. Porosity is experimentally examined byArchimedean approach and Porosimetric analysis on 5 x 5 x 5 mm3 cubic
samples. Furthermore, the residual mechanical properties are determined,
and then how the variation of content of chemical compounds affects
physical and mechanical properties of damaged mortar are discussed.
2. EXPERIMENTAL RESULTS
In this paper, the investigation was aimed at clarifying effect of cement
hydrated production decomposition on physical and mechanical propertiesof fire deteriorated oven – dried mortar. The results contribute to theunderstanding the chemo – physical and physical – mechanical properties
change under elevated temperature.
2.1 Determination of Calcium Silicate Hydrate (CSH)
CSH is the most important chemical compound of cement hydration
products because it contributes structural strength of material. During fire
accidents, CSH still exists up until temperature of 560 °C (Gai F. P. et al.,2006). Based on the hydration of cement powder, hydration products were

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generated and expanded to the free space (Raymond A. C. et al., 1999), it islogical that the pore connectivity was diminished. Therefore, if thedecomposition process of hydrated products occurs, pore – structure must be
disturbed.
The CSH content was measured by Heavy Liquid Separation andMethanol – Salicylic acid technique. In this study, the specific gravity ofheavy liquid solution was set to be 2.30. The percent CSH in whole sample
is shown in Figure 1.
It can be found that the CSH content obviously diminished after 60
minutes of exposure. However, no significant difference between 60 and
120 minutes of exposure time was observed. The reason is because CSH
was substantially decomposed after 60 minutes under elevated temperature(Gai F. P. et al., 2006).

CSH Content (% by weight)

4.00

3.00

2.00

1.00

0

0

20

40

60

80

100

120

Time exposure (min)Figure 1: Amount of CSH in whole sample by weight percentage
2.2 Determination of Calcium Hydroxide (CH)CH, one of major cement hydrate, is known as passivation layer ofsteel reinforcement in reinforced concrete structure; therefore, its
decomposition may affect the durability of reinforced concrete. After
exposed to elevated temperature, CH started to decompose and formed new
carbonate chemical compound. TG/DTA technique was used to determine
CH content in which chemical reaction expressed in Eq. (1) during the test.
For the dissociation of CH, chemical compounds, lime and water, were
newly formed.

Ca(OH ) 2  CaO  H 2O

(1)

According to the law of conservation of mass, the amount of CH canbe determined from the evaporated mass of water. However, thedecomposition of CH is not so significant on strength loss compared withboth invisible and visible cracks due to expansion and shrinkage of newcompound during the formation process (Wei M. L., et al., 1996); therefore,

Modeling ofModelingof oven-driedoven-dried mortar
mortar after
after fire
fire exposure
exposure by
by Rigid
RigidBody
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in this study, the effect of residual content of CH on other properties was
negligible.
2.3 Porosity assessment by Archimedean techniques
Mortar samples were dried at 105 °C for 24 – hour and made asaturated condition by soaking in water for 24 – hour, measured a weight ateach condition. The result is shown in Table 1.
2.4 Porosimetric analysis by mercury intrusion porosimetry (MIP)
MIP is one of the most popular techniques which can determine
porosity and pores size distribution of porous material based on thresholdpressure and pore diameter relation. Owing to its own limitations, incontinuous system, if larger pores can be filled by mercury through small
pore necks only, MIP may not provide the actual results.
Once as hydration reaction proceeds, the pore space becomes entirely
filled and let the isolated pore occurred. Because the pressure introduced tosample during the test can collapse the pore walls to the isolated one(Raymond A. C. et al., 1999), it can be said that one obtained from MIP
technique is closer to actual porosity value than other methods.
As exposing to fire incidents, pore space might be developed again.
Generally, the cumulative volume of mercury intruded must be plotted withrespect to pore diameter in order to know the integrated volume of pore pergram of freeze – dried samples. The result is shown in Figure 2.
It can be observed that the total volume of mercury intruded which
represent the total pore volume for deteriorated samples is greater than acontrol specimen. As shown in Table 1, the Archimedean’s porosity is lessthan those of MIP since the applied pressure did break the blockages
through the existing isolated pore.
Figure 3 shows the relationships between pore diameter and volume of
mercury intruded at any pore size. The pore diameter at characteristic peakfor deteriorated specimens is larger than a control one because of thedecomposition of chemical compounds after fire exposure. In addition, twocharacteristics peaks were observed in burnt samples. One possibility ofthese occurrences is to crush on hydrated products which impede theintrusion path; therefore, the neck size at a given peak may not correspondto the pore diameter.
To focus on deteriorated samples, two peaks are clearly appeared forafter 60 minutes of exposure while there is only one peak for another set ofsamples. It can be explained that the surface and the blockages were almost
broken for 120 minutes of exposure because of deterioration. Furthermore,
due to the variability of material strength after fire exposure, pressure at thesecond peak is reasonable to say that can be varied by size of pore necks or
thickness of framework.

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Cumulative volume 3of mercury intruded(cm /g)0.60

0 minute of exposure
60 minutes of exposure
120 minutes of exposure

0.50

0.40

0.30

0.20

0.10

0
0.01

0.10

1.00

10.00

100.00

Pore diameter (x10 -6m)

Figure 2: Cumulative volume of mercury intruded into samples
Table 1: Comparison of porosity value between Archimedean and MIP
techniques
Total pore volume(MIP)(cm3/g)0.03210.2136
0.3622Porosity(Archimedean)(cm3/g)0.03070.18240.2062Time exposure
(min)
0
60
120
0.014

0 minute of exposure
60 minutes of exposure
120 minutes of exposure

Volume of mercury
intruded3(cm /g)

0.012
0.010
0.008
0.006
0.004
0.002
0
0.01

0.10

1.00

10.00

100.00

Pore diameter (x10 -6m)

Figure 3: Differential curve of mercury intruded into samples
Since an unpredictable crack phenomenon during the test, the total
pore volume sometime represents the porosity value including broken porewalls to a distinct network. Therefore, a porosity value from Archimedean
technique may be preferable way for deteriorated samples.
2.5 3 – point mesoscopic bending test
A 3 – point bending test with roller support at both ends was
conducted on the notched mesoscopic beam with dimension of 10 x 20 x 90mm3. Three LVDTs were set at both ends and mid – span for gathering theload – deflection curve without support settlements.
Figure 4 shows the tendency of reduction in flexural strength. It canbe roughly seen that flexural strength would be diminished with theModeling ofModelingof oven-driedoven-dried mortar
mortar after
after fire
fire exposure
exposure by
by Rigid
RigidBody
BodySpring
SpringModel
Model(RBSM)(RBSM)

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increment of exposure time; however, the results were divided into twogroups. The shaded spots represent the test results whose specimens failed
at artificial crack when applying point load at mid – span. The flexuralstrength was significantly reduced in the range between 0 and 60 minutes of
exposure but there was a slightly recovery in the range between 60 and 120
minutes of exposure. The reason is fire deteriorations in mortar samples
were extremely random in nature. Sometimes, the tested specimens were
probably made from the zone without major cracks of burnt original
samples; however, the difference between the results of two different time
exposure were not so significant and the modulus of rupture for all damaged
mortar samples were less than the value of control group. Another spots
represent the residual flexural strength whose location of failure wasunpredictable due to the behavior of fire damages. Although the reduction inflexural strength was obviously observed with an increment of time
exposure as same as the shaded spots, the residual flexural strength in thisgroup was so small compared with another group. One possibility to explain
this kind of situation is, a severe damaged zone might be chosen to be
testing specimens.
2.6 Calculated shear strength
The shear strength has been experimentally examined; however, thetesting results were inclined to be a compressive strength due toinappropriate size of specimen and the limitation of maximum capacity of
testing machine. Therefore, the preferable way to estimate the shear strength
is to be calculated from their own actual flexural strength by numericalequation proposed by Japanese researchers as shown in Eq. (2).

 max  1.083 f b  1.11
Where

 max
fb

=

shear strength (N/mm2)=

flexural strength (N/mm2)(2)

Miura T. and Sato Y. have proposed this linear equation to predictshear strength from tested flexural strength which was suitable for non –damaged and damaged mortar samples. Therefore, it may be more suitable
way for predicting the shear strength of both control and fire – damaged
samples. The results were shown in Figure 5.
Because the shear strength was calculated from its own flexural strength, thereduction tendency was same as flexural strength results. Although the
reduction in shear strength was obviously observed with an increment oftime exposure as same as the shaded spots, the residual shear strength in thisgroup was so small compared with another group.

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3. RELATIONSHIPS BETWEEN MATERIAL PROPERTIES
3.1.1 Chemical – Physical relation
With respect to two different time exposure, The proposed chemical –physical relation can be used to predict the porosity using residual content
of CSH. Figure 6 shows relationships between porosity and CSH. The
regression lines are obtained for different exposure times.
Since the volatilization of CSH was initiated at 560 °C, the non –evaporable water was gradually escaped out so a cohesive force between
CSH layers was demolished. The porosity in samples exposed to higher
temperature is greater than the lower one because in case of hightemperature the molecular structure of CSH might be substantiallydestroyed. With respect to the increment of maximum temperature from 60
to 120 minutes during fire exposure, pore – structure coarsening was
intensified because of the CSH decomposition phenomenon.
Actually, the content of CH for each fire condition was experimentallytested. However, a mechanism for the rehydration of CH may be able tohappen but it is so complicated and not easy to expect because of theuncertainty of rehydration process (Wei M. L., et al., 1996). One possibilityof this relationship is to say that the rehydration for post - fire samples;
therefore, the pore space may be refilled by new formation of CH. In thisstudy, the changed porosity was assumed to be affected by only thevariation of CSH.
7.00
0 minute of exposure
60 minutes of exposure(at expected location)
60 minutes of exposure(at unexpected location)
120 minutes of exposure(at expected location)
120 minutes of exposure(at unexpected location)

Flexural strength (N/mm2)

6.00

5.00

4.00

3.00

2.00

1.00

0

0

20

40

60

80

100

120

Time exposure (min)Figure 4: Reduction in flexural strength for post – fire samples

Modeling ofModelingof oven-driedoven-dried mortar
mortar after
after fire
fire exposure
exposure by
by Rigid
RigidBody
BodySpring
SpringModel
Model(RBSM)(RBSM)

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9.00
0 minute of exposure
60 minutes of exposure(at expected location)
60 minutes of exposure(at unexpected location)
120 minutes of exposure(at expected location)
120 minutes of exposure(at unexpected location)

8.00

Shear strength (N/mm2)

7.00
6.00
5.00
4.00
3.00
2.00
1.00
0

0

20

40

60

80

100

120

Time exposure (min)Figure 5: Reduction in shear strength for post – fire samples
0.25
60 minutes of exposure(Max. Temp. 927 C)
120 minutes of exposure(Max. Temp. 1010 C)

3Porosity (cm /g)

0.20

y = -0.0629x+0.2377|r|=0.988

0.15

0.10

y = -0.0487x+0.1952|r|=0.932

0.05

0

0

0.50

1.00

1.50

2.00

2.50

3.00

3.50

CSH Content (% by weight)

Figure 6: Relationship between CSH and porosity
3.1.2 Physical – Mechanical relation
As mentioned above, a porosity of mortar sample was estimated by
the residual CSH in sample. Also, mechanical properties would bediminished by the influence of variation of porosity value (Omer A., 2007,
Chang Y. F., et al., 2006, Chi S. P., et al., 2001, Djaknoun S., et al., 2012);
therefore, the relationship between porosity against mechanical properties
must be concerned in order to better understand the actual behavior of bothnon – damaged and fire – damaged mortar.
Regarding two different time of subjecting exposure in this study, theresidual flexural strength has been interpreted in terms of its porosity
predicted by the retained amount of CSH in sample after deterioration. The
regression lines in Figure 7 were obtained for different exposure times andmaximum temperature in furnace.

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7.00
60 minutes of exposure(Max. Temp. 927 C)
120 minutes of exposure(Max. Temp. 1010 C)

6.00

y = -20.232x+6.496|r|=0.875

2

Flexural strength (N/mm )

5.00

4.00

3.00

y = -33.487x+6.903|r|=0.952

2.00

1.00

0

0

0.05

0.10
Porosity (cm 3/g)

0.15

0.20

Figure 7: Effect of porosity on the flexural strengthAlso, shear strength was calculated from its own flexural strength as
expressed in Eq. (2). Therefore, the regression lines of shear strength musthave the same tendency as the relationship between porosity and flexuralstrength.
Since the changes in porosity after subjected to high temperature
treatment up to the target temperature, mesoscopic flexural and shear
strength has been diminished due to the decomposition of strengthcompounds and Van der Waal forces between CSH layers. Figure 7 andFigure 8 show the influence of porosity on the residual strength in which ahigher maximum temperature has a higher strength. In fact, mortar samples
with same porosity value should have the same material strength; however,it was probably recovered depending on several factors such as rehydration
process, post – fire cooling regimes or atmospheric humidity (Wei M. L., etal., 1996, Chi S. P., et al., 2001).
9.00
60 minutes of exposure(Max. Temp. 927 C)
120 minutes of exposure(Max. Temp. 1010 C)

8.00
7.00

y = -21.919x+8.146|r|=0.875

2

Shear strength (N/mm )

6.00
5.00
y = -36.273x+8.587|r|=0.952

4.00
3.00
2.00
1.00
0

0

0.05

0.10
3
Porosity (cm /g)

0.15

0.20

Figure 8: Effect of porosity on the shear strength

Modeling ofModelingof oven-driedoven-dried mortar
mortar after
after fire
fire exposure
exposure by
by Rigid
RigidBody
BodySpring
SpringModel
Model(RBSM)(RBSM)

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4. FULL – SCALE SIMULATION
Until now, all the test results were done on mesoscopic samples but, inpractical, the performance of structural concrete was evaluated in full –scale structure. Therefore, the purpose of this task is to compare only the
maximum load of actual 3 – point bending test in macro scale with thesimulation result using the mesoscopic experimental data. In this study, the60 minutes post – fire specimen was simulated compared with the actual
experimental result at macroscopic sample.
The dimension of full – scale sample used in model construction was shownin Table 2 together with material properties in mesoscopic level. In analysis,
the process was suddenly stopped at descending branch around 70 percent
of peak load. The comparison of actual and simulation data was shown in
Figure 9.
Table 2: Material information used in simulation
Time exposure
(min)
60

CSH
(%W/W)

Dimension
Height(mm)
23.8

Length(mm)
400.0

Thickness
(mm)
46.9

CH
(%W/W)

Ini.

Temp.

Ini.

Temp.

3.29310.47763.8513

1.9698Despite there was some defect made non – smooth graph in damaged
sample during the test, both the load – deflection shape and maximum load
were almost the same with considering the diminishing of material
properties and residual chemical compounds in sample after subjected tofire exposure. In addition, another possibility to explain a small difference
between experimental data and simulation is the size effect. Material
properties of small and macro specimens may not be exactly the same
because the overall performance of full – scale structure is probably
influenced by several factors. Based on the results of the current study, it
can be said that RBSM can be used to predict the maximum applied load for
3 – point macroscopic bending test accurately with material properties inmeso – scale and considering the effect of chemical compounds content inwhole sample; however, the simulation of tensile softening branch need tobe modified in the future prospects.

Applied load (N)

30.00

20.00

10.00

Actual load - deflection curve
Simulated load - deflection curve
0

0

0.05

0.10

0.15

0.20

0.25

Deformation (mm)Figure 9: Experimental data compared with RBSM simulation for 60
minutes after fire

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REFERENCES
Gai F. P., and Zhi S. H., 2006. Change in microstructure of hardened
cement paste subjected to elevated temperature. Construction and Building
Materials 22, 593-599.
Raymond A. C., and Kenneth C. H., 1999. Mercury Porosimetry of
hardened cement pastes. Cement and Concrete Research 29, 933-943.
Wei M. L., Lin T. D., and Powers – Couche L. J., 1996.
Microstructures of Fire – Damaged Concrete. ACI Materials Journal, 199205.
Omer A., 2007. Effects of elevated temperature on properties ofconcrete. Fire Safety Journal 42, 516-22.
Chang Y. F., Chen Y. H., Sheu M. S., et al., 2006. Residual stressstrain relationship for concrete after exposure to high temperatures. Cement
and Concrete Research 36, 1999-2005.
Chi S. P., Salman A., Mike A., et al., 2001. Strength and durability
recovery of fire-damaged concrete after post-fire-curing. Cement andConcrete Research 31, 1307-18.
Djaknoun S., Ouedraogo E., Ahmed B. A., 2012. Characterisation of
the behavior of high performance mortar subjected to high temperatures,Construction and Building Materials 28, 176-86.

Modeling ofModelingof oven-driedoven-dried mortar
mortar after
after fire
fire exposure
exposure by
by Rigid
RigidBody
BodySpring
SpringModel
Model(RBSM)(RBSM)

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6thCURRENT SITUATION ABOUT
RECONSTRUCTION OF HAKOZAKIPENINSULA AFTER THE GREAT EAST
JAPAN EARTHQUAKESae SHIKITA1, Takaaki KATO 2
1
Graduate student, School of Eng., The University of Tokyo, Japanshikita@iis.u-tokyo.ac.jp
2
Associate Professor, ICUS, IIS, The University of Tokyo, Japan
ABSTRACT
It has been 2 years since the Great East Japan Earthquake occurred,and detailed reconstruction planning is ongoing in every area affected bytsunami. Kamaishi City, Iwate Prefecture, where severely damaged in thedisaster, is also in such situation. Especially we focused on Hakozaki
Peninsula in the north part of the city, which consists of 8 villages andmajority of the people gain their livelihood by fishery. After the earthquake,some of the local people started thinking about reconstruction of their own
village. As including external experts in urban planning, they came toconsider about the future of all over the peninsula to be sustainable areaand established a new NPO.
In discussion with the local government on planning of each village,
the concerns incline to be present and narrow ranged matters. However, it
is also necessary to consider and cope with the facing problems since before
the disaster such as aging society and population declining from long-term,wide-areal and comprehensive viewpoint. From this point, this paper
analyzes the current situation and problems in reconstruction of Hakozaki
Peninsula.
Keywords: reconstruction, Kamaishi City, Hakozaki Peninsula,community participation, sustainable, problems.
1. BACKGROUNDDISASTEROF

KAMAISHI

CITY

BEFORETHE

1.1 History
Kamaishi city is located on the south-eastern part of Iwate Prefecture,along the coastal line towards Pacific Ocean, with total area of 441.43
square kilometres. Kamaishi city was well known as a city of fisheries andiron. In 1857, Nambu Domain constructed Ohashi blast furnace at Kamaishi
as the first western-style blast furnace in Japan. Then in 1934, the Meiji
Government established Nippon Steel, when the city population at that time

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reached 40,388 people. At the same time, roads and railways weredeveloped, and the role of Kamaishi Port became significant.
1.2 Population change
Population of Kamaishi City once reached around 100,000 in 1960’s,
but now it is 39,574 (Statistics Bureau, 2010). Figure 1 shows the
population trend in Kamaishi City, DID (Densely Inhabited District)
population, and the number of household, with the data obtained from
National Census. The population from 2010 until 2040 was estimated. From
this graph, it can be assumed that great amount of reducing population
number will occur in the future. The decreasing number of people can beseen on the general population, also in the urbanized area. On the other hand,
the household declining rate is smaller compared to population declining.
Figure 2 explains about the population composition in range of age and sex.
It expresses the state of declining birth rate and aging population.
1.3 Industry
The situation of industry in Kamaishi City is declining. In 2007,
output of primary industry accounted for 3.8% of total, secondary industry
33.3%, and tertiary industry 67.6% (Kamaishi City, 2011). The number of
employees had decreased by about 30% from 1990 to 2010. Particularly, thedeclining of primary industry was significant; it has been halved in 20 years.
Agriculture has decreased about 65% among others (Statistics Bureau, 1990
and 2010). In fishery industry, although the amount of catch of fish have notdecreased so much, output of money has been declining, especially in 10
years from 2000 until 2009 (Kamaishi City, 2011).

Figure 1: Changes in population and households of Kamaishi (Derived
from National Census, 1985, 1990, 2000, 2005 and 2010)

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Figure 2: Population pyramid of Kamaishi City in 2010 (Derived from
National Census, 2010)
2. DAMAGE ON GREAT EAST JAPAN EARTHQUAKE
Kamaishi City suffered great tsunamis recorded in 1896, 1933, 1960 and thelast in 2011. During the Great East Japan Earthquake in March 11th, 2011,by the highest wave of tsunami reaching 10.1m at Kamaishi harbor,
Kamaishi City suffered the total number fatalities 885 people, missing 176
people, evacuees to other area 9,883 people, and evacuees inside the city
633 people (Kamaishi, 2011). The number of dead and missing people ofKamaishi City was the third largest amount in Iwate Prefecture, after
Rikuzentakata and Otsuchi Town. The number of totally collapsed buildings
are 2,954, highly destroyed 396, partially destroyed 291, and damaged 907(Kamaishi City, 2011).
3. CURRENT SITUATION
Location of the refugee has been closed by October 8th 2011, and 3,164
houses of temporary housing were constructed by August 5th 2011, whichwere located in 50 sites in Kamaishi City (Iwate Prefecture, 2012).
Currently, people who lost their own houses mainly by the tsunami areliving in the temporary housing which are scatteredly located inside the cityand other housing outside the city.

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The amount of proceeded debris in the disaster was estimated as 82
tons in Kamaishi City, and it is planned to be removed from the affected
region by March, 2013, and completely processed by March, 2014
(Kamaishi City, Pacific Consultants, 2011). By April 2012, debris removal
reached 50%, and the process rate was 3% (23,000t).
4. RECONSTRUCTION
4.1 Reconstruction plan of the municipality of Kamaishi City
In September, 2011, the Basic Principles of Reconstruction made by
the municipality was enacted, which consists of basic principles and roughtimetable of the reconstruction process from the beginning. Then in
December, 2011, the Basic Reconstruction Plan (Figure 3) was establishedthrough asking opinions from the representatives of the citizens. On theother hands, detailed landscape plan and land use plan haven’t been decided
yet. They will be decided after consultation with communities and now they
are under discussion in July, 2013 (Figure 4).
4.2 Present situation of the area and people related to reconstruction
Sets of interviews had been done in the settlement of Ryoishi and
Shirahama by NPO (Non-Profit Organization) CeMI (Crisis &Environmental Management Policy Institute) and Kato Takaaki Laboratory
in October and December 2011. This was aimed to know the real range ofthe choices of livelihood rehabilitation of each household, and consequently
the potential range of recovery of the communities. The interviews were
subjected to 20 household who had moved to temporary housing. From thisinterview, the following facts were revealed; Fist, affected people strongly
feels anxiety from the uncertainty of the future of their lives. This may leads
to the people moving out who want permanent housing and job in the earlystage of reconstruction. Second, some people don’t put the first priority on
hard measures against tsunami in their villages. This means consideration
about the balance of usage of money on hard measures and other amenities.
Third, there are many people who can’t recover their living on their own
because of economic conditions. Thus support for such people is needed tobe considered to realize their reasonable living and sustainability of theregion.
Also, to stem the declining town is as important as ensuring citizens tolive in. The basic city planning reconstruction of Kamaishi City wasorganized and intended to meets the needs of various kinds of stakeholders
in the city. However, it was not transparent enough in assuring the opinionof the representative organization and citizens, including the details of theresidential area plan and disaster prevention measures. Therefore it should
be considered that the awareness of residents plays role to achievesatisfaction on all stakeholders.

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4.3 Community Participation
After the disaster, community participation across Hakozaki Peninsulaon the north part of the city has been active and the organization for
discussion and activities for recovery of the area including 8 villages has
been established. June 2011 was the start of this movement involving 4
villages together, mainly held by a fisherman in Ryoishi with support from
CeMI. After that, they came to have leaders meeting almost every monthacross the 8 villages. Community members and CeMI has been working
together for the reconstruction process, such as discussing reconstruction
plan of their own village and get advices from planners including Kato
associate professor. In March 2012, a new NPO was established, which wasnamed O-Hakozaki Shimin Kaigi (Community Association of Hakozaki
Peninsula) and it consists of community members, CeMI members, andplanners.
The O-Hakozaki Shimin Kaigi’s activities are participated by four
working groups, starting August 2012. The Working Group 1 manages
community recovery for future, including physical, social, and humanfactors; Working Group 2 involves economic, creating job opportunity andnew industry related to the fisheries; Working Group 3 focuses on humancapacity building such as disaster mitigation and response; and Working
Group 4 focuses on economic factors, based on the resources of this area,and market the products and value addition.

Figure 3: General plan of Kamaishi City in Basic Reconstruction Plan
(Kamaishi City, 2011)
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Figure 4: Draft of land use plan for one of the villages in Kamaishi City
(Kamaishi City, 2012)

5. CHALLENGES AND PROBLEMS
During the reconstruction process, there are several challenges for all of the
stakeholders. First, top-down planning strategies has been applied in thisarea since the very beginning of its history until before the Great East Japan
Earthquake. But after this disaster, with the stream that expectation on
regional decision and residents participation is much bigger than before, themunicipality and the region itself should consider their reconstruction forthemselves. For the reconstruction process, the citizens trust themunicipality to develop and implement the plan. However, many of themunicipalities in the affected area have little experience of making andimplementation of regional planning including making agreement ofresidents, communities and various sectors. This can be applied to thecommunity as well. Thus there needed enough time to think of theircomprehensive and sustainable way of reconstruction of the region. Also,
the planning system that includes municipality community and professionals
such as planners should be made.
Second challenge is that there was not much chance for various people
such as young people and women. Until now, the persons mainly taking partin the discussion are old men. In addition, the top person of the conventionalorganization has very strong influence that other members seem to havedifficulty to state their opinions and take actions based on their ideas. The
other difficulty is the community tends to have short-term view regarding
their economic, social, and built environment situation, while the local
government has a long-term view. The profile of community in the area is

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fishermen and fisheries industry, with their fishermen cooperative union
which aimed for their working purpose.
The present condition of Japan as developed country needs a different
approach for reconstruction than the previous disaster during developing
period. Next challenge is the ageing society of the currently living
population of Kamaishi City, also the declining economic condition. Withmajority of community leaders and most of the people are in the age of 70s
years old. However, there is presence of young generation in Shirahama to
support the reconstruction process. Second, the community members who
are mainly elderly tend to stay in the previous place and/or city, and do notwant to move to the other cities. Based on the interview, community has
strong attachment to the area. The future of the cities ensuring the safety andsustainability issues in comprehensive way, and how to attract the people tostay in this area needs to be taken into account. It is also considered theformation of compact smaller town, to maintain the population andsustainability in the future.
6. CONCLUSION
Currently Kamaishi City and the other affected area in Japan are in the earlyphase of reconstruction from the Great East Japan Earthquake and Tsunami.
Considering the past of Kamaishi City and the present situation of theageing society, Kamaishi City has several challenges to face during thereconstruction process, especially related with the developed state of Japanand the currently applied planning system, also the character of community
to adapt with the new situation.
Further study is to analyze the challenge of the reconstruction process,finding influencing factors, considering the needs of citizens, and consensus
between all stakeholders, ensuring citizen involvement. Then, the analysis is
aimed to find possible suggestion to achieve comprehensive reconstruction
process.
REFERENCES
Kamaishi City, 2011. The Situation of Kamaishi City. Japan
Statistics Bureau, 1990. National Census. Ministry of Internal Affairs
and Communications, Japan
Statistics Bureau, 2010. National Census. Ministry of Internal Affairs
and Communications, Japan
Kamaishi City. 2011. Reconstruction Plan Kamaishi. Basic Town
Plan. Iwate Prefecture, Japan: Kamaishi City. Data obtained on December
22nd, 2011.
Kamaishi City, Pacific Consultants, 2011. Plan of Debris Processing.
Iwate Prefecture, Japan
KamaishiCity.
http://www.city.kamaishi.iwate.jp/index.cfm/8,0,76,425,html
Current Situation
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Iwate Prefecture. 2012. Iwate Prefecture NPO activities.
http://www.pref.iwate.jp/~hp0301/npo-info/ninsho/sinseininshodantaiichiran/ichiran-10(451-500).htm. Data obtained on June 2012.
Website of Reconstruction from the Central Government
http://reconstruction.go.jp. Data obtained on June 2012.
Japan Non-Profit Organization. 2012.
nonprofitjapan.home.jgc.org/npo/mpojp.html. Data obtained on June
2012.

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6thNEIGHBORING SEARCH PROCESS IN
GENETIC ALGORITHM FOR TRAINSCHEDULING PROBLEM
Jetpan WETWITOO1 and Thirayoot LIMANOND2
1
Thailand Accident Research Center,
Asian Institute of Technology, Thailand
jetpanw@ait.ac.th2
Thaioli PLC, Thailand
ABSTRACTOne of the most important issues in railway planning is to optimizing aschedule. Delay is one of the key factors to optimize since decreasing indelay means lower cost to operator and less travel time for passenger. In
this study, Genetic Algorithm combined with First-come First-served rule is
proposed for a Train Scheduling Optimization Problem with variable
departure time. The performance of conventional Genetic Algorithm process
will be compared with Genetic Algorithm with Neighboring Search process.
Optimization analysis was conducted on The Northern Section of State
Railway of Thailand, on 22 segments with 12 trains. Within 1,000
generations, Neighboring Search process enhanced in Genetic Algorithm
offered a 0 minutes delay which is a global optimum. However, without
Neighboring Search, best solution of 3 minutes delay was reached within
1,000 generations. In conclusion, Neighboring Search process with smaller
step with can improves Genetic Algorithm performance by avoiding achance to miss an opportunity to convergence from larger step fromcrossover and mutation process in each generation.
Keywords: railways, optimization, train scheduling problem,
Neighboring Search, Genetic Algorithm, delay, State Railway of Thailand
1. INTRODUCTION
Timetable can be considered as one of the key factor to the railway system.
In terms of scheduling, the management of trains can be consisting of thetime of arrival and departure, from the depot or yards to a platform andincluding a crew scheduling. However, in term of operational timetableespecially in a single track line, it is a principle of how to control the train
between stations flawlessly by the time that train should be arrived anddepart and which siding it should belong to.
Many factors have to be considered from the decision makers in orderto create a timetable, e.g., number of passengers, their origin anddestination, number of rolling stocks, available tracks and sidings, cost ofoperation and travel time. Obviously, the travel time can be one of the major

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factors which effected to other factors as well. For instance, apart from cost,passenger decision on mode choice is also depends on travel time. Too long
travel time can force passenger to other train or other mode. In addition,
travel time is also effect to the cost. Not only cost to railway operator fromoperation cost, less travel time can give more opportunity cost to the user aswell. Thus, it is more convenience to develop the optimization algorithm tominimize travel time since it can be easily convert to other factor by simply
multiply the cost factor or other factor to a given travel time. In single track
railway, travel time consists of three main components, running time
between station, dwell time in station and delay occurred from a waitingtime. Delay is the only part which can be optimize since the other factors are
the constant to the characteristics of railway network. Therefore, in thispaper, delay will be minimized through the best meet-pass plan for a singletrack line.
Study on train scheduling optimization was started by Szpigel (1973),
who develop a linear programming model to determine the best overtaking
and crossing positions in single track railways. Recent development on
methodology and algorithm has been proposed extensively by many
researcher but most of them still based on integer linear programing. Some
researchers propose different methodology. For instance, Huisman etal.(2002) propose queqing network by queqing theory, Corman et al.(2010)
propose taboo search for rerouting train. Also, some of artificial intelligent
based search has been propose. Salim & Cai (1997) considering different
other factors other than travel time based on genetic algorithm. Particalswarm also has been proposed by Ho et al.(2012) in terms of cost
minimization for railway scheduling.
2. METHODOLOGY
In this study, Genetic Algorithm will be used to conduct a search algorithm
to find the best departure time for each train. The departure time of eachtrain is generated through Genetic Algorithm in each generation andallowed to be adjusted within a range of 15 minutes from its actual
departure time. Train operation in each conflict will be governed by a firstcome first-served basis. Hence, only delay affected from departure time will
be minimize in this study. Actual timetable including current departure time,
travel time of each segment by each train was collected from State Railwayof Thailand and will be used as inputs to the problem.
Performance comparison on conventional Genetic Algorithm andGenetic Algorithm with neighboring search will be analyzed in this study.
Conventional Genetic Algorithm (Figure 1) is consisted of three generalmodules, namely, fitness selection, crossover and mutation which decodedin decimal. Proposed neighboring search module will be process after
mutation module as shown in Figure 1.
Concept of neighboring search is derived from mutation process.
However, unlike the mutation which mutated a chromosome into any value

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within a range, neighboring search tries to mutate a chromosome into
nearby possible solution. In this case, if the chromosome was selected to beneighboring mutate from probability of neighboring mutate rate, thechromosome which is a departure time of each train will have a 50/50
chance to mutate +1 or -1 minute from its prior departure time as shown thecomparison in Table 1.

Figure 1: Flowchart of Conventional Genetic Algorithm (left) and Genetic
Algorithm with Neighboring Search (right)

Neighboring Search
Neighboring
Search Process
Process inin Genetic
Genetic Algorithm
Algorithm forfor Train
Train Scheduling
Scheduling Problem
Problem

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Table 1: Example of Conventional Mutation and Neighboring Search
Mutation Suppose if departure time = 3:15
 Allowable range of 15 minutes
Conventional Mutation
Neighboring Search Mutation
 Randomly change departure time 1
Randomly change departure timewithin a range
minute from its default value
Possible solution = 3:00 to 3:14, 3:16 to  Possible solution = 3:14 or 3:16
3:30
 Probability of each solution; P = 1/2
Probability of each solution; P = 1/30

3. RESULT
Analysis was conducted on The Northern Section of State Railway ofThailand, from Phitsanulok Station to Chiang Mai Terminus with actual
inputs of 22 segments with 12 trains and actual train schedule. Each
iterations, Genetic Algorithm was set to execute at 1,000 generation. Within
10 iterations, conventional Genetic Algorithm could reach a minimum
solution of 3 minutes. (Figure 2) However, with Neighboring Search, aglobal optimum solution of 0 minute could be reached 8 times from 10
iterations. (Figure 3) A comparison of the rate of convergence between twoalgorithms is shown in Figure 4. Difference of computational time between
two algorithms is comparatively small and negligible.

Figure 2: Performance of conventional Genetic Algorithm

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Figure 3: Performance of Genetic Algorithm with Neighboring Search
process

Figure 4: Comparison of average performance between two algorithms
4. CONCLUSION
It is proven from the result that Genetic Algorithm with Neighboring Search
Process can reach better solution within a given constrain. The strength of
Neighboring Search comes from a smaller step in each mutation. In a
conventional mutation, each mutation has a main objective to jump pass aboundary of local optimum curve. Nevertheless, each jump might be too big
Neighboring Search
Neighboring
Search Process
Process inin Genetic
Genetic Algorithm
Algorithm forfor Train
Train Scheduling
Scheduling Problem
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and miss an opportunity to reach an optimum solution. By adding
neighboring search process next to mutation process, after each big jump, asmall step is conducted to search nearby solution for a better solution. This
smaller step is proven to be a crucial step to find an optimum solution.
Neighboring Search is specially design for a Genetic Algorithm withdecimal encoded. In mutation process, range of possible solution in decimal
encoded maybe too wide for randomly selected process. Therefore,
Neighboring Search Mutation is added in Genetic Algorithm to fill this gap.
When a solution is already near situated near an optimum solution, little stepfrom Neighboring Search gives an advantage to find nearby optimum point.
In contrary, mutation process has a larger range to mutate so there is lower
probability to find an optimum point. However, departure time result might
be difficult to be implemented because departure time usually assign in 5 or
10 minutes interval.
As mentioned, this study analyze the performance of Neighboring
Search Process under a specific condition; decimal coded and applied fortrain scheduling problem. It is recommended to apply Neighboring Search
Process in other type of problems or other Genetic Algorithm techniques toverify the performance of Neighboring Search Process in Genetic
Algorithm.
REFERENCES
Corman, F., D’Ariano, A., Hansen, I. A., & Pacciarelli, D. (2011). Optimal
multi-class rescheduling of railway traffic. Journal of Rail Transport
Planning& Management, 1, 14–24.
Ho, T., Tsang, C., Ip, K., & Kwan, K. (2012). Train service timetabling inrailway open markets by particle swarm optimization. Expert Systems
with Applications, 39, 861–868.
Huisman , T., Boucherie, R. J., & van Dijk, N. M. (2002). A solvablequeueing network model for railway networks and its validation andapplications for the Netherlands. European Journal of Operational
Research, 142, 30–51.
Szpigel, B. (1973). Optimal Train Scheduling on a Single Track Railway.
Operational Research '72, 343-352.

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6thAN INVESTIGATION OF ROAD BRIDGE
MAINTENANCE SYSTEM IN JAPAN IN
DEVELOPED SOCIETY
Mari NAKASHIMA1 and Kohei NAGAI2
Bachelor course student, Department of Civil Engineering,
The University of Tokyo, Japan
mari@iis.u-tokyo.ac.jp
2
Associate Professor, ICUS, IIS, The University of Tokyo, Japan
1

ABSTRACTThe road assets in Japan have peak of construction in high economic
growth period around 1970s. At that time, constructing bridges as fast andas cheap as possible was all their interest and the maintenance was notconsidered enough. However, as it is called "America in ruins", many
structures in the United States constructed in New Deal withoutmaintenance were rapidly deteriorated and caused tragic accidents. After
those, Japan started to establish a countermeasure to prevent to be "Japan
in ruins."
This research investigates a current situation of road bridge
management system in Japan and clarifies the problems to be solved in thedeveloped society. Many road bridges must be efficiently updated in thenear future, so Japanese government tries to investigate maintenance PDCA
cycle. However, a research showed most managers had not even inspected
bridges. Japanese central government decided to let all local governments
inspect bridges they manage and make plans for maintaining them for longterm by “preventive maintenance”. After this policy, most managers haveinspected all bridges and made their own manuals. It seems inefficient but
Central government could not make unified data base and inspecting
manual because of the decentralized government system. In this situation,
managers in charge of local bridges are local government, which seldom
have enough staff. A research shows that there are some towns in Kochi
Prefecture that have many bridges constructed without considering
maintenance deeply but have only one or two persons in charge of thebridges. Furthermore, they are not enough educated or experienced toinspect and diagnosis whether or not they should repair. For those
governments, diagnosis is difficult and managing bridges is a burden. To
solve this problem, many universities and other organs started researching
to make reliable system for deterioration prediction. However, it is almost
impossible because uncertainty and individual conditions are unavoidable
factors.
Keywords: road bridge deterioration, maintenance, asset management,
limit of deterioration prediction, policy

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1. INTRODUCTION
Many bridges in the United States constructed in New Deal felt down withloss of lives for inappropriate maintenance. From the accidents, we learned
that there is huge economical loss once an accident happens with loss of
lives.(Inoue, 2010) Japan has 157 thousands of bridges over 15m span and
700 thousands over 2m span, and no accidents that cause lives loss havefortunately happened yet, but number of deficient bridges is increasing
every year. This is a problem of developed society which had a construction
boom in high economic growth period. Developed society has the problemof aging society of people at the same time. Especially in local areas, theproblem is serious. Even though local governments are having hard timewith short of public finance and staffs, they have to manage the bridges they
have. The number of national road is 50 thousands and that of bridges
managed by prefecture is 100 thousand, and by municipality is 521
thousand. 77 percent of bridges are managed by local government. National
government tries to support the local governments but it is limited becauseof decentralized government system. This research investigates a current
situation of road bridge management system in Japan and clarifies the
problems to be solved in the developed society.
2. THE NUMBER OF ROAD BRIDGES AND THEIR AGE IN
JAPAN

Figure 1: Number and the age of bridges in Japan (MLIT, 2010)
In 2031, the number of bridges whose age is more than 50 years will
be over 50% of all the bridges.(Figure 1) There is no scientific support thatbridges that are over 50 years old collapse, but the fact that around 50% of
the bridges whose age is over around 50 years need maintenance can be200

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ICUS ICUS
ReportReport
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reliablereliablereasonreason
to sayto those
say thosebridgesbridgesshouldshouldbe inspected
be inspected
and repaired
and repaired
if
if
needed.(Figure
needed.(Figure2) The2) reason
The reason
why why
the ratio
the ratioof deficientof deficient
bridgesbridgesdoes does
not notincreaseincrease
after after
it achieves
it achieves
50 years
50 years
old isoldbecauseis because
seriously
seriously
damageddamagedbridgesbridgesof thoseof thoseare alreadyare alreadyrepaired.
repaired.
In japan,
In japan,bridgesbridges
whosewhose
age isagemoreis morethan 50than 50
increaseincrease
rapidlyrapidly
in a few
in adecades
few decades
althoughalthough
it is impossible
it is impossible
to replace
to replacethem themat at
the same
the sametime intime
thisincondition
this conditionof finance.
of finance.
To flatten
To flatten
the cost
the tocost
thetoyears,
the years,
preventive
preventive
maintenancemaintenancewhichwhichcosts costs
less shouldless should
be started
be started
at present.
at present.
A: No
A: damage
No damageor needlessor needless
to torepairrepair
B: Need
B: Needrepairrepair
depends
depends
on theon thesituation
situation
C: NeedC: Needrepairrepair
immediately
immediately
E1: Need
E1: Need
immediate
immediateactionaction
for forstructurally
structurally
deficient
deficient
E2: Need
E2: Need
immediate
immediateactionaction
for forother otherreasonsreasons
M: NeedM: Needmaintenancemaintenanceserviceservice
S: NeedS: Needdetaileddetailed
investigation
investigation

FigureFigure2: Percentage
2: Percentage
of dteriorationof dteriorationlevel levelversusversus
elapsedelapsedyear (Tamakoshi,
year (Tamakoshi,2011)2011)
3. CASES
3. CASES
OF DEFICIENT
OF DEFICIENT
3.1 Kiso
3.1 Kisoriverriver
bridgebridgeThis Thisis anisexample
an exampleof nationalof nationalroad roaddamage.
damage.
Kiso Kiso
river river
bridgebridge
constructed
constructed
in 1963
in 1963
was seriously
was seriously
damageddamagedat theatconnection
the connection
betweenbetweentruss truss
and girder.
and girder.
It wasIt found
was foundwhenwhenemergency
emergency
investigation
investigation
was taken
was taken
for truss
for truss
bridgesbridgesin 2006.
in 2006.
Until Until
then, then,it wasit not
wasfoundnot foundalthoughalthoughit should
it should
be easilybe easilyfoundfound
if inspecting
if inspecting
was taken.
was taken.
It wasItrepaired
was repaired
right rightafter after
the foundation
the foundation
and andfortunately
fortunately
no accident
no accident
happened.
happened.

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road bridge
bridgemaintenancesysteminJapanJapandeveloped
society
An investigation
An investigationof roadofroad
maintenancemaintenancesystemsystem
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Figure 3: Kiso River Bridge (Mie Prefecture)
3.2 Kanonji Gokuraku Bridge
This is an example of damaged bridge managed by local government.
This bridge was constructed in 1933 and deteriorated seriously because of
airborne chloride. Huge crack in pier would cause accident. Also,
protective covering is too thin so that rod is exposed and rusted. This bridge
need to be repaired or removed. Considering its traffic, removement is better
choice, which is difficult to make consensus with neighbors for in terms ofcultural heritage. For this reason, it is still available to use with limit of 4
ton.

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Figure 4: Kanonji Gokuraku Bridge (Kanonji, Kagawa Prefecture)
4. MANAGEMENT SYSTEM OF
NATIONAL GOVERNMENT
4.1 PDCA cycle
National
government
introduced
maintenancecycle:
inspection, diagnosis, maintenance andrecord. The cycle is going to be thethird cycle. The national plan for inspection decides many points to inspectand diagnosis: 8 points for steel member, 8 for concrete member, and 16 forother and all of them have to be ranked from “a” for good to “e” for bad asshown in Figure 5. And this has to be done for each part of bridges, whichtakes a lot of work. Some parts that has to be inspected are shown in Figure
6. This is one of the reasons why national government could not let all local
governments to use this inspection manual.
In asset management, the ideal cycle is inspection, prediction,assessment and maintenance. It is important to think individual bridges’
management and macro governance separately. The latter cycle cannot beapplied for the individual management because deterioration prediction foreach bridge is almost impossible although it would be the ideal. On theother hand, the cycle can be used for the macro governance to make abudget estimate. The prediction in this case is not deterioration prediction
but that estimated by the number and age of bridges and cost statistically.

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DamageofconcretememberCrackLeakage andfree lime
Detachment
and rod
exposure
Falling out
Damage ofconcrete
stiffener
Floor crack
Concreteflaking
Expansion
gap
abnormality
Rough roadsurfaceOtherdamagePavement
damageBearingdysfunction
othersa-e
a-e

Corrosion

Edge
Middle

a-e
a-e

a-e

Crack

Edge

a-e

Middle

a-e
a-e

Edge

a-e

Middle

a-e
a-e

a-e
a-e
2 edgepanelsMiddleDamageof steelmembera-e

Rupturea-e
a-e

Anticorrosion
deterioration
Anchoragezone damage

a-eExpansion joint
Others

a-e
a-e
a-e
a-e
a-e

Loose anddropped bolt

CommondamageColor change,
deterioration
Leakage andstagnant watersound andvibration
excessive
deflection
Deformation,
deficit
Pipe clogging
by dirt
Subsidence,
movement,
slope
scour

a-e
a-e
a-e
a-e
a-e
a-e
a-e
Substructure
Fulcrum

Figure 5: Inspection items (MLIT, 2004)

a-e
a-e
a-e

Figure 6.1: Name of parts of concrete bridge (NILIM, 2007)

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Figure 6.2: Name of parts of steel bridge (NILIM, 2007)
4.2 Deterioration prediction
For individual bridges, deterioration prediction is almost impossible
because how the environment changes in the future cannot be predicted.
Even if the environmental change can be expected, there are always
unexpected errors in construction, so that still it is impossible to predict how
exactly the bridge deteriorate. Prediction also has an avoidable wide
variability. In the graph of Figure 7, grade of soundness is plotted versus
elapsed year; from “a” for good to “e” for bad. Two approximate lines are
somehow drowned but as can be seen in the figure, this is just an averageand has a wide dispersion. This can be used for macro study for deciding
budget but cannot be applied for individual bridges’ management. As statedabove, central government majors the bridges deterioration by the index
which is calculated by using many inspection data of the local parts. It may
be possible to show the average deterioration of the bridges. However, thecause of collapse is caused by the local damage or failure; usually at theconnection between truss and girder or around the shoe of the girder. Henceeven if the total average assessment is not bad, it does not mean that thebridge is safe.
For these reasons, precise deterioration prediction for
individual bridge has a limit in field.

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Figure 7: Unreliable deterioration prediction (Tamakoshi,2011)
5. LOCAL ROAD BRIDGE MANAGEMENT
5.1 Sustainable maintenance plan
A survey in 2007 showed that 90 percent of municipalities had notinspected their bridges after construction. Although local governments areshort of budget and staff, they are in charge of managing their bridges. To
let local governments manage their bridges efficiently, central government
decided to support only if they inspect bridges and make their own plan to
manage efficiently. It is inefficient that every government make their own
ones but the central government could not make unified manual because
Japanese governance system is decentralized and the central government is
not the manager of those bridges in local area. After this policy, more than95 percent of municipalities inspected their bridges and 81 percent of local
governments made the plans by 2013(Figure 8).

Figure 8: Change of percentage of inspected bridges (MLIT,2013)

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5.2 Diagnosis

40
35
30
25
20
15
10
5
0

Unknown
Clerical staff
Engineer

Kochi
MurotoAki
Nankoku
Tosa
Susaki
SukumoTosashimizu
Shimanto
Konan
Kami
Toyo
Nahari
Tano
YasudaHonzan
Otoyo
Tosa
Okawa
Ino
Niyodogawa
Nakatosa
SakawaOchi
Yusuhara
Tsuno
Shimanto
OtsukiKuroshioNum�er of staff

Thanks for the policy, “inspecting” of the PDCA cycle has been donein most local government. The next phase is diagnosis using their inspecting
data. As it is shown in the research taken in Kochi prefecture, local
governments have only a few staffs for management of bridges as shown in
Figure 9 and it is not a rare case that they are not professional about bridges(Yabe, 2009). The upper graph in figure 9 shows the number of staffs ineach municipality in Kochi. Kochi city which have 45 percent of populationof Kochi prefecture (760 thousands) have many staffs compared to others.
To think of the burden of the staffs, the lower graph shows the number ofbridges to manage per staff. It is difficult for them to diagnose when andwhat kind of maintenance should be done and which bridge is the firstpriority. However, it is difficult to assign enough engineers to 1,742
municipalities. To improve the condition, municipalities should recognize
their responsibility for last decision and ask advice for upper organization if
they cannot diagnose. Those governments usually outsource management to
consultants and ask advice for universities and other research institution
who is supposed to be just advisers. Those organizations may have toomuch effect on the decision. Municipality governments should always makea last diagnosis with responsibility even though they do not have enoughengineers. Being it difficult, they should ask prefecture government whichhave more educated engineers for support. If they also cannot diagnosis,
they should ask regional development bureau. This system shown in Figure
10 makes the management efficient and solves the problem of short ofengineers at local government.

City

Town

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350
300
250
200
150
100
50
0

Kochi
MurotoAki
Nankoku
Tosa
Susaki
SukumoTosashimizu
Shimanto
Konan
Kami
Toyo
Nahari
Tano
YasudaHonzan
Otoyo
Tosa
Okawa
Ino
Niyodogawa
Nakatosa
SakawaOchi
Yusuhara
Tsuno
Shimanto
OtsukiKuroshio

Number of bridge to manage per
staff

400

City

Village

Town

Figure 9: Situation of staffs in municipalities in Kochi

Figure 10: Ideal system of local management
5.3 Data base
To manage bridges efficiently through the nation, unified data basewould be useful because any government is able to find other government’s
data of the same kind of bridge, and the central government can also let all
governments to recall easier when some kind of bridge are figured out thatthose have deficient in shape . However, for the same reason as
management manual, data base of each government is not unified. Central
governmet is editing its own data base for lacal government to recorded
their data. However, for the continuity of the data, it is difficult to let all
local government to use the new database.

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6. CONCLUSION
Even though it is difficult to predict exactly how bridges going todeteriorate, it is the fact that they deteriorate and early and efficientmaintenance before accident happen is necessary. What is needed in thefield is not an exact estimation but index that can be used to account forpeople and make budget. At the same time, making PDCA cycle work isimportant for efficient management especially for local governments that do
not have enough budget and trained staffs.
REFERENCE
Inoue, M., and Fujino, Y., 2010. Study on social impact of the I-35W
bridge collapse in Minneapolis in Japanese, Journal of JSCE, F Vol.66
No.1,14-26
Tamakoshi, T., Okubo, M., and Yokoi, Y., Annual Report of basic
Data on Road Structures In FY 2011 in Japanese, Technical Note of
National Institute for Land and Infrastructure Management No. 693
Ota, S., Suzuki, T., and Miura, M., 2011, Practical Management
Course For Road Administrator in Japanese, Taisei Publisher, JapanThe Office of Mie River and National Road in Chubu Regional
Bureau in Ministry of Land, Infrastructure and Transport
http://www.cbr.mlit.go.jp/mie/oshirase/bridge_refresh/index6.html
Miyazaki, Y., Tokunaga, G., Report of Emergency Investigation of
Steel
Truss
and
Arch
Bridges
http://www.kkr.mlit.go.jp/plan/happyou/thesises/2008/pdf05/01.pdf
Nagoya Institute of Technology, Department of Urban Society
Engineering
http://www.cm.nitech.ac.jp/civil/Structural.html
Yabe, M., An Investigation on Priority of Infrastructure Managementof Residents and Local Government in Mountainous Regions, Graduation
Thesis, Department of Civil Engineering, The University of Tokyo (inJapanese)
Tamakoshi, T., 2011, The Condition of Road Bridge and Problems of
Maintenance (http://limejapan.jp/archives/1039)
System for preventive management of road in Ministry of Land,
InfrastructureandTransport(http://www.mlit.go.jp/road/sisaku/yobohozen/yobohozen.html)
Tamakoshi, T., Kobayashi, H., Takeda, T., and Hiratsuka, Y., 2007,
Technical Note of National Institute for Land and InfrastructureManagement No.381,
Local
Authorities
SystemDevelopment
Center,2013.
(https://www.lasdec.or.jp/cms/1,19,14,151.html).

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THE ROLE OF MUTUAL ASSISTANCE
DURING DISASTER IN AGING SOCIETY
Nao SASAKI1, Muneyoshi NUMADA2 and Kimiro MEGURO3
1
Master Course, ICUS, IIS, The University of Tokyo, Japann-sasaki@iis.u-tokyo.ac.jp
2
Assistant Professor, ICUS, IIS, The University of Tokyo, Japan
3
Professor, Director, ICUS, IIS, The University of Tokyo, Japan

ABSTRACT
For disaster reduction, it is important to consider balanced
application of three types of countermeasures, which are self-support,
mutual assistance, and public support. Vulnerable people such as pregnant
women, small children, the aged, and the physically challenged are limitedin their abilities to help themselves and rely more on help from others andpublic sectors compared to healthy people. Facing rapidly depopulation
and aging society, effective countermeasure for vulnerable people is crucialin Japan as well as in other Asian countries where the similar situation will
occur in the future.
During the time immediately after a hazard attacks, public supportcannot be fully dependent thus mutual assistance becomes critical inproviding support to vulnerable people. However the current disastermanagement plan by the Japanese government does not provide necessary
information about mutual assistance.
In this research, the authors focus on the role of mutual assistance,
based on the analysis of its characteristics and limitation, future adaptation
plan of mutual assistance in the aging society is discussed.
Keywords: vulnerable people, mutual assistance
1. INTRODUCTION
1.1 Significance of mutual assistance to reduce risks
For disaster reduction, it is important to consider balanced applicationof self-support, mutual assistance, and public support. In case of vulnerable
people such as aged and disabled people, pregnant woman, children,
foreigners and tourists, the role of mutual assistance and public supports
will become more important comparing to the case of healthy people.
Japanese government had already recognized the necessity of mutual
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number of countermeasures but many aged people and disabled people were
killed in the 2011 great east Japan earthquake disaster.

Figure 1: Age-specific victims of the 2011 great east Japan earthquake (fromMarch 2011 to March 2012)
Figure 1 shows the number of age-specific victims due to the 2011
great east Japan earthquake, revealed by Cabinet office. “Victim” in the
figure includes not only the dead person during disaster but also the victims
who got killed by indirect effects of disasters. According to Figure 1, thenumber of the victims over 60 years old is as much as 10,085.
When aging society is increasing, ability of community to provide
mutual assistance will decline. Therefore, it is necessary to build system ofmutual assistance which is suitable to characteristics of aging community. In
the following sections, we will introduce the situations surrounding ofvulnerable people and the trends of recent countermeasures which expect tomake use of mutual assistance in a community.
1.2

Situations surrounding vulnerable people and mutual assistance

Vulnerable people are defined as the people who have any kind of
disability and need for others help during disasters. Because of disabilities
or difficulties of avoiding dangers, difficulties in evacuation or living in the
refugee shelters, and difficulty of recovering as fast as healthy people, thesepeople need mutual help and assistance along with special care. (Japanese
Red Cross Society, 2006)
Handicaps are different depending on each person, but the problems
due to handicaps are classified in Table 1. These problems are the factors ofserious bottlenecks in a disaster. Figure 2 shows the critical parameters ineach phase and relationships between factors and disability problems.

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Table 1: Problems of vulnerable people in disaster

Problems
Getting information
Avoiding danger
Delayed moving
Keeping healthBeing adapted tochangesGetting the highstandards of housing
Economicdisadvantage

Contents
Difficulty in getting or understanding the information about
disasters
Difficulty in defending oneself from dangers such as overturned
furniture or stream of moving water
Difficulty or delay in evacuating
Difficulty in keeping their lives without certain medicine , medical
devices, and life support devices
Difficulty in getting adapted to sudden changes of situation suchas living in a shelter
Difficulty in satisfying the housing standards which is higher thanthose of healthy people (e.g. impediment removal)
Difficulty in getting high income due to handicaps

Figure 2: Relationships between disabilities and different phase of
disabilities for vulnerable people in disaster1.3 Recent countermeasures for vulnerable people in Japan
In order to solve above mentioned problems, many countermeasures
have been considered and provided by public sectors. Especially, Japanese
government started to put effort into evacuation supports after the 2004 Mid
Niigata Prefecture Earthquake and a series of flood disasters in 2004, whichcaused the notable death of aged and handicapped people due to delay inevacuation.
The Japanese government revealed the guidelines of evacuation
support for vulnerable people in March, 2005. Following 5 or 6 years, each
local government started to prepare the vulnerable people list and somecommunities made efforts towards their trainings for evacuation supports.
Vulnerable people list contains personal information of vulnerable
people such as their address and their needs in disaster, and list of people
who need to be checked first during disaster for their safety confirmation.
According to a survey by Japanese Cabinet Office from January to March in2013, 32% of local governments didn’t finish the preparation of the list.
Even though the other 68% seem to have lists, only one third of them were

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registered by more than half of vulnerable people in the community. The
reason of this seems the possible conflict with the personal information law,because of which the local governments gather information of only thosepeople who want to register themselves in the list.
Few local governments have persuaded each person to register himself
or herself in the list and getting his or her information, or shared the family
registration information among the relevant institutions.
Japanese government also tried to prepare the evacuation places withspecial consideration of people disabilities. After the great east Japan
earthquake in March 2011, it is said that the numbers of those evacuation
places and nursing care stuffs were not enough when some of the nursing
homes in the area were crowded. (Fujitsu Research Institute, 2012).
In conclusion, the countermeasures were already considered, but theactions taken by local governments were insufficient. The reasons can beconsidered as following three points.
First, the information of vulnerable people was not sufficient.
However, this problem is now regarded as the very important issue andthere is a big action to solve it. In 2013, the Disaster Measures Basic Act,
which is the basic law of disaster management in Japan, has started to oblige
local governments to make a vulnerable people list. This change will ease
for local governments to gather information of vulnerable people.
Second is the limitation of evacuation supports for vulnerable people
with mutual assistance in case of wide affected areas with shorter
evacuation times.
Third is the lacking of supporting people. This problem was observedin many local governments. Some governments cannot find enoughsupporters for all vulnerable people, so that they prioritize them depending
upon the degree of handicaps.
2. RESEARCH OBJECTIVES
To realize the mutual assistance for vulnerable people which should beaccommodated in aging societies or communities, understanding thesituation of vulnerable people is not enough. For this, assessing the potential
capacity of community to support them will be required to make it possible
to utilize the mutual assistance which can be applied in disasters. There aremany researches on the quantification of coping capacity of the local
community but they are not specialized in mutual assistance for vulnerable
people.
Therefore, the purpose of this research is to propose a method forassessing potential capacity to support vulnerable people in disaster areawith main focus on evaluation supports.

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3. ASSESSMENT METHOD
3.1 Necessary characteristics of this method
In this section, the necessary characteristics of this assessment method
are identified with referring the analysis of the existing methods used forevaluation of the coping capacity of the local communities in Japan(Nagamatsu, 2009).
1) We would like to show the components of disaster risk in order todescribe the coping capacity. Disaster risk can be represented by the gap
between coping capacity and the sum of hazard, exposure, andvulnerability. Hazard is just the external force as a factor of disasteroccurrence. Exposure means how much our life is exposed to the disaster.
Vulnerability contains both of the physical distribution of disaster andsocial distribution of vulnerable people. Vulnerable people such as theaged and the handicapped are included to social vulnerability. Whereas
coping capacity is the ability of minimizing these risks. In Japan, theevaluation of vulnerability and that of coping capacity are often
separately done, and there are few evaluation methods which combine
both of them. Therefore, this research will aim to establish a evaluation
method which can evaluate vulnerability and coping capacity together
and can proposes required countermeasures.
2) The unit of each area should be defined. Current research mainly focuseson evacuation required in each unit based on the locations of shelters,such as, elementary school district or community center, etc.
3) The targets of evaluation factors in the community should be set. In thisresearch, stakeholders who can have a relationship with vulnerable
people are the targets, such as, neighborhoods, nursing care staff, district
welfare commissioners, and volunteer fire department, etc.
4) It is necessary to consider the components of coping capacity. This isdivided into risk awareness, exposed capacity, and latent capacity(Nagamatsu, 2009). Risk awareness is the factor of people awarenessabout the risk of the area. Exposed capacity is defined as the explicit
degree of preparedness for reducing the risks. Latent capacity is theimplicit ability of community capacities which is not intended to reduce
the risks, but has a possibility to contribute to it. Some of recent
researches have set a high valuation on the latent capacity. However,when support for vulnerable people is considered, the problem isdisparity of the quality and quantity of care between normal times and in
the disaster periods. Therefore, it is necessary to carefully evaluate latentcapacity. In Addition, coping capacity will include the ability to support
vulnerable people.
The characteristics of this method are summarized as follow.
1) There is a structure to produce the countermeasures.
2) The unit of evaluation area is based on the places of shelters.
3) Target includes the various stakeholders who have a relationship withvulnerable people.
4) Coping capacity should be considered based on how much support
vulnerable people can receive.
In addition, simple representation of evaluation will be preferred.
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3.2 Assessing flow
In this section, the image of assessing flow is introduced. It isnecessary to consider three categories, which are characteristics of hazard,
vulnerable people, and potential support ability. Hazard can be classified as
the range of affecting area and the degree of urgency to evacuate. Table 2
shows the factors and the origin of data in order to evaluate the governing
factors.
Category
HazardVulnerable people

Potential support ability

Table2: Factors of assessing
FactorCharacteristics of thehazards(Earthquake, flood,
landslide, etc)
Distribution of their living
places
The ratio of each kinds ofvulnerable people
Distribution of young
people’s living place
Distribution of shelters
Unionization rate ofvoluntary organization fordisaster prevention

Data

Hazard map
Statistics(GIS)
Statistics
Statistics
Statistics
Needs for getting data fromfield work

First, hazard map and the distribution of shelters are plotted on themap of whole area, mesh size is decided and each mesh is classified based
upon the level of difficulty to evacuate. The levels are showed as the Table
3.
Level
4
3
2
1

Table 3: Level of hazards

Shelters
Not available
Available
Not Available
Available

High risk
High risk
Low risk
Low risk

HazardSecond, in order to produce the index of ability to support, distributionof young people and vulnerable people are plotted. The index is producedon each mesh with considering several situations depending on time zone,
days of the week, and seasons. The index is referred as following.
c(aV / bY)+ d
where:
V: number of vulnerable people,
Y: number of abled people
a: self-help ability of vulnerable people
b: supporting ability of abled people
c: characteristics of disasterd: other factors

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In this research, abled people are defined as people who have no disability
in normal time, and supporting ability of abled people is defined as any
resource which contributes to evacuation supports, for example skills of
nursing care, sign language, knowledge about certain disability, andavailability of evacuation support devices like a car, etc.
After producing index, we can over view the distribution of ability tosupport vulnerable people. Then we can take countermeasures in order toreduce the disparity of ability to support vulnerable people.
The assessing flow is shown in the Figure 3.
Whole area level
Plotting hazard

Plotting shelters

Classification the area
as the level of danger

Plotting vulnerable people

each mesh level

Plotting young people

Weighting as the quality ofyoung and vulnerable
peopleThe necessity of evacuation
supports

Index of ability to support

Producing countermeasures

Figure 3: Assessing flow
3.3 Advantage of this method
In this section, we will explain about the advantage of representing theability of community to support vulnerable people. First, it is easy tocompare the ability of each community, so that we can identify which area
is most dangerous and who should move to the safer area. Second, it can beanalyzed how the ability to support vulnerable people will change ascommunity will be aging in the future. Addition, assessing the ability itself
may encourage the disaster prevention activities in the community orawareness of vulnerable people in the community.
This kind of index can be useful especially in rural area which isunder severe aging and depopulation. Also, in Japan huge earthquakes aresupposed to occur along the Nankai trough in near future, which will hit

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many rural areas. The assessment of capacity to evacuation support will bemeaningful for these areas.
4. PROBLEMS AND FUTURE PLAN
In order to build the assessment method, basic survey is needed. The topics
of survey can be as following.
• Types of vulnerable people e.g. which are easy or difficult to help.
• What is the difference between the care-skilled people and noskilled people in order to help vulnerable people• Categorizing the characteristics of the community• Categorizing the countermeasures based on public support, mutual
assistance, and self-support.
REFERENCES
Japanese Red Cross Society, 2006. Guideline of the evacuation
support for vulnerable people
Fujitsu Research Institute, 2012. Research project report about thephased support system for elderly people from disaster occurrence phase toreconstruction phase
Shigeo, T., 2013. Key points of preparing public supports forvulnerable people in a local government, Municipal Government June (theorgan magazine published by Japan Association of City Mayors), 10-12
Junichi, Y., 2011. Evacuation support for vulnerable people to anatural disasters and introduce of community network, a collection ofresearch paper of Shitennoji University, 21-35
Mashiho, S., 2001. “Vulnerable people to natural disaster” andsupports -Mainly about the shifts of countermeasures and idea after
Hanshin Awaji earthquake-, Japan Society for urban studies annual report
2000, 34, 38-45
Minister of State for Disaster Management, Cabinet Office, 2009.
Research Project Report about the evacuation support for vulnerable people
Shingo, N., Toshinari, N., Yuichiro, U., 2009. How can “Coping
Capacity of the Local Community Against Disasters” be Evaluated?,National Research Institute for Earth Science and Disaster Prevention
Research Report No.74, 1-11.

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THE CALIBRATION OF ROAD NETWORKTRAVEL TIME FUNCTION IN KOREA: THE ISSUES AND SOLUTIONS
Choong-shik LEE1, Hyun-myung KIM2, Seung-hoon CHEON3and Sung-hi AN4
1
Researcher/Transportation Planning Lab, Dept. of Transportation Eng.
College of Engineering, The MyongJi University, South Korea
cndtlr90@naver.com
2
Professor, Director, The MyongJi University, South Korea
3
Doctor, Dept. of National Survey and Analysis, Center for Korea
Transport Database, The Korea Transport Institute, South Korea
4
Researcher/Transportation Planning Lab, Dept. of Transportation Eng.
College of Engineering, The MyongJi University, South Korea
ABSTRACTThe purpose of this research is to improve reliability of the calibrationof road network Travel Time Function(TTF) in Korea with a new
calibration methodology and using cutting-edge data. In this perspective,
there are four research issues; 1) old calibration techniques, 2) data
problems, 3) traffic assignment problems and 4) fixed value of TTF’s
parameters. We are provided with basic data like origin-destination(OD)
traffic volume, network and observed traffic counts from The Korea
Transport Institute Data Base(KTDB) and cutting-edge data like Toll
Collection System OD(TCS OD) and Car Navigation Data(Navi-data) from
Korea Highway Corporation Data Base(KHCDB) and MN-Soft,
respectively.
The validation criterion for the network calibration is that the link
volume error is within ±30%. In addition we developed new calibration
model based Harmony Search(HS) to estimate a set of parameter values of
TTF. Also, traffic volume consistency verification method and observed
traffic volume filtering method are developed to improve accuracy ofobserved data.
Finally, we conduct an analysis how much the level of calibrationindex improved based on the result of this research and suggest future
direction of research.
Keywords: travel time function, BPR function, car navigation data,
harmony search, intra-zonal trip, network calibration

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1. INTRODUCTION
A current road network is used to expect feasibility and demand of varioustransportation policies or infrastructure construction enterprises.
Accordingly, reproducing accurate road network is very important because
these Social Overhead Capital(SOC) Projects generally has greater social
impact and large scale. In order to construct status road network accurately,
estimation of Travel Time Function(TTF)’s parameters is one of the mostimportant elements. However, the problem is that the difference between
two kinds of link traffic volumes observed and estimated by established
TTF is so big that reliability of calibration is not enough.
Therefore, the purpose of this research was to improve reliability of
the calibration of road network TTF and calibrate TTF’s parameters bydeveloping new calibration methodology and using cutting-edge data. We
investigated the reasons decreasing reliability of calibration and the methods
to improve existing calibration system and finally we suggested new
coefficient of TTF’s parameters as both range and value.
2. DATA AND METHODOLOGY FOR CALIBRATION
2.1 Travel Time Function
TTF is the monotonically increasing function to traffic volume. It is
used in traffic assignment model and can represent congested traffic flow
condition when traffic demand exceeds road capacity. For examples,
Wardrop function, Bureau of Public Roads(BPR) function, Conical function,and Irwin, Dodd and Von-Cube function are typical. From among these,
BPR function is the most commonly used TTF; it is used in Korea; and it isexpressed as follows (formula 1):(1)
The variable of BPR function is traffic volume( ) and travel time( ). There
are four parameters, , , , and each parameter’s explanation is in the
Table 1.
Parameters

Table 1: Parameters of BPR function

Explanations
Free flow travel time; determined by field investigation
Capacity of the road; determined by capacity analysis
Coefficient; estimated by calibration or traffic assignment modelFirst two parameters, and , are determined by field data because
these are not related to sensitivity between
and travel time. On the otherhands, other parameters, and , are estimated by calibration or traffic
assignment model because these affect function’s shape for whole region of

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area for function’s shape according to(Figure 1).

Figure 1: Effective area of BPR’s Parameters
2.2 Network and Data
KTDB road network consists of 248 zones, 107,844 nodes and 68,613
links except for centroid-connectors and ramps. In Korea, all roads areclassified into the 16 levels according to attributes such as road type, thenumber of lanes and density of intersection, also each level of road has
different TTF’s parameters.
Table 2: Criteria for classification of TTF’s level
Level
of TTF
1
2
3
4
5
6
7

8

Type
Highway
Urban
HighwayLane# ofintersection
in 1kmNo
intersection

0 – 0.3
Arterial
0.3 – 0.7

Level
of TTF
9
10
11
12
13
14
15

Type

Lane

# of
intersection
in 1km
0.7 – 1.0
1.0 – 2.0

Arterial
2.0 – 4.0

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Figure 2: KTDB road network of Korea
To calibrate road network TTF, Origin-Destination(OD) traffic
volume and observed link traffic volume are needed. These data areprovided from KTDB. The number of OD pairs is 61,256 and the data isclassified into 5 types of vehicle. Also, observed link traffic volume iscounted at 6,818 places by detectors or investigation and it is classified into
12 types of vehicle.
In Korea, cutting-edge data, Toll Collection System OD(TCS OD) and
Car Navigation Data(Navi-data), has currently received a lot of attention infield of transportation. Especially, Navi-data could be usefully applied toverification of zone-network compatibility and many other analyses byprocessing variously like intrazonal trip rate, the number of passing OD
pairs, average travel distance of passing car and OD traffic volume sharing
ratio of link.
2.3 Calibration Criteria
The current calibration criteria of road network is that the error
between observed traffic volume and estimated traffic volume of each linkis inner 30%, and if this criteria is satisfied, it means TTF explain present
trip pattern well. However, even if observed traffic volume of link is

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reproduced by TTF well, travel time is not reproduced well on many links.
Therefore it needs to do analysis about reasons and solutions of this problem.
3. RESEARCH ISSUES
In case of arterials, the hit ratio within 30% range from the observed counts
was 22.6% on average in 2011. This ratio is too low to be get reliability of
TTF, thus, we suggested what decrease reliability of TTF as research issues.
3.1 Calibration Technique
To calibrate TTF’s parameters which maximize the hit ratio, we haveused Golden Section Method or Incremental Method before as calibration
technique, however, these methods have high randomness problem thatcannot find unique global solution. Accordingly, it needs to develop new
calibration model based on global optimization method for reducing
randomness in TTF calibration process.
Also, it needs to diversify calibration criteria like travel speed or time
for detailed calibration of the road network. To apply new calibration
criteria, the data related criteria must be databased, however, it is difficult toget basic data for applying new calibration criteria.
3.2 Data Problems
The data used for calibrating road network has a problem itself thatthe data doesn’t guarantee its accuracy, because OD traffic volume is notobserved data but estimated and observed traffic volume has counting error
from detector. For solving these data problems, therefore, it needs to usehigh accuracy data or find solution correcting data logically.
3.3 Traffic Assignment Problems
Zone system is one of the most effective elements to reliability ofcalibration, nevertheless, traffic assignment based zone system has
fundamental problem. In zone based traffic assignment, OD traffic volume
is only valid when generating zone is different to arriving zone. It is defined
as interzonal trip. In contrast, when generating zone is same with arriving
zone, its OD traffic volume is not existed in estimated traffic volume but
observed volume. It is defined as intrazonal trip (Figure 3).
Intrazonal trip is main cause of under estimated traffic volume and it
affects critical impact to reliability of calibration. To mitigate this impact
from this problem, it needs to separate zone system more detailed or reflect
intrazonal trip to traffic assignment.

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Figure 3: Definition of interzonal trip and intrazonal trip
The above problems are collectively defined as zone-network
compatibility problem. There are two cases of non-compatibility (Figure 4),
and the road network of Korea corresponds to case 1.

Figure 4: Cases of zone-network non-compatibility
Conventional zone system has another fundamental problem thatgenerating and arriving trip are only assigned by centroid connectors. It
makes overestimated traffic volume on some observed links whichconnected directly with connectors. For solving this problem, it needs to
exclude links connected directly with connectors from observed place orseparate zone system more detailed in common with solution of noncompatibility. As a result, we could solve this problem by using Navi-data.

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3.4 Range or Value of TTF’s Parameters
The parameters of TTF have information related to the physicalcondition of road. However, it is impossible that all of each road is
explained by fixed parameters, even though the levels of TTF are separated
very detailed, because physical conditions of each road in a same level of
TTF are different little by little according to spatial range. Therefore, it isneeded to suggest the range of each parameter, then, analyst can use TTF
flexibly.
4. SOLUTIONS
To solve the problems mentioned in research issues, we suggested twosolutions. First was to improve previous calibration system by applying
Harmony Search(HS); it is a kind of global optimization method; andcalibrate new TTF’s parameters. Second was to use cutting-edge data; TCS
OD and Navi-data; for correcting observed traffic volume and analyzing
basic reliability of network by verifying network compatibility. As a result,
we could improve reliability of road network TTF from these solutions.
4.1 Improvement of Calibration System
The TTF estimation problem was formulated as a Bi-level
optimization problem because it has structure which iterates traffic
assignment and adjusting coefficient to optimize objective function. TTF
parameter estimation has the plural combinations of optimum solutions;accordingly it needs to apply global searching optimization method. To
solve this convex problem, TTF calibration algorithm based HS wasdeveloped by Kim et. al.( 2012).
HS is a phenomenon-mimicking inspired by the improvisation processof musicians proposed by Geem(2001). In the HS algorithm, each musician(= decision variable) plays (= generates) a note (= a value) for finding a best
harmony (= global optimum) all together. HS is similar to Genetic
Algorithm(GA), however, HS may overcome the drawback of GA's building
block theory which works well only if the relationship among variables in achromosome is carefully considered. It iterates process to find new solutions
by combinating parts of superior solutions randomly. Based on above
concepts, optimization problem for TTF calibration was configured asfollows (formula 2):(2)

In formula, A and B is the set of links existed observed traffic andtravel time respectively, and is the coefficient which define relative
weights between traffic volume and travel time of error. Also, is traffic

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volume of link a,is travel time of link b, and,andis observed
traffic volume and travel time each.
Using HS TTF calibration algorithm developed by Kim Hyun-myung in
2011, we did estimation of TTF’s parameter. Finally we could find plural
combinations of optimum solutions with solving the local solution inprevious Golden Section Method or Incremental Method.
4.2 Cutting-edge Data
4.2.1 TCS OD
To guarantee accuracy of network calibration, high accuracy data andnew calibration criteria are needed, so we did highway separated calibration.

Figure 5: Network for highway separated calibration
In Korea, almost of highway operates closed toll, thus the TCS OD
from toll to toll could be get by inflow and outflow count at each toll. TCS
OD is more accurate than KTDB OD from zone to zone because it is not
sample data. As a result, we could estimate more reliable parameters of
TTF1 and TTF2 by applying highway separated calibration with TCS OD.
Additionally, we could apply new calibration criteria, travel time, because
highway acquires link data not only traffic volume but also travel time.
Also, we developed axis consistency verification method based thelaw of conservation of traffic volume to correct observed traffic. The
definition of the axis consistency is that the sum of each traffic volume of
upstream and downstream must be same (Figure 6). Consequently, it waspossible to correct observed traffic volume within allowable error rate bycalculating error rate between upstream and downstream traffic volume.

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Figure 6: Definition of the axis consistency
In this research, first, we did highway separated calibration by using
TCS OD; we calibrated TTF1 and TTF2, then we did KTDB network
calibration as the parameters of TTF1 and TTF2 are fixed.
4.2.2 Car Navigation Data
Navi-data are very important and high usable in this research. The
number of links which the Navi-data are acquired is 33,179, and it is 48.4%
of links of KTDB network. By using Navi-data providing detailed startpoint, end point and path of the cars, we could calculate intrazonal trip rate,
the number of passing OD pairs, average travel distance of passing car and
OD traffic volume sharing ratio of each link.
The intrazonal trip rate was especially very valuable data because theintrazonal trips do not simulated by traffic assignment. In general, a half ofobserved traffic counts are intrazonal trip currently. (Table 2).
Table 2: Under estimated rate of arterials (%)

UnderestimatedRate-30% ~ -50%
-50% ~ -70%
-70% ~ -100%
-100%
Total

Arterials
10.2
9.9
16.1
17.5
53.7

TTF
5&6
6.1
6.9
14.0
23.5
50.5

TTF
7&8
9.5
10.1
15.6
19.5
54.7

TTF
9&10
10.0
9.1
17.6
14.8
51.5

TTF
11&12
14.0
10.5
15.9
14.1
54.5

TTF
13&14
12.5
16.2
22.1
5.9
56.7

TTF
15&16
25.0
21.2
17.3
13.5
77.0

With other calculated results including intrazonal trip rate, we could
do link network compatibility evaluation and priority analysis for verifying
basic reliability of KTDB road network. For example, compatibility of eachlink is judged to be as high as intrazonal trip rate is low, the number ofpassing OD pairs is various and average travel distance of passing car islong.
Also, we could verify zone-connector problem by using OD traffic
volume sharing ratio of each link. This result explains overestimated traffic

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volume of observed links which connected directly with connector and give
judgment about appropriacy of current zone system.
5. RSEULTS
5.1 Analysis Results Using Navi-Data
As a result of calculating intrazonal trip rate, average of each TTF’s
rate is about 10% and 30~50% in case of highways and arterials. Namely,
intrazonal trip rate of arterial is much higher than highways. Also it isshowed patterns as lower as the road has low level of TTF; the density ofintersection is high; and the number of lane is many (Figure 7).
Consequently this result solved underestimated traffic volume problem,
referenced (Table 2), of arterials for network calibration.

Figure 7: Analysis results of intrazonal trip rate
From intrazonal trip rate, the number of passing OD pairs and average
travel distance of passing car of each link, we analyzed zone-network
compatibility. As a result of analysis, we understood the facts that there may
be included in some links which not appropriate for traffic assignment based
current zone system and the compatibility of urban road is lower than local
road relatively (Figure 8).

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Figure 8: Compatibility analysis by car navigation data
Additionally, we verified zone-connector problem by using OD traffic
volume sharing ratio. For example, figure 9 is analysis result of generating
OD sharing ratio. In figure, actual generated OD traffic volume of links
connected directly with connectors is about only 21% in a whole zone. It
namely means that 79% of generated OD traffic in a whole zone can’t be
explained by connectors for traffic assignment. This problem cases
overestimated traffic volume of observed links which connected directlywith connector. For mitigate this impact of problem, we could add moreconnectors, but it is not fundamental solution, because it is impossible to set
assigned OD traffic volume of each connectors.

Figure 9: The example of zone-connector problem

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5. 2 Calibration Results
As a result of comparison analysis between previous highway
calibration and current high way separated calibration, the hit ratio of TTF1
and TTF2 was improved 22.6% and 11.6%, respectively (Table 3). From
the result, accordingly, it was proved to appropriate for highway separated
calibration which use more accurate TCS OD than KTDB OD andparameters estimated by HS.
Table 3: The result of highway separated calibration (traffic volume)

Traffic
Volume200% ~
100% ~ 200%
75% ~ 100%50% ~ 75%
30% ~ 50%
15% ~ 30%0% ~ 15%-15% ~ 0%-30% ~ -15%
-50% ~ -30%
-75% ~ --50%-100% ~ -75%
~ -100%±30%±50%Before
0.5
4.7
7.5
6.0
11.3
12.8
15.2
18.5
13.5
6.5
2.9
0.3
0.3
60.0
77.8

TTF 1 (%)
After
0.0
0.0
0.9
1.1
5.2
16.0
23.1
26.5
17.0
7.8
2.2
0.0
0.0
82.6
95.6

Change
-0.5
-4.7
-6.6
-4.9
-6.1
3.2
7.9
8.0
3.5
1.3
-0.7
-0.3
-0.3
22.6
17.8

Before
2.4
5.3
2.4
3.5
5.6
9.1
14.5
26.0
18.6
7.4
0.6
3.5
1.2
68.1
81.1

TTF 2 (%)
After
0.0
1.0
0.0
0.5
7.7
11.1
15.5
30.9
22.2
9.7
1.4
0.0
0.0
79.7
97.1

Change
-2.4
-4.3
-2.4
-3.0
2.1
2.0
1.0
4.9
3.6
2.3
0.8
-3.5
-1.2
11.6
16.0

In case of travel time, we verified it by using difference between
observed and estimated travel time, because it has never used as calibration
criteria, so it is impossible to compare with precious result (Figure 10). In
figure, the dispersion of two values is similar to shape of 45 degrees;
consequently it is justified to be high reproduced rate for travel time.

Figure 10: The result of highway separated calibration (travel time)
Next, we did comparison analysis between previous and this highway
calibration for KTDB network as the parameters of TTF1 and TTF2 from

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highway separated calibration were fixed. In result, the hit ratio of TTF1
and TTF2 was worsened -2.8% and -3.8%, respectively (Table 4); however,TTF2 is improved as much as 3.4% within 50% range, so this is not serious
problem. Also, TTF1 showed an overestimated tendency and TTF2
contrarily did an underestimated it.
The overestimation problem of TTF1 is caused by some particularhighways which included in TTF1, but different from physical conditions ofroad. Therefore it needs to be defined new TTF hierachy to separate thesehighways.
Table 4: The result of KTDB network calibration (highway)

Traffic
Volume200% ~
100% ~ 200%
75% ~ 100%50% ~ 75%
30% ~ 50%
15% ~ 30%0% ~ 15%-15% ~ 0%-30% ~ -15%
-50% ~ -30%
-75% ~ --50%-100% ~ -75%
~ -100%±30%±50%Before
0.5
4.7
7.5
6.0
11.3
12.8
15.2
18.5
13.5
6.5
2.9
0.3
0.3
60.0
77.8

TTF 1 (%)
After
0.7
5.5
9.8
9.8
9.5
13.4
15.5
17.4
11.0
5.3
2.1
0.2
0.0
57.2
72.0

Change
0.2
0.8
2.3
3.8
-1.9
0.6
0.2
-1.1
-2.5
-1.2
-0.9
-0.2
-0.3
-2.8
-5.8

TTF 2 (%)
After
0.0
2.8
0.8
3.4
10.1
8.4
12.4
19.7
23.9
10.1
3.7
3.7
1.1
64.3
84.6

Before
2.4
5.3
2.4
3.5
5.6
9.1
14.5
26.0
18.6
7.4
0.6
3.5
1.2
68.1
81.1

Change
-2.4
-2.5
-1.5
-0.2
4.5
-0.7
-2.1
-6.3
5.3
2.7
3.1
0.1
-0.1
-3.8
3.4

In addition, we analyzed result of KTDB network calibration ofarterial. In case of arterial, the hit ratio within 30% range from the observedcounts was 25.4%, so it was improved as much as 2.8% compared withprevious result. In general, the high TTF roads (TTF5~10) tend to beoverestimated and low TTF roads (TTF11~16) do to be underestimated(Table 5).
Table 5: The result of KTDB network calibration (arterial)

Traffic
Volume200% ~
100% ~ 200%
75% ~ 100%50% ~ 75%
30% ~ 50%
15% ~ 30%0% ~ 15%-15% ~ 0%-30% ~ -15%
-50% ~ -30%
-75% ~ --50%-100% ~ -75%
~ -100%
±30%
±50%

Total
11.4
11
4.5
6.8
5
4.3
5.4
6.5
9.2
14
10
11.8
0.1
25.4
44.3

TTF
5
15.5
13.9
4.2
6.6
3.6
2.7
3.7
3.6
9
9.7
13.7
13.7
0.1
19
32.3

TTF
6
17.5
9.6
7
7.9
7.9
1.8
2.7
10.6
7
16.7
4.4
7
0
21.9
46.5

TTF
7
10.7
11.8
5.4
6.2
4.2
3
3
4.6
7.9
13.8
11.6
17.7
0.1
18.5
36.5

TTF
8
14.2
13.5
3.5
9.2
7.1
5.3
8.7
7.9
7.3
10
7.8
5.2
0.2
29.4
46.5

TTF
9
6.5
9.7
3.4
4.9
3.7
3.5
5.2
7.6
10.6
21.5
8.1
15.3
0.2
26.8
52

TTF
10
17.8
10.3
4.3
9.2
7.4
7.1
7.1
7.4
6.3
10
8
4.9
0
28.1
45.6

TTF
11
4.9
8.3
3.3
4.7
4.7
4.9
7.3
7.6
15.1
19.1
8.2
12
0
34.8
58.6

TTF
12
11.6
9
5.7
8.2
6.4
7.1
7.8
8
9.7
14
6.4
6.1
0
32.6
53

TTF
13
13.4
4.2
1.7
6.7
1.7
4.2
5
5.9
11.7
15.1
16
14.3
0
26.9
43.7

TTF
14
7.9
9.2
5.4
9.2
5.7
4.4
7.9
11.6
8.2
11.1
12.3
7
0
32.3
49.1

TTF
15
17.1
2.9
2.9
2.9
0
5.7
5.7
5.8
14.3
14.3
20
8.6
0
31.4
45.7

The overestimation problem of high TTF roads was judged to becaused by zone-connector problem because many high TTF roads are

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16
7.7
10.8
4.6
1.5
9.2
7.7
4.5
10.8
9.3
29.2
3.1
1.5
0
32.3
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connected by connectors directly. On the other hand, the underestimation
problem of low TTF roads is judged to be caused by high intrazonal triprate.
In general, we could solve underestimation problem in whole networkby applying intrazonal trip rate to calibration system though there are still
many underestimated links. Also, if we solve the problems of TTF5 andTTF7 which satisfy the two lowest hit ratio, it is expected to improvereliability of calibration for whole KTDB network.
Finally, we suggested the result of TTF parameters estimation as
range and applied value for calibration (Table 6).
Level of
Type
TTF
1
Highway
2
3
Urban
Highway
4
5
6
7
8
9
10
Arterial
11
12
13
14
15
16

Table 6: The result of TTF’s parameters(km/h)

110~130
110~12595~10590~10070~9090~10570~8585~10065~8583~100
65~80
80~95
65~75
80~90
60~70
70~80

Range

1700~2100
1750~2150
1700~2000
1900~2200
680~1400
1150~1550
650~1150
1100~1500
630~1000
930~1500
550~800
780~1450
400~600
600~800
350~500
600~800

0.25~0.65
0.30~0.70
0.35~0.75
0.40~0.80
0.47~0.85
0.50~0.70
0.52~0.88
0.55~0.75
0.57~0.90
0.60~0.80
0.58~0.90
0.62~0.82
0.60~0.90
0.66~0.86
0.67~0.90
0.71~0.91

Applied Value(km/h)

1.80~3.50
1.50~3.20
1.60~3.40
1.40~3.10
2.05~2.90
2.00~2.50
1.95~2.80
1.90~2.40
1.85~2.60
1.80~2.30
1.85~2.40
1.80~2.05
1.75~2.30
1.70~1.95
1.65~2.20
1.55~1.85

101
121
98
92
72
90
70
86
68
84
66
82
65
80
62
75

is related to

1700
1900
1700
1900
680
1300
650
1200
630
1100
600
950
580
800
550
780
(

0.55
0.48
0.50
0.42
0.85
0.70
0.86
0.73
0.87
0.76
0.88
0.78
0.89
0.80
0.89
0.82

2.60
2.50
2.40
2.30
2.85
2.20
2.75
2.10
2.60
2.00
2.40
1.90
2.25
1.80
2.15
1.75
)

6. CONCLUSION
From this research, we improved reliability of the calibration of road
network TTF by applying new calibration system; HS and highway
separated calibration; and using cutting-edge data; TCS OD and Navi-data.
Also we suggested new coefficient of TTF’s parameters as both range andvalue.
As a result of calibration of road network TTF, the hit ratio of TTF1
and TTF2 was improved 22.6% and 11.6% respectively in highway
separated calibration. In arterials of KTDB network calibration, the hit ratio
was 25.4%, so it was improved 2.8% compared with previous result andgenerally underestimation problem was solved by applying intrazonal triprate.
In addition, we suggested fundamental problems and future research
direction of calibration system from network compatibility analysis andzone-connector verification by using Navi-data.

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REFERENCES
Geem Z. W., and Geem W. B., 2007. Cutting-edge Optimization
Technique and its Application to the Civil Engineering, The Magazine of
the Korean Society of Civil Engineers, Vol.57, No.1, pp.155-171.
Geem Z. W., Lee K. S., and Park Y. J., 2005. Application of Harmony
Search to Vehicle Routing. American J. Appl. Sci. 2, No.12, pp.1552-1557.
Kim H. M., Hwang Y. H., and Yang I. C. (2012), Calibration of a
Network Link Travel Cost Function with the Harmony Search Algorithm,
The Journal of Korean Society of Transportation, Vol.30, No.5, pp.71-82.
Korea Transport Institute, 2012. The Investigation and Research about
Road Travel Cost Function Construction

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STUDY ON EFFECT OF STREETBLOCKADES ON TRANSPORTATION OF
SERIOUSLY-INJURED VICTIMS TO
HOSPITALS-CASE STUDY OF EXPECTEDTOKYO INLAND EARTHQUAKEFei JIANG
Master Course Student, Graduate School of Interdisciplinary
Information Studies, The University of Tokyo, Japanjiangfei@iis.u-tokyo.ac.jp
Miho OHARA
Associate Professor, ICUS, Institute of Industrial Science
The University of Tokyo, Japanohara@iis.u-tokyo.ac.jp
ABSTRACTTokyo Metropolitan area has high risk of the Tokyo Inland
Earthquake in the future. In case of the earthquake, disaster base hospitals
have missions to accommodate seriously-injured victims and provide
medical treatment for them. According to the lessons from past disasters,
medical treatment within 72 hours after the disaster is essential to increase
the possibility of saving the lives of victims. Rapid transportation ofseriously-injured victims to disaster base hospitals and immediate medical
treatment are very important to avoid their “Preventable Death”.
In this research, effect of street blockades to transportation ofseriously-injured victims to hospitals in case of the expected Tokyo Inland
Earthquake was analyzed considering road network. The maximum
transportable area where victims could be transported to some hospitals by
car within certain time was identified using a software “ArcGIS Network
Analyst”. After the Kobe Earthquake in January 1995, road blockades dueto the debris from collapsed buildings caused problem on medicaltransportation. This research simulated the effect of road blockades on
maximum transportable area. As a result, almost all the areas in Tokyo
Prefecture were accessible to disaster base hospitals within 10 minutes by
car in normal situation. However, maximum transportable area drasticallyshrank when road blockades in case of disaster was considered. This
analysis identified the areas where rapid transportation to the nearest
disaster base hospitals was impossible in case of the earthquake. Increasein the number of disaster base hospitals or dispatch of medical treatment
team with portable facilities such as DMAT (disaster medical assistanceteam) may be necessary in these areas.

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Keywords: Tokyo Inland Earthquake, seriously-injured victims,
disaster base hospitals, transportation
1. INTRODUCTION
It is believed that in the Tokyo Metropolitan Area, massive trench-type
earthquakes with magnitudes of 8 or greater, such as the Great Kanto
Earthquake (1923), will occur at intervals of 200-300 years. Additionally, it
is presumed that several Tokyo Inland Earthquakes of magnitude 7 will
occur prior to a magnitude 8 earthquake, and the imminent possibility in thefirst half of this century has been pointed out. Because of that, many typesof Tokyo Inland Earthquakes are assumed by the cabinet office of Japanese
government and Tokyo metropolitan government with various possible
epicenters and the complicated mechanism of occurrence. According to theestimation of The Tokyo metropolitan government, the number of seriouslyinjured victims will be about 150 thousand in the earthquake with an
epicenter in the northern part of Tokyo Bay (assumed scale of M7.3,
18p.m.). So, it is essential to provide the medical treatment within 72 hours
after the disaster to increase the possibility of saving the lives of victims dueto the lessons from past disasters. Rapid transportation of seriously-injured
victims to disaster base hospitals and immediate medical treatment are very
important to avoid their “Preventable Death”.
A disaster base hospital is responsible to accommodate seriouslyinjured victims whom the medical aid stations are not able to treat, based on
the stations’ request (Tokyo metropolitan disaster base hospital installation
management guidelines, 2003). In Tokyo metropolitan area, there are 70
disaster base hospitals and 182 emergency hospitals, with an average
number of beds of 574 and 158. Figure 1 shows the distribution ofseriously-injured victims based on the occurrence of the earthquake with an
epicenter in the northern part of Tokyo Bay (assumed scale of M7.3,
18p.m.). The darker the color is, the higher the number of the seriouslyinjured victims is. The circles with black dots are the disaster base hospitals
and the empty circles are the emergency hospitals, where the size of thecircles shows the number of beds. According to this figure, the eastern partof the Tokyo metropolitan area has more seriously-injured victims but has
fewer disaster base hospitals.

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Figure 1: Distribution Map of Heavily-injured Victims and Hospitals inTokyo
However, as Figure 2 shows, the eastern part of the Tokyo
metropolitan area is the most severe part according to building collapse. The
debris may cause serious street blockades and traffic jams which could
impede the speed of medical treatment and decrease the possibility of saving
the lives of victims.

Figure 2: Distribution of the Total Collapse of Buildings(Assumption by Tokyo Metropolitan Government)
There are mainly two previous researches on the seriously-injured
victims transported to disaster base hospitals. Ohara (2008) estimated thenumber of seriously-injured victims transported to disaster base hospitals
and emergency hospitals using an entropy model. However, the direct
distance between the location of the injury and the hospital has been used in
calculations instead of the road network. Maruyama (2010) analyzed thelevel of function loss of disaster base hospitals. Road network and decreaseof the speed of transportation due to the street blockades based on theintensity of earthquake were considered. However, it still needs some moredetailed calculations on the effect of street blockades.
Based on the background and previous researches, this paper aims toconsider the road network and the effects of street blockades ontransportation to identify the areas where rapid transportation to the nearest
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disaster base hospitals are impossible in case of the expected Tokyo Inland
Earthquake.
This kind of analysis would become a basis to provide a plan for increasing
number of disaster base hospitals by strengthening existing emergency
hospitals or the dispatch of medical treatment teams with portable facilities
such as DMAT (disaster medical assistance team) in these areas.
2. METHDOLOGY
To identify the areas where rapid transportation to the nearest disaster basehospitals is impossible, “ArcGIS Network Analyst” is a useful tool. It canbe used to identify the maximum transportable area up to which victims
could be transported to a specific hospital by car or on foot within a certain
time. This section describes the outline of methodology to analyze thetraffic situation both in normal and disaster situations using “ArcGIS
Network Analyst”.
2.1 Method of traffic simulation in normal situation
First, the hospital database has been made based on the list of disasterbase hospitals in Tokyo metropolitan area. Next, the map, hospitals and road
network (Table 1) of Tokyo metropolitan area were plotted on GIS. The
maximum transportable areas have been identified for time distance of 5 and
10 minutes traveled by car, using “ArcGIS Network Analyst”. Figure 3
shows the maximum transportable areas within 5 minutes and Figure 4
shows the maximum transportable areas within 10 minutes. According to
the Fire and Disaster Management Agency of Japan, the average time fromthe ambulance call to reach is about 10 minutes. Figure 4 shows the resultthat almost all the area in Tokyo Prefecture is accessible to disaster basehospitals within 10 minutes by car after an ambulance call.

Table 1: Velocity Table of Roads in Tokyo Metropolitan Area

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Figure 3: The Maximum Transportable Areas of Disaster Base Hospitals in
5 Minutes

Figure 4: The Maximum Transportable Areas of Disaster Base Hospitals in
10 Minutes
2.2 Method of traffic simulation in disaster situation
The Method of traffic simulation in disaster situation is almost thesame as that in normal situation except that the road network changes. Here,the effect of street blockades on transportation was considered. Figure 5 isthe flow of method of traffic simulation in disaster situation.

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Figure 5: Flow of method of traffic simulation in disaster situation
At first, using the 250 mesh data of partial damage rate and totalcollapse rate of buildings, building damage rates have been calculated by
the equation below.
Building Damage Rate = Total Collapse Rate + 1/2 Partial Damage Rate
Next, the blockade rates of the links were calculated using building
damage rate. Ieda (1997) surveyed the relationship between building
damage and blockade rates in the affected district in Kobe City after the
1995 Kobe Earthquake. Based on these survey results, two equations are
currently proposed for calculating blockade rates of links; one is theequation used in the damage estimation of the Tokyo Inland Earthquake byTokyo Metropolitan Government. The other is the equation used in thedamage estimation of the Tokyo Inland Earthquake by the Cabinet Office ofJapanese Government. The calculation using the former equation was
defined as case 1 and the latter as case2. In Figure 6, these equations
according to the width of the road are shown in red or green lines.

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Figure 6: The relationship between building damage rate and blockade rateof links
Then, restriction line barriers (Figure 7) or scaled cost line barriers((Figure 8) were added to links. Barriers are feature classes in network
analysis layers that restrict or alter impedances of the underlying edges andjunctions of the associated network dataset. There are two types of line
barriers; one type is restriction line barrier that prohibits travel anywhere theStudy on Effect of Street Blockades on Transportation of Seriously-Injured Victims to Hospitals-Case Study
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barrier intersects the network, and the other type is scaled cost line barrierthat doesn't restrict travel on the edges and junctions it covers; rather, it
scales the cost of traversing the covered edges and junctions by a factor you
specify. For example, a factor of 2 would mean it is expected to take twice
as long as normal.

Figure 7: Restriction Line Barrier

Figure 8: Scaled Cost Line Barrier
According to Hagita (2009), in the case of Hanshin-Awaji Earthquake,the running speed of Route 2 of the Kobe city fell down from 25 km/h ofrush hours in normal situation to less than 3km/h at the day of theoccurrence. Because the running speed fell down to almost 1/10 of thenormal situation, a factor of 10 was adapted as Scaled Cost Line Barriers asshown in Figure 9. Furthermore, according to Ieda’s survey (1997), almost
all the road users gave up using the blockade links or areas which had been

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blocked with 20% or more. Based on this, restriction line barriers wereadded to the links with 20% or more blockades as in Figure 9.
Last, by operating the “ArcGIS Network Analyst” within 10 minutes
based on the changes of roads, the maximum transportable areas in disastersituation were identified.

Figure 9: Relationship between Cost and Blockade Rate of Link
3. SIMULATION RESULTS
The results of inaccessible links of case 1 (Figure 10) and case 2 (Figure 11)
show that most of the inaccessible links are in the eastern part of Tokyo andthere were more inaccessible links of case 1, which led to two inaccessible
hospitals and much shrank of maximum transportable areas (Figure 12) tothe nearest hospitals compared with case 2 (Figure 13). One of theinaccessible hospitals is located at Katsushika district (Figure 14) and theother one is located at Arakawa district (Figure 15). Furthermore, comparedwith the normal situation, the maximum transportable areas also shrankseriously when road block in case of disaster was considered.

Figure 10: Inaccessible Links of Case 1
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Figure11: Inaccessible Links of Case 2

Figure12: Accessible Areas of Case 1

Figure13: Accessible Areas of Case 2

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Figure14: Inaccessible Hospital at Katsushika District of Case 1

Figure 15: Inaccessible Hospital at Arakawa District of Case 1
4. CONCLUSION
This study simulated the effect of street blockades on transportation ofseriously-injured victims to hospitals using “ArcGIS Network Analyst” incase of the expected Tokyo Inland Earthquake. The simulation results
showed that almost all the areas in Tokyo Prefecture were accessible todisaster base hospitals within 10 minutes by car in normal situation.
However, maximum transportable area drastically shrank when road blockin case of disaster was considered. This analysis identified that the rapidtransportation of the eastern part of Tokyo metropolitan area to the nearest
disaster base hospitals would be impossible in case of the earthquake. A
series of measures should be taken in those areas, such as increase in thenumber of disaster base hospitals by new construction or strengthening
existing emergency hospitals, preparation for accommodating victims
rapidly in the hospitals in surrounding areas with little damage, or dispatchof on-site medical treatment team with portable facilities such as DMAT
(disaster medical assistance team).
As future studies, various scenarios should be considered such as
expanding the maximum transportable areas to within 30 minutes or 1 houror changing the parameters of scaled cost line barriers etc. Additionally,
Study on Effect of Street Blockades on Transportation of Seriously-Injured Victims to Hospitals-Case Study
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transportation to the hospitals on foot or to the existing emergency hospitals
should also be considered in the next steps.
REFERENCES
Tokyo Metropolitan Government, 2012. Damage Estimation of Tokyo
Inland Earthquake.
Cabinet Office, Government of Japan, 2013. Damage Estimation of
Nankai Through Earthquake
Ohara, M., Meguro, K.,2008. Estimation of the number of heavily
injured victims transported to hospitals in case of expected Tokyo Inland
Earthquake. Industrial Research vol. 60, No. 6, pp. 33-38.
Sho, L., Maruyama, Y., 2010. Analysis of wide area road network ofdisaster base hospitals in case of expected Tokyo Inland Earthquake,proceedings of undergraduate’s thesis, Chiba University.
Ieda, H., Kaminishi, S., Inomata, I., Suzuki, I., 1997. Street Blockades
in Hanshin Earthquake'95 and Its Influence on Disaster Relief Activities.
Doboku Gakkai No.576/ IV-37, 69-82.
Kagita, K., Yamashiro, T., Hongo, K., 2009. Operating simulation ofpolice rescue corps on the next Tokyo Metropolitan Earthquake. National
Research Institute of Police Science Report 60 (1.2), pp. 30-36.

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A STUDY OF LOW COST GROUT FOR PRE
PLACED AGGREGATE CONCRETE
Michael Coo1, Dr. Thanakorn Pheeraphan2
Structural Engineering Department, Asian Institute of Technology,
Pathumthani Thailand
2
Faculty, Structural Engineering Department, Asian Institute of Technology,
Pathumthani Thailand
1

ABSTRACTIn this study, a high performance low-cost low-cement grout mix for
Preplaced Aggregate Concretingis studied with different fly ash
replacements to identify the effects of this pozzolanic material. Results showthat replacement of fly ash at 20 to 30% improves the flow of grout atconstant w/c and superplasticizer content. Compressive strengths of both
grout and PA concrete mixtures have attained a similar early strength (24h) even after fly ash replacement, attaining as high as 30MPa in 7 days for
20% fly ash replacement. Therefore, a low-cost cementitious grout for PA
concrete can be a viable alternative to high cost commercial products.
1. INTRODUCTION
Preplaced Aggregate Concrete (PA concrete) is a concreting method wherecoarse aggregates are preplaced or packed into formworks first (as called
Preplaced Aggregate) then high performance grouts that can penetrate into
the aggregates filling up all voids are injected. Making a material thatcarries load and behaves in the same way as conventionally casted concrete.
PA concrete is also termed in other literature as “Two-Stage Concreting
Method”, implying that the method is casted in two steps, both being
independent of each other. This method of casting is of great advantage tostructural repair works where very difficult placement conditions andlimited site access are encountered. In most repair works, damaged
structures are already in service for quite some time, and access to repair
site is mostly very limited and provides disturbance. PA concreting method
is done without the need of very large mixer trucks, even on high volume
concrete repair jobs. Formwork materials and coarse aggregates can betransported and installed way before actual grouting can take place, which
can also be done by batches.
As a relatively old technique of concreting work, projects as early as 1937
for the rehabilitation of a tunnel in California has already made use of thismethod of concreting to save on cement costs. PA concrete, surprisingly,
has very limited studies available, even ACI 304, the committee on PA
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concrete, neither research nor performance data is available. This may be
due to the patenting of the method which was granted in 1940.
In PA concreting work, it is obvious that the quality of the grout
dictates the quality of the final concrete product. Since the grout injectedholds the concrete together, it is therefore fundamental to understand anddevelop a high performance grout that is suitable for PA concrete. In orderto reach the optimal mix of grout, the right combination of cement,pozzolans, and other additives that affect grout properties should be studied.
Several researchers have studied and concluded that the use of a lowreactivity, fine grained material improves the necessary workability of
cementitious materials (Erdogan 1997). Flow of grouts can also be
improved by an increase in w/c ratio but at the expense of early strength andpossible cause of bleeding, therefore superplasticizers essential to maintainthis ratio low while attaining the grout fluidity required. Chemical
admixtures have come a long way since the early days of PA concrete,becoming cheaper and more effective through research. The incorporationof superplasticizers however, its effects, proper dosages and reactions withother pozzolanic admixtures should be evaluated in the laboratory before
actual field use. This study addresses the effects of fly ash replacement on
superplasticized cementitious grout, since in PA concrete grout, it isessential to maximize flowability while minimizing grout cost. To maintainthis balance, a low cementitious content groutis used as themain proportion and fly ash replacements are done to maximize the flow ofgrouts without increasing cost.
2. EXPERIMENTAL
2.1. Materials
Specimens for testing mechanical and hardened properties ofpreplaced aggregate concrete is prepared using a binder proportion which
incorporate different replacement levels of fly ash. Sand, water and superplasticizer dosages are held constant. Mix proportions and cost are shown in
Table 1.
Mix
CodeControl
PFA10
PFA20
PFA30
PFA40

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Table 1: Mix Proportion and Costs of Selected Mixtures
Cement(kg/m3)
200
180
160
140
120

Fly Ash(kg/m3)
0
20
40
60
80

Sand
(kg/m3)Water(kg/m3)HRWR(l/m3)

200

80

4

Cost
(THB)
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2.1.1 Cement and Pozzolanic Admixtures
An ordinary Portland Cement Type-1 (ASTM C150) and Fly ash
sourced locally (ASTM Type-F) was used in all specimen preparation.
Typical Chemical Compositions are shown in Table 2.
2.1.2 Chemical Admixtures
Superplasticizer used in the preparation of flowable grout, which islater injected into preplaced aggregate formwork, is a Napthalene Sulfonate
Formaldehyde type admixture. It is classified in ASTM C494 as Type-F,
High-range water reducing admixture. Delivered in a dark brown liquid witha solid content of 41%, a bulk density of 1.2 kg/l, and ph value of 7.5.
2.2. Mixing Procedure
A rotary drill with a plate type mixing head is used to agitate themixtures prepared in 10L batches. In preplaced aggregate work, the quality
and consistency of the grout used dictates the success in the casting
operation. Therefore, in order to achieve a consistent grout for sampling andtesting, a consistent method is used to prepare all mix proportions of grout,which is outlined below:
1. Binder proportions are dry mixed for 30s
2. Sand is added and dry mixed for another 30s.
3. Half of mixing water is introduced into the mixture while half is mixed
with superplasticizer and introduced into the mix.
4. After all liquid is introduced; mixing is continued until a total time of 5
mins. is achieved starting from first contact with water.
All mixtures are done in a laboratory condition typical of an indoorsite in tropical climates in South-East Asian region, with average daily
temperatures of 30±5ºC and around 50% RH.

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Table 2:Physical, chemical and mechanical properties of cementitious
materials used in the study

Chemical Analysis (%)
SiO2
Al2O3
Fe2O3
CaO
MgO
SO3
LOI
Insoluble Residue
C3S
C2S
C3A
C4AF
Physical Properties
Blaine Fineness (cm2/g)
Autoclave Soundness (%)
Initial Setting time
Final Setting timeCompressive strength
(MPa)

2.3

Cement
21
5.3
3.3
66.6
1.1
2.7
2.5
0.1
60
15
8.5
9.7

Fly Ash
20-55
5-40
1-15
7-20
0-5
1.5-4
1

3350
0.02
105 min.
135 min12, 19, 28@
3, 7 and 28
days

2500

Measurements

Grout properties are measured during the fresh state (max 10 mins.
after first contact with water) and hardened state (>24 h after casting). All
measurements are done in accordance with relevant ASTM standards andtest methods including casting and sampling of concrete specimens ofpreplaced aggregate concrete.
2.3.1 Fresh Properties
Spread flow and flow cone method (ASTM C949) tests were
performed in each mixtures right after mixing and samples are collected forsetting time and bleeding measurements which also includes samples ofhardened properties testing of grout.
2.3.1.1 Spread Flow (Mini slump)
Spread flow test is an alternative method to measuring the spread
flow of grout in which is related to the yield stress of the mixture when aviscometer is not available, which is also studied and suggested by several
researchers (Felekoglu, 2006), (Flatt, Larosa, & Roussel, 2005) and(Nepomuceno, Oliveira, & Lopes, 2011).
2.3.1.2 Flow Cone (ASTM C939)
The flow cone test method is an indicator of good flow of grouts forinjection purposes. Used in this study to assess the time of efflux of each
mixtures relating to the plastic viscosity of the grout samples. The apparatus

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is a modified Marsh cone test which is standardized by ASTM (Fig 1)that
consists of a cone funnel with a 12mm diameter outlet pipe where a 1,725
ml of grout will exit through and the time until all the grout has beendischarged is recorded as the efflux time of the grout.

Figure 1: ASTM C939 Flow Cone
2.3.1.3 Bleeding Test (ASTM C940)
Bleeding as it is termed is the appearance of bleed water at the surfaceof the grout, which is an important indication of grout properties in concrete
repair applications because it affects the bond performance of the repair
material to the substrate concrete. In this study a bleeding test from ASTM
C940 is used to measure the relative bleeding of the pastes, in which an
800ml of grout is poured into a cylinder and measuring the bleed water thatshows after 3 hours, which is then divided by the total volume to get thepercentage of bleeding.
2.3.1.4 Setting Time (ASTM C953)
In concrete repair works, the time span a concrete mix is workabledepends on the time of setting of the material, which is measured in thisstudy from ASTM C953, which defines the initial setting as the time thegrout is already starting to stiffen and pouring/injecting will not be possible
and the final setting of the grout as the time the grout has already stiffened.
2.3.1.5 Injection Test
Injection tests are done in order to test the penetrability of grout topreplaced aggregate forms and to visualize the ability of the grout to resist
segregation even after experiencing pressure. In this study, grouts are
injected into 50x100x100mm moulds with preplaced aggregates of about
15% voids. Schematics of the test is illustrated in Figure 2.

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Figure 2: Schematics of Injection tests to simulate PA Concrete casting
2.3.2 Hardened Properties
2.3.2.1 Grout Compressive Strength (ASTM C942)
Preliminary testing of compressive strengths of preplaced aggregate
concrete is done by sampling and testing 50x50x50mm cubic samples asshown in Fig 3. This is to evaluate the compressive strength of grout for
initial selection of mixes and quality control of grouts as it is one of themain sources of strength of the PA concrete which holds the aggregates
together.

Figure 3: High Performance PA grout sample preparation
2.3.2.2 Concrete Compressive Strength
In an attempt to study the relationship between the strength of grout
and the actual concrete strength using the preplaced aggregate method, avalue more suitable for the actual designing of structures to be repaired,concrete cubes (150x150x150mm) are prepared simulating the actual

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concrete casting procedure of a preplaced aggregate concrete, Fig 4 showsmoulds preplaced with coarse aggregate to be pumped with grout, after
pumping and demoulding. These samples are later on de-moulded after 24hours of curing then tested for 1 day strength, and the rest water cured(100%RH) for 3 days and 7 days evaluation of compressive strength. A
2000 KN Universal Testing Machine calibrated independently inaccordance with Japanese, European and American standards is used as thecompression force testing equipment.

Figure 4:Compressive Strength specimen preparation through pumping
2.3.2.3 Drying Shrinkage of Preplaced Aggregate
In repairing concrete structures, especially when cementitious
materials are used for repairing, drying shrinkage of the material whether
the material will last long without cracking which can affect the durability,
strength and protection of the utilities and reinforcements within the
repaired member. Casting method is likewise simulated as that of PA
concrete casting in the field as shown in Fig 5. A linear shrinkage
measurement is done in accordance with the specified procedure outlined in
ASTM C157 with first length measurement starting at 24h after casting,then every day for the next seven days while cured on water.

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Figure 5: Shrinkage Specimen preparation using PA method
3. TEST RESULTS AND DISCUSSION
3.1 Fluidity and viscous flow of grouts with low cement and fly ash
replacement
Grouting work in preplaced aggregate concreting method is evidently
one of the most important characteristic of the work, also mentioned
previously, it dictates the success and failure of the job. An optimum grout
during its fresh state should be cohesive enough without segregation while
stationary with very fluid or free flowing when moving (Collepardi, 1998).
Quality of the grout, as several researchers have also mentioned, depends ona large number of parameters like mixing (Nguyen, Remond, & Gallias,
2011), mineral content (Svermova, Sonebi, & Bartos, 2003), pozzolanic
content (Mirza, Mirza, Roy, & Saleh, 2002),w/c ratio, environmental
conditions (A., Henriques, & Cidade, 2010), mixing time, mixing process,superplasticizer addition (Huang, 2001) and so on. Clearly, there is still alot to understand regarding the flow of fluid grouts. In light of this, startingwith a conclusion from Huang that Fluidity of cement-fly ash grout isgreatly enhanced by the use of a superplasticizer, but when an excessive
dosage is incorporated, segregation and bleeding will occur and adversely
affect the supposedly performance and engineering properties of the
mixtures. This is why, researches like the present one are important in
detecting the proper dosage in a given concrete mixtures and theirinteraction on pozzolanic materials such as fly ash addition. Fig 6 shows theeffect of flyash on the efflux time of grout mixtures with superplasticizer
dosage and water to powder ratio held constant. It can be seen from the

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graph that flyash replacement can reduce the flow of grout mixtures which
are evident from 30 to 40% replacement when superplasticizer dosage is at
2% by weight of binder, higher replacement than this will cause an
excessive bleeding and segregation of the grout mixture and were discarded.
This phenomenon is explained by French researchers (Morin, Tenoudji,
Feylessoufi, & Richard, 2001) that when superplasticizers are dosed to theextreme, a large amount of repulsive forces are introduced into the surfaceof particles, which in turn causes seggragation, and in this case, since thedosage is held at constant it can be hypothesized that due to the finerparticles of fly ash, a higher replacement of ash can mean a higher surface
area that electrostatic repulsive forces can adhere to, therefore segregation
occurs at higher replacement levels of fly ash. Separating heavier sandparticles and disrupting flow of grout.

Figure 6:Efflux time with different fly ash replacement levels
3.2 Mini Slump Cone spread with respect to fly ash content of mixtures
In this section a discussion on the results measured by using the minislump flow method of measuring the relative yield stress of each cement
grouts mixed. Results are shown in Fig 7 . The final spread diameter in
relation to the yield stress and viscosity of the grout is studied upon
byBouvet et. al., which can be attributed to the spread because when thecone is lifted up, the yield stress of the paste is exceeded by the normal
stress exerted by the available weight above it, but after a certain amount of
spread, weight gets lesser and to an extent where the local shear in the grout
is already equal to the yield stress to which the grout stops flow. As
suggested by several researchers, (Bouvet, Ghorbel, & Bennacer, 2010) thata slump flow of 300mm to 400mm approximately corresponds to a very low
value of yield stress approaching that of a Newtonian fluid when in transit.
Results in this study has shown that the influence of fly ash on the slump
flow of mixtures are only noticeable at replacement intervals of 30 to 40%,
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this may be attributed to the spherical shape of the fly ash particles thus the
lubricating effect on the mixtures and the high replacement of fly ash
particles on the mixture, extensive literature review on fly ash can also befound in (Coo, 2011).

Figure 7: Mini slump cone spread of PA grout
3.3 Bleeding (ASTM C940)
The test is done to check the excess water that seeps out of the grout
when mixing is done, bleeding on the concrete surface means that there is a
substantial excess of free water and may adversely affect the engineering
qualities of the grout. All mixtures tested in this study did not show any
bleeding, partly due to the low w/c used and the use of fly ash which
requires a greater amount of water from the higher total surface area thancement. But it is good to note that (Toumbakari, 2002) has concluded thatbleeding on grouts are allowable up to 5% without adverse effects.
Therefore, fly ash replacement on low cement content grout mixtures up to
40% replacement will not be susceptible to bleeding.
3.4 Pumpability
The objective of this study is to determine stability of the mix while
under pressure from pumping that will eventually be used in making a
preplaced aggregate concrete, it is therefore substantial that the grout
mixtures should be pumpable while fresh and will remain stable without
segregation even after the end of pumping. This is shown in Fig 8, a typical
pumping on a transparent prismatic formwork with coarse aggregates
preplaced. After pumping, the excess grout coming out of the exhaust on top
is evaluated for signs of segregation while pumping (water coming out

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first), and later the forms are opened while still wet to check forhoneycombing. No grout mixtures done in this experiment has shown any
segregation and honeycombing, as expected, due to the fluid consistency of
the grout shown using the flow cone test, slump spread and bleeding tests
done as shown from previously done tests. It can therefore be concluded thatflow cone test and slump spread test results are good workability indicators
but resulting values should only be taken as a guide, because even with flow
cone results of around 50 seconds, PA grout mixtures can still be handpumped (0.3 MPa pressure) without any problem, 1/10 of the pressurecommonly used in the field (3MPa) as mentioned in ACI 301

Figure 8: Pumping of PA grout
3.5 Setting Time
Directly related to the allowable time the grout is allowed to stay after
first contact with water is the setting time of the mix, and in the field, thismeasurement is essential in order to estimate the duration grout remains
workable and allowed to be pumped. Setting time is affected by factors suchas water and cement content of mixes and whether a pozzolanic admixture
is added, and also the incorporation of a superplasticizer. In this study, a low
cement content and the incorporation of a pozzolanic admixtures to which
the cement will not experience a premature set due to the low heat ofhydration. Results are shown in Figure 9, 10FA to 40FA on the left side of
the graph are grout mixtures done in this study for Preplaced aggregate
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concrete use, while on the right side, an SCC grout researched previously
for high volume repair applications (coo 2011) . It can be seen from thegraph that cement content alone can produce a significant decrease in setting
time of mixtures, even at high replacement levels of fly ash. But in a general
sense, a 4 hour working time is well suited enough for field application, thelowest setting time recorded.

Figure 9:Setting time measurement of PA grout in comparison toconventional fly ash concrete
3.6 Compressive Strength of Grout and Concrete: A comparison
Compressive strength of grout is done as an initial study to identify
mixtures that has the highest compressive strength in terms of fly ash
replacement. As shown in Fig 10, a high early strength can be achieved withall the grout mixtures tested, except those of 50% and 60% fly ash
replacement, one of the reason is that due to the low starting cement content
of the mixture, a replacement of fly ash at very high degrees will cause asignificant decrease of cement content and could induce retardation of the
mixture. Nevertheless, grouts with replacement levels of 10-40% has shown
an excellent grout compressive strength which can be utilized in preplaced
aggregate grouting work.
Concrete compressive strength on the other hand as casted bypumping the same grout as mentioned above has resulted in a significant
decrease (half) in compressive strength, with the same trend for all
replacement levels of fly ash as shown in Fig 11. Trends like these are
already expected since the strength of the coarse aggregates should befactored into, but a trend of almost always twice the strength of concrete
produced is the strength of the grout injected is a new find.

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Figure 10: Grout Compressive Strength

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Figure 11: Comparisons of grout strength and concrete compressive
strength by pumping
3.7 Preplaced aggregate casting method: Poured and Pumped
Two methods of casting is done to determine the influence of casting
method in PA concrete. One is pumping the grout into preplaced aggregates
and another pouring from top. Figure 12 shows the results obtained, P fromthe mix proportion indicates pumped sample and numerals indicate fly ash
replacement. Negligible differences are noticed in compressive strengths ofconcrete. This indicates that as long as formworks are fully filled with grout,the same hardened concrete properties can be assumed in the same grout
mixture whether grout has experienced pressure from pumping or poured
and allowed to consolidate by gravity.

Figure 12: Compressive Strength of concrete: comparison with pumping
and poured

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4. SUMMARY AND CONCLUSIONSIn this research, a high performance grout with several fly ash replacementsfor preplaced aggregate concrete injection is studied under its fresh andhardened properties. Only binder proportions are replaced with fly ash,other factors such as superplasticizer addition and water-cementitious
materials ratio are held constant. Several conclusions can be drawn from theresults:
4.1

4.2

4.3

4.4

4.5
4.6

Fly ash replacements from 30% to 40% of grout mixtures
resulted in an increase in fluidity of the grout mixtures using theflow cone test.
A 20% increase in slump spread of grouts with fly ash
replacement level of 30% to 40%. This indicates a correlation
between the flow cone testing method and the slump spreadmethod, more study is recommended for further understanding.
With the use of a low cement, low w/c with sand and fly ash
replacement on grout mixtures, bleeding can be minimized or
avoided entirely.
Pumpability of grout mixtures can be visually evaluated by the
proposed method of preplaced aggregate concreting simulation
done in this study. The ability of the grout to fill coarse
aggregate voids can be visually detected and segregation can beseen on the overflow vent of the transparent formwork.
Sensible setting times are achieved by fly ash replacement of PA
grout.
Compressive strengths of grouts can be used to estimate thestrength of actual PA concrete for preliminary study, but castingof pumped specimens are still recommended.

A low-cost low-cement and high performance cementitious grout for
PA concrete application can be achieved by replacing cement by 20 to 40%
of fly ash
REFERENCE
ASTM, Annual Book of ASTM Standards Vol. 4.01, Cement, Lime,
Gypsum, American Society for Testing and Materials, Philadelphia USA,2007
A., B., Henriques, M., & Cidade, M. (2010). Effect of environmental
temperature and fly ash addition in hydraulic lime grout behaviour.
Construction and Building Materials, 1511-1517.

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Bouvet, A., Ghorbel, E., & Bennacer, R. (2010). The mini-conicalslump flow test: Analysis and numerical study. Cement and Concrete
Research, 1517-1523.
Collepardi. (1998). Admixtures used to enhance placing
characteristics of concrete. Cement and Concrete Composites, 103-112.
Coo, M. (2011). Development of a low-cost Repair Concrete. AITThesis. Pathumthani, Thailand.
Felekoglu, B. (2006). Optimization of self-compacting grout mixtures
for repair purposes. Construction and Building Materials, 660-667.
Flatt, R., Larosa, D., & Roussel, N. (2005). Linking yield stressmeasurements: Spread test versus Viskomat. Cement and Concrete
Research, 99-109.
Huang, W. (2001). Improving the properties of cement-fly ash groutusing fiber and superplasticizer. Cement and Concrete Research, 10331041.
Mirza, Mirza, Roy, & Saleh. (2002). Basic rheological and mechanicalproperties of high-volume fly ash grouts. Construction and Building
Materials, 353-363.
Morin, V., Tenoudji, C., Feylessoufi, A., & Richard, P. (2001).
Superplasticizer effects on setting and structuration mechanisms ofultrahigh-performance concrete. Cement and Concrete Research, 63-71.
Nepomuceno, Oliveira, & Lopes. (2011). Methodology for mix designof mortar phase of self-compaciting concrete using different mineral
additions in binary blends of powders. Construction and Building Materials,
317-326.
Nguyen, Remond, & Gallias. (2011). Influence of cement grouts
composition on the rheological behaviour. Cement and Concrete Research,
292-300.
Svermova, Sonebi, & Bartos. (2003). Influence of mix proportions onrheology of cement grouts containing limestone powder. Cement andConcrete Composites, 737-749.
Toumbakari, E. (2002). Lime pozzolan cement grouts and theirstructural effects on composite masonry walls

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A STUDY ON STRATEGY FOR
EFFECTIVE DISASTER INFORMATION DISSEMINATION : CASE STUDY IN THAILAND
Niwat APICHARTBUTRA
Doctor Course Student, Department of Civil Engineering,
Graduate School of Engineering, the University of Tokyo, Japanniwat-a@iis.u-tokyo.ac.jp
Miho OHARA
Associate Professor, International Center for Urban Safety Engineering,
Institute of Industrial Science
The University of Tokyo, Japanohara@iis.u-tokyo.ac.jp
ABSTRACT
Information Dissemination is considered as one of the most important
tools to disaster risk reduction; yet, if poorly managed, it can posehindrances to disaster mitigation process. This paper examines disaster
information dissemination system in Thailand. It intends to investigate how
disaster-related information was disseminated during Thailand’s mostextensive inundation in 2011; and, attempts to draw recommendations.
During the flood, the Thai government tried to establishment disaster
information communication with the people. However, its attempt was not
successful. As a result, this led to frustration of a majority of Thai people
and, in turn, triggered a momentum of information flow in various forms
and means of informal communication. In the latter part of this reportstrategies for effective disaster information dissemination are also proposed.
2011.

Keywords: disaster information dissemination, Thailand flooding

1. INTRODUCTION
In July 2011, Thailand was hit severely by the build-up of waters
brought by a 25% increase in average season rainfall. The inundation later
spread through provinces of the Northern, North-Eastern and Central
Thailand along the Mekhong and the Choa Phraya river basins and became
the country’s most extensive inundation in 70 years. The flood claimed
more than 600 lives; and the World Bank (2011) estimated economic losses
were at 45.7 billion USS. Due to the exorbitant lost, there were a number of
evidence pointing that Thailand was unable to deliver effective disaster
information to the people, which led to poor management of the flood andan urgency to develop a comprehensive framework to manage disaster.

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August 2013, Thailand(Larnard and Sandman, 2011; Coben, 2011; Reuters, 2011; Bangkok Post,
2013).
Flood risk communication studies have been carried out extensively
in the US and Europe as one of the most important tools to disaster risk
reduction. (Cole T.W. and Fellows K.L, 2008; Tinker T.L. and Galloway, Jr.
E.G., 2009; Bradford and O’Sullivan, 2011) In case of Thailand, however,there is a limited number of studies carried out in terms of flood disaster
information dissemination during the worst flood in 2011. Therefore, thispaper intends to investigate how disaster-related information was
disseminated during Thailand’s most extensive inundation in 2011. It
focuses on disaster information dissemination in Thailand; and, attempts todraw recommendations. In this study, strategies for effective disaster
information dissemination are also proposed.
2. THAILAND’S 2011 FLOOD DISASTER INFORMATIONDISSEMINATION
To investigate disaster information dissemination during the 2011 flood, thisreport employs documentary research methodology and breaks lines ofcommunication during Thailand’s 2011 inundation into 2 periods using
October 8, 2011, as the breakpoint because October 8, 2011, was when thePrime Minister of Thailand ordered a setup of Flood Relief Operations
Centre (FROC) as the “one stop service” for flood-related matters including
information and warning dissemination. The 2 periods comprises (1)Thailand’s disaster information dissemination before October 8, 2011, and(2) Thailand’s disaster information dissemination after October 8, 2011.
2.1 Thailand’s disaster information dissemination before October 8,
2011
According to the National Disaster Prevention and Mitigation Plan
B.E. 2553-2557 (2010-2014), Thailand’s latest disaster prevention andmitigation scheme, general requirements have been introduced for effective
disaster information dissemination. The following government agencies
responsible for disaster information dissemination have to makenotifications.
 Thai Metrological Department (TMD) and National Disaster
Warning Center (NDWC) are responsible for close surveillances
and warning at national level.
 Department of Disaster Prevention and Mitigation (DPM)
receives warning information from TMD and NDWC and somerelevant agencies and further disseminates warning information to
the provincial government.
 Provincial government is responsible for the watch-out anddissemination of warning information at provincial level.
 District office is responsible for surveillance and dissemination of
warning information at district level.

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Civil Defense Volunteer and warning information dissemination
network are responsible for surveillance and dissemination of
warning information at community level.

2.2 Thailand’s disaster information dissemination after October 8, 2011
On October 8, 2011, the Prime Minister of Thailand set up the Flood
Relief Operations Centre (FROC) in an effort to solve floods in a coherent
and comprehensive manner. One of FROC main duties was to be the centerof flood-related information to send out important information to people
such as situation evaluation and warning. The Justice Minister was
appointed to take charge FROC along with the Interior Ministry’s Deputy
Permanent Secretary. The Science and Technology Minister was put incharge of operations. The Transport Minister managed FROC’s informationand public relations. Governors of local entities were to follow FROC’sorders in managing the floods in their respective localities and encouraged
to set up provincial versions of FROC. The armed forces as well as othergovernmental agencies were to take orders from the Prime Minister’s
Office. Later in October 20, 2011, the Prime Minister invoked Section 31 of
the 2007 (B.E. 2550) Disaster Prevention and Mitigation Act which gave theprime minister a single control to all officials.
In the setup day, FROC was assigned as the focal point to disseminate
disaster information to people. Apart from regular announcement throughmass media, FROC opted to send information through SMS text messages.
It appointed the National Broadcasting and Telecommunications
Commission (NBTC) to look after the task. NBTC summoned ad hoccooperation with the 3 major mobile phone network carriers in Thailand,
namely AIS, DTAC, and TRUE.
The Government would like tosend information from the
Flood Relief Operations Centre(FROC) through SMS.
Volunteers are invited at
FROC, 1st floor, Don Muang
Airport. Everyday from8.00-21.00 hrs.
Now FROC successfully
controlled water in Prapa
canel.
FROC has already ordered
the Bangkok Metropolitan toimmediately drain flood waterin Don Muang and Laksi.

Table 1: The 4 SMS sent by FROC
Unfortunately, FROC did not mount a united front in its
management of the floods crisis and lost its credibility. (Chinchit, 2011) The
FROC’s chief often gave contradictory statements to the media, issued afalse sense of confidence to the public and, most importantly, delivered
irrelevant information that people did not really need. (Thairath, 2011) Its
first SMS was released on October 11, 2011 and the last one was reported to
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be received on the October 23, 2011. Altogether, it was reported in Thairath
newspaper (2011) that there were 4 messages sent by FROC and the people
were not satisfied with the content of them because they did not serve thepurpose of early warning and evacuation. Figure 1 shows the 4 messages
with translation. On October 24, 2011, FROC officially announced that it
had stopped sending SMS to the people by giving the reasons that “sending
information via SMS messages is limited to 70 alphabets. The limitation
would cause messages confusing and subjected to people’s interpretation.”
Matichon Newspaper (2011) reported that Assumption University’s
ABAC Poll Research Centre surveyed 415 people living in Bangkok andadjacent provinces in October 2011 regarding FROC’s credibility. The
survey revealed that nearly 90 percent of the respondents said they were
confused by announcements from FROC. 87% said they did not trust
information from FROC and 86% said FROC did not provide clear
information as to whether their homes would be flooded. They gave averagescore of 3.36 out of 10.
Now FROC still exists but appears to be inactive under the Ministry of
Natural Resources and Environment.
3. THAI GOVERNMENT AGENCIES RELATED TO FLOODDISASTER DISSEMINATION
Apart from the aforementioned agencies, there are also a number ofgovernment agencies that routinely take part in disseminating flood
information in Thailand. Some send warning and useful information directly
to the people while some are in charge of information gathering, monitoring
as well as issuing warning and useful information to concerned agencies.
This part of the paper attempts to gather relevant agencies that disseminate
disaster information particularly flood-related information to the people.
Through an observation, there are 10 following agencies related to flood
information dissemination in Thailand. Ones marked with have warning
systems.
1. Thai Meteorological Department (TMD)
2. National Disaster Warning Centre (NDWC)
3. Department of Disaster Prevention and Mitigation (DPM)
4. Department of Mineral Resource (DMR)
5. Department of Water Resources (DWR)
6. Department of National Park, Wildlife and Plant Conservation(DNP)
7. Royal Irrigation Department (RID)
8. Hydro and Agro Informatics Institute (HAII)
9. Electricity Generating Authority of Thailand (EGAT)
10. Bangkok Metropolitan Administration
All 10 agencies have established their own means to communicate
with the people mostly through websites and some have certain system thatissues warning to (1) concerned agencies and (2) areas or people that might

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be at risk.
be atTable
risk. Table
1 shows
1 shows
agencies
agenciesthat havethatwarning
have warning
systems,
systems,
while while
Table Table
2
2
showsshows
agencies
agenciesthat havethatmonitoring
have monitoring
function.
function.
To mention
To mention
some, some,
DPM and
DPM and
DMR DMR
have establishedhave establisheda networka networkof trainedof trained
volunteers
volunteers
that canthatdisseminate
can disseminate
disasterdisasterinformation
information
to the tolocal
the people
local people
and collectand collectinformation
informationfrom thefromat-the atrisk sites
risk tosites
send
to back
send toback
centralto central
authorities.
authorities.
DWR DWRhas developed
has developed
early early
warning
warningsystems
systems
in localin areas
local whereareas wheresound soundalarm alarm
can becan
triggeredbe triggered
after after
detectingdetectingdangerous
dangerous
level oflevel
rainfall.
of rainfall.
Table 1Table: Agencies
1 : Agencies
that havethatwarning
have warning
systems
systems
AgenciesAgencies
Thai MeteorologicalThai MeteorologicalContent
of Information
Content ofInformation
Weather to
Forecast
Weather Forecast
DPM to DPM

Means ofMeans ofCommunication
Communication
SMS

SMS

Department
Department(TMD) (TMD)
Flood-related
andinformation
disaster information
Disaster notice,warning notice,
Flood-related
and disaster
Disaster warning

Issuing warnings

Prevention
and Mitigation received fromreceivedfromagencies
relevant agencies SMS
Prevention
and Mitigation
relevant
to relevant agencies

Issuing warnings

to relevant agencies

Department
of Disaster
Department
of Disaster

SMS(DPM) (DPM)
Departmentof Mineral
Departmentof Mineral

Water,
Flood, Geo-hazard
andWater, Flood,
Geo-hazard
andDisaster noticewarning notice
Disaster warning

ResourcesResources(DMR) (DMR)

information
landslide landslide
information& SMS & SMS

Royal Irrigation
Department
River
Water &Information,
Rainfall Information,
Through various
Royal Irrigation
Department
River Water& Rainfall
Through various
(RID)

(RID)

and Large-sizedMedium-Mediumand Large-sized
ReservoirReservoir
channels channels
Water Level
Water Level

ElectricityElectricity
Generating
Generating
AuthorityAuthority
Dam Water
Dam
Level
Water
by Level
Telemeteringby Telemetering
Warning system
Warning systemof Thailandof Thailand(EGAT) (EGAT)

System toSystem
RID andto DPM
RID and DPM

Thai MeteorologicalThai Meteorological

Weather Forecast
Weather Forecast

Mass media
Mass media

Department
Department(TMD) (TMD)(NDWC)(NDWC)

receivedfromagencies
relevant agencies Phone Application
Phone Application(iOS
received fromrelevant(iOS

Issuing warning to people

Nationalwarning
disaster center
warning center
Flood-related
andinformation
disaster information
Massmedia, Mobile
National disaster
Flood-related
and disaster
Mass media,
MobileFlood-related
andinformation
disaster information
Mass media,
Flood-related
and disaster
Mass media,

Issuing warning to people& Android)& Android)
Department
of Disaster
Department
of Disaster

Prevention
and Mitigation received fromreceivedfromagencies
relevant agencies Mr.Warning*
Mr.Warning*
Prevention
and Mitigation
relevant(DPM) (DPM)
Departmentof Mineral
Departmentof Mineral

Issuanceof regarding
Warning regarding
GeoMassSMS
Media,
Issuance ofWarning
GeoMass Media,& SMS &

ResourcesResources(DMR) (DMR)

hazardand landslide
hazard andlandslide

VolunteerVolunteer
Network Network(13,857 volunteers
in 39(13,857 volunteers
in 39
provinces)provinces)

Departmentof Water Resources
Early System
Warningfor
Systemand Warning
Early station
Warning
Departmentof Water Resources
Early Warning
FloodforandFlood Earlyat station at(DWR) (DWR)

1,587
at-risk villages)
LandslideLandslide(in 1,587 (inat-risk
villages)risk areasrisk areas

Department
Departmentof Nationalof Park,
National Park,
Forest flood
Forestandflood
rainfallandinformation
rainfall information
Warning system
Warning system
Wildlife andWildlife
PlantandConservation
Plant Conservation(comprising(comprising
1,034 rainfall
1,034gauge
rainfall gauge
(DNP)

(DNP)

stations) stations)*Mr.Warning
is a network
trainedofcommunity
coordinators
who givewho
warning
peopleto
aspeoplewell asasmonitor*Mr.Warning
is a ofnetworktrained community
coordinators
give towarning
well asandmonitor andsend useful
informationback to central
authorities.
send
useful informationback tocentral authorities.

Table 2Table: Monitoring
2 : Monitoring
Agencies
Agencies
AgenciesAgencies

Content ofContent
Informationof Information

Thai MeteorologicalThai Meteorological
Department
Department(TMD) (TMD)
Rainfall Statistics,
Rainfall Statistics,
Weather Map
Weatherand Map
Forecast,
and Forecast,

Means ofMeans ofCommunication
Communication
Website Website

Meteorological
Meteorological
Satellite Picture,
SatelliteDam
Picture,
Water
Dam
Level
Water
Data
Level Data
Analysis,Analysis,
Meteorological
Meteorological
Information,
Information,
Storm Tracking
Storm Tracking
National disasterNationalwarning
disaster center
warning(NDWC)
center (NDWC)
Flood-related
Flood-related& other disasters& other information
disasters information

Website Website

Department
Department
of Disasterof Prevention
Disaster Prevention
andandFlood-related
Flood-related& other disasters& other information
disasters information

Website Website

Mitigation
Mitigation(DPM) (DPM)
Department
Departmentof MineralofResources
Mineral Resources(DMR) (DMR)
Flood, Water,
Flood,
Geo-hazard
Water, Geo-hazard
and landslideand landslide
information
information
Website Website
Department
Departmentof Water Resourcesof Water Resources(DWR) (DWR)Water Level
Water
Monitoring
Level Monitoringby CCTVby(27
CCTV
sites),(27Water
sites), WaterWebsite Website
Situation Situation
Report, Flood
Report,andFlood
WaterandInformation
Water Information
through throughA StudyA
Strategy
for Effective
Disaster
Information
Dissemination:
Case Studyin
Thailand
Aon
Study
on
for
Disaster
Information
Dissemination:
Case
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ininThailand
Studyon Strategy
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for Effective
Effective
Disaster
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Dissemination: CaseStudy
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Table 2 : Monitoring Agencies
Agencies

Content of Information

Means ofCommunication

Mekhala Centre for Water Crisis
Department of National Park, Wildlife andRainfall Monitoring Stations in National Conservation

Plant Conservation (DNP)

Parks (1,034 stations)Royal Irrigation Department (RID)

River Water & Rainfall Information, Medium- andWebsiteWebsiteLarge-sized Reservoir Water Level
Hydro and Agro Informatics Institute (HAII)

Information on Rainfall and storm by Mini

WebsiteTelemetering System/Satellite/Radar
Electricity Generating Authority of Thailand

Dam Water Level by Telemetering System

WebsiteRainfall Information by radar, Water drainage monitor

Website(EGAT)
Bangkok Metropolitan Administration

by Telemetering

4. FLOOD-RELATED INFORMAL COMMUNICATION
Since the government’s attempt to deliver flood information to the people
was not successful, frustration of a majority of Thai people was developed
and, in turn, triggered a momentum of information in various forms andmeans of informal communication. One of the most popular channels that
Thai people opted for getting information was through the Internet andsocial networks such as Facebook, Twitter, YouTube, and Weblog. There
were statistics showing a sharp increase in messages shared through Twitter
during flood disaster in Thailand from 1.5 million messages a day in normal
time to about 2.2 million messages a day during the flood (Bangkok Biz,2011).
Informal communication during the flood can be grouped into 3
following categories with some examples.
Category I Communication that have already existed before the flood
but its function changed.
 Seub Nakhasathien, YouTube channel, normally served the
purpose of promoting forest conservation. During the flood, Sasin
Chalermlarp, Secretariat-General of the foundation, used thechannel to broadcast his flood analysis. The number ofaccumulative viewers reached almost a million. The most popular
VDO reached 450,000 viewers.
 Muang Ake community used its existing platform such as website
and Facebook page to communicate with its inhabitants within thecommunity during flood crisis. Its original function was to share
general and administrative information.
Category II Communication that was created for flood purpose but
now inactive
 Roosuflood (know and beat the flood) is a YouTube channel thathave anime VDO concerning how to fight the flood. Some VDO
reached more than 300,000 views. The last activity was May
2012.

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ThaiFightFlood, ThailandFlood2011 and NamKunHaiReepBok(when water rises, we tell) are Facebook account that updated
news regarding flood. Their last activities were October and
September 2012.

Category III Communication that was created for flood purpose andhave functioned up till now.
 www.thaiflood.com and its Facebook and Twitter account werelunched during flood in 2010 and now continues to broadcast
flood-related information. Now it has over 70,000 likes.
 Volunteers watchdog for Bangkok flood (in Thai) is Facebook
account that created during the flood and still posts flood
information.
Though social media has proven to be a popular and effective tool forsharing information, it can hinder response efforts especially when theinformation is incorrect, malicious, outdated, and inaccurate. In some cases,
the location of the hazard or threat was inaccurately reported, or somerequests for help were retransmitted repeatedly even after victims were
rescued.
5. RECOMMENDATIONS FOR DISASTER INFORMATIONDISSEMINATION IN THAILAND
The 2011 flood has led to the necessity that the government established acomprehensive framework for better management of flood disaster in the
future. As a result, on 13 February 2012, the government issued theRegulations of the Office of the Prime Minister to improve flood and water
prevention and mitigation by integrating work plans of relevant agencies
and establishing a central unit that gives command on water management.
This directed to the establishment of the Office of the National Water andFlood Management Policy (NWFMP), run by the National Water and FloodManagement Policy Committee, chaired by the Prime Minister, with thehope to make comprehensive national water and flood management policy.
However, information communication and dissemination is one of theessential aspect determining whether or not Thailand is to achieve water andflood management. Some hindrances can pose threat to the credibility andthe government might not be able to achieve what it has hoped for. To avoid
repeating the 2011 flood’s history, the following recommendations areproposed.
 So far, there have been 2 government agencies whose main duties
ought to be the centre of flood-related information in times ofcrisis. The 2 agencies are NDWC and FROC. As a result, there is
still possibility that Thai people will be confused on which
agencies they are to listen to. Choosing one and terminating theother are recommended to create true single command and avoid
redundancy and confusion.
 NDWC should be chosen because it has already established since
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





2005. Though NDWC was set up to respond to Tsunami, its
original function covers every disaster that might occur inThailand.
The government should set up a risk communication unit undereither NDWC or FROC as a focal point to communicate between
the government and the people and an establishment oftrustworthy communication should start from today on.
Existing facilities concerning disaster information dissemination
should be employed to better cater the warning system. Also, it isencouraged that the government would use as many and various
channels as possible to deliver message to the people since thepeople are not homogenous and can be categorized in various
groups like the handicapped and flood-inexperienced.
Since FROC’s messages were reported that they were irrelevant

information that people did not really need. The content of messages

during flood disaster should focus on reporting current situation,issuing warning when needed, and informing of an evacuation
when necessary.
The government should also focus on the prevention andpreparation. Disaster education and preparation should becontinuous even though there is no coming disaster. Since
behavioural changes are more likely if they are self-motivated,rather than imposed, developing a raising awareness of current
flood information sources such as websites, brochures and flood
information campaigns offers potential for empowering
individuals and communities to mitigate flood risk in an
appropriate manner. Means of promoting information sources
includes dissemination through mass media and circulation by
post or through recognised access points such as train station,
council officers, and libraries.

REFERENCES
Bangkok Biz, 2011. Thai Social Network Behaviour during Flood
Crisis (in Thai). October 26, 2011. Retrieve July 2013, fromwww.bangkokbiznews.com
Bangkok Post, 2013. Government shows its intractability. May 20,
2013.
Retrieved
July
2013,
from
http://www.bangkokpost.com/opinion/opinion/350880/ government-showsits-intractability.
Bradford, R.A., and O’Sullivan, J.J., 2011. Improving Communication
Strategies for Effective Flood Risk Management. Unpublished paper for
National Hydrology Conference. Retrieved July 2013, fromhttp://www.opw.ie/hydrology/data/ speeches/05%20-%20Bradford%20%20Improving%20
Communication%20
strategies%20for%20effective%20flood%20risk%20management.pdf
Chingchit S, 2011. Thailand Flood: Not Enough to Destroy theGovernment. In IPRIS Viewpoint, Volume 78, December 2011.

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Coben R., 2011. UN: Southeast Asian Floods Trigger Humanitarian
Crisis.
October17,
2011.
Retrieved
July
2013,
from
http://www.voanews.com/content/ thai-soldiers-rush-to-reinforce-bangkokflood-walls-132036703/ 146797.html
Cole T.W., and Fellow K.L., 2008. Risk Communication Failure: A
Case Study of
New Orleans and Hurricane Katrina. In Southern Communication
Journal, Volume 73, Issue 3, Page 211-228. Routledge, London.
Larnard J., and Sandman P. M., 2011. Over-Reassuring Thai Crisis
Communication about the Great Flood: When “Restoring Trust” is too
Muchto
Expect.
Retrieved
July
2013,
from
http://www.psandman.com/col/Thai-flood.htm
Matichon, 2011. ABAC Poll Revealed Majority Distrusted FROC and
Evaluated 3.36 from 10. October 18, 2011. Retrieve July 2013, fromhttp://www.matichon.co.th/news_detail.php?newsid=1318911293&gr
pid=03&catid=03
Reuters, 2011. Thai Govt Faces Lawsuit over Flood Crisis Handling.
December
22,
2011.
Retrieved
July
2013,
from
http://www.trust.org/item/?map=thai-govt-faces-lawsuit-over-flood-crisishandling/
Thairath, 2011. People unsatisfied: FROC SMS were useless (inThai).
October24,
2011.
Retrieve
July
2013,
from
http://www.thairath.co.th/content/tech/211564
Tinker L.T., and Galloway E.G., 2009. “How to communicate flood
risks effectively” in Journal of Business Continuity & Emergency Planning.
Volume 3. Number 3.
World Bank, 2011. The World Bank Supports Thailand’s Post-Floods
Recovery Effort. December 13, 2011. Retrieved July 2013, fromhttp://www.worldbank.org/en/news/feature/2011/12/13/world-banksupports-thailands-post-floods-recovery-effort

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Student ReportREPORT FROM STUDENT PARTICIPATION ON
6th JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURE
DATE: August 6-7, 2013
The 6th Joint Student Seminar on Civil Infrastructure was a combined programme of an in-houseseminar and a field trip. This year, the seminar was held at Dipak C. Jain Room, SASA
International House, Chulalongkorn University, on August 6th, 2013. It consisted of 5 main parts;
opening remarks, invited speakers, student presentations, closing remarks and an award-presenting
banquet. On the following day, August 7th, 2013, the participants went on a field trip of visiting
places of cultural and engineering significance. The trip started at Hua Lamphong’s construction
site where a tunnel of the new MRT Blue Line was being excavated, then, Hua Lamphong train
station - Thailand’s central hub of the train system. In the afternoon, the participants visited culturalsites as such Kao Sarn Road, Kalayanamitra Temple and Bowonniwet Temple.

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PRESENTATION SESSIONOPENING REMARKS
Dr. Akiyuki Kawasaki, Asian Institute of Technology (AIT), gave opening remarks,
signifying the commencement of the 6th Joint Student Seminar on Civil Infrastructures. He firstexpressed his sincere gratitude for the committee members for their tremendous support to theadministration of the seminar, offering his special thanks to Dr. Hyunmyung Kim, Dr. Kunnawee
Kanitpong and Dr. Withit Pansuk. He then mentioned the objectives of the seminar, which are toexperience an international seminar, to improve presentation skills in English, to share research
information and to build friendship. He wrapped up his opening remarks by encouraging thestudents to actively contribute to the seminar as it could widen their horizons.
INVITED SPEAKERS
Two distinguished speakers were invited to deliver presentations. Dr. Jeong Whon Yu, Ajou
University, Korea, talked on the topic “Big Data and Transportation Information Platform in
Korea”. The term “Big Data” is referred to as a collection of data that is so large and complex that it
becomes unmanageable if they are dealt with traditional data processing applications. Information
platform in Korea was also presented.
The other speaker was Mr. Kunakorn Pragthong, a representative of Hilti (Thailand) Ltd. He
delivered a presentation on the topic “Understanding Post-Installed Anchor Systems & Code
Design Requirements”, which gave the audience an introduction to anchor systems. His talkdemonstrated that chemical anchors can be a practical alternative, or better in some cases, tomechanical anchor. Yet, it is very important for an engineer to choose the right type of anchor forthe optimal result.
STUDENT PRESENTATIONSTwenty students from AIT (Thailand), Chulalongkorn University (Thailand), the Universityof Tokyo (Japan), Myong Ji University (Korea), and Seoul National University of Science andTechnology (Korea), gave presentations and participated in an exchange of ideas. Topics ofpresentations were grouped into 3 parts, chaired by Dr. Akiyuki Kawasaki, AIT, Dr. Kunnawee
Kanitpong, AIT, and Dr. Sung Bong Chung, Seoul National University of Science and Technology,
respectively.
CLOSING REMARKS
Dr. Kunnawee Kanitpong, AIT, delivered significant reviews, comments and closing
remarks of the 6th Joint Student Seminar on Civil Infrastructure. She stressed that all theparticipants’ performance surpassed expectations and it was a really good opportunity to givepresentations and exchange ideas in an international environment. She also recommended us toattend more seminars both student or professional seminars. Last, she wished all the participants
success and good luck in study in the future.

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AWARD-PRESENTING BANQUET
In the evening of August 6th, 2013, a dinner banquet was held as a platform for theparticipants to further exchange ideas and develop networks. Three excellent presenters were also
awarded at the banquet. The excellent presenters were chosen according to criteria such as research
quality, oral presentation, and enquiry-answering. Another criterion was added this year, which was“contribution during Q&A session”. Judges consisted of lecturers, invited speakers, andchairpersons.

The students awarded “Excellent Presenters” with the panel of judges
One of the judges’ remarks was that “this year the quality of research was very high. There
were 6 presenters that earned high scores and judges had a very difficult time to choose only 3
excellent presenters.” The following students were awarded “Excellent Presenters”. Order does not
signify rank of scores.
1.
2.
3.

Mr. Tsubasa Sasaki, University of Tokyo (The impact of flow velocity on microbial
precipitation of calcium carbonate among soil)
Ms. Fei Jiang, University of Tokyo (Study on effect of street blockages on transportationof seriously-injured victims to hospitals : case study of expected Tokyo inland
earthquake)
Mr. Seemanta Bhagabati, AIT (Comparison of benefits for political boundary and natural
boundary from hydropower development in the Mekong)

Excellent Presenters 2013: (left to right) Mr. Tsubasa Sasaki, Ms. Fei Jiang
and Mr. Seemanta BhagabatithReport from
Reportfrom Student
Student ParticipantsParticipants on
on TheThe 66thJoint
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FIELD TRIP
On August 7th, 2013, participants went on a field trip to visit places of engineering andcultural significance.
HUA LAMPHONG CONSTRUCTION SITE VISIT
Hua Lamphong is the area where Bangkok’s main railway and the construction site of the
new MRT Blue Line (Subway) are situated. The field trip started at the construction site of the newsubway blue line, officially named as Metropolitan Rapid Transit (MRT) Chaloem
Ratchamongkhon Line. The project was aimed to complete within 4 years. So far, it has been in aprocess of excavating the main tunnel. With help from Japan, the project uses Tunnel Boring
Machine (TBM) to make a tunnel of about 2.8 kilometres in length. We were granted an
opportunity to go down the tunnel and see the real TBM. Down in the tunnel, safety is the firstpriority.

(left to right) inside the briefing room, the entrance of the tunnel(left to right) inside the tunnel, the mouth of the TBM
After that, we went to Hua Lamphong train station, officially named Krungthep station. The
station was opened on June 25th, 1916, after six years' construction. The site of the railway station
was previously the national railway's maintenance centre, which was moved to Makkasan in June
1910.

(left to right) Hua Lamphong or Krungthep Station, a locomotive awaiting for its journey,participants inside the station

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CULTURAL SITE VISIT
In the afternoon, we visited cultural sites of Bangkok including 3 temples and Khao Sarn
Road. Kalayanamitr Temple, Sraket Temple & Golden Mountain, and Bowonniwet Temple were
the temples we explored and they were unique for their significance. Kalayanamitr Temple has thebiggest braze Buddha’s image in Bangkok and was built for the King Rama III. Sraket Temple &Golden Mountain has a beautiful golden pagoda situated on the top of a mountain. With 500-stepstaircase, the top of the mountain provided a picturesque view of Bangkok. Bowonniwet Temple
contains a number of Chino-Portuguese and Victorian buildings.

At Kalayanamitr Temple : (left to right) Kalayanamitr Temple from across the river, students
shaking sticks in a bamboo cylinder to read their fortunes, the giant brass bell believed to bringgood luck to the person who rings it

At Sraket Temple & Golden Mountain : (left to right) students starting climbing up to the Golden
Mountain, Bangkok from the top of the Golden Mountain, the golden pagoda

Inside Bowonniwet Temple
Khao Sarn Road comes from the word "Khaosan" which can be translated as "milled rice", areminder that in former times the street was a major Bangkok rice market. Now, Khaosan Road has
developed into “Backpacker’s heaven”.

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What was special about this trip was that we had a chance to experience different modes oftransportations, which were “river bus” and “canal bus”. Both were established as an alternative to
avoid road congestion during rush hours of Bangkok. The river bus took us across the Chao Phraya
River in about 5 minutes and the cannel bus made the journey from the outer ring of Bangkok to theinner city fast and interesting.

(left to right) Khaosarn Road, a pier of the river bus, inside the cannel bus

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Impressions from the Student ParticipantsThis is my second time to attend the Joint
Student Seminar on Civil Infrastructures. I
was very excited, because I could meetwonderful friends from different countries!
This seminar not only gave me a valuablechance to present my research in front of aninternational audience but also provided mean occasion to further widen my perspectives
on civil infrastructures and services. I had agreat moment with students, researchers andprofessors by sharing the knowledge. It was a
precious experience to me and thanks ICUS
for giving us this opportunity (by Sung-Hi An)
I was very glad that I had a chance toparticipate in this seminar since it gave avariety of invaluable experiences – travelingaround Bangkok and seeing what was goingon in Thailand, getting to know what kind ofresearch the students from overseas were
engaged in, to name a few. Although I wasproud of getting myself the presentation
award, I believe that there were many otherpresenters who provided more fascinating
talks in a very skilful and effective way. So, I
learned a lot from them and felt that it would
help me change for the better as a presenter. Finally, I would like to offer
my sincere gratitude for everyone who put forth effort to make the seminarsuccessful, and who helped the whole trip to Thailand become this valuable
and memorable. Thank you all. (by Tsubasa Sasaki)
The 6th Joint Student Seminar on Civil
Infrastructures was a great platform foramateur researcher students to present theirwork in an international environment. It wasa great pleasure to attend the seminar again
this year, as I attended it last year as well. It
was a wonderful experience for me as I madenew friends from a wide variety of fields andcountries, and got to know about the parallel
researches going on. I really enjoyed theseminar as well as the field trip. I am reallyhonored to receive the excellent presenter
award, my first such award in an international
audience. I would like to express my sincere thanks to all the participants
who shared their work, judges for their valuable comments and feedbacks aswell as to the organizing committee who made this event a great success. I
am looking forward to attend it next year.
(by Seemanta Sharma Bhagabati)
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August 2013,2013, Thailand
AugustThailandThe 6th Joint Student Seminar on civil
Infrastructure has given me a wonderfulexperience. This was my first time to make apresentation in front of foreign students andresearchers. So, I was a little uneasy at first.
However, it was a precious experience toreceive advice about my research and to
discuss it with students and professors fromother universities. I was motivated by students’presentation and their researches, and enjoyedtalking with students. I really appreciated thisopportunity of joining the seminar. From now
on, I will do my best in advancing researchand improving my English communication skills. (by Noriko Okamura)
I spent very good days and had a wonderfulexperience at the 6th Joint Student Seminar on
Civil Infrastructure. This seminar was myfirst experience of going abroad and making apresentation at an international platform. Not
only did I learn how to improve my English
and presentation skill, I also had a chance tobe exposed to different cultures. As the
program went on, I realized that the seminar
had given me valuable attributes. At first, I
found that making and English presentation
was a very hard task, but I was able todevelop confidence gradually. In the future, I
will surely use my experience earned from this seminar to benefit my future
research. (by Ryoichiro Hoshino)
I was very glad to have an opportunity to takepart in the 6th Joint Student Seminar on Civil
Infrastructures. Through the conference, wehave shared and gained a lot of new
knowledge and experiences. The judges andspeakers from different fields of researchcreated the multidisciplinary atmosphere forthe seminar. As for me, this was my first time
to listen about concrete technology but it wasvery interesting and attention-grabbing. In mypoint of view, all of my fellows were wellprepared and some had excellent presentation.
From them, I have learnt more about presentation, question and answer
skills. The questions and comments from judges and audience will help me
improve my research work. In addition, this was a change for international
students exchange culture and expand friendship network. Although there
was only 2 days, we had many good memories together, especially in thefield work in Bangkok. I really enjoyed the field work and deeply impressedby the harmony between modernization and tradition in Bangkok.
(by Dang Anh Nguyet)

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The 6th Joint Student Seminar on Civil
Infrastructure was very exciting and gave mea wonderful experience. The seminar gave me
the first opportunity to make a presentation in
a foreign country. This was also the first time
for me to travel abroad. Before the seminar, I
was very strained over travel arrangement andpresentation in a foreign country; but, after
the seminar, I think I would want to do it
again. I have learned a lot of things from theseminar and I would like to make use of theexperience for my future research.
(by Shunya Kimura)The 6th Student Seminar was a goodexperience for me. This was my first time togive a presentation in English. I was nervous
because you knew my English level is low.
My lately interest is railroad operation in
Japan. So I wanted to talk to Japanese student.
But I wasn’t given opportunity because I
thought my English level. I think it’s lucky
for me to ask my presentation by somebody
and chair. Frankly I didn’t understand about
half presentation because my major istransportation investment and planning. But I
got many major’s information through talking to somebody else; civilengineering, urban planning, chemistry, tools, foreign culture and so on. The
seminar gave me an opportunity to think about the importance and necessityof infrastructure and learning English, and I would like to attend thisseminar next year.
P.S. Thanks to my professor for let me attend this seminar. Also, thanks to
Dr. Akiyuki Kawasaki for the answer “otsukaresamadesita” and I want tomeet and talking about Japan’s infrastructure(railroad) if I go to Tokyo.
(By Baekkyu Namkung)It was a wonderful experience for me to join
the 6th Joint Student Seminar on Civil
Infrastructure. I have never had an experienceof making a presentation on my research infront of students from other countries before,
but this seminar gave me such opportunity.
Not only did I aim to use better English but
also make my presentation understood to theaudience. Also I had an enjoyable time taking
with students from other countries during
lunch and dinner. This seminar had helped
improve my English skill, so that I can makeany presentation fearlessly than before. I want to research more deeply andmake a presentation in English in front of people from different countries.
(by Aya Fujikawa)
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AugustThailandIt was a very good opportunity to me for joining
this seminar. With the objectives to encourage
students and researchers to practice theirpresentations and share their studies, all ofparticipants were got the new experience andalso the new knowledge. The benefits did not
only relate to my specific field of study, but also
covered to all the fields of civil infrastructures. I
have got new friends as well. They are very
impressing to me both their friendliness andbrilliance. Their hard working studies had been
proved to their abilities beside their tolerance. I
have appreciated them. Moreover, I also want to express my thankful to theorganizer team, the seminar will not be successful without them.
(by Tharadol Punlop)
The 6th Joint Student Seminar on Civil
Infrastructure was my first opportunity that I
had taken part in an academic seminar. It was avery nice experience for me and I enjoyedmyself very much. Since participants werefrom many different countries and they had
different backgrounds, it was very interestingto get to know each other and exchange
opinions. Also, the seminar had added aninternational aspect to my research and ways ofthinking. I am convinced that this opportunity
had given motivation for my research in the
future. Moreover, the field trip further created a space where we could
exchange views deeply with counterparts that were not acquainted. Pleaseallow me to extend my thankfulness to everyone taking part in this seminar.
(by Shunya Suzuki)
This is my 2nd time to attend this joint student
seminar. Last year I attended as an audience inthe 5th joint student seminar, I thought it was
useful seminar for the non-native English
speaker to practice how to make a presentationand discussion in English speaking environment.
After one year passed, I have attended thisseminar again as a speaker. However, it was not
only good chance for practicing the presentation
skill, but it was also a very good chance to havea new friendship with all participants. We madea useful discussion and gave some comments toother speakers at time of presentation and talked about casual things likeshopping, sight-seeing in Thailand. It will be one of my unforgettable
memories. (by Onnicha Rongviriyapanich)

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I fortunately had experienced giving
presentation in English before, but in thisseminar, thanks to its warm atmosphere, I
could join the discussion much moreinteractively by making and answering
questions. Especially, I was glad to have hadquestions from 3 professors from Korea andstudents, in which I could get important
viewpoints. It will benefit every country if weinternationally cooperate and exchange
experience and knowledge about recovery
after disasters. Also, I really enjoyed the field trip with students from civilengineering. Since it was my first trip to Thailand, the temples in Ayutthaya
and townscape in Bangkok impressed me very much. It was pleasure tocatch a glimpse of Thai history and culture. At last, I am grateful toeveryone who supported and took part in this seminar. (by Sae Shikita)
The Joint Student Seminar on Civil
Infrastructures provided me a very greatchance to a variety of new research related tocivil works. It is such a pleasant time for me tolisten to the new topic which some of themwere never been heard before in my working
experience. However, due to a large number ofparticipants, it may be little bit exhausted tojoin the seminar from 8am to 6pm and since
many participants have not enough time for
their sightseeing around Bangkok. Therefore,
my suggestion is, should the seminar be divided in two days? I’m looking
forward to join this seminar again. (by Jetpan Wetwitooait)
It was a great experience to join the 6th Joint
Student Seminar on Civil Infrastructure. Allof the presentations made by invited speakers
and students were very interesting and had a
lot to learn. As a bachelor course student, it
was the first time for me to make apresentation on my research. I was frustrated
by my inability to answer the questions raised
from the floor; at the sam time, it gave a great
motivation to make my better research andpresentation. My sincere thankfulness is to allof you who gave me this wonderful
opportunity. (by Nakashima Mari)

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AugustThailandThis seminar was very exciting because it was
the first time to introduce my own research to
students from foreign universities. Having
fruitful discussion and receiving commentsfrom students and professors were a precious
experience to me. Also, exchanging
viewpoints with students during coffee
breaks, lunch and dinner was enjoyable. The
time I spent during the seminar had motivatedme to improve my research, English, andpresentation skill. The accommodations andseminar arrangement were so good that there
was no need to worry even though you had never been abroad. Finally, I
would like to show my appreciation to professor, secretary, seniors, andstaffs both at the seminar and in the laboratory for giving me this chanceand helping me for everything. (by Nao Sasaki)
The 6th Joint Student Seminar on Civil
Infrastructures gave me opportunity to speak
presentation in front of foreign students. Last
year, I attended this 5th seminar as an
audience. That time, I was worried about
presentation in English. However, I got new
and valuable experience from this seminar andit was meaningful that we met each other who
are studying in same field. Also, I was sorry toparticipate in field trip together due to mypersonal reason. Next year I will probably join
this seminar again. I hope that I will speak
more advanced presentation and make relationship with all students.(byChoong-shik Lee)I would like to offer my sincere gratitude to allorganizers of this seminar and those who
helped me during the field trip in Thailand. At
first, I was little worried since it was my firsttime to present in English in an international
environment. However, I made it and gained
confidence after this seminar. It not only
provided us an opportunity to present our
research in English and communicate withstudents from other different fields, but also
gave us a chance to gain friendship. I reallylearned a lot from this seminar and I will use
this experience to my further study and keep contact with friends gained
from t his seminar in the future. (by Fei Jiang)

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It is such a great opportunity to present mywork and listen to what others are working on
and the progress of researches in civil
infrastructures, especially by students likeme. This may be the second time I’ve joined
this seminar but indeed a lot has changed
since my last attendance. Just two years havepassed but the quality of work andenthusiasm of presenters has evolved intomuch greater depts. It has always been
exciting and a good opportunity to practiceand brush up on my presentation skills, butmore importantly, on making my presentation as simple as possible whilestill including all the important details. Finally, I would like to extend mysincere gratitude to the organizing committee and to the attendees for the
excellent seminar and friendships fostered. (by Michael Coo)
When I realized that I was granted an
opportunity to participate in the 6th Joint
Student Seminar on Civil Infrastructure in
Bangkok, I was a bit worried because myacademic
backgroundwasdifferent.
However, the experience I had from theseminar was somewhat valuable andmemorable. For the student presentation
session, I have learnt new genre of expertiseand interesting researches. Most importantly,
I have met new friends from Japan, Thailand
and Korea. This network of friendship, I
trust, will be very useful for us as an informal platform to exchange ideas
and useful information in the future. For the field trip, I was very impressedby the construction site visit. The real TBM machine looked more majestic
than the image I saw on documentary channel, If I had not participated inthis program, I would not have had a chance like this. My thankfulness is
not enough to express how I felt and I do hope that this program willcontinue as it gives priceless opportunities for students. (by Niwat
Apichartbutra)

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6th Joint Student Seminar on Civil Infrastructure
Venue: Sasa International House (Room: Dipak C. Jain), Chulalongkorn University
Date: 6 August 2013
Time
08.00-08.30
08.30-08.40
08.40-09.00
09.30-10.15
10.15-11.00
11.00-11.15
11.15-11.30
11.30-11.45
11.45-12.00

13.00-13.15
13.15-13.30
13.30-13.45
13.45-14.00
14.00-14.15
14.15-14.30
14.30-14.45
14.45-15.00

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Topic

Page 1Name of speaker
Dr. Akiyuki Kawasaki, AITRegistration
Opening remarks
Group photo & Coffee break
Invited Lectures (chair: Dr. Hyunmyung Kim, Myong Ji University)
Big Data and Transportation Information Platform in
Dr. Jeong Whon Yu
Korea
Ajou University
Understanding Post-Installed Anchor Systems & Code
Mr. Kunakorn Pragthong
Design Requirements
Hilti (Thailand) Ltd
Student Presentation 1 (chairs: Dr. Akiyuki Kawasaki, AIT)Car Navigation Data Analysis for Transportation
Ms. Sung-Hi An
Planning
Myong Ji University
The impact of flow velocity on microbial precipitation
Mr. Tsubasa Sasaki
of calcium carbonate among soil
University of Tokyo
Comparison of benefits for Political boundary and
Mr. Seemanta Bhagabati
Natural boundary from Hydropower development in
AIT
the Mekong
Evaluating thermal comfort in city life and its relation
Ms. Noriko Okamura
to socio-economic activities
University of Tokyo
Lunch
Student Presentation 2 (chairs: Dr. Kunnawee Kanitpong, AIT)Development of real wind turbine blade vibration
Mr. Ryoichiro Hoshinomeasurement techniqueby 3D motion analysis
University of TokyosoftwareForest carbon estimation using the combination of
Ms. Dang Anh Nguyet
LiDAR data and high resolution imagery in Ludikhola
AIT
Watershed, Nepal
Three-dimensional simulation of flow of fresh
Mr. Shunya Kimura
concrete in multi-dimensional steel bar arrangement
University of Tokyo
by MPS
Application of Risk Management to forecasting
Mr. Baekkyu Namkung
Transportation Demand
Seoul National University of
Science and Technology
Characterization of Air Quality in Global Mega-Cities
Ms. Aya Fujikawa
by Remote Sensing and Inventory Data
University of Tokyo
Structural equation models regarding to inter-city van
Mr. Tharadol Punlop
transit in Thailand: A case stuy of difference of travel
AIT
distance
Basic research for quantitative analysis of oilMr. Shunya Suzuki
contaminated soil purification by microorganisms
University of Tokyo
Modeling of oven-dried mortar after fire exposure by
Ms. Onnicha Rongviriyapanich
Rigid Body Spring Model (RBSM)
Chulalongkorn University
Coffee Break

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Venue: Sasa International House (Room: Dipak C. Jain), Chulalongkorn University
Date: 6 August 2013
Page 2Student Presentation 3 (chairs: Dr. Sung Bong Chung, Seoul National University of Science and Technology)
15:30-15:45
Current Situation about Reconstruction of Hakozaki
Ms. Sae Shikita
Peninsula after the Great East Japan Earthquake
University of Tokyo
15.45-16.00
Neighboring Search Process in Genetic Algorithm for
Mr. Jetpan Wetwitooait
Train Scheduling Problem
AIT
16.00-16.15
An investigation of road bridge maintenance system in
Ms. Nakashima Mari
Japan in developed society
University of Tokyo
16.15-16.30
The role of mutual assistance during disaster in the
Ms. Nao Sasaki
aging society
University of Tokyo
16.30-16.45
The Calibration of Road Network Travel Time
Mr. Choong-Shik Lee
Function in Korea : The Issues and Solutions
Myong Ji University
16.45-17.00
Study on Effect of Street Blockades on Transportation
Ms. Fei Jiang
of Seriously-Injured Victims to Hospitals-Case Study
University of Tokyo
of Expected Tokyo Inland Earthquake
17.00-17.15
A study of low cost grout for Preplaced Aggregate
Mr. Michael Coo
ConcreteAIT
17.15-17.30
A Study on Strategy for Effective Disaster Information
Mr. Niwat Apichartbutra
Dissemination: Case Study in Thailand
University of Tokyo
17.30-17.45
Review comment
Closing remarks
18.00
Dinner at Sasa International House,
Presentation Award Excellence Presenter Ceremony
Thai 6 speakers (AIT 5, Chula 1)
Korea 3 speakers (Myong Ji University. 2, Seoul National University of Science and Technology 1)
Japan 11 speakers (University of Tokyo 11)
Total no. 20 speakers*10 minute presentation and 5 minute discussion

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6th Joint Student Seminar on Civil Infrastructure
Field trip Program
Date: 7 August 2013
Time
08.00

Descriptions
Meeting at lobby of the Sasa International House- Visit Construction site of new MRT Blue Line (Underground)09.30 -11.00
at Hua Lampong
http://www.mrta-blueline.com/- Hua Lamphong Railway Station
http://en.wikipedia.org/wiki/Hua_Lamphong_Railway_Station
11.30-12.00- Wat Kalayanimit
http://www.encyclopediathai.org/sunthai/rkosin31.htm
Lunch- Kao Sarn Road http://en.wikipedia.org/wiki/Khaosan_Road
12.30-16.00- Wat Ratchanadda
http://bangkokforvisitors.com/ratanakosin/loha-prasat/
16.00
Departing from Wat Ratchanadda
17.00
Returning to Sasa International House
18.00
Transfer to the airport
For the field trip information:- Dress : polite , not sandal shoe, not t-shirt (Visit Construction site)- Proper dresses should be worn, no shorts, no dresses showing bare shoulders(Visit Temple)

Expected No. (Bus)
Japan
Thai
Korea
Total

288

No. of Student
112
13

No. of Faculty
Organizing team 2
4

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