4th Joint Student Seminaron Civil Infrastructures
1-2 August 2011
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
Myongji University, Korea
and
Chonnam National University

Edited by
Dr. Hyunmyung Kim, Dr. Akiyuki Kawasaki, ,Dr. Kyung-Ho Park , Dr. Thirayoot Limanondand Dr. Kunnawee Kanipong

Organization Teams
Prof. Worsak Kanok-Nukulchai (AIT, Thailand)
Dr. Pennung Warnitchai (AIT, Thailand)
Dr. Kyung-Ho Park (AIT, Thailand)
Dr. Kunnawee Kanipong (AIT, Thailand)
Dr. Thirayoot Limanond (AIT, Thailand)
Dr. Masahiko Nagai (AIT, Thailand)
Dr. Mukand Singh Babel (AIT, Thailand)
Dr. Pakawat Sancharoen (SIIT, Thailand)
Dr. Withit Pansuk (Chulalongkorn U, Thailand)Prof. Kimiro Meguro (UT, Japan)
Prof. Haruo Sawada (UT, Japan)
Dr. Akiyuki Kawasaki (UT, Japan)
Dr. Shinji Tanaka (UT, Japan)
Dr. Wataru Takeuchi (JSPS, Thailand/UT, Japan)
Dr. Hyunmyung Kim (Myongji U, Korea)
Prof. Yongtaek Lim (Chonnam National U, Korea)
Prof. Hee-Jung Kang (Konkuk U, Korea)

4th JOINT STUDENT SEMINARON CIVIL INFRASTRUCTURES
August 2011
PREFACEIn this era of rapid globalization, having an international sense andbroad 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 and joint student seminar in July 2008, July2009 and July 2010, respectively, and following this success the “4th Joint
Student Seminar on Civil Infrastructures” was held on 1-2th August, 2011.
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 65, consisting of 2 faculties and 63
students from Chonnam National University, Sirindhorn International
Institute of Technology, Thammasat University, Chulanongkorn University,
The University of Tokyo and Asian Institute of Technology.
On the first day, we had a presentation session, having 2 faculties’
lectures and 16 students’ presentation. The topics covered wide range areasof civil engineering and every student did their best in his/her presentation.
During the seminar, students and faculties had lively exchange of views
beyond their specialty, culture and nationality. At the end of the seminar,
excellent presentation awards were given to the following 4 students.
1. Mr. Sathita Taothong from Thailand
2. Mr. Michael Coo from Thailand
3. Mr. Soti Rajendra from Japan
4. Ms. Sho Oh from JapanOn the second day, we had a field visit to the PRUKSA PRECAST
Company Limited (a Precast concrete factory); visit Ayutthaya (Wat Chai
Wattanaram Temple, Wat Phrasrisanphet, Wat Mahathat, Wat Rat Buranaand Ayutthaya Floating Market)
The seminar was quite successful and fruitful: this seminar gave not
only knowledge and information but also a lot of other stimuli to thestudents. 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.
HYUNMYUNG KIM, AKIYUKI KAWASAKI,KYUNG-HO PARK, THIRAYOOT LIMANOND
AND KUNNAWEE KANIPONG

ICUS Report 57

Table of Contents
Page
Opening Session
SECTION 1

Invited Lectures
Green Highways for Environmental and Economical Sustainable
Development
Kunnawee Kanipong

SECTION 2

1

Student Presentations
3D numerical simulation of stacked twin-tunnel
Viet-Dung Tran

27

A framework of optimal sensor placement in highway traffic operations
Tian Jiang

37

Study of aluminium and magnesium alloy as sacrificial anode for corrosion
protection of structural steels by immersion test
Aris Mahasiripan

49

Seismic retrofitting of non-engineered masonry houses using bamboo-bandmeshSoti Rajendra

63

SSI effect on seismic response of high-rise building in Jakarta, Indonesia
Kullachai Tantayopin73

Study on risks and human evacuation plan in Koto delta area in Japan,
during large flood
Makinodan Kohei

83

Influence of surface complexity to GCPs distribution
Sudniran Phetcharat

93

A preliminary feasibility study of railway construction in Korea
Juhye Kim

105

The use of fly ash concrete with expansive agent on concrete repair
Michael Coo

115

Analysis of mid rise structure made by timber and steel -simulation of
shaking table tests by time history response analysisTachibana Kazuki

139

An empirical analysis of success and failure factors for public-private
partnerships in tool road investments
Sathita Taothong149

4th Joint Student Seminar on Civil Infrastructures

August 2011, Thailand

Effect of gas and oil pipelines in sensitive areas to environmental change
Nakao Yushi

161

Effects of environmental condition on corrosion of structural steel in marine
environment of Thailand
Prasong Permsuwan

169

Application of elastic wave measurement to model tests using benderelements
Wang Xiang

181

Water resources development in Konto basin, Indonesia: is thatenvironmentally sound?
Md. Reaz Akter Mullick

191

A study on long-distance evacuation measure to large-scale flood disasters inJapanese three major metropolitan areas
Nakai Hiroyuki

201

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Registration

Opening ceremony (Prof. Worsak)

Dr. Akiyuki Kawasaki

Prof. Kimiro Meguro

Dr. Kunnawee Kanipong

Mr. Viet-Dung Tran

Mr. Tian Jiang

Ms. Aris Mahasiripan

4th Joint Student Seminar on Civil InfrastructuresiAugust 2011, Thailand

ii

Mr. Soti Rajendra

Mr. Kullachai Tantayopin

Mr. Makinodan Kohei

Mr. Sudniran Phetcharat

Ms. Juhye Kim

Mr. Michael Coo

Mr. Tachibana Kazuki

Ms. Sathita Taothong

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Mr. Nakao Yushi

Mr. Prasong Permsuwan

Ms. Sho Oh

Md. Reaz Akter Mullick

Mr. Nakai Hiroyuki

Closing ceremony (Mr. Masahiko Nagai)

Excellent Presentation from Japan

Excellent Presentation from Thai

4th Joint Student Seminar on Civil Infrastructuresiii

August 2011, Thailand

Presentation Precast concrete factory

Presentation Precast concrete factory

Presentation Precast concrete factoryiv

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Seminar: Group Photo at AIT CC (Morning)

Seminar: Group Photo at AIT CC (Afternoon)

4th Joint Student Seminar on Civil Infrastructuresv

August 2011, Thailand

Field Trip: Group Photo at PRUKSA PRECAST Company Limited

Field Trip: Group Photo at Ayutthayavi

4th Joint Student Seminar on Civil Infrastructures

Invited Lectures

GREEN HIGHWAYS FORENVIRONMENTAL AND ECONOMICALSUSTAINABLE DEVELOPMENT
Kunnawee Kanitpong
School of Engineering & Technology, Asian Institute of Technology,
P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
kanitpon@ait.ac.th1August 2011, Thailand

2

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development3August 2011, Thailand

4

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

5August 2011, Thailand

6

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

7

August 2011, Thailand

8

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

9August 2011, Thailand

10

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

11

August 2011, Thailand

12

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

13

August 2011, Thailand

14

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

15

August 2011, Thailand

16

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

17

August 2011, Thailand

18

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

19

August 2011, Thailand

20

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

21

August 2011, Thailand

22

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development

23

August 2011, Thailand

24

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Green Highways for Environmental and Economical Sustainable Development25

August 2011, Thailand

26

4th Joint Student Seminar on Civil Infrastructures

Student Presentations

3D NUMERICAL SIMULATION OF
STACKED TWIN-TUNNEL
Viet-Dung Tran1
1
School of Engineering & Technology, Asian Institute of Technology,
P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
vietdungtran0324@gmail.com

ABSTRACTIn tunneling construction, one of the most important issues is theprediction of the tunneling-induced ground movement. In this paper, a 3D
simplified numerical simulation is conducted with two key parameters: thepartial released stress ratio αdec and the length of the unlined zone Ldec. The
effects of model parameter αdec are investigated. The tunneling-induced
ground movement from simulated result are analyzed in longitudinal andtransverse directions and compared with the measured data obtained fromthe settlement makers of Bangkok Blue Line subway project. The results of
the numerical simulation may be used for the prediction of tunnelinginduced ground movements in the planned South Blue Line Extension
subway project.
Keywords: tunneling-induced ground movements, numericalsimulation, stacked twin-tunnel, Bangkok subway tunnel

1. INTRODUCTION
The first phrase of Bangkok Blue Line Subway project has been completed
and opened to public in 2004. Due to the thick soft clay layer, Earth
Pressure Balance Shield (EPB) was used to maintain control of the
excavation face in order to prevent the settlement. The tunneling-induced
ground movement can be reduced by applying the balance horizontalpressure compare with Earth pressure. However, control settlement does notstop as the face. In most case, the secondary settlement (after the shield has
passed) is more significantly than the settlement experienced as the machinemines past a point. This secondary settlement depends on different aspects,
such as the over-cut of the shield, the lining properties, the effective of thegrouting, the time delay to install the support system. Tunneling-induced
ground movement in soft clay is the most important issue to undergroundconstruction in urban area. The surface and subsurface ground movements
are significant caused damage to the foundations of existing structures. To
ensure the safety of the underground constructions and adjacent building
when excavated the tunnel, prediction of the extension and the magnitude oftunneling-induced ground movement need to be made.
Numbers of researches have been done to analyze the tunnelinginduced ground movement. Generally, these researches can be divided into27

August 2011, Thailand

3 methods: empirical, analytical and numerical methods. Empirical method
assumes the distribution of ground movement with some coefficients. These
coefficients are determined through fitting field or centrifuge observations(Peck 1969, O’Reilly & New 1982). Meanwhile, analytical method focuseson the boundary conditions around the opening during the tunnel
construction process. The surface and lateral ground movement can be
defined base on the assumption of uniform radial displacement (Sagaseta
1987, Verruijt & Booker 1996) or oval-shaped radial displacement(Loganathan & Poulos 1998, Park 2004, 2005) pattern around the tunnel.
To overcome the limitations of empirical and analytical method, inrecent years, numerical simulation becomes much more common toconsider the excavation in urban area (Leca 1996, Dasari et al. 1996,
Stallebrass et al. 1996, Cheng et al. 2008). However, numerical modelingoften deals with a lot of complex aspects, for example non-linear behaviorof soil, magnitude of face pressure, installation of defining support, soilstructure interaction (soil-tunnel, soil-pile). In order to simplify these
aspects, the combination with the gap due to over-cut of the shield machinelead to the assumption of stress reduction factor. Various authors havesuggested various values of stress reduction factor. Panet (1973) suggested a
33% for the stress reduction factor. Meanwhile, Muir Wood (1975)
recommended 50% for this value according to his closed-form solution.
Negro (1988) found the stress reduction factor by combining the radialdisplacements and a full range of diagrams in a variety of geometric andgeotechnical. Kim et al. (2006) introduced a reliable method to determine
the value of stress reduction factor for different soil strengths, tunnel depth
and in-situ stress ratios. Panet & Guenot (1982) used the stress release factor
λ to simulate for the 3D problem. The method consists of excavation of thecore elements and application of a gradual release of boundary stresses tosimulate the tunnel advance. The in-situ stresses along the excavatedboundary are gradually reduced to a certain percentage of the originalstresses to simulate the installation of a linning at a certain distance from theface. Mrouh & Sharouh (2008) introduced a 3D simplified model technique
to simulate the excavation of a single tunnel. The model bases on
convergence-confinement concept, in which the out-balance forces causedby tunnel excavation are released partially at different construction stages.
This study considers the tunneling-induced ground movement ofvertical stacked twin-tunnel. The 3D numerical simulation is conductedusing 2 keys parameter: partial released stress ratio αdec and the length of theunlined zone Ldec. The case history is chosen from the first phase of
Bangkok Blue Line Subway project (Zone 4T from Lumphini to Bon Kai).
The surface settlements are analyzed in terms of surface settlement at tunnel
face (wa), in the unlined zone (wb) and due to complete released stress (wc).
The simulated result is compared with the measured data.

28

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2. BANGKOK BLUE LINE SUBWAY PROJECT
The first phase of the Bangkok Blue Line Subway project has 20 kilometersof twin-tunnel which is subdivided into 2 main tunnel sections, namely, theNorth Tunnel Section and the South Tunnel Section (Figure 1). The
tunneling construction was done mostly in soft ground, using EPB shield.
Each of twin tunnels is 6.3 m in outer diameter and 5.7 m in inner diameters.
In some areas, the twin-tunnel were stacked over each other in order toavoid pile foundations of existing fly-over bridges along the route. The
lower tunnel (Southbound) was excavated first and then the upper tunnel(Northbound) was excavated. The Northbound was located in the stiff clay
layer, but in some sections where the tunnel was very shallow, the tunnelcrown was located within the soft clay layer. The Southbound mostly was
excavated within the sand layer.

Figure 1: MRT Bangkok Blue Line
During the underground construction, the surface settlement makers
were used to measure the maximum surface settlement at the centerline
during excavation. These makers were installed approximately 50 m interval
along the tunnel alignment. Moreover, in some chosen location, a line ofsurface settlement makers were installed to monitor the ground settlement intransverse direction. The reading was taken overtime covering a period
before the shield approaching and after the shield passing.

3D numerical simulation of stacked twin-tunnel

29August 2011, Thailand

3. NUMERICAL SIMULATION
3.1 Modeling mesh
The analysis described in this paper was performed using finite
different element program FLAC3D. Figure 2 shows the typical modelingmesh adopted from the program. The tunnel axis is along the Y-axis, whichis called the longitudinal direction, X-direction is the transverse direction
and Z-direction is the depth direction. The geometry model for numericalsimulation is designed with the length of 100 m long in the longitudinal
direction, 80 m wide in the transverse direction and 40 m depth in thevertical direction.

Figure 2: 3D modeling meshes
The left and right boundaries are hinged to prevent the movement in
the horizontal direction, but are free to allow the movement in the verticaldirection. The width of left through right boundaries is widely set to avoid
the boundary effect to the perimeter of the tunnel. The bottom boundary is
set in to the stiffest soil material and is fixed against vertical movements.
3.2 Material models and parameters
The constitute law used for the soil elements is elasto-plastic
associated Mohr-Coulomb model. Figure 3 shows the typical soil profileand value of soil properties simulating in FLAC3D. The values of soil
properties are considered based on the soil test results of South Contract inBangkok Blue Line Subway project. Bulk modulus (K) and shear modulus(G) are related to Young modulus (E) and Poisson’s ratio (ν), by following
equations:30

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

K=E
3(1 − 2ν )G=E
2(1 + ν )

(1)

(2)

Figure 3: Soil properties
The water table located at 10 m depth below the ground surface. The
lining and grouting were modeled by using shell element. The lining had 0.3
m of thickness. The properties of the lining are also shown as in Figure 3.
3.3 Tunnel advancement simulation procedure
The simulation procedure, based on force controlled approach, isconsidered in this study using two main parameters: the partial stress releaseαdec and the length of unlined zone Ldec. The value of αdec is varied toinvestigate the effects of this parameter on the tunnel-induced groundmovement. While the value of Ldec is equal to the shield diameter (1D = 6m). The step by step procedure in the simulation of excavation and shieldadvance can be summarized:
In the first step of excavation, (a) remove the ring of soil element
equal to 7 m ahead of the shield (6 m for the length of the shield and thelength of unlined zone (Ldec) and 1 m for the supported zone (Llin) where thelining will be installed), (b) release stress around the tunnel, (c) install lining
1 m at the end of the excavated, (d) grout the removed area and measure the3D numerical simulation of stacked twin-tunnel

31August 2011, Thailand

displacement at the tunnel boundary and at the surface, (e) back fill material
and apply face pressure to prepare for the next excavation.

Figure.4: Step for tunnel advance
Figure 4 shows the steps for tunnel advance: (a) remove the ring ofsoil element ahead of the tunnel face, (b) release the stress using αdec, (c)activate the lining element in removed area with the length of Llin, (d) grout
the removed area, release completed stress and take the measurement ofground movement, (e) apply the pressure at the tunnel face. Then repeat
from (a) to (e).
3.4 Simulation results
Figure 5 shows the numerical results of surface settlement for a single
and twin tunnel (SB&NB), using αdec = 0.2, 0.4 and 0.6, together with themeasured one. Ldec = 6 m is used. In single tunnel, the surface settlement
start to occur -24 m (= -6D) before the shield reaching and continue to 42 m(= 7D) after the shield. The value of surface settlement at tunnel face (wa),in the unlined zone (wb) and due to complete released stress (wc) is
considered and compared with the maximum surface settlement (wmax).
From the ratio of (wa/wmax), (wb/wmax) and (wc/wmax), it can be seen that thesurface settlement mostly occurred at the tunnel face (35 ~ 39 % of

32

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

maximum surface settlement) and in the complete released stress zone (39 ~42 %). In stacked twin-tunnel, the surface settlement already occurred
before the Northbound excavated. The value of this addition surface
settlement is equal to the maximum surface settlement occurred when the
excavation of the Southbound tunnel is finished. This is also the main
reason that the surface settlement in the Northbound mostly occurred at thetunnel face. The ratio of (wa/wmax) shows that 73 ~ 90% of maximum
surface settlement occurred before the shield reaching and at the tunnel face.
5

Surface Settlement (mm)

0
-5
-10
-15
-20
-25
-30
-35
-40

SB
NB
SB (0.2)
SB (0.4)
NB (0.2)
NB (0.4)

-45
-50
-55
-50

-40

-30

-20

-10
0
10
Shield position (m)

20

30

40

50

Figure 5: Simulated results in longitudinal direction (SB&NB)
5

Surface Settlement (mm)

-5

-15

-25

SB (CS-4C)-35

SB (CS-4D)
NB (CS-4C)
NB (CS-4D)-45

SB (0.4)
NB (0.4)

-55
-40

-30

-20
-10
0
10
20
Distance from center line of the tunnel

30

40

Figure 6: Simulated results in transverse direction (SB&NB)

3D numerical simulation of stacked twin-tunnel

33August 2011, Thailand

Table 1: Comparison for parameters of Peck formula
SB
NB
Mroueh &Shahrour (2008)
Attewell (1977)
O’Reilly & New (1982)
Oteo & Sagaseta (1982)

i/D

Vs/Vexc (%)

1.63 ~ 1.72
1.27 ~ 1.49
1.17
1.25
0.68 ~ 1.22
0.77 ~ 1.43

1.66 ~ 2.35
2.59 ~ 3.86
0.26
1~5
0.5 ~ 3The comparison of the surface settlement in transverse direction
between the simulated results and the measured data is shown in Figure 6.
As same as the measured data, the settlement trough is narrow andsymmetric. The maximum surface settlement occurred in the center line of
the tunnel’s alignment. The values of the parameters of Peck formula (Peck,1969) are compared with the values suggested in previous researches, as
shown in Table 1.

4. CONCLUSION
Numerical simulation of vertically stacked twin-tunnel in the BlueLine Subway project has been performed. The simulated results arecompared with the measured data obtain from the settlement maker of Zone
4T in Bangkok Blue Line Subway project. Similar with the measured data,
the simulated surface settlement in longitudinal mainly occurred before theshield reaching and after the shield passing. The settlement trough is narrowand symmetric. The simulated results show that the model parameter αdec
highly affects the maximum surface settlement. As αdec increases, themaximum surface settlement increases. The measured surface settlements
can be ranged within αdec = 0.2 ~ 0.6. The results of the numericalsimulation may be used for the prediction of tunnelling-induced groundmovements in the planned South Blue Line Extension subway project.

REFERENCES
Attwell, P.B. (1977). Ground movements caused by tunneling in soil. 1st
Conference on Large Ground Movements and Structures, Pentech
Press, London, pp 812-948.
Cheng, C.Y., Dasari, G.R., Chow, Y.K., Lueng, C.F. (2008). Finite element
analysis of tunnel-soil-pile interaction using displacement controlledmodel. Tunnelling and Underground Space Technology, 22, 450-466.
Dasari, G.R. (1996). Modelling the variation of soil stiffness during
sequential construction. PhD dissertation, University of Cambridge.
Kim, H.J., Eisenstein, Z., Chae, B.G., Jeong, C.H. (2006). Estimates ofstress reduction factors for the tunnel design. Tunneling UndergroundSpace Technology, Vol 21, pp 1-6.

34

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Leca, E. (1996). Modelling and prediction for bored tunnels. Proceedingsof International Symposium on Geotechnical Aspects of UndergroundConstruction in Soft Ground, Balkema, London, pp 27–42.
Longanathan, N., Poulos, H.G. (1998). Analytical prediction for tunnelling
induced ground movement in clay. Journal of Geotechnical andGeoenvironmental Engineering, Vol. 124 No. 9, pp 846-856.
Mroueh, H., Shahrour, I. (2008). A simplified 3D model for tunnel
construction using tunnel boring machines. Tunnelling andUnderground Space Technology, Vol. 23, pp 38-45.
Muir Wood, A.M. (1975). The circular tunnel in elastic ground.
Geotechnique, Vol.25 No.1, pp 115-125.
Negro, A.Jr. (1988). Design of shallow tunnels in soft ground. Ph.D.
Thesis, Department of civil engineering, University of Alberta.
O’Reilly, M.P., New, B.M. (1982). Settlements above tunnels in the United
Kingdom – their magnitude and prediction. Proceedings of theInternational Conference Tunnelling ’82, Institution of Mining andMetallurgy, London, pp 55-64.
Oteo, C.S., Sagaseta, C. (1982). Prediction of settlements due tounderground openings. International Symposium on Numerical
Methods in Geomechanics, Zurich, 13-17 September, pp 654-659.
Panet, H. (1973). La stabilite des ouvrages souterrauns-soutenement etrevetement. Rapport de recgercge, 28, Paris: Laboratoires des Ponts et
Chaussees.
Panet, M., Guenot, A. (1982). Analysis of convergence behind the face of atunnel. Tunneling 82. The Institution of Mining and Metallurgy, pp
197-204.
Park, K.H. (2005). Analytical solution for tunnelling-induced groundmovement in clays. Tunnelling and Underground Space Technology,
Vol. 20 No. 3, pp 249-261.
Park, K.H., (2004). Elastic solution for tunnelling-induced groundmovement in clays. International Journal of Geomechanics ASCE, Vol.
4 No. 4, pp 310-318.
Peck, R.B. (1969). Deep excavations and tunnelling in soft ground.
Proceedings of 7th International Conference on Soil Mechanics andFoundation Engineering, Mexico City, State-of-the-Art volume, pp225-290.
Sagaseta, C. (1987). Analytical of undrained soil deformations due toground loss. Geotechnique, Vol. 37 No. 3, pp 301-320.
Stallebrass, S.E., Grant, R.J., Taylor, R.N. (1996). A finite element study ofground movements measured in centrifuge model tests of tunnels.
Proceedings of International Symposium on Geotechnical Aspects ofUnderground Construction in Soft Ground, Balkema, London, pp 595600.
Verruijt, A., Booker, J.R. (1996). Surface settlements due to deformation ofa tunnel in an elastic half plane. Geotechnique, Vol. 46 No.4, pp 753756.

3D numerical simulation of stacked twin-tunnel

35August 2011, Thailand

36

4th Joint Student Seminar on Civil Infrastructures

A FRAMEWORK OF OPTIMAL SENSOR
PLACEMENT IN HIGHWAY TRAFFICOPERATIONS
Tian Jiang1, Marc Miska2, Kuwahara Masao3 , Shinji Tanaka4
and Takashi Oguchi5
1
PhD student, School of Eng., The University of Tokyo, Japan
xiaodark@iis.u-tokyo.ac.jp
2
Research Associate (Program Director), Smart Transport Research Centre,
Queensland University of Technology, Australia
3
Professor, Graduate School of Information Sciences,
Tohoku University, Japan
4
Lecturer, ICUS, IIS, The University of Tokyo, Japan
5
Professor, IIS, The University of Tokyo, Japan

ABSTRACT
While research focuses on using available traffic data sources toimprove traffic models and operation systems, less work is devoted
answering the question of how and where to collect traffic data, so that
control systems can perform in an optimal, and cost-efficient manner. In thispaper, we present a framework to assess traffic detection systems, byintroducing the level of detection as value to allow for an objective
comparison of multiple detector placement scenarios. In contrast to existingstudies, proposing new or alternative detector locations based on errors oftravel time or traffic state estimators, our approach is solely based on
network parameters, detection technology, and traffic demand, which allows
the usage of the framework for network operations, as well as planningpurposes. By translating traffic operation goals into data demand functions,and detector capabilities, combined with their location, into data supply
functions, it is possible to optimize detector locations with well know tools
from operations research. The latter one is important, since it allows forincluding additional boundary conditions, such as costs.
Keywords: detection, optimization, highway

1. INTRODUCTION
To ensure the mobility of our cities, dynamic traffic management became
essential to operate existing road networks in the most efficient way.
Models estimate traffic conditions, predict traffic states in the short andlong-term, and provide traveler information to keep the traffic flow as fluid
as possible. Those models are fed with real-time traffic data, collected fromthe roadside or transmitted from probe vehicles in the network, and theirperformance depends strongly on where the data were collected, and ofwhich quality the traffic data are.

37

August 2011, Thailand

Given the importance of the traffic data collection, the detector placement
problem is a relatively less explored research topic in transportation.
Relevant research includes the work done in transportation planning for
obtaining accurate origin-destination trip matrices (Yang et al, 1998) andseveral simulation-based studies identifying the relationship between
detector location and travel characteristics on arterial roads (Sisiopiku et al,
1994; Thomas, 1999; Oh et al, 2003). One real-world traffic data study on
the detector placement problem for freeways (Edara et al, 2008), found that
accurate travel time estimates varied by location and traffic conditions, andthat evenly spaced detectors did not necessarily provided better results.
Common in those studies is that they result in case specific, nontransferable optimizations. The real-world study developed a method,
including GPS data collection and mathematical tools, to effectively
determine preferable detector locations for the objective to minimize thetravel time estimate error. However, no hint was given what effect the newdetector locations would have on incident detection, or how they could
serve for additional purposes such as feeding ramp metering algorithms. The
underlying problem is that these studies optimize the input for a specificalgorithm, instead of actually identifying the information gained from thetraffic detection.
In contrast to those existing studies, our approach aims to identify thatknowledge and to optimize it for various combinations of algorithms.
Further, the framework is solely based on network parameters, detection
technology, and traffic demand, which allow the usage of the framework for
network operations, as well as planning purposes. To achieve this, wetranslate traffic operation goals into data demand functions, and detector
locations and capabilities into data supply functions. Then it is possible tooptimize detector locations with well-known tools from operations research.
This becomes important when considering additional boundary conditions,such as costs, which for a highway detection scheme is often the limiting
factor.

2. DATA SUPPLY FUNCTION
In a first step, we have translated detector capabilities and positions into
data supply functions. For this, we looked at a possible speed profile of alink with an active bottleneck as shown in Figure 1.

38

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 1: Speed profile with categorization of traffic state on a link withactive bottleneck
In free-flow condition, a detector placed anywhere on the link, will
give a good indication of the average speed on the link. The same can besaid for a completely congested link. When however, the link is only partly
congested, the knowledge gained from just one detector is limited. Placed in
the free-flow or congested zone, the validity of the measurement isrestricted to the homogeneous zone, and if placed in either the breakdown oracceleration zone, the information gathered is valid only on the spot. Basedon these assumptions, we can develop a data supply function that indicatesthe gained knowledge based on the traffic state and the detector position as
follows:⎧ϕ n −1,v , s n −1,0 < x < s n −1,1⎪
− s n,0ϕ n −1,vs
⎪ ϕ n −1,v − 1
x + n −1,1,⎪ss n −1,1 − s n,0n − 1,1 − s n ,0⎪⎪
ϕ i ,v = ⎨1, s n,0 < x < sn,1⎪
⎪ ϕ n + 1,v − 1 x + s n + 1,0 − s n,1ϕ n + 1,v ,⎪ sn + 1,0 − s n,1
s n + 1,0 − sn,1⎪⎪⎩ϕ n + 1,v , sn + 1,0 < x < sn + 1,1

s n −1,1 < x < sn,0(1)
s n,1 < x < sn + 1,0

where :
ϕi, v : speed data supply by detector i

ϕ n -1, v : speed data supply at neighbor upstream free - flow or congestion area n - 1
ϕ n + 1, v : speed data supply at neighbor downstream free - flow or congestion area n + 1
s n,0 : transition point from break down or acceleration phase to free - flow or congestion
phase
s n,1 : transition point from free - flow or congestion phase to break down or accelerationphase

ϕ n − 1,v = 1 −| vn − vn − 1 |vn − 1v n : speed in free - flow or congestion area n

ϕ n + 1,v = 1 −| vn − vn + 1 |vn + 1

A framework of optimal sensor placement in highway traffic operations

39

August 2011, Thailand

We assume that each detector has a “valid range” and the speed
collected by the corresponding detector can be used to depict the speed
condition in the “valid range”. A valid range consists of five zones: thehomogeneous zone where the detector placed, two neighbor homogeneouszones (one is in upstream and the other one is in the downstream), twotransition zones (break down or acceleration). The supply function cancompute the detection accuracy in valid range and calculate values ofaccuracy for these five zones. Detection accuracy is assumed to be 100% in
the homogeneous area where the corresponding detector is placed. In thetwo neighbor homogeneous zones, the accuracy is calculated by speed
difference between the real speed and detection speed, which means when
the detector speed is used to depict the speed in its neighbor areas, the moredifference between the real speed and detection speed, the less detection
accuracy obtained. Based on the detection accuracy in three homogeneouszones, data accuracy in transition zones could be calculated by linear
functions to ensure smooth connection between two homogeneous zones.
To illustrate the behavior of the function, Figure 2 shows the knowledge
function for speed along the link.

Figure 2: Speed data supply along the link when one detector is placed incongestion area
To test these assumptions, we have modeled MEX C1 Route of theTokyo Metropolitan Expressway (MEX) in the microscopic simulator
VISSIM (see Figure 3).

40

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 3: MEX C1 Route network in the Vissim simulation
By Running Dynamic Traffic Assignment (DTA) models in Vissim,
we can simulate traffic conditions in MEX. Based on the simulated speed
information, dynamic bottleneck activities and queue lengths can bemonitored in detail. Most vehicles using MEX have onboard devices forelectronic toll collection (ETC). With access to this ETC data, we were able
to determine the real origin-destination (OD) matrices for 30-minute timeintervals for the network. We selected the morning peak from 6am to 9am
as study period.

A framework of optimal sensor placement in highway traffic operations

41

August 2011, Thailand

Figure 4: Traffic state of the C1 network in the morning peakFigure 4 shows the traffic state of a ten-kilometer link with several
bottlenecks in a time space diagram.
With this information and our data supply function definition, wewere then able to determine the real information gathered by a detector at
any given place. Figure 5 shows the example of a detector being placed at4000 meter. The area around the detector location, and bounded by thewhite lines, equals the knowledge space over time. At the beginning of thesimulation period, since most parts of the link is in free flow condition, thedetector can provide accurate speed data up to the end of the link. With thebottleneck active in the downstream, the knowledge area decreases and thedetector can only provide the information up to the nearest queue tail. For
the last 40 minutes, when the queue is back up to the detector location, thedetector starts to collect the speed information in the queue. Since thevehicle speed in the congestion area is fairly constant, the knowledge
increases again. To verify the result, we compared the actual traffic
measurements on MEX with detectors placed along the link, to check thecorrelation between the measurements with the expected correlation fromthe simulated traffic state. The comparison showed, that the prediction of
the queue tail, and hence the correlation of the speed measurements waswell replicated.

42

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 5: Full knowledge map of the link provided by the detector at
location 4000m
Similar to speed data supply, those functions can be found for all
measurement values.

3. DATA DEMAND FUNCTIONS
Compared with data supply functions, data demand functions are
independent from detector types and locations. Data demand is determinedby the way how traffic authorities use the data. Different types of data need
be collected in essential spots and even a whole link to support traffic
management measures or provide travel information to drivers. According
to highway management policies, traffic authorizes decide which traffic
management measures are selected to improve their services consideringcongestion conditions, safety issues and environment impacts. We define ageneral data demand function as:χ i,δ = gη , ρ ( x)where :
χ : traffic data demand [%]x : position [m]i : link i(2)

δ : data type
η : Traffic management measuresρ : Management policies

A framework of optimal sensor placement in highway traffic operations

43

August 2011, Thailand

The data demand functions are static and do not vary with traffic
conditions. The data demand function for a link is the maximum over all
demands at a certain location. In a very crisp definition this would lead to ademand of either 0 or 1 at position x along the link. However, byintroducing weights, based on policies of the road authority, it is possible toemphasize certain demands. We choose several general traffic dataapplications as examples to introduce how to determine the data demand
based on the functions we defined above. Many basic or advanced
methodologies have been developed for traffic management measures. Eventhe same traffic management measure has several algorisms to achieve it,which leads to different data demands. For instance, a lot of practicalmethodologies are developed to improve accuracy and reliability of travel
time prediction. The comparison of data demands for two different
methodologies is shown in Figure 6

Figure 6: Comparison of data demands for two methodologies of travel time
estimation.
Midpoint algorithm is a classical methodology which uses speed
collected at the detectors and the spacing between the detectors. The
midpoint between two neighbor detectors is used to determine the valid
range of the speed data, and theoretically less distance between the detectors
more estimation accuracy is obtained (Kristin et al, 2008). Therefore, as
shown in the upper part of Figure 8, when travel time from A to B needs tobe predicted based on midpoint algorithm, basically all speed information
between A and B are required to fully implement the methodology(CHUNG et al, 2004). On the other hand, some statistical travel time
estimation models do not needs link speed data but passing time data instead.
Based on passing time at location A and B, historical travel time data arerecorded to forecast travel time in the next time period. Apparently different
methodologies for same management measure needs various types of data at
different locations. Therefore the data demands should be carefully
determined based on the methodologies selected for highway management
measures.

44

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

4. EVALUATION AND OPTIMIZATION
A traffic detection scheme is evaluated by comparing the data demand andsupply function, introduced as level of detection:(3)
The value indicates the percentage of the data demand that can besupplied by the sensors placed along a link. This value is objective and agood indicator for planning or changing detection installation plans.
To optimize the detection of the road we have to maximize the levelof detection by moving sensors in the link or by adding sensors, dependingon the budget constraints.
The usual optimization cycle has the following steps:• Determine potential spot for road way sensor deployment and aminimal unit length for potential spots• Determine total number of available detector according to the budget
constrains• Generate an initial set of acceptable samples of detector placement
randomly• According to genetic algorism (GA), randomly crossovers among of
the samples are implemented and an offspring set is obtained.
• Select samples with high LOD as a new parent set to generate newoffspring set.
• Repeat the procedures above to newly generated offspring till a prespecified number of generations

5. PRELIMINARY CASE STUDY
To test the framework under real world conditions we have chosen C1
Route of Tokyo Metropolitan Expressway (MEX) for case studies, due tothe data availability through the International Traffic Database(http://www.trafficdata.info).
C1 Route, located in the center of Tokyo, connects other routes of
MEX network such as Route 3 Shibuya Line, Route 4 Shinjuku Line, Route
5 Ikebukuro Line and etc.. The C1 Route carries a huge amount of traffic
and is highly congested during the rush hours.
As we introduce in the Data Supply Functions section, traffic state
analysis is the key point to determine the speed data supply conditions. We

A framework of optimal sensor placement in highway traffic operations

45

August 2011, Thailand

use actual ETC data as inputs of traffic simulation to identify the speed
distribution in the study area, and a rush hour sample is shown in Figure 4.
As demand for the section firstly we chose travel time estimation for theentire ten kilometer link. By using the optimization methodology weintroduced in Evaluation and Optimization Section, the optimal sensor
placements are determined with limited number of detector automatically as
shown in Figure 7

Figure 7: Optimal detector locations of a ten kilometer link on MEX C1
Route for travel time estimation.
According to the traffic state visualized in Figure 4, we can observe
the Frequency of detectors placed in a congested area (from 2500m to7500m) is higher than other parts of the link. To verify the placement results,
we compare level of detection (LOD) for optimal placements with LOD foreven spacing placements as shown in Figure 8

Figure 8: Comparison of optimal placements and even spacing placements

46

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

LOD for optimal placements are higher than simply even spacing
placements, particularly when the number of detectors are quite few such as
the total number of available sensors are 3 or 4.

6. DISCUSSION
In this paper we have introduced the concept of network and link knowledge
maps that represent the information that can be gathered from a network bymonitoring only. To allow this, we have introduced data supply functions,which determine the coverage area in which they gather reliable information.
Combining this information with historical data and incident statistics, leads
to a solid basis for detector location optimization. While a link based view
seems very straight forward, the complexity increases quickly when
extending to network scale, and by adding more requirements and data
supply functions. Future work will include the addition of more data supply
functions, and the extension of the link based method to a network based
methods, which will require link boundary conditions. Further, with
commonly accepted methodologies for estimating measured values based on
local measurements, we will include knowledge maps that can consider
estimated information in the optimization.

REFERENCES
Yang, H., and Zhou, J. Optimal Traffic Counting Locations for
Origin-Destination Matrix Estimation. Transportation Research Part B, Vol.
32, 1998, pp. 109-126
Sisiopiku, V.P., Rouphail, N.M., and Santiago, A. Analysis ofCorrelation Between Arterial Travel Time and Detector Data from
Simulation and Field Studies. Transportation Research Record, No. 1457,
1994, pp. 166-173
Thomas, G. The Relationship Between Detector Location and Travel
Characteristics on Arterial Streets. Institute of Transportation Engineering
Journal, Vol. 169, 1999, pp. 36-42
Oh, S., Ran, B., and Choi, K. Optimal Detector Location for
Estimating Link Travel Speed in Urban Arterial Roads. In Proceedings of
the 82nd Annual Meeting of the Transportation Research Board,
Washington DC, 2003
Edara, P., Guo, J., Smith, B. L., McGhee, C., Optimal placement ofpoint detectors on Virginia’s freeways: Case studies of Northern Virginia
and Richmond, Report No. VTRC 08-CR3, Virginia Transportation
Research Council, Richmond, 2008
Kristin Tufte, Sirisha Kothuri, Report No: OTREC-RR-08-02
Assessment and Refinement of Real-Time Travel Time Algorithms for Use in
Practice, Final Report October 2008
Chung, E., Warita, H., Bajwa, S. and Kuwahara, M. (2004). Travel
time prediction: issues and benefits. 10th World Conference on Transport
Research, (CD), Istanbul, Turkey

A framework of optimal sensor placement in highway traffic operations

47

August 2011, Thailand48

4th Joint Student Seminar on Civil Infrastructures

STUDY OF ALUMINIUM AND
MAGNESIUM ALLOY AS SACRIFICIALANODE FOR CORROSION PROTECTION
OF STRUCTURAL STEELS BYIMMERSION TEST
Aris MAHASIRIPAN1, Ekkarut VIYANIT2*, Wanida PONGSAKSAWAD2,
Benya CHERDHIRUNKORN2,3, Somnuk TANGTERMSIRIKUL4,5 andPakawat SANCHAROEN5
1

Graduate Student, School of Civil Engineering and Technology (CET),
Sirindhorn International Institute of Technology, Thammasat University,
Pathumthani 12121 Thailand
amrissy@hotmail.com
2
Researcher, National Metal and Materials Technology Center (MTEC), 114
Thailand Science Park, Pathumthani 12120 Thailand
3
Researcher, Material Science Major, Department of Physics, Faculty of
Science and Technology, Thammasat University, Thailand
4
Professor, School of Civil Engineering and Technology (CET), Sirindhorn
International Institute of Technology, Thammasat University, Thailand
5
Researcher, Construction and Maintenance Technology Research Center(CONTEC), Sirindhorn International Institute of Technology, Thammasat
University, Pathumthani 12121 Thailand

ABSTRACTIn this paper, structural steel, which is hot-rolled steel grade SS400
according to JIS G3101 was determined corrosion rate and corrosion
behavior of material by potentiostatic polarization technique. In this test,
real seawater and artificial seawater were used as the electrolyte solution.
Real seawater was collected from Gulf of Thailand. And artificial seawater
was synthesized by dissolving sodium chloride (NaCl) that is comparable interm of Cl- ion concentration to that of the real seawater. The hot-rolled
steel grade SS400 attached with aluminium and magnesium sacrificial
anode were determined corrosion rate by immersing in the artificial
seawater for a period of 1 month. The results of surface morphology showedthat hot-rolled steel grade SS400 attaching with aluminium sacrificial
anode has denser surface morphology than specimen attached withmagnesium sacrificial anode. So aluminium anode can protect steel
corrosion in marine environment better than magnesium anode.
Keywords: Corrosion, Cathodic Protection, Sacrificial Anode,
Carbon Steel, Marine Environment

49August 2011, Thailand

1. INTRODUCTION
Currently, the steels are widely applied to build the many engineering
structures such as pipelines and marine structures including ships,
submarines and offshore structure. These structures are normally located in
the soil and seawater. So they are rapidly deteriorated by corrosion. The
corrosion is a major problem affecting safety and serviceability of thestructures because it causes reduction of sectional area of the steel members.
Consequently, consideration of corrosion control methods are essential for
maintain the serviceability of the structures.
The problems of corrosion become a crucial for structural steel in marine
environment. Structures should be well maintained for its designed life.
Corrosion science, protective engineering and galvanic corrosion areimportant topics. Cathodic protection is one of the ways to protect structuralsteel from corrosion which has mainly two categories: impressed current
cathodic protection (ICCP) and galvanic anode cathodic protection. ICCP
has to applied current for protecting steel that it has apply. Graphite, copperor platinum are used as anode in this systems. In contrast, galvanic anodecathodic protection or sacrificial anode doesn’t need applied current tosystem. Sacrificial anode was used as material for giving electron to protect
structural steel which is a cathode by electrochemical method.
Cathodic protection technique is not widely used in Thailand because of its
complicating both in design and application. Former steel structures are only
constructed in atmospheric. Due to expansion of economic system in the
recent years, marine and underground structures such as coated facilities andpipelines made from steels, are extensively constructed in the industrial area.
Sacrificial anodes are initially applied to protect corrosion in Thailand.
However, it is essential to study the protective behaviour of sacrificial
anodes to protect structural steels in Thailand.
Aluminium is popular to be used for making sacrificial anode because of its
high current capacity, low cost, long life and light weight. Magnesium
anode has highly negative potential and dissolve too rigorously in seawater.
But it is useful to protect structure in high electrically resistivity
environment. AZ91D magnesium alloy is among the best lightweight
structural materials with a relatively high strength to weight ratio. Therefore,
magnesium attracts special attention of researchers working in automotive
and aircraft industry [1]. This is because magnesium alloy is the mostactive metal in the galvanic series [2], and a magnesium alloy component isalways the active anode if it is in contact with other metals.
Aluminium alloy anode or Al-Zn-Mg alloy is one of sacrificial anode forthis experiment which suitable for cathodic protection. It was developed in
the recent years. The system is more efficient with respect to the superficial
activation of anode (preventing the formation of superficial aluminium
oxide film) [3].

50

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2. EXPERIMENTAL
2.1 Materials
2.1.1 Steels
The specimens for this study were hot-rolled steels grade SS400 according
to JIS G3101, SM490YA according to JIS G3106 and SMA490A according
to JIS G3114. The surface areas of specimen were 20 cm2 and 40 cm2.
Specimen for immersion test is SS400 hot-rolled steel which was weldedwith copper cable by current power of 90 V. Then specimen was coated all
surfaces except the top surface by epoxy resin as shown in figure 1.

Figure1: Cathode specimen before immersion test
2.1.2 AnodeTwo types of sacrificial materials were used as an anode: AZ91D
magnesium alloy ingot and aluminium alloy (Al) ingot. All specimens were
grinded by emery paper No.80 – 600 before tested corrosion behavior [4].
Specimen for immersion test is AZ91D magnesium anode and aluminium
anode in figure 2 which was drilled by screw with copper cable for attaching
between cable and specimen then specimen was coated all other surfaces
except the top surface by epoxy resin as shown in figure 3.
Aluminium anode

Magnesium anode

Figure 2: Anode specimen

Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
51

August 2011, Thailand

Figure 3: Anode specimen before immersion test
2.2 Testing
2.2.1 Spark emission testing
SS400 hot-rolled steel specimen size 150 mm and 150 mm was grinded by
emery paper No. 80 before tested chemical compositions by spark emission
spectrometer2.2.2 Microstructure testing
SS400 hot-rolled steel, AZ91D magnesium alloy and aluminium alloy were
cut with the gauge diameter and length to the size of 15 × 15 mm. It were
grinded by emery paper No. 80 – 4000 and polished by diamond paste 1 – 9
μm. Then it was etched by 6% by volume nitric acid for measuring
microstructure at 200X by optical microscope (OM). Specially AZ91D
magnesium alloy must measured microstructure and was analyzed its
element by Scanning electron microscope (SEM) at 2000X together with
Energy dispersion spectrometer (EDS) technique.
2.3 Analysis of the polarization curve
Polarization curve was conducted to determine corrosion rate byelectrochemical testing and use potentiostatic technique to measured
potential and current density of materials. AZ91D magnesium alloy waschosen because it give electron more than aluminium alloy. In thisexperiment, AZ91D magnesium alloy, aluminium alloy and SS400 hotrolled steel were used as working electrode (WE). Saturated calomelelectrode (SCE) was used as a reference electrode (RE), and platinum rod(Pt) was used as electric path. Anodic potentiostatic polarization of carbon
steel and weathering steel electrode with actual sea water as the solution
was achieved using potentiostatic /galvanostat EG&G 273A at a rate of
0.167 mVs-1.
2.4

Solution preparation

Electrolyte solution for this study is real-seawater from Gulf of Thailand
which was determined its ion concentration by Ion Chromatography (IC).
Real-seawater from 5A has the highest chloride ion concentrative. Then
artificial seawater was prepared by dissolve sodium chloride (NaCl) in deionized (DI) water to be same chloride concentration as in real-seawater.

52

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2.5

Immersion testingHot-rolled steel grade SS400 was used as a cathode, AZ91D magnesium
alloy and aluminium alloy were used as a sacrificial anode which were
immersed in artificial seawater as shown in figure.4

CathodeAnodeFigure 4: Installation specimen for immersion test
2.6

Surface analysis

2.6.1 Sacrificial anode
After immersion test anode specimen was cleaned by chromium trioxideand phosphoric acid (CrO3 + H3PO4) with 85 degree celcius and 10 minutein G1 standard for cleaning specimen.
2.6.2 Steels
After immersion test specimen was cleaned by 50% hydrochloric acid (HCl)
10 minute and then dry out the moisture. Specimen was studied surface
morphology by Scanning Electron Microscope (SEM).
3 RESULTS AND DISCUSSION
3.1

Materials selection and physical properties

Chromium (Cr), nickel (Ni) and silicon (Si) element content can increase
corrosion resistance of structural steel in marine environment. Table 1
showed that chemical composition of SS400 hot-rolled steel compare toothers steel: SM490A and SM490YA, SM490A has Cr content higher thanothers. It is the highest corrosion resistance. SS400 have the lowest Cr
content makes it is the worst corrosion resistance. So it should install
sacrificial anode to protect corrosion and failure in this study.

Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
53

August 2011, Thailand

Table 1: Chemical composition of three types of structural steels.
Chemical composition (wt %)Material
C

Si

Mn

P

S

Cr

JIS G3101

0.14-0.22≤ 0.300.30-0.65≤ 0.045≤ 0.050-SS400

0.047440.17401

1.03980.010120.00606

0.02572

JIS G3114≤ 0.20≤ 0.55≤ 1.60≤ 0.035≤ 0.035-

SM490A

0.11499

0.396190.36757

0.076080.00503

0.68137

JIS G3106≤ 0.20≤ 0.55≤ 1.60≤ 0.035≤ 0.035-

SM490YA

0.070250.21799

0.352440.00887

0.015630.03082

Figure 5 showed microstructure of structural steel. SS400 hot-rolled
steel has many grain boundaries and has ferrite phase increasing corrosionof SS400 hot-rolled steel.
SS400

SM490A

SM490YA

Figure 5: Microstructure of three types of steels by optical microscope 200X
Figure 6 showed that microstructure of sacrificial anode both of Al
anode and Mg anode Microstructure of aluminium alloy and magnesium
alloy are same that have three phase consists of matter, solid solution (Aland Mg compound or phase alpha) and phase of aluminium element orphase beta. Phase beta (β) and phase alpha (α) are found in microstructure.
Alpha phase increases corrosion resistance because it has more mixing
compound that has aluminium and magnesium content than others phase.
Aluminium alloy has higher corrosion resistance than magnesium alloy
because aluminium alloy has higher amount of alpha phase.
Aluminium anodeβ

Magnesium anodeααβFigure 6: Comparing microstructure between aluminium alloy andmagnesium alloy.
3.2

Real-seawater analysis

Table.2 showed the result of anion concentration in real-seawater
determined ion by ion chromatography (IC). Rack location of real-seawater

54

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

has chloride ion, sulfate ion bromide ion and fluoride ion are a lot higher
than other locations.
Table 2: Anion concentration in real-seawater from Gulf of Thailand.
Location of realseawater
Coastline
In the sea
Middle between
coastline and sea

3.3

Chloride18821.87
19231.71

Ion concentration (ppm)
SulfateBromide3041.6058.21
3088.4559.65

19586.46

3147.58

60.75

Fluoride
4.09
4.06
4.13

Corrosion behavior of materials

3.3.1 Steel tested in real-seawater
Polarization curve can describe the current density and potential of materials
behavior as shown in figure.7. It was shown in table.3 that polarization
curve of SS400 hot-rolled steel which has the highest corrosion current
density about 0.77 × 10-6 A/cm2 and corrosion potential about -0.7092 volts.
Analyzed corrosion rate by tafel slope between cathodic polarization curveand anodic polarization curve, was 0.008 mmpy. When chloride
concentration is increased in location of seawater can effect to corrosion
current density and corrosion potential of polarization curve is increased
too. Due to corrosion rate of materials which tested in seawater is increased.

Figure 7: Example of polarization curve of structural steel grade SM490YA
hot-rolled steel in real-seawater

Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
55

August 2011, Thailand

Table 3: Corrosion potential and current density of three types of structuralsteels in real seawater.
Location

Coastline

In the sea

Middle between
coastline and sea

Ecorr

Icorr

Corrosion Rate

(V)

(A/cm2)(mmpy)

SS400-0.8821

4.62E-06

0.0440SM490A-0.8462

0.05E-06

0.0015

SM490YA-0.7005

0.95E-06

0.0099SS400-0.8799

7.06E-06

0.0736

SM490A-0.8672

0.58E-06

0.0060SM490YA-0.7092

0.77E-06

0.0080SS400-0.8852

6.28E-06

0.0654

SM490A-0.6778

0.18E-06

0.0018SM490YA-0.7088

0.19E-06

0.0021

Materials

3.3.2 Steel tested in artificial seawater
Polarization curve can describe the current density and potential of materials
behavior as shown in figure.8. It was shown in table.4 that polarization
curve of SS400 hot-rolled steel which has the highest corrosion current
density about 8.18 × 10-5 A/cm2, corrosion potential about -0.6315 volts andcorrosion rate was 0.752 mmpy. So that SS400 hot-rolled steel has to beprotected by cathodic protection to extend its service life in marine
environment.

Figure 8: Polarization curve of structural steel grade SM490YA hot-rolled
steel in 3.22%NaCl

56

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Table 4: Corrosion potential and current density of three types of structuralsteels in artificial seawater.
Location

Coastline

In the sea

Middle between
coastline and sea

Ecorr

Icorr

Corrosion Rate

(V)

(A/cm2)(mmpy)

SS400-0.6367

6.47E-05

0.752

SM490A-0.6523

4.64E-05

0.539

SM490YA-0.6430

7.38E-05

0.858

SS400

-0.6313

8.18E-05

0.951

SM490A-0.6439

5.64E-05

0.655

SM490YA-0.6275

9.13E-05

1.060

SS400

-0.6394

7.03E-05

0.817

SM490A-0.6657

4.50E-05

0.523

SM490YA-0.6438

7.39E-05

0.859

Materials

All results of polarization curve of steel in real-seawater and artificial
seawater, corrosion rate are shown in table.3 and table.4. They are shownthat steel tested in artificial seawater has corrosion rate more than tested it inreal seawater. This is due to lower pH and higher conductivity of artificial
seawater as explained later. The results conflict theory that corrosion ratefrom laboratory should less it than from real seawater. However, candiscuss the results cause of pH and conductivity of electrolyte. pH is lesswhich is effected to high corrosion rate and conductivity is more effected tohigh corrosion rate too.
3.3.3 Anode tested in artificial seawater- Magnesium anode
Figure 8 showed that polarization curve of AZ91D magnesium alloy
which has potential approximate -1400 mV vs SCE and corrosion current
density about 1 × 10-7 A/cm2.

Figure 8: Polarization curve of AZ91D magnesium alloy in 3.22%NaCl
artificial seawater.
- Aluminium anode
Figure 9 showed that corrosion potential of aluminium alloy was around 1250 mV and less than that of magnesium alloy because aluminium alloy
gives electron less than magnesium alloy.
Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
57

August 2011, Thailand

Figure 9: Polarization curve of aluminium alloy in 3.22%NaCl artificial
seawater.
When comparing polarization curve between magnesium alloy andaluminium alloy, polarization curve of aluminium alloy has passive film
more stable than magnesium alloy. Therefore, aluminium alloy tend to show
higher corrosion resistance than magnesium alloy also aluminium alloy has
weight loss less than magnesium alloy which is suitable for making
sacrificial anode in marine environment.
Table 5: Corrosion potential and current density of three type of aluminium
anode and magnesium anode in artificial seawater.
Location
Middle between
coastline and sea

3.4

Materials
Magnesium anode
Aluminium anode

Ecorr (V)
-1.4710-1.2264

Icorr (A/cm2)
1.87E-05
9.72E-06

Corrosion Rate (mmpy)
1.64
1.48

pH and conductivity of solution

Real-seawater and artificial seawater were analyzed their pH andconductivity by water analyzer. Result showed that real-seawater from threelocations have higher pH than artificial seawater tested in laboratory as
shown in table.6. So corrosion rate of structural steel in artificial seawater ismore than tested in real-seawater because artificial seawater has low pH.
But, conductivity of real-seawater solution is lower than artificial seawater.
It means that when testing corrosion in artificial seawater gives a higher
corrosion rate than tested in real-seawater.
Table 6: Comparing pH and conductivity between real-seawater andartificial seawater
Conductivity
SeawaterPosition

pH
(mS)

Shore

7.30

66.4

7.35

68.9

7.38

69.2

5.42

72.3

[3.14%NaCl]
Far
Real-seawater[3.17%NaCl]
Rack[3.22%NaCl]

Artificial-seawaterRack[3.22%NaCl]

58

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

3.5

Immersion testing analysis

In this section, relationship between corrosion current and time of
immersion in artificial seawater was explained. As shown in figure.10 steel
was attached with Al alloy showed more stable current exchange thanspecimen attached with Mg alloy from figure.11. The current exchangemeans that both of anode can reduce corrosion rate of steel, but aluminium
anode has lower maintenance cost than magnesium anode.

Figure 10: Relationship between day of immersion and current exchange
from aluminium anode to protected cathodeFigure 11: Relationship between day of immersion and current exchange
from magnesium anode to protected cathode3.6

Surface morphology

3.6.1 Sacrificial anode
After immerse steel in artificial seawater is found that magnesium anode
was used up, but aluminium anode was remained. Then surface was cleaned
by CrO3 + H3PO4 to measure weight loss of anode and surface morphology.

Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
59August 2011, Thailand

Table 7: Surface morphology of sacrificial anode which is attached bydifferent amount of aluminium anode after immersion test
SurfacepreparingAluminium anode

Magnesium anode

Beforecleaning

Aftercleaning

3.6.2 Steels
Figure 12 showed that surface morphology of SS400 hot-rolled steel whenhas no sacrificial anode to reduce corrosion. Rusting on clearly be observedon the surface.
Table 8 showed the surface morphology of steel when it was protected byaluminium anode at difference anode to cathode ratio. Area ratio between
steel to anode as 1:20 is more effective than 1:40 ratio because anode cangive more electron to steel.

Figure 12: Surface morphology of SS400 hot-rolled steel without sacrificial
anode
Table 8: Surface morphology of SS400 hot-rolled which is attached bydifferent amount of aluminium anode
SurfacepreparingAl : Steel = 1 : 20

Al : Steel = 1 : 40

Before cleaning

After cleaning

Table 9 showed that surface morphology of SS400 hot-rolled steel
when using magnesium sacrificial anode to protect corrosion. Surface

60

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

morphology of specimen with magnesium anode was insufficient to protect
steel from corrosion. This is due to the fast consuming of magnesium anodein seawater.
Table 9: Surface morphology of SS400 hot-rolled which is attachedby different amount of magnesium anode.
Surface preparing

Mg : Steel = 1 : 20

Mg : Steel = 1 : 40

Before cleaning

After cleaning

Table 10 showed the result of measured during weight loss. It is
obtained by calculate current measured during immersion testing in the
duration of 30 days. Weight loss can be calculated by current flow fromsacrificial anode to cathode. Equation.1 is used to calculate weight loss
I ×t × M
W =……….Equation.1
n× F
W = Weight loss of steel (g)I = Current (A)M = Atomic weight for iron equal to 55.85 g/mol
n = number of exchanging electron, equal to 2
F = Faraday’s constant is equal to 96500 coulombs/molt = Time (second)

Weight loss from equation.1 is used to calculate corrosion rate by equation.2
for predict service life of sacrificial materials.
87600W
Corrosion Rate =……….Equation.2
DAT
W = weight loss from equation.1 (mg)
D = Density of steel (A/cm2)
A = Surface area of steel (cm2)
T = Immersed time (s)Table 10 showed that weight loss and corrosion rate of aluminium
anode and magnesium anode. Alumiunium anode is consumed less thanmagnesium anode. Then aluminium anode can use last longer thanmagnesium anode to protect corrosion of steel in cathodic protection by use
sacrificial anode in marine environment.

Study of aluminium and magnesium alloy as sacrificial anode for corrosion protection of structural steels by
immersion test
61August 2011, Thailand

Table 10: Weight loss and corrosion rate when using sacrificial anode
Sacrificial anode typeArea ratio between
anode and cathode

Weight loss per
30 day (mg)Corrosion rate
(mmPY)

1 : 20564.050.4359

1 : 401713.16

0.66201 : 20120.0093

1 : 40450.1739Magnesium alloy

Aluminium alloy

4 . CONCLUSION

This study can conclude that structural steel grade SMA490A has corrosion
rate less than SS400 hot-rolled steel because it has Cr content higher thanothers. So it is necessary to improve SS400 hot-rolled steel which can beused as structural steel in marine environment due to its lower cost thanothers. Cathodic protection was used to protect steel from corrosion.
Aluminium anode can be used to protect steel same as magnesium anodebut magnesium anode is consumed at the higher rate. Therefore, aluminium
alloy is suitable more than magnesium alloy to protect steel corrosion inmarine environment.
ACKHOWLEDGEMENTS

The study was supported by Failure Analysis of Metal and Materials
Corrosion (FAMC) laboratory, National Metal and Materails Technology
Research Center (MTEC) a member of National Science and Technology
Development Agency (NSTDA).
REFERENCES
A. Eliezer, E.M. Gutman, E. Abramov and Ya. Unigovski., 2001.
Corrosion fatigue of die-cast and extruded magnesium alloys, Journal oflight metals1, 1(1): 179-186.
G. Song, B. Johannesson, S. Hapugoda and D. StJohn., 2003.
Galvanic corrosion of magnesium alloy AZ91D in contact with analuminium alloy, steel and zinc, Corrosion science, 46: 955-977.
M.A. Talavera, J. Juarez-Islas and J. Genesca., 2003. Electrochemical
testing of Al-Zn-Mg sacrificial anodes, Corrosion science, 46: 955-977.
ASTM G5, 1999, Standard reference test method for making
potentiostatic and potentiodynamic anodic polarization measurement.

62

4th Joint Student Seminar on Civil Infrastructures

SEISMIC RETROFITTING OFNON-ENGINEERED MASONRY HOUSESUSING BAMBOO-BAND MESH
Rajendra SOTI1, Sathiparn NAVARATNARAJ2, Muneyoshi NUMADA3
and Kimiro MEGURO4
1

Graduate Student, Department of Civil Engineering, University of Tokyo, Japansoti@risk-mg.iis.u-tokyo.ac.jp
2
Post-Doctoral Research Fellows,
Institute of Industrial Science (IIS), University of Tokyo, Japansakthi@risk-mg.iis.u-tokyo.ac.jp
3
Research AssociateInternational Center for Urban Safety Engineering (ICUS), IIS, University ofTokyo
numada@iis.u-tokyo.ac.jp
4
Professor, Director, ICUS, IIS, University of Tokyo, Japanmeguro@iis.u-tokyo.ac.jp

ABSTRACTThe collapse of unreinforced masonry structures, which are widelydistributed around the earthquake prone regions of the world, is one of thegreatest causes of death in major earthquake events. This paper presents aninnovative retrofitting method for masonry structures, which uses bamboo
band arranged in a mesh fashion and embedded in a mortar overlay. In
order to determine the effectiveness of the proposed retrofitting technique,shake table tests were conducted using retrofitted and non-retrofitted 1/4
scaled masonry houses and each house was subjected to sinusoidal groundmotion inputs. Based on the experimental results, the retrofitted specimen
exhibited good seismic performance by withstanding a more than twice
input energy than non-retrofitted specimen.
Keywords: unreinforced masonry, bamboo-band mesh, shaking tabletest

1. INTRODUCTION
The collapse of the unreinforced masonry building induced by theearthquake events is one of the greatest causes of the human casualties
around the world. The failure of unreinforced masonry structurescontributes to more than 60 % of the structural damage of masonry
structures (Mayorca and Meguro 2004). Around 30 % of the world's
population live in adobe construction (Houben & Guillard) and large
proportion of the structures are located in earthquake prone regions. Thus,
strengthening of unreinforced masonry structure is indispensable need toreduce the casualties significantly.

63

August 2011, Thailand

Till date, several types of retrofitting methods have been developedfor unreinforced masonry structures. Retrofitting technique for developing
countries should consider not only the effectiveness in terms of seismic
performance but also the issues like economic viability, cultural adoptability
and material as well as technological availability. Under the aforementioned
circumstances, PP-Band Retrofitting Technique is one of the appropriate
retrofitting techniques and different aspects of this method have alreadybeen researched in Meguro Laboratory, in the Institute of Industrial Science,
The University of Tokyo. On the other hand, another strengthening
technique, which uses bamboo band meshes as a strengthening system, hasbeen proposed and different aspects are being researched in Meguro
laboratory.
Bamboo-band retrofitting technique is simple enough to be understoodand applied by layman without any prior expertise. Shake table tests werecarried out to understand the dynamic response of masonry buildings, crack
propagation, failure mechanism, and overall effectiveness of the newly
developed retrofitting technique.

2. EXPERIMENTAL PROGRAM
2.1. Specimen Details
Two models were built in the reduced scale of 1:4 using the un-burnt
bricks as a masonry units and cement, lime and sand (1:2.8:8.5) mixture as
mortar with c/w ratio of 0.33. Even though the materials used were fromJapan, great attention was paid to make the models as true replica of brickmasonry building in developing countries in terms of masonry strength.
Both models represented a one-story building with roof. As Figure 1 shows,the dimensions of both buildings were 950mm×950mm×720mm with 50mm
thick walls and the sizes of door and window in opposite walls were
243mm×485mm and 325mm× 245mm, respectively. The size of the adobe
brick used was 75mm×50mm× 35mm. Surface finishing was applied bothon retrofitted and non-retrofitted buildings. These two building wereidentical in terms of geometry, construction materials, mix proportion,
construction process and technique and other conditions that may affect thestrength of the model house. The cross section of the band used was
8mm×0.75mm and the mesh pitch was 40mm. Surface finishing was applied
to both specimens.

64

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure1: Model dimension (mm) (without roof)
2.2. Retrofitting Procedure
Bamboo band mesh was first prepared on a square grid in a way thatone band crosses over another band in different layers at subsequentcrossing points. This process was quite similar to the basket weaving
process. The straw, which was used to ensure hole during model
construction, was removed. Straw was placed at approximately 200 mm
pitch. In case of existing structures holes can be prepared by drilling throughthe wall. The prepared mesh was then installed both on outside and inside of
the wall and wrapped around the corner of the house. The inside and outside
meshes were connected by the Polypropylene strings (PP strings) whichwere passed through the hole. The overlapping and wrapping of the meshes
was also made around the opening and roof. Figure 2 illustrates the overall
retrofitting procedure.

1. Preparing bamboo-bandmesh in size
2. Securing holes using thestraw
duringmodel
construction
2

1

3. Wrapping the building
from inside and outsideby the mesh3

4. Connecting inner andouter meshes by PP string
and overlapping aroundthe opening and roof
4

Figure 2: Retrofitting procedures

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh

65August 2011, Thailand

2.3. Instrumentation
The test was carried out in the shaking table facility available in theInstitute of Industrial Science, the University of Tokyo. The size of theshaking table is 1.5m X 1.5m. It has six degrees of freedom and operates infrequencies ranges from 0.1 to 50 Hz. It has a maximum displacement
capacity of ± 100 mm and the maximum weight of the specimen that can betested is 2 tons.
2.4. Input motions
Sinusoidal motions of frequencies ranging from 35 Hz to 2 Hz andamplitude ranging from 0.05g to 1.4g were applied to obtain the dynamic
response of both retrofitted and non-retrofitted structures. Figure 3 shows
the typical shape of the applied sinusoidal wave. The number of cycles wasconstant for all frequencies. Thus, lower frequency input motion had longer
duration.

Figure 3: Input Sinusoidal motion
Loading was started with a sweep motion of amplitude 0.05g andfrequency ranging from 2 to 50 Hz for identifying the dynamic properties ofthe models. The sequence of loading is given in Table 1. The numbers intable indicate the run numbers. General trend loading was from higher
frequency to low frequency and from lower amplitude to higher amplitude.
Higher frequencies motions were skipped towards the end of the runs.

3. CRACK PATTERN AND FAILURE BEHAVIOUR
At the end of each sinusoidal ground motion, inspection of the specimen
was carried out. In addition, observed cracks were marked to highlight theirlocations. The crack formation for both specimens is shown in Figure 4 andFigure 6 after 42nd run of input motion. The initial crack patterns for both
specimens were similar. However, these cracks widen in each successive
loading in case of non-retrofitted model and new cracks appeared andpropagated in the retrofitted model. For non-retrofitted model, no major
crack was observed up to run 25. Initial crack was appeared from Run 26.
At run 26; minor cracks were observed close to connection between roof

66

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

and south wall. Run 31 caused crack in point close to connection between
roof and south and north wall. Similar cracks were also observed in top ofeast wall and its adjacent wall. ’X’ shaped cracks were observed in south
wall
Table 1: Loading sequence
Frequency (Hz)Amplitude(g)
2 5 10 15 20 25
1.4
50
1.2
54 49
1.0
48
0.8
53 47 43 40 37 34
0.6
52 45 42 39 36 33
0.4
51 44 41 38 35 32
0.2
46 25 24 23 22 21
0.1
18 17 16 15 14 13
0.05
10 09 08 07 06 05
Sweep
01,02

30 35

31
30
29
20
12
04

28
27
26
19
11
03

Loading sequence
For both specimens

Loading sequence
After non-retrofitted
specimen
collapsedcompletely

In run 33, cracks from the corner of the door opening propagated up to
the top layer of the wall. Existing cracks appeared from the previous run
were propagated up to the bottom of the wall at run 38. Run 40 caused thefalling of surface finishing from south wall. Large damages were observedin the run of 43 at which separation between east wall and its adjacent walls
was occurred with the significant detachment of surface finishing from thewalls. The run 44 caused the total separation of top part of the East-North
corner from the specimen. At run 45, all the top part of the north and south
wall totally separated from the specimen and roof was totally supported byeast and west walls which are perpendicular to the shaking direction. The
run 47 led the non-retrofitted building to total collapse (see Figure 5).

East

North

West

South

Figure 4: Crack patterns of non-retrofitted building model after run 42

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh

67August 2011, Thailand

Figure 6: Non-retrofitted building model after run 46 (L) and 47(R)In case of the retrofitted building model, similar cracks in the case of
non-retrofitted building started from top corner of the door opening in therun 27. Run 28 caused the propagation of the existing vertical cracks to the
top corner of the door opening. In addition, some vertical and diagonal
cracks were also observed around the window opening. The new inclined
cracks were appeared in south wall at the run 40.Lots of cracks wereobserved at run 43. The inclined cracks originated from the corner of thewindow opening were extended to the top and bottom layer of the wall. ’X’
shaped cracks were appeared in north and south wall with few detachmentof surface finishing from the specimen. At run 47, most of the existing
cracks were extended to the top and bottom layer of the walls. Most of the
new cracks were concentrated in the bottom parts of the walls. This run
caused the significant detachment of surface finishing from the specimen.
Widening of existing cracks with the formation of few new cracks wascontinued to the run 49. At run 50, most of brick joints were cracked andfew brick units fell down from the bottom part of the door opening. There
was a large gap in some part of the specimen between the brick units and themesh was broken at the corner of the wall. At run 52, the building lost theoverall integrity and collapsed completely (see Figure 7).

East

North

West

South

Figure 6: Crack patterns of retrofitting building model after 42 run

68

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 7: Retrofitting building model after 51 run (left) and 52 run (right)
4. PERFORMANCE EVALUATION
The performance of the models was assessed on the damage level of thebuilding at the different level of shaking. The damage level categories are
specified on the Table 2. The Japan Meteorological Agency (JMA) seismic
intensity scale is a measure used in Japan to indicate the severness of groundmotions. JMA seismic intensity is a single number ranging from 0 to 7 andit describes the degree of shaking at a point on the Earth’s surface. The JMA
intensities were calculated based on the input motions to the structure atdifferent runs. Table 3 shows the performance of model houses withdifferent JMA intensities.
Table 2: Damage categories
D0: No damage
D1: Light Structural
damageD2: Moderate structural
damageD3: Heavy structural
damageD4: Partially collapse

D5: Collapse

No damage to structure
Hairline cracks in very few walls. The
structure resistant capacity has not been
reduce noticeably.
Small cracks in masonry walls, falling ofplaster block. The structure resistant capacity
is partially reduced.
Large and deep cracks in masonry walls.
Some bricks are fall down. Failure inconnection between two walls is observed.
Serious failure of walls. Partial structural
failure of roofs. The building is in dangerous
condition.
Total or nearly collapse.

The collapse of the non- retrofitting building is observed at 47th run at
JMA 5+. The retrofitted model performed moderate structural damage levelat 47th run at which the non-retrofitted model collapsed. Moreover, moderate

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh69August 2011, Thailand

performance continued to 48th run. The retrofitted building sustained JMA
6- before going to complete collapse.
Figure 8 shows the performance of the model house with respect tothe duration of shaking. The non-retrofitted specimen collapsed at a time
when a retrofitted performed heavy structural damage. The collapse time
was extended to 70 sec for retrofitted specimen than non-retrofitted
specimen.
The arias intensity was initially defined by Arias (Arias A., 1970) as
π t 2
Ia =
(1)
∫ a (t ) dt
2g 0

and was called scalar intensity. It is directly quantifiable through theacceleration record a(t), integrating it over the total duration of the shaking.
The arias intensity is claimed to be measure of the total seismic energy
inputted to the specimen from the ground.

Figure 8: Damage level comparison
Figure 9 shows the performance of the specimen based on arias
intensity. From the results, retrofitted model could withstand twice more
input energy than non-retrofitted model.

70

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 9: Seismic capacity comparison

CONCLUSIONSThis paper discussed the result of a shaking table test carried out using nonretrofitted and retrofitted hose model by the bamboo band mesh as astrengthening system. The dynamic behavior of models was analyzed andfailure behaviors and performances were evaluated. The result showed thatthe bamboo-band mesh retrofitting technique enhances the seismic resistantcapacity of the masonry building model significantly. The retrofitted
masonry building could withstand more than twice input energy than nonretrofitted specimen. Bamboo is universally available construction material
and its use for retrofitting works not only enhances the seismic resistantcapacity of new and existing building but also promote the local business inthe vicinity.

REFERENCES
Sathiparan, N.: Experimental study on PP-Band Mesh Seismic
Retrofitting for Low Earthquake Resistant Masonry Houses, Ph.D.
Dissertation, The University of Tokyo, Japan, 2008.
Meguro, K., Mayorca, P. Guragain R., Sathiparan, N. and Nesheli N.:
Shaking Table Experiment of Masonry Buildings and Effectiveness of PPBand Retrofitting Technique, Seisan-Kenkyu, Vol. 6, pp. 30-33, 2009.
Nazir, S., Sathiparan, N., Numada, M. and Meguro, K.: Experimental
Study of Seismic Behavior of Scaled Non-Engineered Masonry structures
retrofitted by PP-Band Mesh, Bulletin of Earthquake Resistant Structure
Research Center, No. 43, pp. 77-84, March 2009.
Sathiparan, N., Mayorca, P., Meguro, K.: Seismic Study of ¼ Scale
Unreinforced Adobe Masonry Models Retrofitted with PP –Band Meshes,

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh71August 2011, Thailand

Bulletin of Earthquake Resistant Structure Research Center, No. 42, pp. 8594, March 2009.
Alidad Hahemi and Khalid M. Moslam.: Shake –table Experiment on
Reinforced Concrete Structure Containing Masonry Infill Wall, Earthquake
Engineering and Structural Dynamics, Vol. 35, pp. 1827-1852, 2006.
Roko Zarnic, Samo Gostic, Adam J.Crewe and Colin A. Taylorm,
Shaking Table Tests of 1/4 Reduced Scale Models of Masonry Infilled
Reinforced Concrete Frame Buildings, Earthquake Engineering andStructural Dynamics, Vol.30, pp. 819-834, 2001.
Sathiparan, N., Mayorca, P. and Meguro, K.: Experimental Study on
Static and Dynamic Behavior of PP-Band Mesh Retrofitted Adobe Masonry
Structure.7th International
Conferenceon
Urban
Earthquake
Engineering(7CUEE), March 3-5, Tokyo Institute of Technology, Tokyo,
Japan, 201072

4th Joint Student Seminar on Civil Infrastructures

SSI EFFECT ON SEISMIC RESPONSE
OF HIGH-RISE BUILDING IN JAKARTA,INDONESIA
1James FRANK1, Kullachai TANTAYOPIN*1
School of Engineering & Technology, Asian Institute of Technology,
P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand
kullachai@gmail.com

ABSTRACT
High-rise buildings are conventionally analyzed under fixed baseassumption without considering soil-structure interaction (SSI) because it
provides a conservative design. However, recent studies show thatconventional fixed base analysis may underestimate the seismic response
and lead to unsafe design. This study deals with the effects of SSI for atypical high-rise building in Jakarta, Indonesia. Nonlinear time history
analysis is performed and the inclusion of seismic SSI is modeled throughdynamic beam on nonlinear Winkler foundation method. The results showthat the peak story shear and moment are reduced when considering SSI,
while, in the first floor level, SSI analysis gives a larger response than thatof the non-SSI analysis. Therefore, the effects of SSI for the typical example
building are not always beneficial and may be detrimental in some part ofthe building.
Keywords: soil-structure interaction, high-rise building, nonlinear
seismic response

1. INTRODUCTION
High-rise buildings are conventionally analyzed under fixed baseassumption without considering soil-structure interaction (SSI) because it
provides a conservative design. However, recent studies show thatconventional fixed base analysis may underestimates the seismic response
and this may lead to unsafe design.
Mylonakis et al. (2000) investigated the role of SSI on the collapse of
18 piers of the Hanshin expressway during Kobe earthquake and found thatthe effects of SSI might cause collapse of Hanshin Expressway. Jeremic etal. (2003) studied the influence of SSI on seismic response of an elevatedhighway bridge (the I-880 viaduct) and found that the SSI can have bothbeneficial and detrimental effects. Yue and Wang (2009) studied theinfluences of SSI on the high-rise frame shear wall structure in Fujian
province of China. The results show that effects of SSI amplified dynamic
response of structure in some stories which indicated that SSI cannot be
neglected in analysis.

73August 2011, Thailand

The previous studies indicate that fixed base analysis on seismic
response can lead to unsafe design and the SSI effects shall be investigatedfor each different site. This study discusses the SSI effects on the seismic
response of high-rise building for a typical high-rise building in Jakarta,
Indonesia. As Indonesia has been well known with its high seismic activity(Irsyam et al., 2008), the seismic response of the high-rise building in
Jakarta is important to be analyzed.

Figure 1: Transversal building cross section and layout of building plan

2. STUDY BUILDING
The selected typical building consists of 18-story floors and 4-levelbasements with central concrete core wall system. The basements are
essentially larger in floor plate area comparing to the tower foot print area asshown in Figure 1. The gravity framing system at the tower is reinforced
concrete post-tensioned beam with reinforced concrete slab. The gravity
framing system at the ground floor and basement floor is reinforced
concrete flat-slab with drop panel at the slab-column connection. The
structure configurations are briefly described in Table 1.
Soil investigation and laboratory soil test were conducted to identify
the subsoil condition at the site. The subsoil at the site can be divided into 3
major zones. The upper zone is occupied with recent deposits which are low
in strength. The old deposits at the middle zone are competent as a bearing
layer. The older deposits at the lower zone are also competent but less stiff
than the middle zone. The generalized soil profile is presented in Figure 2
including shear wave velocity (Vs) obtained from field downhole seismic
test. With the presence of relatively deep basements (4-level), the building is
originally designed with raft foundation which is able to sit directly on the
middle zone. In order to study the effects of SSI for deep foundation, thebuilding is also designed using pile foundation. Bored pile with the diameter74

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

of 800mm and the length of 24m is utilized to support the building. The 2D
dynamic analysis is performed in transversal building cross section.
Table 1: Structure model configuration
Vertical element
Level

B4
B2-B3
B1
L1
L2
L3-L11
L12-L16
L17-L18

Core wall
thickness
Outer

Inner

m
0.45
0.45
0.45
0.45
0.45
0.45
0.25
0.25

m
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25

Horizontal element

Column
size

fc'

Beam
sectionflatslab
thickness

m2
0.90x0.90
0.90x0.90
0.90x0.90
0.90x0.90
0.90x0.90
0.90x0.90
0.80x0.80
0.75x0.75

MPa
50
50
50
50
50
40
32
32

m2
0.45x0.65
0.45x0.65
0.45x0.65
0.45x0.65
0.45x0.65

m
0.250
0.175
0.200
0.200
0.150
0.150
0.150
0.150

S-Wave Velocity (Vs)(m/s)
0ZONE 1

ZONE 2FILL

γ = 17 kN/m3

Cu = 60 kPa

CLAY 1

γ = 17 kN/m3

Cu = 60 kPa

CLAY 1b
SAND 2

γ =16.5 kN/m3
γ = 20 kN/m3

Cu = 150 kPa
φ ' = 38 Deg

CLAY 2

γ = 18 kN/m3

Cu = 300 kPa

SAND 2
CLAY 2γ = 20 kN/m3
γ = 18 kN/m3

φ ' = 38 Deg
Cu = 300 kPa

SAND 2γ = 20 kN/m3

φ ' = 38 Deg

CLAY 3aγ = 17 kN/m3

Cu = 150 kPa

CLAY 3

γ = 18 kN/m3

Cu = 250 kPa

ZONE 3

200

400

600

800

fc'

MPa
32
32
32
32
32
32
32
32

Max Shear Modulus (Gmax)
(MPa)
0

200

400

600

800 1000 1200 1400

Figure 2: Generalized soil profile

3. NONLINEAR MODEL
The building is expected to exhibit the nonlinear behavior under severe
earthquake level. In order to identify the actual response of the building, thebehavior of the SSI and structure elements need to be described using
nonlinear model.

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh75August 2011, Thailand

3.1 SSI Model
The nonlinear SSI is modeled based on the dynamic beam on
nonlinear Winkler foundation (BNWF) method suggested by Boulanger etal. (1999) using the lateral load-displacement (p-y) element of the pile. The
nonlinear (p-y) element conceptually consists of elastic, plastic and gap
component in series. The gap and plastic components represent the nearfield component while the elastic component represents the far-fieldcomponent. Radiation damping is modeled using a dashpot in parallel withelastic component. The nonlinear behavior of the plastic, closure and dragsprings is described using the following equations:
⎡
⎤
cy50
⎥
p = pult − ( pult − p0 ) ⎢pp⎢⎣ cy50 + y − y0 ⎥⎦

n

(1)

⎡
⎤
y50
y50
−
p c = 1.8 pult ⎢
g
g ⎥
+
−
⎣ y50 + 50( y0 − y ) y50 − 50( y0 − y ) ⎦

(2)

⎡
⎤
y50
⎥
p d = Cd pult − (Cd pult − p0d ) ⎢gg⎢⎣ y50 + 2 y − y0 ⎥⎦(3)

3.1.1 Pile Foundation
Boulanger (2000) originally implemented the nonlinear SSI model
into Open Sees computer program platform for pile foundation case using
three types of nonlinear SSI element: nonlinear (p-y); nonlinear (q-z);
nonlinear (t-z) to represent the load-displacement behavior of lateral load
capacity; bearing at pile tip; skin friction of the pile, respectively. In thisstudy, only the PySimple1Gen command is used to generate nonlinear (p-y)
elements along the pile in Open Sees developed by Brandenberg (2004).
The influence of two other nonlinear elements is insignificant to the
response of the pile.
3.1.2 Raft Foundation
Raychowdhury and Hutchinson (2008) modified the loaddisplacement relationship of the pile foundation to be applied for shallow
foundation case and calibrated the model based on shallow footing test. The
nonlinear behavior of SSI for raft foundation is described using three
nonlinear SSI elements: nonlinear (p-y); nonlinear (q-z); nonlinear (t-z)
which represent the load-displacement behavior of lateral passive earthpressure at footing side; bearing capacity of footing; friction resistancealong base of the footing, respectively. The nonlinear (p-y), (q-z), and (t-z)
for shallow foundation case are implemented in Open Sees as PySimple2,
QzSimple2, and TzSimple2 material models, respectively.

76

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57a) Lumped plasticity modelb) cyclic response of lumped plasticity model

Figure 3: a)Lumped plasticity model, b)cyclic response of lumped plasticity
model
3.1.3 Structural Element
The nonlinear behavior of the structural element is described using
lumped plasticity model which consists of elastic element and concentrated
plastic hinge at each end as shown in Figure 3a. Concentrated plastic hinge
is modeled using rotational spring where the monotonic backbone curve iscalibrated from the experimental reinforced concrete test. Figure 3b shows
the cyclic response of the nonlinear structural element using lumped
plasticity model. The monotonic backbone curve of the concentrated plastic
hinge is described by five parameters: yield moment (My), yield chordrotation (qy), hardening stiffness (Ks), capping rotation (qcap), and postcapping stiffness (Kc). These five parameters are determined based on themodeling parameter by Haselton et al. (2008) as recommended in
PEER/ATC-72-1 (2010). The monotonic backbone curve of theconcentrated plastic hinge is defined using Clough material model in
OpenSees platform.

4. SEISMIC INPUT MOTIONSThe seismic input motions for the building are specified based onIndonesian Seismic Code SNI 03-1726-2003. The code determines theseismic design load based on design basis earthquake (DBE) corresponding
to the uniform seismic hazard level of 10% probability of exceedance in 50
years. According to the code, Jakarta city is located in seismic Zone 4 withthe peak base rock acceleration of 0.20g. Based on the subsoil condition at
the site, the site can be categorized as medium soil site. The nonlinear time
history analysis is required to be performed in maximum considered
earthquake (MCE) level which is assumed to be 1.5 times the DBE level.
The design response spectrum for MCE level is therefore defined foranalysis.

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh

77August 2011, Thailand

No.

Table 2: Selected earthquake ground motions
EarthquakeYear
Denotation
Mw
Event

PGA
(g)

1

Cape Mendocino

1992

CM-EUR-090

7.0

0.18

2

Hector Mine

1999

HM-H-000

7.1

0.27

3

Imperial Valley

1979

IMP-CH-012

6.5

0.27

4

Loma Prieta

1989

LP-HSP-000

6.9

0.37

5

Superstition Hills

1987

SH-PR-360

6.5

0.30

6

Mentawai Island

2007

MEN-ISL-HNN

7.9

0.13

7

South Sumatra

2007

SO-SUM-HNN

8.4

0.04

1.4

1.2

Spectral Acceleration (g)1.0

CM-EUR-090
HM-H-000
IMP-CH-012
LP-HSP-000
MEN-ISL-HNN
SH-PR-360
SO-SUM-HNN
DBE
MCE

0.8

0.6

0.4

0.2

0.0
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Period (sec)Figure 4: Response spectra of scaled ground motions
Seven earthquake records are used in this analysis to satisfy seismic
design code requirement (UBC 1997, EURO8) for average response. Fivestrong earthquake records from worldwide database and two earthquake
records from inside Indonesia area are selected to cover the wide range ofearthquake variation as shown in Table 2. The ground motions are scaled tomatch with the MCE response spectrum by using spectral matchingsoftware RspMatch2005 (Hancock et al., 2006) as shown in Figure 4. Freefield site response analysis is then performed using SHAKE91 software(Idriss and Sun, 1991) to provide the seismic input motions along thedifferent depths considered.

78

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

5. ANALYSIS RESULTS
In order to identify the effects of SSI on seismic response of the buildings,
the nonlinear time history analysis is performed for three foundation cases:
fixed (Non-SSI), raft (SSI), and pile (SSI) foundation cases and the analysis
models for the three foundation cases are shown in Figure 5.

a) Fixed (Non-SSI)b) Raft (SSI)c) pile (SSI)

Figure 5: Fixed, raft and pile foundation models
5.1 Modal Analysis
Table 3 shows the results of natural period for non-SSI and SSI
analyses. It is shown that the effects of SSI cause to increase natural periods
as shown in Table3. The natural mode shapes of the building essentiallyhave the similar pattern for the three foundation types as shown in Figure 6.
A large difference of the natural mode shapes are only found at thebasement level for the 3rd mode.
Table 3: Natural periods of building
Natural Period (sec)Foundation Model
Mode 1Mode 2Mode 3Fixed (Code)

1.632

-

-

Fixed

1.425

0.297

0.130

Pile

2.269

0.372

0.165

Raft

1.835

0.355

0.179

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh

79August 2011, Thailand

Mode 1

20

20

15

15

15

10

10

10

-0.6

-0.4

-0.2

5

0

0
-0.8

Fixed Case
Pile Case
Raft Case

5

5

-1

Mode 3

Mode2

20

0

0.2

0.4

0.6

0.8

1 -1

-0.8

-0.6

-0.4

-0.2

0
0

0.2

0.4

0.6

0.8

1 -1

-0.8

-0.6

-0.4

-5

-5

-0.2

0

0.2

0.4

0.6

0.8

1

-5

Normalized lateral Eigen Vector

Figure 6: Natural mode shapes of building
5.2 Nonlinear Time History Analysis
The nonlinear time history analysis is performed to obtain the seismic
response of the building. The seismic responses of the building aredetermined in term of force and deformation responses. The force responses
are described as shear force and bending moment while the deformation
response are described as relative displacement and interstory drift ratio.
5.2.1 Force Response
The average response profiles of shear force and bending momentfrom the seven earthquake ground motions are summarized in Figure 7.
Non-SSI analysis shows the maximum response of shear force and bending
moment except for the first floor level where the maximum responses areprovided from SSI analysis. The responses of shear and bending moment
above the basement level for pile and raft foundation cases are similar while
the different responses are only found at the basement level with the larger
response resulted from pile foundation case.

a) Peak Shear Forceb) Peak Story Moment

Figure 7: Average response profile of force

80

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57a) Lateral story displacementb) Interstory drift ratio

Figure 8: Average response profile of displacement
5.2.2 Deformation Response
The average response profiles of the peak story relative lateraldisplacement and the interstory drift ratio from the seven earthquake groundmotions are summarized in Figure 8. The maximum relative lateraldisplacement and interstory drift ratio are provided from pile foundationcase while the minimum values are provided from non-SSI case. The effectsof SSI increase the deformation response of the building. The maximum
relative displacement at the roof level increases about 55% to 100% for raftand pile foundation cases, respectively, compared to fixed foundation case.

6. CONCLUSION
The effects of SSI have been considered for a typical high-rise building in
Jakarta, Indonesia. The results show that SSI analysis reduces seismic
responses in term of peak story shear and moment. However, at the firstfloor level, the moment response from the SSI analysis gives a larger
response than that of the non-SSI analysis. Therefore, the effects of SSI for
this typical building are not always beneficial and may be detrimental insome part of the building.

REFERENCES
Boulanger, R. W., Curras, C. J., Kutter, B. L., Wilson, D. W., andAbghari, A., 1999. Seismic soil-pile-structure interaction experiments andanalyses. Journal of Geotechnical and Geoenvironmental Engineering, Vol.
125, No. 9, pp.750-759.
Boulanger, R. W., 2000. The PySimple1, QzSimple1, and TzSimple
material documentation. Document for the OpenSees platform.
Branderberg, S. J., 2004. PySimple1Gen OpenSees command.
Document for OpenSees platform.
FEMA, 2000. Prestandard and commentary for the seismic
rehabilitation of buildings. Report No. FEMA–356, Federal Emergency
Management Agency.

Seismic retrofitting of non-engineered masonry houses using bamboo-band mesh81August 2011, Thailand

Hancock, J., Lamprey, J. W., Abrahamson, N. A., Bommer, J. J.,
Markatis, A., McCoy, E., and Mendis, R., 2006. An improved method ofmatching response spectra of recorde earthquake ground motion using
wavelets. Journal of Earthquake Engineering, Vol. 10, Special Issue 1, pp.
67-89.
Haselton, C. B., Liel, A. B., Taylor Lange, S., and Deierlein, G. G.,
2008. Beam-column element model calibrated for predicting flexural
response leading to global collapse of RC frame buildings. PEER Report
2007/03, Pacific Earthquake Engineering Research Center, University ofCalifornia, Berkeley, California.
Idriss, I. M., and Sun, J., 1991. User's Manual for SHAKE91. Center
for Geotechnical Modeling, Department of Civil and Environmental
Engineering, University of California, Davis, CA.
Irsyam, M., Dangkua, D. T., Hendriyawan, Hoedajanto, D., Hutapea,
B. M., Kertapati, E. K., Boen, T., and Petersen, M. D., 2008. Proposed
seismic hazard maps of Sumatra and Java Island and microzonation study ofJakarta city, Indonesia. Journal of Earth System Science, Vol. 117, pp.865878.
Jeremic, B., Kunnath, S., and Xiong, F., 2004. Influence of soilfoundation-structure interaction on seismic response of the I-880 viaduct.
Journal of Engineering Structure, Vol.26, Issue 3, pp.391-402.
Mylonakis, G., Gazetas, G., Nikolaou, S., and Michaelides, O., 2000b.
The role of soil on the collapse of 18 piers of the hanshin expressway in the
Kobe earthquake. Proceedings of 12th World Conference on Earthquake
Engineering, New Zealand, Paper No. 1074.
PEER, 2007. PEER Column Database. Pacific Earthquake
Engineering Research Center, University of California, Berkeley, California.
PEER/ATC-72-1, 2010. Modeling and acceptance criteria for seimic
design and analysis of tall buildings. Applied Technology Council,
Redwood City, California.
Raychowdhury, P., and Hutchinson, T., 2008. Nonlinear material
models for Winkler-based shallow foundation response evaluation.
Proceedings of GeoCongress 2008, New Orleans, Louisiana, March,
pp.686-693.
SNI, 2003. Indonesian Seismic Code for Buildings. SNI 03-17462003, Standard National Indonesia, Department of Public Work.
Yue, M., and Wang, Y., 2009. Soil-structure interaction of high-risebuilding resting on soft soil. Electronic Journal of GeotechnicalEngineering, Vol.3, Bund. D, pp.1-20

82

4th Joint Student Seminar on Civil Infrastructures

STUDY ON RISKS AND HUMANEVACUATION PLAN IN KOTO DELTA
AREA IN JAPAN, DURING LARGE FLOOD
Kohei MAKINODAN1, Miho OHARA2 and Kimiro MEGURO3
1

Master Student, Department of Civil Engineering,
The University of Tokyo, Japanmakinodan@risk-mg.iis.u-tokyo.ac.jp
2
Assosiate Professor, Interfaculty Initiative in Information Studies/Institute of Industrial Science,
The University of Tokyo, Japan
3
Professor, Interfaculty Initiative in Information Studies/Institute of Industrial Science,
The University of Tokyo, Japan

ABSTRACTIn recent years, it is reported that the Koto Delta Area in Tokyo whose
elevation is lower than sea level, has large-scale flood risk. When flood
occurs, the municipalities give the evacuation counsel or evacuation order.
However people don’t evacuate to the area which flood doesn’t reachbecause people don’t have the imagination of flood risk. For these reasons,people living in the area which flood reach will be severely damaged. Inorder to reduce this damage, it is important to study action of emergent
evacuation after flood occurs. This paper focuses on detail analysis of floodrisk of the whole Koto Delta Area using GIS data of flood, population
distribution and building inventory. Then, using the result of analysis, we
discussed the appropriate human evacuation plan of the area. Based on theobtained results, we can conclude that if people use only evacuation centers
officially designated by local municipalities, about 75% of them can’t
evacuate successfully. However, if they use existing tall buildings asevacuation centers, they can all evacuate successfully.
Keywords: Flood, Risk analysis, Evacuation

1. INTRODUCTION
East part of the Tokyo Metropolitan area has high risk of large-scale urban
flood. Especially, Koto Delta Area whose elevation is lower than sea level issurrounded by rivers and expected to face the severe problems of humanevacuation in the future flood disaster. According to the Cabinet Office inJapan, the maximum predicted number of death is approximately 3,500 andthat of displaced number of people is approximately 720,000 1) in case thatdrainage pump station, drainage pump vehicle and water gate areunavailable. In this paper, considering whole Koto Delta as a study area,83August 2011, Thailand

we carried out detail analysis of flood risk of the area using GIS data offlood, population distribution and building inventory.
N

30km

Saitama
Tokyo

Chiba

Kanagawa

Koto Delta Area

Figure 1: Location of Koto Delta Area

2. IMPACT OF LARGE-SCALE FLOOD ON KOTO DELTA AREA
Detail flood risk analysis of the area was conducted using GIS in a 125mmesh with flood data from Cabinet Office2) and additional information likepopulation distribution and building inventory. This paper discuss on large
scale flood impact and problems on evacuation.
2.1 Expected inundation in Koto Delta Area
Figure 2 shows inundated depth and inundated area which comes up
2m of depth in most of the area. In this area, residents can’t use lower
buildings (below 2 floors) and the number of these buildings is 49,449
among total 114,646 buildings.
昼間人口/population
なし
Daytime

浸水深(ｍ)
Depth(m)a) 1 hour later

84

5.
0

-2
.0

-1
.0

-5
.0
2.
0

1.
0

.5
00

0.
5

084
3
84
421
41
21
42
-4
02
40
4
25
-7
1
71
97
98
-1
47
66

原点Z

b) 6 hours laterc) 24 hours later
Figure 2: Alteration of inundated depth

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2.2 The area where it is impossible to walk
Figure 3 shows the area where people can’t walk after flood occurs
estimated by comparing inundated depth to limited depth for walking
calculated by equation(1) 3)in each mesh. It is impossible to walk in mostinundated area.
h L = 0.7 × (1－u/2.5)
h L : limited depth for walking (m)，u：velocity(m/s)
昼
間人
口/ なし
Day
population(1)

Whether people can walkor not

歩行可能判定

084
3
84
421
41
21
42
-4
02
40
4
25
-7
1
71
97
98
-1
47
66

possible
歩
行可能
impossible
歩行不可能a) 1 hour laterb) 6 hours laterc) 24 hours later
Figure 3: The area where it is impossible to walk
2.3 The number of damaged wooden houses
The number of damaged wooden houses is also estimated. Table 1
shows the relationship between hydrodynamic pressures and damaged
wooden houses. Hydrodynamic forces are defined as multiplication ofinundated depth and square velocity. Figure 4 shows the number of wooden
houses which fall under different levels described in Table 1. The number ofwooden houses which are slightly damaged is 628, the number of damaged
wooden houses which have difficulties in living and the number of wooden
houses which can be washed out is 283.
Table 1: Relationship between hydrodynamic pressure and damaged
wooden houses4)5)
Pressure(m3/s2)
Level 1(0～under 1.5)
Level 2
(1.5～under 2.5)
Level 3(2.5～ under 20)
レベル4(more than 20)

Damaged wooden houses
Houses hardly have the potential to be damaged
Wooden houses are damaged somewhat
Wooden houses can be damaged, then people
can’t live in these wooden houses
Wooden houses have the potential to wash out

Study on risks and human evacuation plan in Koto delta area in Japan, during large flood

85August 2011, Thailand

1km

1km

1km

Number
建物数(棟)

Number
建物数(棟)

0

0

1～8

1～9

9～18

10～21

Number
建物数(棟)

19～28

22～37

0

29～47

38～49

1～39

48～16750～98

40～137a) Level 2b) Level 3c) Level 4
Figure 4: The number of damaged wooden houses
3. STUDY
DISASTERON

APPROPRIATEEVACUATION

AT

FLOOD

In this section, considering the area where flood can reach within 1 hour, we
discuss the appropriate human evacuation plan. Considering some places as
evacuation center, we compare the capacity in evacuation centers to thenumber of people who should evacuate at the flood disaster. Based on theobtained result, we discuss how many people can be accommodated infollowing 3 cases.
(1) In case of using evacuation centers officially designated by local
municipalities(2) In case of using municipal building as evacuation center in addition tocase (1)(3) In case of using other tall building as evacuation center in addition tocase (2)Also, based on location of evacuation centers, required time from eachplace to evacuation center is estimated in the area where flood can reach
within 1 hour.
3.1 Capacity for evacuation
3.1.1 Capacity of evacuation centers officially designated by local
municipalities
Figure 5 shows the capacity in evacuation centers officially designatedby local municipalities and the number of people who should evacuate there
under case (1). The capacity in each evacuation center is defined as thenumber subtracted the capacity in gym, 1st floor and 2nd floor from capacityin each evacuation center during peacetime which is published inmunicipalities. The number of people who should evacuate is defined as
floor area in each house, which are under area of more than 1.5m3/s2(hydrodynamic pressure) and more than 2m (inundated depth), divided byfloor area per capita in the target area. Table2 shows capacity and thenumber of people who should evacuate in each evacuation centers. The
estimated number of people who should be evacuated to each evacuation
center is bigger than the capacity in most evacuation centers. The difference86

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

between the number of people who should be evacuated and the capacity is
40 times in one center. Figure 6 shows the number of people who should be
evacuated in each district of the town before and after using evacuation
centers officially designated by local municipalities. The capacity inevacuation centers is insufficient in most district of the town and there isvariation of the number of people who can be accommodated.
凡例

Legendcapacity
収容可能人数
number of people
避難
人数
who
shouldevacuate①745,1770②463,1099③105,4311⑤391,863④307,863⑧330,2213⑥693,899⑦603,1722⑪600,1623⑨501,2489⑩304,2489⑫609,1763⑬1058,318

Figure 5: The capacity and the number of people who should evacuate
凡例

Legend(number of people who should evacuate)

beforeapplying
case1
利用前
避難人
数

1363 ，2198

after
case1
利
用applying
後避難人
数2511 ，2563
1289 ，2014
1696 ，1748
38 ，3831073 ，1435 1192 ，1602

1907 ，2379
1095 ，1803
838 ，1345

924 ，1237
1150 ，1653-149 ，1273

Figure 6: Alteration of the number of people who should evacuate in eacharea

Study on risks and human evacuation plan in Koto delta area in Japan, during large flood87August 2011, Thailand

Table 2: Capacity and the number of people who should evacuate in each
evacuation center
Evacuation designated by local
municipalities①

Kanegafuchi junior high school②

Sumida elementary school③

Sumida second elementary school④

Tsutsumi elementary school⑤

Umewaka elementary school⑥

Mukoujima junior high school⑦

Terajima second elementary school⑧

Yahiro elementary school⑨

Terajima elementary school⑩

Terajima third elementary school⑪

Azuma fifth elementary school⑫

Azauma second junior high school⑬

Terajima junior high school

Capacity
745
463
105
307
391
693
603
330
501
304
600
609
1,058

Number of people
who should evacuate
1,770
1,099
4,311
863
863
899
1722
2,213
2,489
2,489
1,628
1,763
318

3.1.2 Additional capacity of evacuation using municipal buildings
Figure 7 shows the capacity in municipal buildings in this area such asapartment administered by municipalities. The capacity in each municipal
building is calculated based on the floor area of corridors in municipal
buildings divided by floor area per capita. The total capacity in the targetedarea is 40,409. This number is bigger than the total number of people whoshould evacuate. However, the distribution of municipal buildings is not
even. Many people can’t reach local evacuation centers. Therefore, case (2)is insufficient as emergency evacuation centers after flood occurs.
3.1.3 Additional capacity using tall buildings for evacuation
Figure 8 shows the capacity in evacuation centers officially designatedby local municipalities, municipal houses and other tall buildings and thenumber of people who should be evacuated in each district of the town.
Existing tall buildings which are non-wooden house and more than 4 stories(for example, apartment, office and educational facility) are considered asadditional evacuation centers. The number of people who should be
evacuated is bigger than the capacity in most districts of the town. Also, in
the area which doesn’t have enough capacity of accommodation, if
municipalities appropriately guide people to evacuate in the centers next tothese areas, all people can be accommodated in safer location

88

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

凡例
凡例
Legend
収容可能人数
capacity
収容可能人数
10708
2577
858
6589 6642
2494
3615

523

275
488
4854

245

123

293

155

Figure 7: Capacity in case 2
凡例
Legend
凡例

number of people
要避難人数
要避難人数
who
should evacuate
収容可能人数
収容可能人数
capacity

1938,2198

4105,2563
3916,2014
9284,383

1007,1748
4685,1602

3746,1435

6597,1237

4345 , 1803
5547,1345

4479,2379
3960,1653
4441,1273

Figure 8: Capacity in case 3
3.2 Required time to evacuation centerIn this section, time required for evacuating people to evacuation
centers is estimated. We estimated required time from central point of
125m-mesh which is used in GIS to each evacuation centers officiallydesignated by local municipalities in the area which flood reaches within 1
hour. At the beginning, distance from each evacuation center to each central
point of 125m-mesh is measured by using GIS. Then required time for
evacuation was calculated as distances divided by walking speed, which ofadult male is 1.5m/s, adult female is 1.3m/s and elderly people and children
is 1.0m/s based on empiric formula of Fruin6). Figure 9 shows the number ofmeshes per each required times in the area which flood reaches within 1
hour. In most mesh, required time is under 10 minutes. So, the distribution

Study on risks and human evacuation plan in Koto delta area in Japan, during large flood89August 2011, Thailandof evacuation centers officially designated by local municipalities iscomparatively even.
Figure 10 shows the worst timing that an adult male should startevacuation to evacuation centers officially designated by local
municipalities. After this timing, he become impossible to evacuate as flood
water depth exceed the limited depth for walking explained in equation (1).
Red meshes in this figure show the area that residents don’t have lead timefor evacuation before flood starts. Therefore, despite the distribution ofevacuation centers is even, some residents don’t have enough time for
evacuation.
60

The number of meshes

Male adult
成人男性
50

Female adult
成人女性

40

高齢者・子供childrenElderly people,

30
20
10
0
1

2

3

4

5

6

7

8

9

Required time (minutes)

Figure 9: The number of meshes per each required time
Worst timing for an adult male
Theforlimitedtime inforeachevacuation
evacuation
mesh

beforeflood
floodoccurs
occursbeforebelow10
10minutes
minutesbelow
below
below20
20minutes
minutes

below30
30minutes
minutesbelow
below
below40
40minutes
minutesbelow
below50
50minutes
minutes

morethanthan5050minutes
minutesmore

Figure 10: Worst timing for an adult male for evacuation in each mesh3.3 Proposal of appropriate evacuation plan
In this research, emergency evacuation for residents is discussed. In
the view point of capacity and the number of people who should evacuate,
we can conclude that if people use only evacuation centers officiallydesignated by local municipalities, about 70% of them can’t be evacuated90

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

successfully. However, if they use existing tall buildings as additional
evacuation centers, they all can be evacuated successfully. In the view pointof location of evacuation centers, although the distribution of evacuation
centers officially designated by local municipalities is even, there are many
areas where people don’t have enough time for evacuation before flood
starts. In order to solve this problem, it is important to provide evacuation
counsel or evacuation order to residents earlier before flood starts
considering the required time for evacuation.

4. CONCLUSIONSIn this research, considering whole Koto Delta as a study area, we carriedout detail analysis of flood risk of the area using GIS data of flood,
population distribution and building inventory. Then, using the result ofanalysis, we discussed the appropriate human evacuation plan of the area.
Although emergency evacuation for residents after flood is discussed in thisresearch, some problems related to isolated residents in many buildings areremained. From now on, it’s important to study appropriate preparedness forevacuation and increase resident’s awareness on the flood disaster risk.

REFERENCES
Cabinet Office, Japan,
http://www.bousai.go.jp/jishin/chubou/suigai/100402/100402_shiryo_
2.pdf, 2010, 4, 39-41.
Bureau of Urban Development Tokyo Metropolitan Government,
Survey on Land Use in Tokyo Metropolitan Area.
Kyozo SUGA，Preliminary Study on Feasible Safe Evacuation in
Flood Disaster, Annual Journal of Hydraulic Engineering, 1995, 2, 879-882.
Yoshiki KAWATA and Hajime NAKAGAWA, Flood Disaster in the
MISUMI RIVER - Flooding and Damages of Houses - , Disaster Prevention
Research Institute Annuals, 1984, 4, 179-196.
Satoshi SATO, Fumihiko IMAMURA and Nobuo SHUTO,
Numerical Simulation of Flood and Damage to Houses – A Case of the
Yoshida River due to Typhoon No.8610 -, Annual Journal of HydraulicEngineering, 1989, 2, 331-336.
John J. Fruin, PEDESTRIAN - Planning and Design -, Kajima
Institute Publishing Company Ltd, 1976

Study on risks and human evacuation plan in Koto delta area in Japan, during large flood91August 2011, Thailand

92

4th Joint Student Seminar on Civil Infrastructures

INFLUENCE OF SURFACE COMPLEXITYTO GCPS DISTRIBUTION
Sudniran Phetcharat , Masahiko Nagai and Taravudh Tipdecho
RS & GIS, Asian Institute of Technology, Thailand
sudniran@gmail.com
ABSTRACT
Geometric correction is unavoidable in order to increase precisionand eliminate the displacement of positions in the images taken by satellite
imaging due to the positioning of the receiver and the curvature of the earth.
The geometric correction process of satellite image with no physical sensor
parameter provided requires small group of GCPs. The problem is how are
GCPs to be distributed so that precision is achieved. Generally GCPs isdistributed evenly throughout the surface but that is not appropriate when
there are different types of the surfaces because more relief displacement
occurs at high level. The polynomial model is one of the most useful tools
for geometric correction requiring GCPs to determine the coefficients of theequation for the unknown variables. In this study, conventional 3D
polynomial equations 2nd order is employed as the tool to determine GCPs
distribution characteristic. The principle of GCPs distribution in this study
is based on influences of the complexity of the surfaces. The surfaces are
divided into two complexity types consisting of low complexity and highcomplexity area. Results of the comparative study for GCPs selection basedon the principle of evenly distributed and the principles of complexity
through the examination of accuracy of RMSE by ICPs at 68 points was
2.11 pixels and 0.96 pixels respectively. This leads to the conclusion that thetransformation from object space to image space using polynomial and a setof GCPs along with consideration of the complexity of the surface yields
results with higher accuracy than the conventional evenly distributed
method. This is because GCPs distribution corresponds with surface
complexity. The results of this study reduce the time needed for trial anderror in selecting the GCPs’ location and can be applied for the different
images by assigning a weighting criteria for high and low complexity to 55
and 45 percent respectively.
Keywords: surface complexity, ground control points, polynomial
coefficients function

1. INTRODUCTION
Aerial photographs or satellite images are the basic data used in the analysisof data in the geographic information system (GIS), remote sensing, or
mapping. The image acquisition process derived from the different type of
sensors used. Ayoub (2008) revealed that geometrical distortions of current
pushbroom satellites such as SPOT, ASTER, Quickbird, and HiRise can be93August 2011, Thailand

categorized under two main causes: the modeling inaccuracy of the CCD
sensor geometry and the jitter of the instrument’s platform during image
acquisition.
Distortion of the image is unavoidable, especially inphotogrammetry. Likewise, high resolution satellite images are prone togeometric distortions and to correct these imprecision, geometric corrections
is a vital process (Arif, 2006). Major distortions include internal distortionand external distortion. The internal distortion is the distortion within asystem that can be adjusted by using the characteristics of a sensor such aslens distortion which is adjusted using co-linearity equation. The external
distortion is an outside error from external sensor characteristics such as thecurvature of the Earth, change the condition of the atmosphere, reflectancecharacteristics of light and altitude of the surface in the vicinity. Theseerrors are difficult to control and they directly affect pixel position making
them inconsistent with the position on the ground. The characteristics of theground depths, high, low and unevenness can especially cause distortion in
the manner of relief displacement. In some cases the images used in theanalysis have no information on physical sensor model subjected toadjustment by other appropriate models. One of the many models used isthe polynomial model, a simple and convenient model for analyzing. Thismodel can transform the object coordinates to the image coordinates andconvert data from 3 dimensional to 2 dimensional data. This transformationby using ground control points may be obtained from the survey using
integrated information from ground survey, photogrammetry or GPS which
consists of the coordinates X, Y. and Z. Results of the transformation are thecoefficient of polynomial equations. Generally, the polynomial equations
order in photogrammetry will not exceed the 3rd order, because it may
result in errors rather than accuracy.

2. STUDY AREA AND GCPs AVAILABILITY
The area studied is located in Chiang Mai and Lampoon, Northern
provinces of Thailand. The distance from Bangkok to the location isapproximately 600-km. The area studied has a variety of surface
characteristics consisting of both flat area and highly complex surfaces. The
coordinates of the target area at the upper left is 98.83040E, 18.7702N andat the lower right is 99.02550E, 18.5918N. The interested area covers
approximately is 406 square kilometers of land. Surface complexity
observed from DEM shows diversity of surface type. High complex area(mountainous terrain) covers around 30% of the land in the north-west zone
and the remaining 70% in the eastern and southern zones of the image is
low complex areas (urban). The image in Figure 1 showing the landscape of
the area was acquired from worldview-1 on February 23, 2008. Worldview1 image satellite is operating at an altitude of 496 kilometers with very highresolution 50 cm panchromatic at nadir. Geolocation accuracy specificationof the instrument is 6.5 m CE90 at nadir, with actual accuracy in the rangeof 4.0 – 5.5 m CE90 at nadir, excluding terrain and off-nadir effects.

94

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 1: Study area in Chiangmai and Lampoon, Thailand.
There are 78 points GCPs and Independent Check Points (ICPs) for
the image provided by Land Develop Department of Thailand (LDD) withan accuracy of 0.2-m. Acquisition method the position of GCPs and ICPs byusing static and kinematic GPS survey.

3. 3D POLYNOMIAL MODEL
Polynomial Model is the equation used to represent the existing data. A
polynomial equation can be created by determining the coefficient of thecontrol point, which depends on the nature and complexity of surface data.
In general, the polynomial equations used for modifying the images areoften found in the 2nd order or 3rd order. Independents variables used tocreate an equation consists of variables X, Y and Z where X and Y are thecoordinates of the plane and Z is the level of control points. Dependent
variables are the position of the pixels rp and cp where rp is the position of
the image on the horizontal axis, the cp is the position of the image on thevertical axis. Polynomial modeling is used as a tool to create new surface
model with enhanced accuracy both in the shape and position of the image
by using control points to determine the coefficients of the model. The
coordinates’ functions are as shown below.
(1)(2)
where;
rn = row of pixel in image
cn = column of pixel in image
Xn, Yn, Zn = ground coordinate values
Functionis as follows;
2nd order Polynomial

for second order polynomial can be created

YZ+a7X2+a8Y2+a9Z2 (3)YZ+a7X2+a8Y2+a9Z2 (4)Influence of surface complexity to GCPs distribution

95August 2011, Thailand

In general, polynomial transformations with terms up to the first order
can model a rotation, a scale, and a translation and are computationallyeconomical. As additional terms are added, more complex warping can beachieved.
The aj, j = 0, 1, ... , 9 is the coefficient that is unknown. Coefficient aj
can be calculated using the least square method for the error E of all data ndeviated from the function
as follow;(5)
Then associate the minimal errors with E by differentiation and assign
the equation to equal to zero, causing the equation to include the following
sub-10 equations.
(6)
Results of the equations will be as shown in the matrix which includes
the following sub-10 equations.

=(7)

The square matrix size (10x10) on the left side of this equation is asymmetric matrix with known value along with the vector size (10x1) on theright side of this equation. The value coefficients a0, a1, a2, ..., a9 can becalculated by using the methods of solving equations by Invert Matrix.

4. GCPs SELECTION AND
DISTRIBUTION METHOD

DISTRIBUTION

BY

EVEN

GCPs should be evenly distributed throughout the whole image (Jingtao,2009). Selection of control points in order to generate geometric corrections
is usually done by evenly distributing GCPs throughout the area used in thesquare grid. The advantage of even distribution is that it makes it easy to
calculate and allocate the position of GCPs. The disadvantage is the limited
accuracy and is not considering the topography that makes the accuracy ofthe model unreliable.
The location of GCPs for each point of that method may be located at
inaccessible locations such as water bodies or mountain peaks. Therefore
this method makes it easy to locate positions on the image but difficult toactualize on the field.

96

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

5. SURFACE COMPLEXITY CLASSIFICATION
Complexity of surface is shown the different surface or terrain characteristic
at any selected point compared with the around that point. Generally, thecomplexity of a surface area is classified by the intensity of the variety ofhigh and low lands. In some cases, at any high areas may have low
complexity and at any low areas maybe more complex. The key factors
identifying terrain complexity consist of height and the Standard Deviation(STDEV) of the heights in the interested area.
In the conventional GCPs distribution is there is only one area/zone,
not classify the surface characteristic on the images, some area consists ofdifferent types of surface characteristic/complexity. Consequently, the areathat is used to determine the distribution of GCPs should be split into twozones. Each zone is to be classified by its height and standard deviation(STDEV).
Many histogram-based threshold approaches (Boukharouba, 1985)
regard finding a threshold in the histogram for binarizing an image asapproximating the histogram with two distribution functions. Quen-Zong(2005) proposed the procedures to find the Change-point detection fromcumulative histograms as threshold selection by determine maximum distantfrom line L to cumulative histogram, line L is connected between firstpoints to last point on cumulative histogram.
5.1. Changing Point Detection by Using Height Distribution
Zoning height can be identified using data collected from each grid
point of DEM data. This data is then used to create a graph of frequency
distribution. According to Quen-Zong (2005), the changing point can be
determined using the percentage of cumulative data from the cumulative
curve.

Figure 2: Histogram of Height and Cumulative Distribution

Influence of surface complexity to GCPs distribution

97August 2011, Thailand

Figure 2 shows the distribution on the X-axis as height in meters, theleft side of the Y-axis is the frequency of each height interval and right sideof the Y-axis is the cumulative percentage of each height interval.
Information from Figure 3 identifies the changing height point as 308
meters.
5.2 Changing Point Detection Using Standard Deviation (STDEV)Distribution
STDEV is achieved as the result of the calculation process using a 3x3
matrix for each height pixel. The STDEV result represents and locates thecenter of the matrix and the same calculation is used to determine the
STDEV for the entire image. To create a standard deviation histogram the
proposed method by Quen-Zong (2005) is also used in the determination of
the changing point.

Figure 3: Histogram of Standard Deviation Distribution
Figure 3 shows the distribution of STDEV value on the X-axis is withthe left side of the Y-axis being the frequency of each STDEV interval andright side of the Y-axis being the cumulative percentage of each STDEVinterval. The changing STDEV point in this figure is 3.

6. CLASSIFICATION/ZONING
After the changing points are found, both height frequency distribution and
STDEV frequency distribution information will be available for use toclassify the surface into zones. The classification is done by choosing aheight of greater than 308 meters and the STDEV of more than 3 as a highcomplexity zone and those areas with the height of less than 308 m and the
STDEV value of below 3 are to be classified as the low complexity zone.

98

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 4: Two zones are separated by complexity classification
Zoning via Global Mapper software is shown in the Figure 4. Herethe area is divided into 2 zones; high complexity at the north-east area andlow complexity at the south-west area. The zones in the image are
separated by the yellow line.

7. NUMBER OF GCPS FOR EACH ZONE
The model used in the geometric correction for identifying the number andpositions of GCPs in this study is the 2nd order of 3D polynomial. There areten variables used in the form of coefficients of the polynomial consisting of
a0 to a9. The required amount of GCPs for 2nd order is fixed ten points for animage. Imagery is classified into two zones using the zoning method andthe ten points are distributed to those two zones. The number of GCPs to be
distributed for each zone is determined through consideration of both thearea size and the level of complexity.
The proportion of the area size (measured from the image) between
the high and low complexity zones without considering the level ofcomplexity are at the rationed as 0.35 and 0.65 parts respectively. The trial
weights for level of complexity are assigned to 0.55 - 0.80. This study
proposes the method of calculation for the number of GCPs in each zone as;(8)where;
rGCPi = GCPs Requirement of Zone itGCP = Total GCPs Requirement (10 points in this study)
Ai = Proportional Area Size of Zone i
Wi = Trial Designed Weight for Zone i

Influence of surface complexity to GCPs distribution

99August 2011, Thailand

29.3
22.8
16.9
12.4

60.3
50.0
39.5
30.3

GCPs
Required

Area x Wt.
%
Influence

WeightArea (%)

GCPs - N

Area x Wt.
%
Influence

35 0.55 19.3 39.7
35 0.65 22.8 50.0
35 0.74 25.9 60.5
35 0.81 28.4 69.7

10 GCPs
4 65 0.45
5 65 0.35
6 65 0.26
7 65 0.19

GCPs - N

Event.
Distr.
I
II
III
IV

WeightCases

Area (%)

Table 1: Trial weighting the complexity and amount of GCPs for each zone
High Complex
Low Complex
Total

6
5
4
3

10
10
10
10

Table 1 shows five scenarios for the case being studied. In the firstscenario, GCPs is evenly and proportionally distributed throughout the area
based on the fundamental principles of the using ten GCPs points over theentire image on a regular basis. For all cases, the area sizes are the same inproportion. As an example, the calculation for case 1 is processed under the
information criteria of the high complexity area having the influence weightof 0.55 and an area proportion of 0.35 (fixed) and the low complexity area
having an influence weight is 0.45 and an area proportion of 0.65 (also
fixed). Based on the information, the amounts of GCPs distributed in eachzone are calculated to be 4 and 6 in the low and high complexity zones
respectively. Results of the other scenarios are calculated as shown in the
Table 1.

8. ALLOCATION OF GCPS LOCATION USING TRIANGULATION
Positioning of GCPs in each zone should be distributed suitably. This is tomake sure that the error can be distributed equally to each zone, which inthis study selection of GCPs is selected by using a triangle shape, because
triangle is more stability shape. Number of GCPs from the previous section
was selected from the existed GCPs provided by Land Development
Department Thailand (LDD). Triangles created should be similar in size.
Selected locations of GCPs are shown in the images below.

(a) Evenly Distributed

100(b) Case I 4 – 6

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57(c) Case II 5 – 5(d) Case III 6 – 4(e) Case IV 7 - 3

Figure 5: GCPS allocation for each trial cases(a) Evenly Distributed (b) Trial Case I (c) Trial Case II (d) Trial Case
III (e) Trial Case IV
The first is the case scenario where GCPs is evenly distributed
throughout the area. Latter scenarios in cases 1 to 4 represent proportional
distribution of GCPs. For example, the interpretation of in the case scenario
III, is that the number 6 – 4 means there are 6 GCPs distributed in the highcomplexity zone and 4 GCPs are distributed in low complexity zone.

9. RESULTS AND ACCURACY ASSESSMENT
Evaluation of the accuracy of the methods selected for determining the
distribution of GCPs is a measure of errors that occurred after thetransformed pixel is compared between position on the image after
transformation and the same position on the ground points. Accuracy of thetransformation can be measured using the Root Mean Square Error (RMSE)
with all 68 Independent Check Points (ICPs) scattered throughout the image.
The equation used in the calculation is;(9)where;
nxact and yact
xest and yest

= the number of control points= actual location of control point x and y= estimated location of control point x and y

Table 2: RMSE result of the check points
RMSE Result
Average X-Y,
ICP – X, Pixels
ICP – Y, Pixels
PixelsTotal High Low Total High Low Total High Low
Event.Distr. 2.28 3.57 0.70 1.94 3.02 0.62 2.11 3.30 0.66
Case 1
1.21 1.81 0.58 0.65 0.74 0.59 0.93 1.27 0.59
Case 2
1.77 2.68 0.76 0.66 0.78 0.58 1.22 1.73 0.67
Case 30.74 0.87 0.65 1.17 1.42 0.99 0.96 1.15 0.82
Case 44.47 6.01 3.14 6.37 8.43 4.64 5.42 7.22 3.89
Case No.

Influence of surface complexity to GCPs distribution

101August 2011, Thailand

Table 2 shows the errors that occurred when GCPs distribution are
selected from different complexity weighting. Even distribution is a popularmethod for selecting GCPs amount and location. This is done by applying
the same weight to every zone. In this case, the 2 zones weighting are %50
each and the complexity of surface is ignored. Result shows RMSE averagevalue of 2.1pixels. The last case allocated a weighting value at the
maximum value of %80, as a result, yielding the RMSE value at the highaverage of 5.42pixels. RMSE value is low in two cases, 1 and 3, where theweighting proportions are 55and %75with the resulting RMSE average of
0.93and 0.96 respectively.

10. CONCLUSION
The study on the importance of the level of complexity or nature of thesurface height in relations to the selection of GCPs and its location in eacharea using the 2nd order 3D polynomial confirms that there are affects to theaccuracy of the transformation, especially in the high land or mountain area.
Proportional increase in the number of GCPs at the high area or highcomplexity area will reduce errors. The study results show that the average
error is reduced to 55% and for high area decreased by 87% when the
proportion of high complexity to low complexity is 55: 45.
To transform object space to image space with a set of GCPs,
especially when the image does not have physical sensor model thecomplexity of the image should be considered. Other factors that should betaken into account are the height distribution and the standard deviation
distribution by using DEM. In addition, this study can be applied to aerial
photographs or satellite imaging especially as guidance in identifying thenumber and location of GCPs for each complexity area, especially in the
study area that have a variety of surface types.
This study is helpful to the researcher in reducing the time for trial anderror used for the selection of the GCPs location. It can also be applied tothe different images by assigning trial weighting and classifying/zoning of
surface complexity using load free DEM site and distributing suitable
numbers of GCPs for high and low complexity zones.

11. FURTHER STUDY
In this study, analysis and distribution of GCPs location was used only on asatellite image from woldview1. For higher reliability, further studies
should be done using other satellite imagery tools such as ALOS and
THEOS etc and confirm precisions through using comparative results.

102

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

REFERENCES
Arif, F.; Akbar, M.; An-Ming Wu., 2006. Geometric Correction ofHigh Resolution Satellite Imagery and its Residual Analysis, Advances inSpace Technologies, International Conference on Digital Object Identifier,pp. 162 – 166.
Ayoub, F.; Leprince, S.; Binet, R.; Lewis, K.W.; Aharonson, O.;
Avouac, J.-P., 2008. Influence of camera distortions on satellite image
registration and change detection applications, Geoscience and Remote
Sensing Symposium 2008, IGARSS 2008, IEEE International, Volume 2,
pp.II-1072-II-1075.
Boukharouba, S.; Rebordao, J.M; Wendel. P.L., 1985. An amplitude
segmentation method based on the distribution function of an image.
Graphical Models Image Process.29, pp. 47–59.
Chang Kang-tsung., 2006. Introduction to Geographic Information
Systems, 3rd ed.; McGraw-Hill, United States, pp. 128.
Jaebin Lee; Changno Lee and Kiyun Yu., 2011. Autoregistration ofHigh-Resolution Satellite Imagery Using LIDAR Intensity Data. KSCE
Journal of Civil Engineering, 15, 375-384.
Jingtao Zhang and Bo Cheng., 2009. Distribution optimization ofground control points in remote sensing image geometric rectification basedon cluster analysis, conference on Medical Imaging, Parallel Processing of
Images, and Optimization Techniques, Proc. SPIE 7497, 749707,
doi:10.1117/12.833671.
Quen-Zong Wu; Hsu-Yung Cheng; Bor-ShennJeng., 2005. Motion
detection via change-point detection for cumulative histograms of ratio
images, Pattern Recognition Letters, Volume 26, Issue 5, pp. 555-563.
Sezan, M.I., 1985. A peak detection algorithm and its application tohistogram-based image data reduction. Graphical Models Image Process.
pp. 47–59.
Toutin, T.; Briand, P.; Chénier, R., 2004. GCPS requirement for highresolution satellite mapping, Proc. XX Congress ISPRS, Istanbul, Turkey
Tsai, D.M., 1995. A fast thresholding selection procedure formultimodal and unimodal histograms. Pattern Recognition Lett. 16 (6),653–666.
Zhang Wangfei; GaoJianguo; XuTianshu; Huang Yanru., 2009. The
Selection of Ground Control Points in a Remote Sensing Image Correction
Based on Weighted Voronoi Diagram, Information Technology andComputer Science, ITCS 2009. International Conference on, Volume 2, pp.
326-329

Influence of surface complexity to GCPs distribution

103August 2011, Thailand

104

4th Joint Student Seminar on Civil Infrastructures

A PRELIMINARY FEASIBILITY STUDY OF
RAILWAY CONSTRUCTION IN KOREA
Yongtaek LIM1, Juhye KIM2, Cheol PARK2
Professor, Chonnam National University, Korea
limyt@chonnam.ac.kr
2
Msc Student, Graduate School of Chonnam National University, Korea
1

ABSTRACT
Recently a significant interest for railway, environmental-friendly
transportation mode, has been increased due to reducing green house gas ofCO2, and Korean government has focused on rail-oriented public
transportation policies. Under this environment, several economic
evaluations of the railway construction have been performed. For thispurpose, conventional travel demand forecasting steps, such trip generation,
trip distribution, mode choice and trip assignment, has been used. Throughthis process and economic evaluation method, economic verification has
been performed. In this study we review economic feasibility of railway
construction for Indeogwon-Suwon railway construction project in Korea.
Keywords: railway, traffic demand, preliminary feasibility study,
economical analysis

1. INTRODUCTION
A preliminary feasibility study for railway investment can analyze throughcomparing costs, spent in constructing railroads, and benefits, which
occurred as a result of it, and traffic demand is one of the factors, that has
significance impact on estimation of cost and benefits when evaluating
validity. Through estimation of traffic demand, it evaluates whether thatbusiness would be promoted or not and investment priorities and it isutilized in calculation of proper supply scale and impact analysis of areas,
constructing railway has impact on.
These traffic demands could be estimated in various ways, but in caseof our country, 4 stages model is the most utilized. The reason of it isphased consistency of traffic demand's estimation process, which model
implies, could be understood easily even to amateurs. 4 stages traffic
demand estimation method is a method that estimates traffic demand basedon traffic zone successively by dividing it into 4 stages: trip generation, trip
distribution, mode choice and trip assignment.

105

August 2011, Thailand

2. METHOD
2.1 Set analysis range
Base year in this estimation of traffic demand is 2010 and public
launch year is 2020 and analysis target year is 2059, which is a 40 years
after opening. But due to capital area's network of capital area'stransportation head office is to 2021 and O/D will be provided to 2036, set
the final analysis year as 40 years after the opening but suppose that benefits
are constant after 2036, middle analysis year of this business. Influence oftraffic business is a spatial range, that should be concerned when analyzing
feasibility of the business because of occurrence of "distinguishable changes
in traffic pattern' by project implementation and setting a influence as aregion, included in evaluating economic feasibility of business, could have
impact directly on economical analysis so it should be carefully set. Sphereof influence is separated into two: sphere of direct influence and sphere ofindirect influence and explanations of each are as follows.
Sphere of direct influence is a spatial range, which needs specific O/D
and construction of network to analyze minutely effect of project
implementation as a adjacent land geographically and it should include all
enforced section of that business, which changes of traffic pattern occurred
directly according to project implementation. Sphere of Indirect influence isa region, except sphere of direct influence area among geographical range,
which should be included in calculation's scope of benefits due tooccurrence of traffic pattern's changes and it includes sphere of direct
influence and its range is greater than or equal to sphere of direct influence's.

Figure 1: Choosing sphere of influence of this business106

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2.2 Selection basic data and revisionFirst we should set a basic data for demand analysis considering
administrative district of business area. Basic data examples, this study's
demand analysis uses, is a O/D and network data, provided by 『 2010
Renewal data of capital area's transportation head office(2010. 5) 』 of
capital area's transportation head office but it carried out an analysis byreflecting additionally revision of network error, which was in influencesphere and future network of roads and developmental projects. For trip
generation and trip distribution, it used O/D, which has public confidence
and was suggested professional research institute, vicariously.
It reflected additionally for business, which project implementation
was allowed among future developmental plan, not included in capital areatransportation head office's O/D, selected as a basic data, and revised
population planning, which was incorrectly reflected, among developmental
plan, included already.
Also it is judged that there is a high possibility of excessive or
depopulation estimation of people, getting into and out at each station inestimating of prospective demand of business route, so it departmentalizedzones targeting influence sphere, where business would be enforced.

Figure 2: Network configuration
2.3 Setting base year of railway trip assignment model
For railroad, it judged whether model properly reflects reality bycomparing observed getting into and out population, counted according toroute and stations by the railroad central operation body.
Observed getting into and out population at that time is a people, who
get into and out through certain station's gate through AFC (Automatic Fare
Collection) and transit passengers, who get into and out at other stations, areexcluded. Because each railroad central operating body couldn't get a data

Influence of surface complexity to GCPs distribution

107August 2011, Thailand

about transit passengers of each station, who doesn't pass the gate. Adding
the number of passengers, who get into and out at the certain station, and thenumber of transit passengers, who transfer from other routes, is a total
getting into and out population. Because getting on and off population,
estimated at each station, in model is a total population, so pure population,
except transit passengers among them, should be compared with observed
population.
Table 1: Observed getting into and out population and assignments getting
into and out population of base year's station
Boarding passengers

StationObservation Estimation

Gunpo
UiwangSungkyunkwan
University
HwaseoSuwon
Seryu
Byeongjeom
Sema
Osan University
Osan
Seonbawi
Seoul
Racecourse
Seoul Grand
Gwacheon
Indeokwon
Pyeongchon
Beomgye(unit : person/day, %)Alighting passengers

Error
Observation Estimation Error rate
rate

7,567
7,783

8,132
7,420

7.47
-4.66

7,032
7,387

7,798
6,961

10.89
-5.77

16,161

18,415

13.95

15,07019,823

31.54

8,700
43,971
4,764
15,376
1,841
1,562
9,285
4,822

10,53348,702
4,956
17,757
1,615
1,422
9,450
4,507

21.07
10.76
4.03
15.49
-12.28
-8.96
1.78
-6.53

8,726
44,686
4,390
15,243
1,705
1,520
9,095
4,032

9,551
50,201
4,992
15,191
1,465
1,394
10,119
3,999

9.45
12.34
13.71
-0.34
-14.08
-8.29
11.26
-0.82

6,312

7,641

21.06

6,606

6,537

-1.04

7,559
5,297
17,882
12,76628,334

7,854
5,292
18,957
15,12825,571

3.90
-0.09
6.01
18.50
-9.75

8,028
4,673
18,00912,64028,651

7,408
4,862
16,07414,016
31,112-7.72
4.04-10.7410.89
8.59

2.4 Mode choice
Share of means about project implementation should be recalculated
because there is a demand, transferred from plane when estimating demandof railroad unlike general street network business. Mode choice model
among traditional traffic demand 4 stages model could become various
forms of models like trip distribution․ means sharing or means sharing․route assignment according to estimation phase and aim. In preparatory
feasibility survey, integrated model of means sharing and route assignmentis usually utilized to calculate rates of switching means according tochanged network at implementing project.
This paper used Incremental Logit model, which can draw consistency
by grasping characteristic of passenger's passing behavior, as a basic model.
But in the case of occurring Zero cell to solve Zero cell problem, which

108

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

would occur when new means are introduced, Additive Logit model could
be utilized restrictively.
Incremental Logit calculates probability of selection of eachalternatives using variation of utility by changes of explanatory variables.
That is, it calculates usefulness of when project implemented and not andcomputes new means sharing rate, which considers changes of usefulness.
This model is a transformed way from K factor Logit and revision pile,applied to fit observation sharing rates and sharing rates of model, no need
to applied because observation sharing rates is considered together withchanges in utility.
This methodology is usually used in United States and Europe and it
has advantage that it excludes effect by heap constant and in could more
properly reflect effect of project implementation.

: Selection probability of means for project implementation: Sharing observation rates of means for not implementing project: Changes in utility of means before and after project
implementation
Table 2: Changes in volume of trip and means sharing rates as a result ofmeans sharing in 2020
Classification
trip
Means
sharing
rates

Car

Bus

Taxi

SubwayKTX

not
implementing

23,365,648

16,924,836

3,303,544

9,955,379

24,436

implementing

23,353,533

16,911,582

3,303,247

9,981,414

24,067

notimplementing

43.61%

31.59%

6.17%

18.58%

0.05%

implementing

43.59%

31.57%

6.17%

18.63%

0.04%

2.5 Trip assignment
Railways passing is generally composed of access pass, from starting
point to railroad means, railway pass and access pass, to destination. Whenseparating it by time, it could be distinguished into time of outside(accesspassing time, waiting time and boarding time) and time of inside. Also forthe passing case, transit between rails, occurred, transit time(included intime of outside) could be added. Railway does not consider capacity
restriction in trip assignment process and it basically choose line and routebased on optimal strategy.
2.6 Estimating prospective traffic demand
After estimating volume of traffic and traffic characteristics for
project not implementing and implementing by assigning O/D of each

Influence of surface complexity to GCPs distribution

109August 2011, Thailand

means, obtained through calculation process of base year, it suggests
changes in volume of traffic of major roads and railway following project
implementation.
Following graph shows maximum thru passenger from 106 station to
108 station as a result of predicting passenger demand of each station in2020 and 2036.
Table 3: Result of predicting passenger demand of each station in 2020

station
101
102
103
104
105
106
107
108
109
110
111
112
113
Total

down (Suwon districts)
Thru
Boarding
Alightingpassenger
29,352
029,3525,845
2,030
33,166
1,983
1,452
33,6981,255
926
34,027
6,592
7,648
32,97011,771
4,503
40,2384,436
9,958
34,716
17,63810,01042,343
3,108
7,534
37,917623
6,448
32,092291
29,756
2,627
141
1,747
1,021
0
1,021
0
83,03583,033(unit: person/day)up (Indeogwon districts)
Thru
Boarding Alighting
passenger
033,736
0
2,520
5,480
33,736
1,502
2,158
36,6941,106
1,240
37,3507,612
6,912
37,482
4,400
10,33536,782
12,0805,791
42,717
9,522
19,72636,431
8,666
4,085
46,6337,116
590
42,05432,996
191
35,528
1,976
226
2,723
973
0
973
90,46990,470

It is a picture, that shows changes in getting on again population ofaround route for double track subway project implementation between
Indeogwon and Suwon in 2020. Getting on again population of construction
line and line number 4(Gwacheon districts) has been increased and that ofshinbundang line, line number 1, section between Indeogwon and Kumjungof line number 4 has been decreased.

110

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 3: Thru passenger change of surrounding line in 2020
2.7 Computation benefits
Benefits, which occur by implementation of transportation investment,
are divided into two: direct benefits, transportation aspects and indirect
benefits, social benefits due to transportation improvement. Direct benefits,
which happen to users for transportation utilities when transportation
business like road and railroad implementing, are reduction of vehicle
operating costs, reduction of travel time, decrease in car accident, increasein comfort, improvement of punctuality and improvement of stability.
Among these, making reduction of vehicle operating costs, reduction of
travel time and decrease in car accident as a monetary value is a relatively
easy but making increase in comfort, improvement of punctuality andstability as a currency value is difficult because their effect could be varied
according to satisfaction of individual. Meanwhile, for the case of railroad
business, there is a need to reflect benefits by transit demand of flight andshipping and decrease benefit of accident/delay according to improvementof crosswalk but measuring them is not easy.
Indirect benefits are ripple effects, that occur to every people
regardless of using traffic facilities when enforcing transportation businessand are reduction of environmental costs, effect of regional development,
expansion of market area and reforming effect of industrial structure. For
the case of railroad business, benefits of wealth by reduction of maintenance

Influence of surface complexity to GCPs distribution

111

August 2011, Thailand

and administrative expense of highway, due to transit demand, andretrenchment of parking space's opportunity cost, owing to reduced parking
demand and cut of traffic congestion and road space during construction
would be additionally considered.
Table 4: Results of calculating benefits(unit: one million won )Benefits of reduction
in trip time
year

2020
2026
2031
2036

Road

Railroad

Total

110,137
125,650
144,622
148,321-13,654-10,113-8,413-8,12696,482
115,537
136,209140,195

Benefits of
reduction in
opera
ting
costs
19,423
20,58021,334
22,219Benefits of
reducein caracciden
ts
1,788
1,972
2,010
2,030

Benefits of
reduceinenvironmental
costs
2,500
2,836
2,922
2,972

Benefits of
reduction inparking costs

Total

5,041
8,112
9,121
9,054

125,234
148,964
171,523
176,470

Table 5: Summary of economical analysis results(unit: one hundred million won)Classification
Current value of benefits
Current value of costs
B/C
NPV

The task
16,024
20,8170.77-4,793

3. CONCLUSION
Analysis about economic feasibility helps accurate understanding aboutbusiness making us grasp how much economic values this business has.
Furthermore, information about economic feasibility of the business isutilized as a data, the most basic and essential in analysis at policy-level.
Economic evaluation is a process, that apprehends economic
feasibility and finance of the business through calculation like B/C ratio,
NPV and IRR and to supplement error of various estimation, used ineconomic analysis, it fulfills sensitivity analysis about impact that changes
in major variables has on economic feasibility. Especially, economic
analysis judges economic feasibility of that targeting business by comparing
benefits that make changes in traffic pattern as a monetary value, which
occurred at traffic network in influence sphere due to project
implementation and size of costs, which occurred for the entire period ofanalysis due to project implementation

REFERENCES
Deasik YUN, 2001. travel demand forecasting 4 steps, Transportation

112

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

demand analysis, Korea. 33-217
Kangsu KIM, 2008. Mode choice, Preliminary feasibility study
standard guide correction repletion research of road and railroad partbusiness (the 5th editions). The Korea Development Institute. Korea. 265276.

Influence of surface complexity to GCPs distribution

113

August 2011, Thailand

114

4th Joint Student Seminar on Civil Infrastructures

THE USE OF FLY ASH CONCRETE
WITH EXPANSIVE AGENT ON
CONCRETE REPAIR
Michael COO1 and Thanakorn PHEERAPHAN2
Graduate Student, Structural Engineering Department,
Asian Institute of Technology, Thailand
mikecoo@gmail.com
2
Professor, Structural Engineering Department, Asian Institute ofTechnology, Thailand
thanakorn@ait.ac.th
1

ABSTRACTThis study was performed to develop a low-cost concrete repair
material with the use of an industrial waste material with pozzolanic
properties such as fly ash by replacing 20% to 50% of the designed binder
proportion, and to test the mechanical as well as semilong term hardened
properties of this mixture which includes compressive strength, splitting
tensile strength, slant shear strength and flexural strength of repaired
concrete prisms. To evaluate the field performance of this developed repair
material in actual service, a full-scale beam repair procedure was also
simulated. Results have shown that the replacement of 50% fly ash and the
addition of 7% expansive agent is the optimum mix proportion for use in thetarget applications with both normally compacted and selfcompacting
grade. The developed concrete attains 7 day compressive strength, splitting
tensile strength and slant shear strength of 42 MPa, 3 MPa, and 7 MPa
respectively and a free shrinkage of <100 microns in 120 days with a lowcracking tendency classification from ASTM C1581 test. The full-scalebeam testing showed that the performance of the repair material is adequate
for use on repairing of the member and to restore the original load capacityof the member. Costing only 20% more (1,480 vs.1,200 THB for a m3 ofrepair concrete to ASTM Type 1 cement only binder), significant
improvements in many (or all) areas are achieved, e.g., 40% increase in
splitting tensile strength, low cracking tendency and <100 microns
shrinkage in 120 days. Therefore achieving a concrete repair material withsimilar properties as those currently available in the market at a fraction of
the cost (2050 THB vs. 34,000 THB)
Keywords: concrete; repair; fly ash; expansive agent; ring Test;
dimensional stability; cracking Tendency; Restrained Shrinkage; Free
Shrinkage; Beam Repair;115August 2011, Thailand

1. INTRODUCTION
During service, concrete structures are expected to provide safety against
failure as well as appropriate serviceability for the expected loads.
Overloading, exposure to weather and other deteriorating substances may
cause concrete structures to lose their intended service requirements.
Appropriate action needs to be taken in order to restore service requirementsof these structures. Concrete structures may have a lower initial cost of
construction, but without proper maintenance and adherence to loading
limits, these structures tend to deteriorate and lose their initial design intent.
Repair and structural strengthening are two major approaches undertaken to
restore the design intent of these structures.
In the past 25 years, repair and rehabilitation of concrete structures inwestern countries have been a major industry due to the deterioration of
structures built during their construction boom 4 decades ago. Annual
spending averaging at $21 Billion is spent on concrete repair in the UnitedStates alone (Emmons & Sordyl, 2004). This explosive growth of the repair
and rehabilitation industry results in the need for more improvement andunderstanding of concrete repair materials. Asia is also fast approaching thisstructural deterioration phenomenon due to the construction boom in thecontinent during the past decade. Rapid building of structures and the lackof comprehensive concrete durability data during that time led buildings todeteriorate at an unexpected and rapid rate, making repair and strengtheninga growing industry in the Asian region. It should be noted that Thailand also
has not been secluded from the durability and early cracking of concrete
structures; survey shows that deterioration started even just after a decade of
construction.
The concrete repair industry is growing in Asia, and the demand for
concrete repair materials is also on the rise, but until now, proprietary andexpensive repair materials are used, making repairs costly and forcing
structure owners to delay repair jobs, which will eventually become aconcern on safety and serviceability of the structure. Effective, durable andconvenient-to-use concrete repair material, especially at an economical costthat can be adopted to be used on high-volume repair applications istherefore developed.
Cracking has been a major concern in the use of conventional concrete
as a repair material in deteriorated structures, as not only unsightly surface
repairs are a concern but also the performance of the repaired portion of thestructure. Repairing repairs is a common encountered problem when normal
concrete is used; this is due to the restrained condition that the existing
concrete is imposing on the fresh repair material, which will eventuallyaffect the serviceability and long-term performance of repair (Kovler &Bentur, 2009). To mitigate and reduce cracking, suggested solutions fromACI Committee 223 includes the use of expansive cement. However,expansive cement is not produced by local cement manufacturers, whichmakes attaining the material uneconomical and impractical for low-cost
repair works. Expansive additive is instead made use of in this research

116

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

since it is a more cost-effective alternative, as well as being available
locally. Expansive additives, although mitigates cracking on repair
materials, can incur additional cost to the final production of repair concrete,thus; it is proposed in this research to add fly ash, an industrial wastematerial which is collected in the magnitude of 3 million metric tons per
year in Thailand alone (Tangtermsirikul, 2005).
With the replacement of fly ash on the total binder of the repair
concrete, an economical blend of binder can be attained as well as
durability, shrinkage, fresh mechanical ability and strength characteristic
improvement from binders based on Ordinary Portland Cement (Siddique,2003) (Ghosh, 1981) (Bilodeau & Malhotra, 1992) (Naik, Singh, & Hossain,1994) (Shafiq & Cabrera, 2004).

2. EXPERIMENTAL PROGRAM
The program is divided into 3 parts: (1) Development of a repair concretefor general purpose repair applications, with sub parts as checking the freshand hardened properties of the proposed mix proportions until target
requirements (Table 1) are met and (2) this blend of binder is designed witha modified coarse and fine aggregate content and the addition of limestone
powder to pass a self-compacting grade concrete according to slump flow,
L-box and V-funnel target requirements (Table 2) are met. (3) Repair beammock-up test, which will be carried upon after final mix proportion has been
decided according to the target requirements and comparing the totalmaterial cost as illustrated in Figure 1.

Figure 1: General Overview of Repair Concrete Development
Table 1: Repair Concrete Target Requirements

Table 2: Self Compacting Concrete Fresh Properties Target Requirements

Influence of surface complexity to GCPs distribution

117August 2011, Thailand

A total of 5 beams with the following properties is tested (a) Control
beam without repair (b) Normal concrete repaired beam at 7 days curing (c)Same beam as b but repair is allowed to stay for 28 days (d) SCC Concrete
repaired beam at 5 days curing (e) same as beam number 4 but repair isallowed to stay for 28 days.

3. MATERIALS
3.1 Cement
A locally produced Ordinary Portland Cement (OPC) conforming to
ASTM classification TYPE 1 is used throughout the experiment.
3.2 Aggregate
Coarse aggregate used is crushed limestone, laboratory sieved to a
maximum size of 13mm and fine aggregate of limestone sand which passed
sieve opening No. 4 and retained on sieve opening No. 100, as per
requirement from ASTM C33 with Fineness Modulus of 2.74. All
aggregates are locally quarried, washed with laboratory water and air dried
before use.
3.3 Fly ash
Pulverized fly ash obtained from Mae Moh Power Plant in Lampang
Province of Thailand, which meets the general requirements of ASTM as
Type-F.
3.4 Expansive Additive
Commercially available expansive additive classified as Type-K
expansive cement when added to portland cement.
3.5 High Range Water Reducing Admixture (HRWR)
The super-plasticizer used for the production of Self Compacting
Concrete (SCC) is a commercially available High Range Water Reducing
Admixture which is polycarboxylic based liquid admixture that complies
with ASTM C494 Type A & Type F, High Range water reducing
admixture.
3.6 Mid-Range Water Reducing and Retarding Admixture (MRWR)
Chemical admixture used for normally compacted concrete (NCC) inthis study to improve fresh properties while maintaining a fairly low w/c
ratio is a commercially available liquid admixture that complies with ASTM
C494 Type-A (Water-Reducing).

118

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

4. CONCRETE SPECIMEN PREPARATION
Aggregates are washed and air dried, weighed using digital laboratory scalealong with the cement, fly ash, water and MRWRA/HRWR. Specimens areprepared on a laboratory temperature of 25-32°C and a relative humidity of
50-55% and cured at the same curing temperature and humidity. All
specimens prepared are left in the mould for 24 hours, de-moulded, water
cured for 7 days and air dried until time of testing.
4.1 Concrete Mixing and Fresh Properties Evaluation
Concrete is mixed using a laboratory mixer then slump according to
ASTM C143 checked and recorded as T0 which is the time right after
mixing, slump is then continuously monitored for 120 minutes at 30 minuteintervals. Initial and final setting time of fresh concrete is also tested using
ASTM C403 method where coarse aggregate is seived out of the mixture
and penetration resistance is checked until 3.5 MPa and 27.6 MPa as initialand final setting time of the mixture.
4.2 Test on Hardened Concrete
Tests on hardened concrete include Compressive strength which
complies with ASTM C33 for mixing, curing and specimen sampling.
Splitting tensile strength using 100 mm x 200 mm cylinder specimens,
tested at 1, 7, and 28 days. Free shrinkage measurement from all thesamples are also monitored using ASTM C157 method up to 120 days ofcuring. All samples tested on hardened concrete are water cured for 7 days
and then air dried thereafter, maintaining a laboratory curing condition of 50± 5% RH and 25 ± 5°C Temperature throughout the curing period.

5 CONCRETE PROPERTIES EVALUATION
5.1 Compressive Strength
Compressive Strength testing is made in accordance with BS 1881
Part 116 using 150 mm x 150 mm x 150 mm cubic specimens. The
compressive testing machine used is a “Shimadzu” Universal Testing
Machine of type UMH 200A set at a maximum loading of 200 Tons.
Loading rate is limited to 0.15 to 0.35 MPa/s or 0.35 to 0.8 Ton/s in thisspecimen.
5.2 Splitting Tensile Strength
Tensile strength of specimens was tested in accordance with ASTM C
496 splitting tensile strength method. Concrete cylinders with the dimensionof 100 mm x 200 mm are casted and tested horizontally. The same load
testing equipment from the compressive strength testing but is set to have aload limit of 100 Tons.

Influence of surface complexity to GCPs distribution

119August 2011, Thailand

5.3 Slant Shear Strength
Concrete being used as a repair material should bond to a certain
degree with the substrate concrete, in this study, the use of a slant shear testin compliance with ASTM C882 is used due to the simple and effective
approach in determining the bond strength between two materials, which inthis case the substrate concrete and the repair concrete.
This method of testing is chosen because of the practicality andsuitability to our application since this method subjects the bond surface
between the two different materials into shear and compressive stresses
which are also happening on the actual concrete structures being
repaired.This method is done by first cutting of the substrate concrete into
30° angles and placed at 100mm x 200mm cylinder molds then repair
concrete is casted on top.
5.4 Free Shrinkage Test
Free or unrestrained shrinkage of specimens are taken in accordancewith ASTM C157. A prismatic specimen is casted, cured and measured,using a digital length comparator with measurement reading capability up to
1x10-6 m/m. Relative length of samples is taken as the difference between
the starting readings of the samples at 1 day and the consequent days
reading divided by the gague length of the sample which is the distance
between the head of two stainless steel bolts which in this case is set to240mm. Length comparator equipment is set to zero at every test of thesample.
5.5 Restrained Ring Test
Completely restrained specimens are casted on a steel ring thatcompletely restrains dimensional shrinkage of the concrete by the ring. This
test is done in accordance with the specifications and suggestions of ASTM
C1581. The objective of this testing procedure is to capture the real timestrain of the concrete during hydration, drying, curing and finally determine
the cracking strain of the sample under a completely restrained condition.
Ring specimens are prepared by casting the concrete on a steel ring with
attached strain gages and connected to a data logger to monitor strains.
Strain measurement is done on the steel ring assuming that the casted
concrete is completely transferring strain on the restraining steel ring andtherefore, strains on concrete while on the fresh and hardening state ismonitored until the time of cracking.

6. RESULTS AND DISCUSSION
6.1 Fresh Concrete Properties
All mixtures exhibit excellent workability even with a low w/c ratio
due to the addition of a plasticizing admixture and even better after120

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

replacement of fly ash from the binder. Mix proportions are shown on Table
3 and Table 4. Minimum initial slump after mixing and after 2 hours of thesamples are 22 cm and 6 cm respectively, samples with the replacement of
fly ash exhibit a higher initial slump but with a similar final slump after 2hours except that of 50% fly ash exhibits an even higher final slump whencompared to the control mix. Fresh properties of concrete containing fly ash
are also observed to be more workable when compared to the control
concrete based upon the ease of sampling, casting on moulds and vibration
time until consolidation. This can be explained by the generally acceptedcharacteristic of fly ash which in addition or replacement to concretebinders, reduces water demand for a given workability compared to pure
Portland cement concrete due to the spherical and smaller size of fly ash
particles, therefore making mixes with fly ash more workable. In thisresearch, Slump of fly ash concrete are investigated and shown in Table 5
and graphed values in Figure 2 to Figure 3.

Figure 2: Effect of Fly ash replacement on Slump of concrete

Table 3: Selected Mix Proportions of Normally Compacted Concrete
Specimens

Influence of surface complexity to GCPs distribution

121August 2011, Thailand

Table 4: Slump and Setting Time

Table 5: Mix Proportions of Self Compacting Concrete Specimens

6.2 Influence of Fly ash Replacement and Expansive Additive on Fresh
Properties of Concrete
Since workability is one of the important factors in the developmentof concrete mix designs especially on repair applications since placement
concerns and work done on concrete is more complicated than theplacement of normal concrete which is important to assess its performanceand ease of application while on the fresh state in the field. Figure 2 showsthe effect of fly ash on slump loss of fresh concrete. It can be observed thatas fly ash replacement increases, a slight increase in initial slump occurs,
but with the replacement level of 30% a sharp decrease in slump loss isobserved at the time interval between 30 minutes and 60 minutes. The
increase in workability of concrete mixtures containing fly ash has also beenstudied by Hobbs and Brown and concluded that as replacement levels of
fly ash in cementitious materials increase by 10%, the compacting factor
and overall workability increased as the same order as it would byincreasing water by 3%, which is essential in high strength concrete andrepair applications where a low water content is needed for early strengthand durability requirements while lowering the required dosage of122

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

plasticizing admixtures in order to achieve a desired workability for theintended application.
6.3 Effect of Expansive Additive on Slump loss of Concrete with Fly ash
replacement
The addition of expansive agent on binders of concrete with fly ash
replacement increased the initial and final slump of the concrete mixture up
to 6 cm with the 50% fly ash replacement and 4 cm for the lowest
replacement level of 20% fly ash mixture as shown on Figure 3. These
results have validated the question whether the addition of more fines or
cementitious material in our mixture, specifically expansive additive in
powder form, will affect the fresh properties of the mixture and whether the
additional dosage of super plasticizer is needed. Although with an additionof expansive agent on the concrete mixture, the addition of fly ash inmagnitudes of 10% from other researchers (Hobbs, 1983) is still effective in
reducing water demand and therefore increasing the slump of the mixture
without adversely requiring an additional water demand or the addition ofsuper plasticizers to meet the required fresh properties of this mixture.
Figure 3 shows the similarity of slump loss on different fly ash replacementwith expansive additive.

Figure 3: Effect of Fly ash with Expansive agent on slump
6.4 Setting TimeThe rate of stiffening of the concrete mixture is expressed in setting
time, codes on the application and placing of concrete allows the cement tobe worked upon and placed until initial setting of the mixture is reached. In
this repair concrete developed, a minimum setting time of 2 hours is set as atarget in order to allow time for delivery and compensate for the delay andcomplications of actual site work. As seen on Figure 4, all setting times
including the control concrete is more than the set target, but with thereplacement of fly ash in 10% intervals, initial setting time is delayed forabout 20 minutes for the 20% and another 20 minutes for the 30% mixture,

Influence of surface complexity to GCPs distribution

123August 2011, Thailand

but when replacement level reached 50%, a steep increase of greater than 1hour is achieved from control. These findings have also correlated with theresults by several researchers (Joshi, 1993) which concluded that fly ash
when replaced in binders of concrete mixtures produces a retarding effect.

Figure 4: Setting Time Comparison between mixtures
6.5 Self Compacting Concrete Fresh properties
Properties of the Self-compacting Concrete developed are in Table 5.
These target requirements meets the specification of ACI 237 and the more
stringent requirement suggested by researchers (Hwang, Khayat, &Bonneau, 2006).
Table 5: Results of Developed SCC concrete

HARDENED PROPERTIES
6.6 Compressive Strength of Concrete with Fly ash Replacement
Compressive strength of different fly ash addition is compared as afunction of curing time is plotted in Figure 5. This graph shows the mixtures
where fly ash is replaced as part of the binder as a step in finding the propermix proportion for the repair concrete being developed. As seen from thegraph, a higher 7 days early strength than the control concrete can beachieved by using a 30% fly ash replacement.

124

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 5: Concrete Compressive Strength
6.7 Compressive strength of concrete with fly ash replacement with theaddition of expansive agent
For the improvement of early compressive strength of repair concrete,
the addition of an expansive additive is chosen since the concrete mix
proportion is already employing a low water to binder ratio and it isessential to maintain this for early strength. This addition of expansive agent
improves early strength of the concrete as well as bond strength ,which will
be presented in the following sections, due to the formation of ettringite
crystals, this evolution is discussed further in other researchers (Hu & Li,
1999), which also concludes that the addition of powder form expansive
agents improve the compressive strength of mixture.
Figure 6 illustrates the compressive strength between samples withand without addition of expansive additive and the control concrete. Thisgraph shows that in this case, compressive strength decreased for the 30%
fly ash mixture after the addition of the expansive additive while the 50%
fly ash mixture gained more strength after the addition of expansive agent.
This phenomenon exhibited by the mixture may be due to the earlypozzolanic reaction of the 30% fly ash replacement in which without an
expansive agent, gains early strength but otherwise, a different reaction isobserved, due to the ettringite crystal formation which occurs at roughly thesame time as the mixture gains strength, and one hypothesis is that these
two reactions occuring together can cause a reduction in the concrete
strength.

Influence of surface complexity to GCPs distribution

125August 2011, Thailand

Figure 6: Concrete Compressive Strength with Expansive Additive
6.8 Compressive Strength of Self Compacting Concrete with 50% fly
ash replacement and the addition of expansive agent
Based on the results until this point, we can see the advantages on theearly compressive strength gain produced by the addition of an expansive
agent while the fly ash replacement gains strength on the long term while
lowering the total cost of the mixture. In this section, this same binder
proportion is modified in order to comply with the SCC grade concrete
specifications (Hwang, Khayat, & Bonneau, 2006). Final SCC mix
proportion is shown in Table 4. The modification of this binder content tobe made into SCC broadens the application range of this mixture being
developed. Moreover, it is also essential to check the properties of the SCC
mixture, and in this section, the compressive strength is compared with thecontrol mixture and the normally compacted concrete. Results are shown in
Figure 7 and it can be observed that by changing the aggregate content andadding more fines such as limestone powder in the mixture, similar 3 day
strength can be achieved but a slightly delayed strength gain thereafter.

Figure 7: Effect of mixture modification from NCC to SCC concrete of 50%
Fly ashreplacement and expansive agent addition

126

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

6.9 Free Shrinkage of concrete with fly ash replacement
With the compressive strength results presented in the previous
section, it is also essential to determine the dimensional stability of theconcrete material since one of the most crucial performance of the repair
material according to several researchers is the tendency of the material tocrack due to drying shrinkage and incompatibility to the substrate concrete.
Concrete mixtures with fly ash replacement at different magnitudes arecompared with their free shrinkage to determine whether a type of mixture
is suitable for the repair of structures, in this study, a shrinkage limit of lessthan 300 microns is set, noting that this limit is set above the 400 microns
minimum of USACE/ICRI manuals of concrete repair practice, this limit
however, does not ensure that the repair material does not crack, only that
this concrete proportion will generally be applicable to concrete repair. A
free shrinkage plot of the concrete being studied in this research which has
replacements of different levels of fly ash is presented in Figure 8. Free
shrinkage of the concrete mixtures with 20% and 30% fly ash replacements
exhibits a high free shrinkage, even greater than the control concrete, but it sobserved that the 50% fly ash replacement has exhibited a free shrinkage
train similar to that of control concrete.

Figure 8: Free shrinkage of samples with fly ash replacement
6.10 Free shrinkage of concrete with fly ash replacement and expansive
agent addition
Free shrinkage of the following mixtures with fly ash replacements andexpansive agent addition is presented in this section; these mixtures arecompared to the control concrete and the 50% fly ash concrete sinceshrinkage of the 20% fly ash samples have already exceeded limiting
shrinkage strain of 300 microns. Figure 9 shows that the expansive agent
addition to the fly ash concrete has been effective in drying shrinkage
reduction of the mixture to 92 microns in 120 days of curing comparing to
the 262 microns for the 50% fly ash admixture alone; from this mixture a
65% decrease in shrinkage of the concrete is observed. The SCC mixture

Influence of surface complexity to GCPs distribution

127August 2011, Thailand

noting that it has the same binder proportion as the NCC counterpart
mixture, has an identical free shrinkage strain as the NCC mixture. These
two mixtures has shown good dimensional stability when compared to othermixes in this study, therefore more trials are done in this mix to determine if
other properties meet the requirements.

Figure 9: Free shrinkage of samples with 50% fly ash replacement andexpansive addition
6.11 Splitting Tensile Strength
The splitting tensile strength is tested in accordance with ASTM C496 to
evaluate the tensile strength in the concrete developed under load. Results
are shown in Figure 10, 1 day, 7 days, and 28 days cured sample is takenfrom the prepared specimens to evaluate the tensile strength of the mixtures.
Results shows that the replacement of fly ash without expansive additives in
mixtures based on Figure 9 resulted in a decrease of tensile strength of 19%
during early age but with the exception of the 50% replacement (CFA50)
which gained a significant 50% addition during the 7 days of curing, thisresults only verifies that concrete with low levels of fly ash can reduce thetensile strength of mixtures while high volume fly ash due to more fines byvolume in the mixture can increase the tensile strength capacity of theconcrete which also correlates with the compressive strength of the concretethat has followed the same trend. Comparing these results with the additionof expansive additive, a similar trend is also observed where early age
tensile properties of the concrete with 20% and 30% fly ash replacementseven with the addition of expansive additive tensile strength decreases, butwith the replacement of 50% fly ash to the binder, an increase in tensile
strength is observed even at early ages which is nearly the same magnitude
as the one without expansive additive. Therefore, based on these test results,
a conclusion that expansive additive to fly ash concrete may help increase
the tensile strength but at a very minimal level and a later age of theconcrete. Self-compacting concrete however has exhibited a 41% for the 28
day cured specimen. This good reaction may be an indication that the128

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

addition on limestone powder on fly ash concrete improves ettringite growthon the hydration of cement and therefore promotes a higher tensile strength
on concrete; this chemical hydration reaction is elaborated further in thereferenced research (Weerdt, Kjellsen, Sellevoid, & Justnes, 2011.

Figure 10: Splitting Tensile Strength
6.12 Slant Shear Strength
Slant shear test results of selected mixture are shown in Figure 10,comparing the control mixture coded as CFA0E0 with the 50% fly ash
replacement (CFA50), a 60% decrease in early bonding strength and 100%
decrease after 28 days curing, this can be justified due to the delayed
strength gain of the concrete found on the compressive strength of the same
sample. This however is not the same trend when an expansive agent is
added to the mixture, even with 50% fly ash replacement of the mixture, the
1 day bonding strength of the mixture is similar to the control concrete,
which is mostly due to the addition of an expansive agent which improves
bond strength as also observed by other researchers (Halicka, 2009). Thissame binder content when modified into SCC has decreased bonding
strength when compared to the NCC counterpart, but still 9% and 7%
greater than the control mixture in terms of bonding. This property of the
SCC mixture may be due to the high fines on the SCC mixture.

Influence of surface complexity to GCPs distribution

129August 2011, Thailand

Figure 11: Slant shear strength of selected specimens
6.13 Ring Test (Cracking Tendency)
In order to determine the sensitivity to cracking of the developed
mixture, a cracking tendency test is performed as per ASTM C1581
specification. This test consists of casting a concrete ring around a steel
ring. The top and bottom surfaces are sealed so that drying will only beinitiated on the outer circumferential surface. This ring specimen restraint
configuration is chosen to be performed in this study due to certain
advantages over other test methods, one of the main advantage is that whenconcrete is casted as a ring specimen, no stress concentrations are imposedon the concrete specimen unlike the linearly restrained specimens, therefore
allows the concrete to undergo internal volumetric change, stressdevelopment, creep and creep relaxation evenly throughout the element, andwith the circumferential surface exposed to drying and the top and bottom
portion of the ring sealed, cracks are readily visible, which indicates that the
maximum tensile strain capacity of the concrete has been reached. The
recording of the strains transferred by the concrete on the internal steel ring,
compressive strain can be captured and taken as the internal tensile stress ofthe concrete specimen. This tensile strain capacity of the specimen can betaken as a general value and specific to the concrete mixture at a given
curing condition since this maximum tensile strain comes from the internal
hydration, deformation and relaxations of the concrete specimen alone,
therefore through this setup, concrete can be partially isolated from otherfactors that may interfere with the internal strain mechanisms of the sample.
There are 3 separate samples evaluated for cracking tendency in this study,
with each representing each steps in the development of the repair concretein this research. The first concrete specimen tested is the control concrete(CFA0E0), this mix proportion has high cement content (485 kg/m3) and a
low water-cementitious material ratio. This proportion is tested to know thetensile strain propagation of plain concrete without any binder modification;
cracking of the concrete specimen is shown in Figure 11.

130

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 11: Typical cracking of Restrained Ring specimen
6.14 Control Ring specimen
Two ring specimens of the control sample is casted and monitored forstrain development then calculated for the stress rate with respect to the ring
dimensions, results and testing data are presented in Table 6, detailed
explanation on how to determine these testing data and linear regression
results are found in ASTM C1581 standard. Calculating the stress rate andcomparing to the cracking tendency classification from ASTM C1581,
results suggests that the control mix has a high potential for cracking, andbased on the graph shown on Fig. 12, the strain of the specimen increased
rapidly and reaching the tensile strain capacity at a little less than 3 days,
this shows that by using plain concrete as a repair material, especially whenrestrained by an old concrete, this material will crack when the strain of thematerial will reach 150 microns.
Table 6: Stress Rate of Control Concrete Specimen

Influence of surface complexity to GCPs distribution

131August 2011, Thailand

Figure 12: Control Specimen Ring Strain Plot
50% Fly ash replaced Ring specimen
Through finding out the stress rate of the control specimen which isvery high, a 50% fly ash mixture is tested, to verify that a delayed reaction
through pozzolanic activity of the fly ash can minimize the strain
propagation of the concrete element allowing more time for the concrete torelieve stresses through stress relaxation or creep, as researchers (Hossain &Weiss, 2004) who have studied the stress propagation on concrete ring
specimens have observed. Tabulation of the data is shown in Table 7, asshown, the stress rate of the mixture has decreased significantly, but it isobserved that the value is just 3% lower than the low-cracking potential
limit set by ASTM. Moreover, a cracking time of 33 days exceeds thelimitations of USACE for concrete materials used in repair, the plotted
figure with strain against curing duration is plotted in Figure 13, it showsthat the strain propagation of the 50% fly ash replaced mixture is indeed
more slowly than the control mixture, and through this delayed strain
propagation, stress relaxation of the sample takes place.
Table 7: Stress Rate of 50% fly ash replaced Concrete Specimen

132

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 13: Strain propagation of 50% fly ash replaced concrete and control
concrete
50% Fly ash replaced with Expansive Admixture addition Ring
specimen
Strain measurements of 50% fly ash addition concrete, with andwithout expansive agent is presented in Figure 14 and the stress rate
calculations are presented in Table 8. Results as seen from the graph thatwith the addition of expansive agent to the 50% fly ash concrete, a veryminimal (<50 microns) deformation of the concrete specimen can beachieved, moreover the maximum strain from this specimen read -110
microns on the negative end and 60 microns on the positive end, which fromobservation of cracking at the surface of the concrete, this is still notdeleterious since the concrete have not shown any cracking during the test
duration. It is also noticed in the graph that expansion of the concrete started
at around 5 days of curing then a gradual decrease in shrinkage occurred
until around 28 days of curing, and beyond this period a minimal
deformation is recorded. As for the results of the calculation of stress rate, avery low stress rate is occurring from the specimen, but it should also be
noted that these stress rate calculations are based on the linear regression of
the graph, which is considering the median of the values and assuming alinear relationship from the casting of the specimen until continuous curing.
Table 8: Stress Rate of SCC 50% fly replaced Concrete Specimen withexpansive addition

Influence of surface complexity to GCPs distribution

133August 2011, Thailand

Figure 14: Strain propagation of 50% fly ash replaced concrete, with andwithout expansive agent addition
6.15 Full Scale reinforced concrete beam repair
In this section, results of load against mid-span deflection of therepaired beams attained from third-point loading of full scale beamspecimens. Control beams are cured for 90 days before repair material iscasted, no surface preparation or bonding agent is used in the beam surface.
This testing is to evaluate the inherent performance of the repair material
under actual service.
Normally Compacted Concrete beam repaired on compression zone
The normally compacted concrete repair material developed is
performance tested on a simulated beam repair procedure. This repair iscasted on the compressive zone of the substrate beam that is cured for 60
days and tested after 7 days curing of the repair material. Results from thetest as shown in Fig. 15 suggest that at 7 days of curing, the repaired beam
can already sustain a load similar to that of the control beam without repair.
An increase of 50% of the elastic range of the specimen and an increase of
6% on the ultimate load capacity of the beam is also observed after 7 days
curing of the repair material. These results represent that the repaired
members can be open to service 7 days after repair casting.

134

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 15: Load against mid-span deflection of control and NCC repairedbeam
Self Compacting Concrete repair on tensile zone
Results of the SCC repaired beam from the tensile zone compared tothe control beam without repair is presented in Figure 16. Casting of the
SCC material at the bottom of the beam is done without consolidation.
Load-testing is done after 5 days curing of the repair material, results haveshown that a similar elastic deflection with the control beam is observed
during the elastic stage of the graph, but an elastic limit of 9 tons is achieved
with the repaired SCC beam while the control beam 4 tons, a 225% increasein elastic load capacity of the beam. Moreover, a similar ultimate strength of
the beam is observed during failure of the specimen.

Figure 16: Load against mid-span deflection of control and SCC repaired
beam

Influence of surface complexity to GCPs distribution

135August 2011, Thailand

Long term behaviour of NCC and SCC concrete repair
Beam where the repair material is casted and left to cure for 28 days is
discussed in this section. Curing of the specimens is 7 days moist curing andair dried thereafter, beams after casting the repair material is left outdoors
and exposed to weather. Cracking or the delamination of the repair material
is visually monitored during this time, but none has occurred after 28 daysof curing. Load testing of long term specimens are also presented in Figure
17 to present the long term performance of the members, which as theresults suggest, the original performance of the control beam has been
restored after repair.

Figure 17: Load against mid-span deflection of control and SCC long term
repaired beam
6.16 Cost
This section compares the properties of the developed repair concrete
to that of similar commercial repair concrete products currently offered in
the market. Performance from data sheets of three commercial products
supplied in mortar bags and extended with coarse aggregate are used for thecomparison of performance. Cost of commercial product for a yield of one
cubic meter of repair concrete is calculated based on the manufacturers’
specified proportion of packed material to coarse aggregate ratio for a yieldof one cubic meter. While the cost of the developed concrete is calculated
based on the raw cost of each material and therefore, noting that additional
cost aside from the base materials such as development, testing andmarketing are not considered in the cost. Tabulated values of theperformance of three commercial products and the developed concrete areshown in Table 9.

136

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Table 9: Tabulated properties of commercial products vs. developed repair
concrete

Cost Comparison with commercial product of similar properties andapplications
Based on the data shown in Table 9, a repair concrete of similar
performance to highly priced commercial products is developed at thefraction of the cost (94.5% lower cost than the cheapest product considered).
The price of the developed repair concrete binder considered should be
noted that it is calculated based on the cost of raw materials and based on
the current market price as of research period, unit prices of these materials
are shown in Table 10 and binder costs of different fly ash replacementswith expansive agent brand S in appendix D.
Table 10: Unit Cost of Binder Materials

CONCLUSIONS
Based on the results obtained from the experimental study, thefollowing conclusions can be drawn:
1. The normally compacted repair concrete type with a total binder of
485 kg/m3 with 50% fly ash replacement and 7% expansive agent addition
can be effectively used as a low-cost, low-cracking tendency, dimensionallystable(>100 microns), and high early strength (42 Mpa in 7 days)
concrete repair material.
2. A self-compacting repair concrete with the same binder proportion
can achieve a high slump flow (810 mm), good passing ability, nonsegregating, high early strength(33MPa in 7 days), low shrinkage (>100
microns) and low cracking tendency material (>60 days no crack).
3. Full-scale reinforced concrete beams repaired with the developedmaterial exhibited enhancement of performance in terms of higher load

Influence of surface complexity to GCPs distribution

137August 2011, Thailand

before first cracking and an increased elastic limit of the repaired beam incomparison to the original beam without repair.
4. A low-cost (2,050 THB/cu m) concrete repair material is developedwith a performance similar to those of expensive concrete repair materials.

REFERENCES
Emmons, P., & Sordyl, D. (2004). Vision 2020: A vision for theconcrete repair protection and strengthening industry.
Kovler, K., & Bentur, A. (2009). Cracking sensitivity of normal andhigh-strength Concretes. ACI Materials Journal, V. 106,No. 6.
Siddique, R. (2003). Effect of fine aggregate replacement with class F
fly ash on the mechanical properties of concrete. Cement and Concrete
Research 33 , 539-547.
Ghosh, R. (1981). Creep of fly ash Concrete. ACI Journal 78 , 351387.
Bilodeau, A., & Malhotra, V. (1992). Concrete incorporating highvolumes of ASTM Class F flyashes: mechanical properties and resistance todeicing salt scaling and to chloride-ion penetration. . ACI Special
Publication, 319-349.
Naik, T., Singh, S., & Hossain, M. (1994). Permeability of concrete
containing large amounts of fly ash. Cement and Concrete Research 24 ,913-922.
Shafiq, N., & Cabrera, J. (2004). Effects of initial curing condition on
the fluid transport properties in. Cement & Concrete Composites 26 , 381–
387.
Joshi, L. S. (1993). High strength concrete with high volumes ofCanadian subbituminous coal ash. Third International Symposium on
Utilization of High Strength Concrete.
Hwang, S., Khayat, K., & Bonneau, O. (2006). Performance-based
specifications of self-consolidating concrete used in structural applications.
ACI Materials Journal, V. 103 No. 2.
Hu, S., & Li, Y. (1999). Research on the hydration, hardening
mechanism, and microstructure of high performance expansive concrete. 29.
Weerdt, K., Kjellsen, K., Sellevoid, E., & Justnes, H. (2011). Synergy
between fly ash and limestone powder in ternary cements. Cement andConcrete Composites , 30-38.
Halicka, A. (2009). Active repairs of structures with materials basedon expansive cement. Archives of Civil Engineering.
Hossain, A. B., & Weiss, J. (2004). Assessing residual stressdevelopment and stress relaxation in restrained concrete ring specimens.
Cement and Concrete Composites, 531-540.

138

4th Joint Student Seminar on Civil Infrastructures

ANALYSIS OF MID RISE STRUCTURE
MADE BY TIMBER AND STEEL-SIMULATION OF SHAKING TABLE TESTSBY TIME HISTORY RESPONSE ANALYSIS
Kazuki TACHIBANA1, Mikio KOSHIHARA2
1
M.Eng., Graduate Student School of Eng.The University of Tokyo, Japanktachi@iis.u-tokyo.ac.jp
2
Associate Professor, IIS, The University of Tokyo, Japan
Kos@iis.u-tokyo.ac.jp
ABSTRACT
Recently, environmental issues such as global warming have gainedinternational attention with worldwide focus on ways to reduce humanimpacts. The efficient use of wood materials is one of the effective methodsof reducing environmental load from the standpoint of “carbon fix” and“suppression of carbon-dioxide emissions”. Based on this, the developmentof mid-to-high-rise wooden buildings in a safe and effective manner has
attracted attention world wide. Mid-rise and high-rise wooden structures
are gaining popularity in many countries including Japan. The paper
discussed about seismic ability of Mid-rise and high-rise wooden structureson the shaking table test. The shaking table test is NEESWood 2009, US andJapanese researcher did. The specimen is 6 stories timber and first story is
steel structure. I simulated P-δcurve by using the test results. For them we
could simulate specimen story period, displacement and shear force.
Keywords: shake table testing, seismic response, mid-rise building,light-frame wood, earthquake.
1. INTRODUCTION
Recently, environmental issues such as global warming have gainedinternational attention with worldwide focus on ways to reduce humanimpacts. The efficient use of wood materials is one of the effective methodsof reducing environmental load from the standpoint of “carbon fix” and“suppression of carbon-dioxide emissions”. Based on this, the developmentof mid-to-high-rise timber buildings in a safe and effective manner has
attracted attention world wide. Mid-rise and high-rise timber structures are
gaining popularity in many countries including Japan. Some companies
have begun to go as high as four stories in wood and the Japanese.
Government has mandated that a certain percentage of public structures bemade of wood. This increasing interest and government mandate serve as
the motivation for this paper.

139August 2011, Thailand

In July 2009 a full-scale mid-rise light-frame apartment building was
subjected to a series of earthquakes at the world’s largest shake table in
Miki, Japan. On NEESWood project, US and Japanese researchers worked
jointly to test the building. We already considered about the structuraldesign of the building in relation to Japanese Building Codes. The purposeof this study is simulation of shaking table tests by time history response
analysis on the result of the test.
2. SHAKING TABLE TEST
2.1 Specimen
This specimen consisted of a single story of steel with six stories ofwood on top. Figure 1 shows a panoramic view of the specimen. The
timber building was approximately 18.14m x 12.14m (60ft x 39ft) in planview and about 16.82m (56ft) tall. The design details of the building werequite extensive and only a basic description of the structural configuration isprovided here for brevity. The floor plan, layout plan and the direction are
shown by Figure 2. Shear walls in Capstone building used mostly 2x6
framing and 12mm (15/32 inch) T&G Oriented Strand Board (OSB) on
either one or both sides. The nail which we used is “10d common” (length:
7.6mm, diameter: 3.8mm). The nail schedule was mostly 2”/12” or 3”/12”in the lower floors. Incorporation of a new wall type was also investigatedin the project. A double mid-ply wall (mid-ply wall) system designed tohandle high shear demand that exceeds the capacity of traditional shear
walls was included. Also because of the high shear capacity of shear walls,
high strength shear screws were used at the top and sill plates of the shear
walls instead of bolts. These SDS screws had a tested ultimate capacity of
approximately 1 kip per-connector in shear. Studs are D.fir from 2nd to 4thstory and S-P-F from 5th to 7th story. Gypsum wall board (GWB) was
installed on all inside walls except mid-ply wall and ceilings with tape andputty on all joints except the wall-to-ceiling joints and corners. Each shear
wall stack included glulam beams as shear collectors in between stories. The
glulams were fully supported by shear walls except in one line between theelevator shaft and stairwell where they acted as beams since no bearing
and/or shear wall was present. Finishing as many joints as possible wasdesirable in order to provide realistic damage inspection results. For
reference, the origin is the left corner, and the short dimension of thebuilding is designated as the X direction and the long dimension as the Y
direction. (Following Figure 2) Wood shear walls were designed as stacked
wall systems with a combination of steel rod hold-downs (ATS) withmechanical shrinkage compensating devices at each end to prevent
overturning, reduce uplift, and remove slack from the tie-down system thatwould otherwise develop from in-situ reductions in wood moisture content
and natural settling of the structure.

140

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 1: The panoramic view of specimen
Single shear wall
EV hall
12,141

Mid-ply wall

Double shear wall
Non-shear wallx

18,142yFigure 2: The floor plan and layout plan

2.2 Test plan
We used Nothridge CanogaPark wave by changing times. The time isdecided by Hazard level on return period. In Seismic test (ST) 1and 2, thetension of Steel brace was loosed. The brace in ST3~5 was tied. Table 1
shows more detail information.
Table 1: Seismic test schedule
Seismic test
1
2
3
4
5

Test date

Brace situation

6/30/2009

Loose brace

7/6/2009
7/14/2009

Tied braced

Hazard level
50%/50 yr
7%/50 yr
50%/50 yr
10%/50 yr
2%/50 yr

Scalingfactor
0.53
1.40
0.53
1.20
1.80

X
0.19
0.50
0.19
0.43
0.65

PGA (g)
Y
0.22
0.58
0.22
0.50
0.75

Z
0.26
0.69
0.26
0.59
0.88

2.3 damage inspection
After shaking table test, we did damage inspection. The damage wasonly the clack of gypsum board and the place was mainly corner of opening.
Figure 3 shows the example. By the damage inspection, it was clarified that
there are much damage especially at 4th and 5th story. It shows a similar
result in story drift at 3rd ~ 5th story.
Analysis of mid rise structure made by timber and steel –simulation of shaking table tests by time history
response analysis141

August 2011, Thailand

Figure 3: Damage inspection(L:5th story south center wall R:4th story EV hall)
3. MODELING
3.1 Test result
Displacement measured by image processing. The measuring points
exist 7 points per story. The circle and square points in Figure 4 show theplace setting sensors. The circle one is the sensor measuring displacement.
The square one is the sensor measuring acceleration.
Test result is a spatial model actually. We averaged all measured
points per stories for changing mass model from spatial model. Story drift isthe value taking Shaking table data or averaged data at lower story fromaveraged displacement at one story. The average about acceleration follows
that (center x 2 + 4 corner)/6. Shear force can calculate that the acceleration
multiply mass.

142

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Single shear wall
EV hall
12,141

Mid-ply wall

displacement
Double shear wall

acceleration

Non-shear wallx

18,142yFigure 4: Position of measuring sensor

3.1 Specimen
We made a specimen model for analysis. The model was based on test
results. Because we can see it didn’t achieve Pmax, We expressed tri-linear
before Pmax. Figure 5 shows an example to make Skelton curve. SC in the
figure means Skelton curve.
Force[kN]

1200
1000
800
600
theory
ST 5
ST 2
ST 1
SC
on T est
Disp.[cm]

400
200
0
0

1

2

3

4

5

Figure 5: Skelton curve from test result
4. ANALYSIS
4.1 Time historical analysis
We analysis by non-linear, time historical analysis is “increment
displacement express” based on Wilson θ. Formula 2 show “increment
displacement express”. Formula 2 consist Formula 1 in Figure 6. The
Damping matrix is the following formula 3 and 4.
− [M ]{&y&0 } = [M ]{&x&} + [C ]{x&} + [K ]{x}(1)Analysis of mid rise structure made by timber and steel –simulation of shaking table tests by time history
response analysis143

August 2011, Thailand
Q(y)

k(t)

yn

y

yn+1

Figure 6: increment displacement express− mΔ&y&0 = mΔ&x& + cΔx& + k (t )ΔxΔx = xn+1 − xn , Δx& = x&n+1 − x&n(2)

Δ&x& = &x&n+1 − &x&n , Δ&y&0 = &y&0 n+1 − &y&0 n[M]: mass matrix, [C]: damping matrix, [K]: stiffness matrix
cij = cij* ω ( j )hij( j ) = cij* (2kij )(3)

cij = 2hij( j ) kij ω ( j )⎡c11 L c1 j ⎤[C ] = ⎢⎢ M O M ⎥⎥⎢ ci1 L cij ⎥⎣⎦⎡k11 L k1 j ⎤[K ] = ⎢⎢ M O M ⎥⎥⎢ ki1 L kij ⎥
⎣
⎦

(4)

4.2 Analysis model
We used magara model for timber light frame and normal model forsteel as historical models. Figure 7 shows each historical model. The
parameters of magara model are based on the study for light frame at past.
Table 2 shows the parameters. I didn’t consider historical after Pmax onnormal model here because even ST5 didn’t achieve Pmax.

144

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57ⅰ) magara modelⅱ) normal model
Figure 7: analysis model

Table 2 magara model parameters for light frame
α
0.25

β
0.75

γ
0.5

ε
0.6

ζ
1.04

Pop
Pmax x
0.125

4.3 EARTHQUAKE WAVE
We used measured earthquake wave on shaking table for loosing timelag. Figure 8 shows Nothridge CanogaPark 180% wave, Y direction. On
next term, we compare analysis to test result for using this wave.
[gal]

1000
500
0
-500

0

5

10

15

20

25

30

35

-1000

40
[sec]

-1500

Figure 8: Nothridge CanogaPark 180% Y direction (measured wave)
5. RESULT
Figure 9 shows the analysis result on ST5 Y direction because thedisplacement of Y direction is bigger than X direction. The result shows
analysis result of 2nd story is bigger than the test result. But it is safety value.
We can see not fit on 1st story. (Figure 10) Figure 11 shows time history
shear force comparing analysis to test. The result shows period is mainly
fitting and safety value. Safety vale means analysis result is bigger than test
result.

Analysis of mid rise structure made by timber and steel –simulation of shaking table tests by time history
response analysis145

August 2011, Thailand

Shear[kN]

1500

1000

7F

500

0
Disp.[cm]-500
Analysis
Skeleton curve-1000

T est result
-1500
-5

-3

-1

1

3

5

Shear[kN]

1500

1000

6F

500

0
Disp.[cm]-500
Analysis
Skeleton curve-1000

T est result
-1500
-5

-3

-1

1

3

5

Shear[kN]

2000
1500

5F

1000
500
0
Disp.[cm]-500
Analysis-1000

Skeleton curve-1500

T est result

-2000
-5

-3

-1

1

3

5

Shear[kN]

2000
1500

4F

1000
500
0
Disp.[cm]-500
Analysis-1000

Skeleton curve-1500

T est result

-2000
-5

146

-3

-1

1

3

5

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Shear[kN]

2000
1500

3F

1000
500
0
Disp.[cm]-500
Analysis-1000

Skeleton curve-1500

T est result

-2000
-5

-3

-1

1

3

5

Shear[kN]

2000
1500

2F

1000
500
0
Disp.[cm]-500
Analysis-1000

Skeleton curve-1500

T est result

-2000
-5

-3

-1

1

3

5

Shear[kN]

2500
2000
1500

1F

1000
500
0
Disp.[cm]
-500
-1000

Analysis-1500

Skeleton curve-2000

T est result

-2500
-3

-2

-1

0

1

2

3

Figure 9: Shear force- story displacement comparing
Test result to Analysis on ST5
[cm]

6
4

5F

Analysis
Test result

2
0
-2

0

5

10

15

20

25

30

35

40
[sec]

-4
[cm]

0.6
0.4
0.2
0
-0.2 0
-0.4
-0.6
-0.8
-1

1F

Analysis
Test result

5

10

15

20

25

30

35

40

[sec]

Figure 10: Time history response (story displacement)
[kN]

3000
2000

1F

Analysis
Test result

1000
0
-1000

0

5

10

15

20

25

30

35

40
[sec]

-2000

Figure 11: Time history response (story shear force)

Analysis of mid rise structure made by timber and steel –simulation of shaking table tests by time history
response analysis147

August 2011, Thailand

6. CONCLUSION AND RECOMMENDATIONS
We simulated P-δ carve by time history analysis using Skelton carve on
Test result. The simulation result shows the following methods:
・ The result show 4th and 5th story displacement is bigger than theother stories as the test result. This is a characteristic change
happened actually,
・ Period almost fit the test result.
・ On 1st story, analysis displacement didn’t fit the test result. But
shear force by analysis show safe for test.
This is not theory model such as simply summing each Skelton curves
on each story. Therefore we will consider the way to make Skelton curve.

REFERENCES
Japan 2x4 home builders association: Structural calculation guidelines
for light frame wooden structure. 2007
Kazuki TACHIBANA, Hiroshi ISODA, Tomoya OKAZAKI, Mikio
KOSHIHARA, Evaluation of the NEESWOOD Capstone Test Specimen
using Japanese Building Codes, WCTE2010
Architecture vibration theory, Yorihiko OSAKI, syoukoku-sya, 1996
NEESWood Project: Structural and Environmental Engineering,
University at Buffalo, State University of New York, Buffalo, available online at http://www.nsf.gov/news/newsmedia/neeswood/
The Building Center of Japan: The Building Standard Law of Japan.
June 2004
Takuya MAGARA, Tatsuya MIYAKE, Hiroshi ISODA, Shinichiro
TAMORI, Akira SASAGAWA, A STUDY ON THE MAXIMUMSEISMIC RESPONSE DISPLACEMENT OF WOOD FRAME
CONSTRUCTIONS : Prediction of the maximum seismic response
displacement of single-mass system based on energy input ratio, Journal ofstructural and construction engineering. Transactions of AIJ (506), 75-82,
1998-04-30.

148

4th Joint Student Seminar on Civil Infrastructures

AN EMPIRICAL ANALYSIS OF SUCCESSAND FAILURE FACTORS FOR
PUBLIC-PRIVATE PARTNERSHIPS IN
TOLL ROAD INVESTMENTS
Sathita TAOTHONG1, Piyapong JIWATTANAKULPAISARN2
and Kunnawee KANITPONG3
1
Research Associate, Asian Institute of Technology, Thailand
sathita_taothong@hotmail.com
2
Transportation Engineer, Bureau of Planning, Department of Highways,
Thailand3
Assistant Professor, Transportation Engineering Program, Asian Institute ofTechnology, Thailand

ABSTRACTAs the infrastructures and public services require a high investment
cost and the government has a limited of budget, a new approach called
Public-Private Partnerships (PPPs) is introduced to overcome thisproblem. This PPPs scheme allows the private sectors to participate andfinance in public infrastructures and public services projects. Toll road andhighway is one of the infrastructures that popular used PPPs scheme as asolution to that problem. The objective of this study is to empiricallyexamine the success and failure factors of the PPPs toll road and highway
projects using the logistic regression technique. Five main success andfailure factors: project’s country attributes, past experiences with PPPs tollroad and highway project, government support, project characteristics andthe private sector’s attributes are proposed for the analysis. The estimation
results draw conclusions that all of the sub-factors in the project’s country
attributes factor such as project’s region, corruption level, democraticlevel, income level, GDP growth and political stability take an importantrole to the outcome of the project. The past experiences with PPPs toll roadand highway project also with the private sector’s attributes factor and thetype of Private Participation in Infrastructure (PPI) seem to have effects tothe project as well. One interesting point from the results is that the projectroute appeared to be significant to the success of PPPs toll road andhighway project, especially if considering with the existing of competing
route factor
Keywords: Public-Private Partnerships, toll road and highway,
Logistic regression model, success factors, failure factors.

149

August 2011, Thailand

1. INTRODUCTION
1.1 BackgroundIn every country, fundamental infrastructures and public services suchas electricity, telecommunications, water services, waste management,
public transportation, health care, fire service and social services are theprinciple facilities for their citizen’s living life. In the past, the public sector
solely owned and provided the capital for implementing these fundamental
infrastructures and public services. But because of theses infrastructures andpublic services require a high investment cost and the government has alimited of budget, a new approach namely Public-Private Partnerships(PPPs) is introduced to overcome the problems. This approach named
Public-Private Partnerships (PPPs) is a scheme that allows the private
sectors to participate and finance in public infrastructures and public
services projects by entering into a certain period of contractual agreement.
1.2 Objective
This study aims to examine the success and failure factors for PPPs
toll road and highway projects as to enhance our understanding of theinfluences of each factor, which could in turn lead to a better direction for
implementing PPPs toll road and highway projects in the future.

2. PREVIOUS STUDIES RELATED PPPS TRANSPORT PROJECTS
2.1 Previous study topic in PPPs transport projects
Among the previous studies related PPPs transport project, toll roadand highway is the most attention sector with various topics chosen.
Financing is one of the general topics which are focused by many
researchers. The detail of the investment program together with the value ofmoney and the project’s return were analyzed in order to consider theworthiness of the projects (e.g. Reeves, 2005; Mayer, 2007). Moreover,revenue sharing risk, risk transfer and bidding process were sometimes
included in these financial studies (e.g. Mayer, 2007; Kalidindi, 2009; A.B.
Alonso-Conde et al., 2007). According to the PPPs framework studies, notjust only the steps and the process of launching PPPs projects wereidentified (e.g. Sussman and Ward, 2006; Queiroz, 2007; De Jong and Rui
et al., 2010), but the policy, regulations and agreements were also the maincontents of these studies’ topic neither (e.g. Chan and Subprasom, 2007;
Goodliffe, 2002). However, risk allocation is another title that was widelyconcerned and can be combined with other topic studies as well. In the risk
allocation studies, several types of risk that related to the PPPs projects wereclassified (e.g. Ogunlana and Abednego 2006), and the risk transfer
strategies were discussed (e.g. Singh and Kalidind, 2006; Medda, 2007;
Phang, 2007). In addition, other topics such as success and failure factors intransport PPPs and concession contract had also been in the list of transport
PPPs studies either. These studies identified and analyzed the factors that150

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

have an influence to the success of the project (e.g. Galilea and Medda,
2009; Renato E.Reside, Jr., 2009; Tam, 1999; Bagchi and Paik, 2001). It isobserved that most of the studies in this success and failure factors used thecase studies in many countries to be the key answers.
2.2 Success and failure factors in PPPs transport project
From the previous studies, many researchers have proposed anddescribe numerous factors in terms of descriptive context which related tothe success of the PPPs projects. These factors can be concluded andclassified into six aspects: Public sector, Private sector, The public or users,
Financial, Risk and Contractual arrangement (e.g. Chen and Subprasom,
2007; Jacobson and Choi, 2008; Mahalingam, 2010; Ogunlana and
Abednego, 2006; Queiroz, 2007; Sussman and Ward, 2006; Tam, 1999;
Tang et al., 2009; Zhang, 2005).
However, there are some studies that focused only on success andfailure factors in terms of empirical analysis (Galilea and Medda, 2009;
ADBI , 2009). The research by Galilea and Medda (2009) focuses on thefactors that have the effects to the success and failure of PPPs transport
projects in all four sectors: toll road and highway, seaport, airport andrailroad and mass public transit. Its objective is to investigate the role ofthree main factors in the success of the transport PPPs: national experience,the presence of private investors, and the influence of multilateral lenders,by logistic regression model. Meanwhile, the working paper by ADBI
(2009) focused on the factors which stress the failure of PPPs projects in all
infrastructures sectors: Energy, Telecom, Transport and Water. The
objective of this empirical research is to estimate the factors that account for
the greatest level of stress in infrastructure projects; Regulation,
Tariff/Political, Legal and Institutional framework, Macroeconomiceconomic conditions during the operations phase, Economic conditions
during project design phase, structure of transaction, Multilateral andbilateral support, Contract, Nationality, Country’s fiscal capacity and
Sectoral dummies, by probit regression model.

3. METHODOLOGY
3.1 Proposed success and failure factors
This study is the extension of Galilea and Medda’s work in the topic
of “Analyzing the influence of national political and economical factors on
the success of public-private partnerships in transport” which focusing on
the success and failure factor in PPPs transport projects that cover in all
sectors. To specify only in toll road and highway project, five main successand failure factors have been modified and added from Galilea and Medda(2009) for their influence’s analysis which are the project’s country
attributes, past experiences with PPPs toll road and highway project,
government support, project characteristics and private sector’s attributes asshown in the Table 3.1.
An empirical analysis of success and failure factors for public-private partnerships in toll road investments 151

August 2011, Thailand

Table 3.1: Comparison between Galilea and Medda’s success and failure
factors and this study
Galilea and Medda(2009)

Factors

Project’s country attributes
9
Region9Corruption level
9
Democratic level
9
Income level
9
GDP growth
Political stability
×
Past experiences with PPPs toll road and highway projectThis study
9
9
9
9
9
9

Government's past experiences

99

Private sector's past experiences
Government support
Government support
Project characteristics
Project route
Existence of competing route
Route length
Type of Private Participation in
Infrastructure (PPI)Private sector’s attributes
Number of private investors
Percentage of private sector
ownership

×

9

×

9

×
×
×

9
9
9

×

9

9

9

9

9

3.2 Data and variables
The data used in this study is mainly taken from the Private
Participation in Infrastructure Projects. This study also utilizes other sourcesof the data such as the index values from the Transparency International,
The Economist, the World Development Indicators (WDI) by The WorldBank and the World Bank Governance Indicators. Online Google map isalso used for examining the characteristics of the PPPs toll road projects.
3.3 Model
In this analysis, binary logistic regression model is used which
represents two possible values: success or fail. The formula using in themodel is as followed
P (Y ) =1
1 + e −Y

(1)

Where
And P = probability of the success
Y = dependent variable

152

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Xi = independent variables
β = vector of parameterTo estimate the unknown parameters (β), Maximum Likelihood
Method is used which selects values of the model parameters (β) thatmaximize the log-likelihood function which shown below
N

N

i =1

i =1

l = log L ( β ) = ∑ Yi log ( Pi ) + ( N − ∑ Yi ) log (1 − Pi )(2)

Where P = probability of the success
Y = dependent variable
N = number of observation project
Dependent Variable (Y)The dependent variable (Y) is equal to 1 if the project is successfulwhich is in the status of Concluded, and Operational (more than 50, 60 and
70% of the concession period). On the other hand, the dependent variable(Y) is equal to 0 if the project is failed which is in the status of Cancelledand Distressed.
Independent Variable (X)Many independent variables are created in this study in order toanalyze their influence and significant to the success of the PPPs toll roadand highway project. The name of the created variables and the definitions
are shown in the Table 3.2.
Table 3.2: The variable’s names and definitions
Variable
Region1EAP
Region3LAC
Corruption

Democratic

PoliticalStab

GDPavgIncomeLM

Definition
1 = The project is located in East Asia and Pacific region, 0= otherwise
1 = The project is located in Latin America and the
Caribbean region, 0 = otherwise
Corruption index of the project’s country during theconcession period provided by the Transparency
International which ranges between 0-10 points (minimum
point shows the most corruption of the country)
Democratic index of the project’s country during theconcession period provided by The Economist whichranges between 0-10 points (maximum point shows themost democratic governance of the country)
Political Stability index of the project’s country during theconcession period provided by The World Bank
Governance Indicators which ranges between -2.5 to 2.5(maximum point shows the most stable governance of thecountry)
The average of the annual Gross Domestic Product growthof the project’s country during the concession period
1= The project is in the Low or Lower Middle Income

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 153

August 2011, Thailand

SuccessGovExp
FailGovExp
SuccessPrivExp
FailPrivExp
GovSupport
Urban
Parallel

UrbanParal

RouteLength
PPIGreenfield
NumberPrivate
PrivatePercentage

level country classified by The World Bank, 0 = otherwise
Number of past successful project (concluded andoperational) done in the country at the moment of the PPPs
project’s financial closure
Number of past failed project (cancelled and distressed)
done in the country at the moment of the PPPs project’s
financial closure
Number of past successful project (concluded andoperational) done by the same private companies at the
moment of the PPPs project’s financial closure
Number of past failed project (cancelled and distressed)
done by the same private companies at the moment of the
PPPs project’s financial closure
1 = Hosting governments provide financial support interms of fixed government payment to the private sectors
to reduce the financial risk of a project, 0 = otherwise
1 = The project is located within the same urban area, 0 =otherwise
1 = There is a parallel road far from the road project in therange of 0-5 km
2 = There is a parallel road far from the road project in therange of 6-10 km
3 = There is a parallel road far from the road project in therange of 11-20 km
4 = There is a parallel road far from the road project morethan 20 km / no parallel road
The multiple between Urban variable and Parallel variablewhich means there is another existing road project whichruns parallel to the road project located within the sameurban area
The length of the road project in kilometer unit
1 = The project which a private entity or a public-private
joint venture builds and operates a new facility for theperiod specified in the project contract, 0 = otherwise
Number of private companies involved in the project
The percentage of the project company that is owned byprivate sectors

4. EMPIRICAL RESULTS
4.1 Estimation results
Estimation results from total of 218 PPPs toll road projects in thestatus of Concluded, Cancelled, Distressed and Operational can be divided
into three project operating duration; more than 50, 60 and 70% of theconcession period as shown in Table 4.1.

154

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Table 4.1: Estimation results
Operating Duration of the project≥ 50% of the concession period

Variablemodel 1model 2model 3

Corruption

0.3374**0.1862-0.066GDPavg0.4293*0.7211*0.7287

PrivatePercentage

0.0358*0.1587**0.1605*

Region1EAP-3.8623*-10.2126**-8.4436

Region3LAC-1.9333-5.0776**-3.9146-0.7193-0.9811

Democratic≥ 60% of the concession
period
model
model
model
1
2
3
1.55070.29920.2915*0.47670.8803 0.8194**
0.1938 0.1953
0.0271****3.3564 18.331 11.949
**
9**
8**
7.4163 6.0599
1.2236****3.0850 2.4438******
3.1200 2.1626
1.3924**≥ 70% of the concession period
model
1

model
2

model 30.13181.9960*0.52970.43340.84930.60390.0301*
3.7328*1.9198***1.6038**0.0112**0.1763***20.7526***
8.9994***0.1589**11.3798**
4.4572**-3.294-2.58463.7644

3.1888**0.10540.01450.19690.2382

IncomeLM

1.6663*5.4427**

4.1081*

SuccessGovExp

0.01760.1007**0.05780.0176

0.06680.0806

FailGovExp-0.0480.02140.012

0.0081

0.09420.19150.0065

NumberPrivate

0.04840.09050.09050.04540.20160.21760.08160.25190.21262.9142***0.4053*2.5859***2.7181***2.5716**0.2936-0.4468-0.3727

PoliticalStab

2.4880***2.6349***SuccessPrivExp-0.3295**-0.2269

FailPrivExp-0.5580*-0.6370*0.65470.8358*-0.5041-0.5802

GovSupport-1.2993-0.7165

2.03730.45323.51182.12175.2954*0.03462.5646*0.00045.6990**0.4527
3.1595**0.00183.5497**5.3519*-5.2266-0.0455-0.4016

2.12682.90510.00370.0011Urban-3.6959**-3.9565**

Parallel-0.0694-0.2485

UrbanParal

1.6423*2.0301*

RouteLength-0.0017-0.0036-2.2170***

PPIGreenfield
constant-3.1447-6.4552-3.2345

3.2142

3.0524

4.6-2.77894.04034.965429.245
7

53.4871-21.588-18.4984

104

105

76

71

Log-likelihood-75.1673-47.27-42.8439

65.12833.5104

observation

197

158

153

141

109

***
**
*

-3.9270*means the coefficient of the variable is significant at the 1% levelmeans the coefficient of the variable is significant at the 5% levelmeans the coefficient of the variable is significant at the 10% level

4.2 Success and Failure factors
Based on the empirical analysis, there are a number of factors that
could explain the success and failure of PPPs toll road projects. These
includeAn empirical analysis of success and failure factors for public-private partnerships in toll road investments 155

August 2011, Thailand• Region. The project located in East Asia and Pacific region or Latin
America and the Caribbean region is more likely to be distressed orcancelled, compared to those located in other regions (e.g. Europe andCentral Asia, Middle East and North Africa, South Asia and Sub-SaharanAfrica regions).
• Corruption level. The results show that PPPs toll road projects incountries with high levels of corruption are more likely to be cancelled or
stressed as the private sectors may confront with the excessive cost due tothe influence from the politics such as they have to pay for the special
authorization or permission or pay for the bribe in order to win the projectbidding.
• Democratic level. The higher democratic level of the project’s
country can be implied that the government do not have the full power inevery decisions which can cause the delay and tend to encourage the failureof the project.
• Income level. It is possible that the project located in the Low or
Lower Middle Income level country would meet with the successful,compared to those projects in Upper Middle Income level country.
• GDP growth. The higher GDP growth rate of the project’s country
would support the successful as it represents the less probability of being
failed by the economic crisis which encourages the initiation of theinvestment by the private sector. Moreover, the demand to travel in the
country with strong economic growth is higher than in the country with low
GDP growth rate.
• Political stability. In case the project located in the country withhigh level of government stability, the outcome would be successful. In casethe government is changed too frequent, it will affect the continuation of the
project’s progress. The new government may not support the project as it is
the previous government’s work.
• Past experience with PPPs toll road and highway project. The
results reveal that the project located in the country with higher number ofpast successful projects and the project invested by the private companies
which have lower number of past failed project is likely to be successful. It
is convinced that the government that had been achieved in arranging PPPs
agreement in the past will attract the private sectors to invest in more new
projects and the private sector who has had good experiences inconstruction, operating, financing and managing the PPPs toll road andhighway projects, would better understand the strengths and weaknesses of
the PPPs project.
• Project route. The results confirm that the project located within
urban area tends to be cancelled or distressed due to the cost overrun fromland acquisition process, demolishing existing buildings and facilities ortraffic management payment during the construction phase. Moreover,project in the urban area may have to compete with another parallel route orother modes of transport as well.
• Potential competing roads when combining with the location of the
project route. The project located within urban area with farther distancefrom the parallel roads is more likely to successful, compared to those156

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

located nearby the parallel road, with the reason that the demand is lessshared between these two road projects.
• Type of Private Participation in Infrastructure (PPI). The Greenfieldproject has a higher tendency to achieve the successful than the Brownfieldproject. Since the project in this type of private participation is a new built
infrastructure, the private companies who would like to participate in thisproject may also have to do and take the risk in the construction section,
apart from the operation part. Besides, the demand of the new toll road andhighway project is difficult to forecast as there has never been a statistic
data of the road user existed. Consequently, the inaccurate demand forecast
could occur.
• Percentage of private sector ownership. The success of the projectcorresponds with the higher percentage of the private sector ownership. The
private sector can get the high rate of returns or incentives from higher
private’s percentage owned. This caused the project to be monitored andmanaged in the best efficient processes.
However, some factors appear to be insignificant in this study
included the past failed experiences of the government, government support,
competing route, the route length and number of private investors.

5. CONCLUSION
5.1 Summary
This empirical analysis reveals evidence on the success and failure
factors of the PPPs toll road projects as follows:• The project in the East Asia and Pacific region and Latin America
and the Caribbean region have a probability to face with the failure• The project located in the low corruption level and low democraticlevel country or Low or Lower Middle Income level country tends
to be successful• The project located in the country with higher political stability andhigh GDP growth rate is likely to meet with the success• The higher number of past successful projects that have been donein the country and the lower number of past failed project thathave been done by the same private companies encourages the
project to be successful• The road project which located within urban area is likely to meetwith the failure• In case the project is located within urban area, the farther of the
distance between the road project and another competing route hasless effects the failure of the project• The Greenfield project tends to be successful easier than the
Brownfield project• The higher percentage of the project company that is owned byprivate sectors is corresponded with the success of the project

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 157

August 2011, Thailand

5.2 Implications
From the estimation results about the project’s country attributes, they
are useful for the private investors who want to participate in the PPPs tollroad projects. In this regard, they could use this information as an indicatorfor making investment decisions. In case the project is not located in the
country with these potentially successful factors, it is necessary to more
carefully examine the risks associated the project and the project’s contract
agreement as well as to prepare themselves in tackling issues that are likely
to occur.
The finding of the significant of the past experience on PPPs toll roadis useful implications for both public and private sectors. The public sector
can decide whether or not private firms with unsatisfactory records on theprevious PPPs toll road project(s) should be allowed to join in the bidding
process. On the other hand, there could be probably less risk of the project
failure for private firms if they decide to participate in the project in acountry with more experience on successful PPPs toll road projects.
For the project characteristic factors, the type of the project route andthe type of Private Participation in Infrastructure seem to be significant andhave explicit effects on the success of the PPPs toll road projects. Thesefindings could help both the public and private sectors to consider thelocation of the project and be aware of the construction risks that could
emerge from the project. Essentially, solutions to address the issues ofexcessive time and costs need to be prepared if the construction of toll roadprojects is designed to take place within urban areas.
The presence of competing routes could lead the PPPs toll roadprojects in urban areas to be failed. While these suggest that planning anddesign of toll road projects in urban areas should consider alternative routealignments as far as possible from any major parallel roads. Thus, possible
mechanisms to mitigate the demand risks need to be seriously taken into
account in designing PPPs structures and allocating risks associated with the
project.
With respect to the type of Private Participation, it is found that
Greenfield PPPs toll road projects are more risky than Brownfield projects.
It is confirm the need to more explicitly take into account a higher degree ofrisks inherent to the Greenfield investment as a result of several factors. The
design of well-defined contractual structures and the provision ofavailability payment could reduce risk and increase the chance of the project
being successful.

REFERENCES
ADBI (Asian Development Bank Institute), 2009.
GlobalDeterminants of Stress and Risk in Public-Private Partnerships (PPP) in158

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Infrastructure in ABDI Working Paper Series, No. 133, 2009, Edited by
Renato E. Reside, Jr.
Chen, A., Subprasom, K., 2007. Analysis of regulation and policy ofprivate toll roads in a build-operate-transfer scheme under demand
uncertainty. Transportation Research Part A 41 (6), 537-558.
Galilea, P., Medda, F.,2009. Analyzing the Influence of National
Political and Economic Factors on the Success of Public-Private
Partnerships in Transport. Transportation Research Board Compendium ofPapers 09 (3854), 01-16
Khanom, N.A., 2010. Conceptual Issues in Defining Public Private
Partnerships (PPPs). International Review of Business Research Papers 6(2), July 2010, 150 -163.
Tam, C.M., 1999. Build-operate-transfer model for infrastructuredevelopment in Asia: reasons for successes and failures. International
Journal of Project Management 17 (6), 377-382.
Zhang, X., 2005. Critical Success Factors for Public-Private
Partnerships in Infrastructure Development. Journal of Construction andManagement 131 (1), 3-14.

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 159

August 2011, Thailand

160

4th Joint Student Seminar on Civil Infrastructures

EFFECT OF GAS AND OIL PIPELINESIN SENSITIVE AREAS TOENVIRONMENTAL CHANGE
Yushi NAKAO1 and Haruo SAWADA2
Graduate Student, Graduate School of Engineering,
University of Tokyo, Japan
nakao@iis.u-tokyo.ac.jp
2
Professor, Institute of Industrial Science, University of Tokyo, Japan
1

ABSTRACTThis paper aims to assess the impact of pipeline on the surrounding
environment focusing on the Trans-Alaska Pipeline System as a heat source.
Based on the Landsat-7 thermal band, surface temperatures are comparedamong the pipeline, the service road for the pipeline, and the surrounding
fields. Both the pipeline and the road tend to show lower surface
temperatures than the surroundings in winter, and higher surface
temperatures in summer.
Keywords: Landsat, surface temperature, thermal band.

1. INTRODUCTION
1.1 Background
The 1,287.961 km long Trans-Alaska Pipeline System delivers crude
oil from the Prudhoe Bay oil field (70.2846°N 148.6796°W) to an oil port in
Valdez (61.130833°N, -146.348333°W), the most northern ice-free port in
North America. Its construction began in 1975 and opened for service in
1977. Nearly 700 km of the pipeline is equipped with heat-pipes and iselevated above ground to prevent thermal interactions between the warm oil
which can reach 63°C at maximum and the continuous and discontinuous
permafrost that it passes over (U.S. Department of the Interior Bureau ofLand Management, 2004). Still, vegetation clearance upon construction andmaintenance along with global warming continue to affect the groundthermal conditions (Smith et al., 2010).
Research has concentrated on the effect that the harsh and vulnerable
environment has on the pipelines (Seligman, 2000), rather than the effectthat the pipeline brings to the surrounding environment. Satellite based
remote sensing, which observe vast areas with the same sensor system, will
have the potential to find out the impact of the pipeline to the surrounding
environment.

161

August 2011, Thailand

It is also a very important ability of satellite remote sensing to chaseor trace the effects and land cover change over many years.
1.2 Objective
This study aims to assess the pipeline’s effects on the surrounding
environment, focusing on the impact of its heat.

2. METHODOLOGY
2.1 Framework
Figure 1 shows the flow how the surface temperatures on and near the
pipelines are acquired.
The geological information of the pipeline and the service roadrunning parallel to the pipeline is derived manually by the interpretation ofLandsat image, and the surface temperatures alongside the facilities arecalculated and placed in order. The temperature difference between the pixel
containing the pipeline/road and the adjacent pixel is calculated throughout
the image.
Band 2, 4, 7

Location ofpipeline & road

Landsat image

Assessment
Band 6 high
DN to Kelvingain DN
Figure 1: Assessment of surface temperatures around the pipeline andservice road

2.2 Data Used in This Study
The satellite data used are shown in table 1. Both represent the same
area, one in August and the other in April. The area subjected contains thenorth-end, or the beginning of the pipeline to about 130km ahead throughmostly continuous permafrost plain and some hilly areas toward the end of
the scene. There is a gravel road (the Dalton Highway, Alaska Route 11)
along the pipeline built as a supply road for constructing and servicing thepipeline and oil facilities. The location of the pipeline and the roadis shownon the entire Landsat image of Figure 2 (left, from point A to point B),
which were acquired on August 31, 2000. The location of the image in
Alaska is shown on the map of Figure 2 (right).
Table 1: Satellite data used for evaluation of ground surface temperature
Path / Row

Date

Local time

72 / 1172 / 11

2000/8/31
2001/4/27

13:20:52
12:20:03

162

Sun
elevation
28.88°
34.32°Sun azimuth

Cloud cover

172.40°172.71°0.05 %
23.30 %

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

A

B

Figure 2: Landsat image on August 31, 2000 (left) and its cover area (right)
2.3 Converting the Digital Number of the Landsat Thermal Band to
Temperature
Estimation of ground surface temperature is carried out using thethermal band data of the Landsat-7 Enhanced Thematic Mapper Plus
(ETM+). In this study, the high gain of band 6 was used. The digital
numbers (DN) of band 6 with a range between 0 and 255 is converted toradiance values using the following values specific to the individual scene
as shown in the equation below.
Lmax− Lmin
R=
× (QCAL − QCALmin) + Lmin
QCALmax− QCALmin(1)
In this case QCAL(Quantized calibrated pixel value)max=255,
QCALmin=1, QCAL=DN, Lmax=12.650, and Lmin=3.200.
The radiance data is then converted to degrees in Kelvin using the following
equation.
1282.71
K=⎛ 666.09 ⎞
ln⎜
+1⎟
⎠
⎝ R
(2)

3. RESULTS AND DISCUSSIONS
3.1 The Pipeline
Figure 3 shows the temperature difference between the pixel including
the pipeline and the pixel right next to it in August and figure 4 shows thetemperature difference in April. The temperature difference between the
pixel including the pipeline and two pixels next to it is on figure 5 forAugust and figure 6 for April.

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 163

August 2011, Thailand

(K)

A

B

Figure 3: Temperature difference of the pipeline and the adjacent pixel in August
(K)

A

B

Figure 4: Temperature difference of the pipeline and the adjacent pixel in April

(K)

A

B

Figure 5: Temperature difference of the pipeline and the second next pixel in August

(K)

A

B

Figure 6: Temperature difference of the pipeline and the second next pixel in April
Spatial resolution of thermal pixel is 60m by 60m and table 2 shows
the number of pipeline pixels with values higher than, equal to, or lower
than the adjacent pixels. The pixels with the pipeline tend to show lower
surface temperatures in summer and higher temperatures in winter.

164

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Table 2: Number of pixel with temperature higher, equal to, or lower
compared to the neighboring pixel
Neighboring
pixel
AugustApril

next pixel
second next
next pixel
second next

# of pixels withvalue higher than theneighboring pixel
1865
1673
1742
2211

# of pixels withvalue equal to theneighboring pixel
447
125
1536
794

# of pixels withvalue lower than theneighboring pixel
2178
2692
1117
1390

3.2 The Road
Figure 7 shows the temperature difference between the pixel including
the road and the pixel right next to it in August and figure 8 shows thevalues in April. The temperature difference between the pixel including theroad and two pixels next to it is on figure 9 for August and figure 10 for
April.
Table 3 shows the number of pipeline pixels with values higher than,equal to, or lower than the adjacent pixels. The impact of the road results inlower temperature compared to the surroundings in summer and higher
temperature in winter.

(K)

A

B

Figure 7: Temperature difference of the road and the adjacent pixel inAugust(K)

A

Figure 8: Temperature difference of the road and the adjacent pixel in April

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 165

BAugust 2011, Thailand

(K)

A

B

Figure 9: Temperature difference of the road and the second next pixel inAugust(K)

A

B

Figure 10: Temperature difference of the road and the second next pixel in
April

Table 3: Number of pixel with temperature higher, equal to, or lower
compared to the neighboring pixel
Neighboring
pixel

AugustApril

next pixel
second next
next pixel
second next

# of pixels withvalue higher than
the neighboring
pixel
1237
736
3828
4074

# of pixels withvalue equal to theneighboring pixel
316
69
195
68

# of pixels withvalue lower than
the neighboring
pixel
2750
3498
280
161

4. CONCLUSIONS AND FUTURE WORKS
The thermal effect of the pipeline and its service road on the environment
was discussed. Both the pipeline and the service road show the same trendthat the surface temperature is lower than the surroundings in summer andhigher in winter. However, the thermal trend is not evident for the pipeline.
This is most probably because the road is wider and represents a larger
portion in a 30m pixel compared to the pipeline, which consists of a 1.2m
pipe with a narrow access trail. This is to say that by surveying
environmental change around roads in a similar environment of a pipeline,
environmental change around pipelines may be estimated.
Additional study must be done on scenes in different months andlocations. Because the geological information of the pipeline and the road is

166

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

acquired manually, its accuracy must be ensured for proper assessment. The
actual temperature measured at the site may also become necessary tosupport the results.

REFERENCE
Barsi A., Hook S., Palluconi F., Schott J., Raqueno N., 2003. Landsat
TM and ETM+ thermal band calibration. Canadian Journal of Remote
Sensing, vol. 29, no. 2, 141-153.
Khrenov N. N., 2010. Reliability of chilled gas pipelines in permafrost.
Soil Mechanics and Foundation Engineering, vol. 47, no. 1. 26-31.
U.S. Department of the Interior Bureau of Land Management, 2004.
Environmental Assessment of the Proposed Reconfiguration of the TransAlaska Pipeline System, EA-03-009 BLM Serial Nos. AA-5847 and FF12505
Seligman B. J., 2000. Long-Term Variability of Pipeline±Permafrost
Interactions in North-West Siberia. Permafrost and Periglacial Processes,
vol. 11 no. 1, 5-22
Smith S L. and Riseborough D W., 2010. Modelling the thermal
response of permafrost terrain to right-of-way disturbance and climate
warming. Cold Regions Science and Technology vol. 60, 92–103

An empirical analysis of success and failure factors for public-private partnerships in toll road investments 167

August 2011, Thailand

168

4th Joint Student Seminar on Civil Infrastructures

EFFECTS OF ENVIRONMENTAL
CONDITION ON CORROSION OF
STRUCTURAL STEEL IN MARINEENVIRONMENT OF THAILAND
Prasong Permsuwan 1, Pakawat Sancharoen 2, Somnuk Tangtermsirikul3
Paiboon Sreearunothai4, Ekkarut Viyanit5 and Wanida Pongsaksawad 5
1

Graduate student, School of Civil Engineering and Technology, Sirindhorn
International Institute of Technology, Thammasat University, Thailand
Prasong_civilnu@hotmail.com
2
Researcher, Construction and Maintenance Technology Research Center,
Sirindhorn International Institute of Technology,
Thammasat University, Thailand
3
Professor, School of Civil Engineering and Technology,
Sirindhorn International Institute of Technology,
Thammasat University, Thailand
4
Lecturer, School of Bio-Chemical Engineering and Technology (BCET),
Sirindhorn International Institute of Technology,
Thammasat University, Thailand
5
Researcher, National Metal and Materials Technology Center (MTEC),
114 Thailand Science Park, Thailand

ABSTRACT
Structural steels which are hot-rolled steel grade SS400 according to
JIS G3101, hot-rolled steel grade SM490YA according to JIS G3106 andhot-rolled atmospheric corrosion resisting steel grade SMA490A according
to JIS G3114 were exposed from January 2010 until January 2011 tomarine and industrial environment at Maptaphut industrial estate, Rayong,Thailand. The specimens were exposed in two conditions that were
atmospheric and tidal zone. The corrosion behaviors of specimens werestudied by means of weight loss measurement according to ISO8407,
compositions of rust layers by XRD analysis and characteristic of rust layer
by SEM analysis. Also, environmental conditions such as temperature,
relative humidity, and deposition rate of chloride were monitored monthly.
The corrosion rates of specimens were also measured by potentiodynamic
polarization in laboratory. The results showed that corrosion rate at 12
months of carbon steel was higher than that of weathering
steel.Furthermore, specimen in tidal zone showed higher corrosion rate
than atmospheric zone obviouslydue to effects of biofouling and wet-dry
condition. The result can be used for material selection and designing
service life of steel structure in Thailand.
Keyword: corrosion, marine, atmospheric, tidal, carbon steel,weathering steel, chloride.

169

August 2011, Thailand

1. INTRODUCTION
Structural design of both reinforced concrete and steel structures consider
mainly the strength of members. But durability design or service life design,
which also affects the safety of structure, is rarely concerned. Particularly,
the steel can be deteriorated rapidly when it is subjected to corrosive
environmental condition.Carbon steel and weathering steel are used in steel
structure including high-rise building, bridge, offshore platform, sheet piling,steel pile and coastal facilities. Normally, steel structures in marine
environment are deteriorated by atmospheric corrosion. Also, some part ofthose steel structures such as steel piles or sheet piles are immersed in thesea water. So the area between the lowest sea level and the highest sea levelis influenced by tidal zone corrosion.
Atmospheric corrosion is an electrochemical reaction occurring on
metal surface in a presence of thin film electrolyte. In case of atmosphericcorrosion, structural steel in marine environment deteriorates primarily byclimatic factors such as temperature, relative humidity, and the presence ofchloride and SO2. Normally, rust formed on surface of carbon steel isporous with poor adherent, and cracked at the outer part (Castano et al.,2010). This phenomena causes oxygen, water, Cl- and SO2 from outside topenetrate to the steel substrate easily (Yuantai et al, 2009). For corrosion
resisting steel, at the early stage, the rust is formed similarly to the carbon
steel. But as the exposure time increased, the rust layer becomes morecompact, denser, and tighter (Renato, et al., 2003). For this reason, the rustlayer acts as the barrier to inhibit corrosive species and then the rate ofcorrosion is decreased.
The compositions of rust layer have been observed by several
researchers.Mainly, the compositions of rust found in marine environment
are composed of lepidocrocite (γ-FeOOH), goethite (α-FeOOH), akaganeite(β-FeOOH) and magnetite (Fe3O4). The γ-FeOOH is usually formed in theearly stage and transformed to α-FeOOH and Fe3O4. This α-FeOOH isimportant key to reduce corrosion becauseits compact and dense structure
prevent penetration of water, oxygen and corrosive species from theenvironment. However, area with presence of chloride is often observed βFeOOH which has porous and loose structure.
In Thailand, environmental conditions are different from othercountries. There is no sufficient data of atmospheric corrosion to model orpredict steel corrosion in order to design service life of steel structures inThailand. Also, there is limit information on atmospheric and tidal corrosion.
In addition, the standard currently used to design steel structures in Thailand
was adopted from other countries. It is essential to study the corrosion
behavior of structural steel in Thailand. The aim of this paper was toinvestigate the corrosion behavior of SS400, SM490YA and SMA490A
steels in the marine environment of Thailand.

170

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2. EXPERIMENTAL PROGRAM
2.1 Specimen preparation
The materials tested in this study are three kinds of structural steel; 1)
hot-rolled steels for general structure grade SS400 according to JIS G3101,
2) hot-rolled steels for welded structure grade SM490YA according to JIS
G3106, and 3) hot-rolled atmospheric corrosion resisting steels for welded
structure grade SMA490A according to JIS G3114. Their chemicalcompositions determined by spark emission spectrometer are shown in
Table 1 together with the specified values in the standard. ISO 8565
standard specification suggests the following procedures for specimen
preparation before exposure test. Specimens were cut from the steel plate to
the size of 150×70×3 mm for SS400 and SM490YA steels, and 150×70×5
mm for SMA490A steel. The mill scale on the surface of the specimens wasmechanically removed by sand blasting and then wet-polished by 600 grade
sand paper on all surfaces. Finally, the specimens were rinsed with distilledwater and ethyl alcohol, dried by blower, weighted and immediately placed
in desiccators before the test.
Table 1: Chemical compositions of different types of steel
Steel types
Standard JIS G3106
SM490YA
Standard JIS G3101
SS400
Standard JIS G3114
SMA490AC≤ 0.200.070
0.047≤ 0.180.115

Si≤ 0.550.218
0.174
0.15-0.65
0.396

Mn≤ 1.65
0.352
1.039
≤ 1.4
0.368

Chemical composition (% by wt)
P
S
Cr
Ni
Cu
≤ 0.035≤ 0.035
0.009
0.016
0.031
0.073
0.154
≤ 0.05≤ 0.05
0.010
0.006
0.026
≤ 0.035≤ 0.035
0.45-0.75 0.05-0.30 0.30-0.50
0.076
0.005
0.681
0.16
0.368

Al
0.013
0.021

Mo
0.015
0.003

2.2 Exposure test
Exposure site is located at Maptaphut industrial estate, Rayong,Thailand as shown in Figure 1. The position coordinates of this location is
12° 39’ northern latitude and 101° 10’ eastern longitude. Even though,environment of exposure site is marine environment but this area is also anindustrial estate with coal- power plant, oil refinery, chemical industry, etc.
The experiment was conducted from January 2010 to January 2011. The
exposure test was conducted in two conditions which were atmospheric andtidal zone exposure.

Effects of environmental condition on corrosion of structural steel in marine environment of Thailand

171

August 2011, Thailand
Marine and industrial environment at
Maptapud Industrial Estate, Rayong, Thailand

A

B
Rack No. 2
Highest level of sea waterN

Rack No. 1

Platform No.12A

Lowest level of sea water

Platform No.4

Figure 1: Details of the exposure site:(A)atmospheric exposure

and(B)tidal exposure
For atmospheric exposure, two exposure racks according to ISO 8565
were set on the high voltage transmission tower platform No. 4 only. Rack
No.1 faced to the East and rack No.2 faced to the North directions with therack slope of 45° to the horizontal as shown in Figures 1 and 2.
For tidal exposure, sample was placed in the plastic box and installed
under the transmission tower platform number 4 and 12A of which water
quality are different. The position of the boxes was set in the middle
between the highest and the lowest sea water level in order to simulate wetdry condition as shown in Figures 1 and 2. Environmental conditions suchas temperature and relative humidity were monitored monthly by sensors
connected to a data logger near the rack No.1. The results are shown in
Table 2.

Figure 2: The exposure rack of atmospheric zone (left) and tidal zone
(right)Chloride deposition rate was measured monthly by using the wet
candle method according to ISO 9225, and surface of wet candle exposed toatmosphere is 100 cm2. Also, the wet plate modified from standard wet
candle method was installed in 4 directions in order to measure effect ofwind directions on chloride deposition rate. The results are shown in Table
2.The sea water properties, mainly chloride and sulfate concentration, were
measured by Ion-chromatography. The results are shown in Table 2.
The compositions of corrosion products were characterized by X-ray
diffraction (XRD) method using X-ray diffractometer (Rigaku TTRAX III
50 kV 300mA) with Cu target. The scan is performed at a speed of 10°/min,a scan step of 0.02 at 2θ and range from 10° to 70° at 2θ. The characteristicof rust layer was observed by SEM analysis using JEOL model JSM-6301F.

172

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

After 1, 3, 6 and 12 months, exposed specimens and monitored data
were collected for analysis. Corrosion products were removed chemicallyby HCl acid etching according to ISO 8407. After corrosion products had
been removed completely, the specimens were rinsed with ethanol, dried
with blower and weighted to measure weight loss.
Table 2: Atmospheric conditions and sea water properties

Atmospheric conditions
Monthly average Monthly averageMonthly averagetemperature (°C) relative humidity (%) Cl deposition (mg/(m2.day))
30.6
61.3
22.5

Sea water properties
Chloride (ppm)
Sulfate (ppm)
Tower4 Tower12A Tower4 Tower12A
19586
18821
3147
3041

Table 3:Chloride deposition rate of each direction
Wet-plate Direction faced to
Chloride deposition rate
2

East

North

West

South

27.75

27.39

27.94

39.43

(mg/(m .day))

3. RESULTS AND DISCUSSION
3.1 Environmental characteristics at exposure site
The environmental characteristics measured at the exposure sites areshown in Table 2. According to ISO 9223, environmental conditions areclassified in term of chloride deposition rate (class S) and sulfur dioxide
deposition rate (class P). Also, Time of wetness (TOW) (classτ), the periodof time when relative humidity is greater than 80% and temperature isgreater than 0ºC, is also used for the classification. Environmental conditionof exposure site is classified as S1 because chloride deposition rate is 22.47
mg/m2.day which is between 3-60 mg/m2.day. Environmental condition isclassified as class τ3 because TOW is 9.1% which is between 3 <τ ≤ 30 %.
Also, the corrosive categories of the exposure site can be classified as C2(low) to C4 (high) class by S1 and τ3 depending on classification of sulfur
dioxide. As shown in Table 3, the result of the wet plate shows that chloride
deposition of rack No. 2facing north is higher than that of rack No. 1facing
east because of the wind direction.
3.2 Thickness loss
Figure 4 shows examples of the surface appearance of SS400 steel
after 12 months exposure at atmospheric and tidal zone. In atmosphericzone, dark-brown rust product uniformly covered the entire surface while
uneven surface was observed in specimens exposed to tidal zone and werecovered with tightly attached biofouling. Other types of steel showed similar
appearance after 12 months exposure.
Generally, the corrosion rate of steel exposed to real environment wascalculated in term of thickness loss. The equation used to calculate thickness
loss is shown in Eq.1

Effects of environmental condition on corrosion of structural steel in marine environment of Thailand

173

August 2011, Thailand

C =W × 104

ρA

(1)

where C is the thickness loss (µm), w is the weight loss (g) of specimen
after being exposed, ρ is the density of the steel (7.86 g/cm3) and A is the
exposed surface area of the specimen (both sides) (cm2).
Figures 5a and 5b show thickness loss of SS400, SM490YA andSMA490A steels exposed to atmospheric zone for 12 months on rack No.1
andrack No.2, respectively. In both exposure racks, the thickness loss ofSS400, SM490YA and SMA490A steels continuously increasedfrom the
beginning to 12 months but the rate of corrosion decreased with increasing
time. Especially, corrosion rate of SMA490A steel obviously decreased at
the long term because rust layer was more compact and denser.

(a)
(b)
(c)
Figure 4: Appearance of SS400 steel: (a) before exposure, (b) after 12months of exposure in atmospheric condition and (c) after 12 months of
exposure in tidal condition
30

SM490YA

SS400

25

SS400

25

SMA490A

Thickness loss (µm)

Thickness loss (µm)

30SM490YA

20
15
10
5

SMA490A
20
15
10
5

0

0

0

3

6

9

Exposure time (months)

12

15

0

3

6

9

12

15

Exposure time (months)(a)(b)
Figure 5: Thickness loss of specimens versus exposure time at atmosphericzone on; (a) rack No.1 and (b) rack No. 2
The thickness loss of SS400, SM490YA and SMA490A steels
exposed to tidal zone after 12 months under platform No.4 and 12A are
shown in Figures 6a and6b. In tidal zone, the thickness loss of specimens isvery high (greater than-200 µm) after 12 months of exposure.Both platforms,
thickness loss of SMA490A was the lowest because of its alloying
composition. Particularly, rate of corrosion of specimens under platform
No.4 shows almost constant increasing without effect of protective rustlayer. As the results, the thickness loss of specimens under platform No.4 issignificantly higher than those under platform No.12A. This is because
different water quality such as higher O2and higher Cl- concentration

174

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

content which causes fast growing of biofouling as well as accelerates steel
corrosion was observed under platform No.4.
500

500

SM490YA

SM490YA

Thickness loss (µm)

Thickness loss (µm)

SS400

400

SS400

400

SMA490A
300

200

SMA490A
300

200

100

100

0

0
0

3

6

9

12

15

0

3

Exposure time (months)

6

9

12

15

Exposure time (months)(a)(b)
Figure 6 :Thickness loss of specimens versus exposure time at tidal zone
under; (a) platform No.4 and (b) platform No.12A
3.3 Corrosion product analysis
3.3.1 Cross section of rust layer
The cross section analysis of steel exposed to atmospheric zone for 12months showed irregular layer of corrosion product with thickness about 2570 µm. And rust layers were less compact as shown in Figure 7a. This layer
showed some sites with localized corrosion, which probably act as anodes in
the first steps of atmospheric corrosion (Castano et al., 2010). In tidal zone,
the cross section showed thicker layer of corrosion products than that foundin atmospheric zone as well as some site with deep localized corrosion asshown in Figure 7b. The thickness of the layer of corrosion products wasapproximately 25–200 µm. The deeper localized corrosion may becontributed by biofouling. It attached on part of steel surface and caused
severe local corrosion. This can be the weak point of structures.

resin

resin

steel

steel

Local corrosion(a)(b)
Figure 7: Rust layers on the SM490YA steel exposed for 12 months as;(a) atmospheric zone and (b) tidal zone
3.3.2 Composition of rust layer
The main phases of the rust layers formed on the steels exposed toatmospheric zone for 12 months were γ-FeOOH, followed by β-FeOOH and

Effects of environmental condition on corrosion of structural steel in marine environment of Thailand

175

August 2011, Thailand

α-FeOOH as shown in Figure 8a. It can be seen that the main phaseobserved on SM490YA, SS400 and SMA490A was γ-FeOOH. It normallyoccurred at the beginning of corrosion. Then with the interaction of oxygen
and water, γ-FeOOH partly or entirely transformed to more stable α-FeOOH(Zise et al., 2007). As the result, there was low amount of α-FeOOH thatrecently formed on the steels. Generally, β-FeOOH was only found in highchloride containing environment. As many researchers, investigated theeffect of chloride ion on the transformation of rust formed on the carbon
steel and observed that the content of β-FeOOH increased with theconcentration of Cl ions in the environment. Kamimura et al. (2006) also
reported that β-FeOOH was electrochemically active and the existence of βFeOOH influenced the corrosion behavior of weathering steel exposed atmarine site.
For tidal zone, the phases of rust layer formed on the steel exposed for 12months in both locations was mainly Fe3O4 followed by γ-FeOOH, αFeOOH and β-FeOOH, respectively as shown in Figure 8b. Peaks of Fe3O4
phases had high intensity with a small intensity of other phases. For
occurring of Fe3O4, many researchers reported that γ-FeOOH formed on thesteel surface did not only reacts with the oxygen and water and become αFeOOH, but also reacts with Fe2+ dissolved from the iron and forms Fe3O4.
As the results, there was Fe3O4 in rust layers only in tidal zone. There weremany phases with high intensity because in severely corrosive sea water,rust layers formed very fast and rapidly transform to another phases.
Anyway, rust layers in tidal zone did not act as efficiently protective rustlayers like that of atmospheric zone because rust layers formed in this zone
cannot inhibit corrosive species penetrated to surface of steel. For calcite
phase, it was composition of biofouling shell attaching on the corrosion
products.

(a)

176

γ-FeOOH
β-FeOOH
α-FeOOH

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

(b)

Fe3O4
γ-FeOOH
β-FeOOH
α-FeOOH
CalciteFigure 8: XRD spectra of corrosion products obtained from SM490YA for
12 months as; (a) atmospheric zone and (b) tidal zone
3.4 Corrosion rate
The prediction of corrosion rate of steel exposed to atmospheric condition
was proposed by several researchers as a bi-logarithmic equation as shownin Eq.2. This equation usually fits well with corrosion behavior over time.
C = AtB (2)where C is the thickness loss (µm), t is the exposure time (month), and A, B
are constants. The constants A and B can be determined by plot in log-log
coordinate between steel thickness loss and exposure time. The regression
analysis was conducted in log-log coordinate to determine values of
constants A and B value.
For corrosion rate prediction equation, several researchers considered
the B value as the growth law of corrosion product: parabolic or linear.If the
B value is in the range of 0.5 to 1, the corrosion products do notsignificantly protect the steel surface from corrosion and is called a linear
growth. On the other hand, if the B value is lower than 0.5, it indicates aparabolic growth by formation of protective rust layers to reduce rate ofcorrosion in the long term.
The corrosion rates per year and the values of A and B constant in all
exposure zones of SS400, SM490YA and SMA490A steels were calculated
from the bilogarithimic equation and shown in Table 4.
For atmospheric zone, the corrosion rate of rolled steel (SS400 and
SM490YA) is higher than corrosion resisting steel (SMA490A) in bothracks because B value of SS400 and SM490YA are higher than 0.5 whichindicate high rate of corrosion. The key factor causing the difference ofcorrosion rate is the chemical composition of the steel. The corrosion rate of
SMA490Ais the lowest because the corrosion resisting steel contains somealloy elements such as Cu, Cr, and Ni that improve the corrosion resistanceas shown in Table 1. Q.C. Zhang et alhave studied the distribution ofalloying elements during formation of rust layers. They reported that thepresence of Cu restrains the supply of oxygen, retards the anodic dissolution
and reduces the electronic conductivity of rust layers. For this reason, theEffects of environmental condition on corrosion of structural steel in marine environment of Thailand

177

August 2011, Thailand

corrosion rate of SMA490A steel is reduced. The corrosion rate of rackNo.2 is higher than that of rack No.1 for all types of steel because rack No.2
is affected by the wind direction making higher Cl- deposition rate on thespecimen surface from the results of wet-plate. This shows the importanceof the effect of local environmental conditions on steel corrosion. From theresult, the corrosive category of environment at exposure site can be defined
as class C3 (medium) based on ISO 9223.
In case of tidal zone corrosion, the corrosion rate of tidal zone issignificantly more aggressive than atmospheric zone. The corrosion rates oftidal zone and atmospheric zone show the difference of more than 10 times.
The high Cl- concentration and wetting in tidal exposure accelerated
corrosion process byformation of β-FeOOH. Although the α-FeOOH phase
was formed on both carbon steel and weathering steel but the rust layers
were not act as the protective barrier causing the high B value. Because, the
α-FeOOH phase was not uniformly formed on the surface of steel and thenrust layers cannot sufficiently protect the Cl- penetration. Also, the effectofbiofouling increased corrosion rate significantly. Biofouling is considereda major factor to control the corrosion behavior since the biofoulingattached to the surface of steel tightly. Biofouling consumes oxygen, while
supplies acid due to its excretion. As a result, steel subjects to extra
corrosion due to acid (Coetser, 2005).Eventually, the corrosion affectingfrom biofouling is local corrosion which is more severe and difficult forpredicting the rate. In term of location, corrosion of specimens under
platform No.4 is more severe than that of the specimens under platform
No.12A because the differences in Cl-, sulfate and oxygen concentration.
Moreover, specimens under platform No.12A is located in calm sea waterand low water quality that has low oxygen concentration causing slow
activity and slow growing of biofouling.
Table 4: Corrosion rate and constant values of steels
Steel type

Condition
AtmosphericSM490YA
Tidal

Atmospheric
SS400
Tidal
Atmospheric
SMA490A
Tidal

LocationCorrosion rate (µm/year)

A

B

Rack 1

5.25 0.59

22.68

Rack 2

25.86

6.59 0.55

Under 4461.4254.45 0.86

Under 12A

218.6391.62 0.35

Rack 1

22.21

4.83 0.61

Rack 226.50

6.81 0.55

Under 4480.2965.46 0.80

Under 12A

238.6068.71 0.50

Rack 1

19.93

4.63 0.59

Rack 2

23.53

7.93 0.44

Under 4398.5361.66 0.75

Under 12A

196.1848.31 0.56

The typical rates of corrosion for structural steel suggested by BS
6349-1 standard are shown in Table 5. BS 6349-1 standard recommends the
corrosion rate for steel structure design in various exposure zones. The
results of this study show that average corrosion rate of the steels in

178

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

atmospheric zone areapproximately 23.45 µm/year. So the standard is
acceptable to be used to design steel structures in atmospheric zone in
Thailand. On the other hands, the results of average corrosion rates of thesteels in tidal zone in this study are approximately 332.27 µm/year. The
standard recommends maximum limit of 100 µm/year. Therefore, thestandard is not suitable for steel structure design in tidal zone in Thailand.
Table 5: Typical rates of corrosion for structural steels temperate climates(BS 6349-1)
Corrosion rate μm/year Results of the study μm/year
Exposure zone
Mean Upper limit* SM490YA SS400 SMA490A
Atmospheric zone
40
100
25
26
23
Splah zone
80
170
Tidal zone
40
100
461
480
398
Seawater immersion zone 40
130*The upper limit is the 95% probability values

4. CONCLUSIONS
1. Corrosion resistance of SMA490A steel is the highest in all conditions
because itschemical compositions improve the characteristic of protective
rust layers.
2. Corrosivity of sea water in tidal zone is significantly higher thanatmospheric environment. This phenomenon is contributed by the effects of
biofouling and high chloride concentration. Corroding mechanism of
biofouling must be concerned in tidal zone corrosion and studied in the
future.
3. Calculation of corrosion rate specified in the BS 6349-1 standard might
not be suitable for structural steels exposed to Thailand environments
especially for tidal zone corrosion.

ACKNOWLEDGEMENT
The research was funded by the Electricity Generating Authority ofThailand (EGAT).

REFERENCES
Castano J.G., et al, 2010, “Atmospheric corrosion of carbon steel in
Colombia”, Corrosion science, 52, pp. 216-223.
Coetser S. E. and Cloete T. E., “Biofouling and bio corrosion inindustrial water systems”, Critical reviews in microbiology, 31, 2005, pp.
213-232.
Kamimura, T. et al., 2006.“Composition and protective ability of rustlayer formed on weathering steel exposed to various environments”.
Corrosion Science, 48(9), pp.2799-2812.

Effects of environmental condition on corrosion of structural steel in marine environment of Thailand

179

August 2011, Thailand

Renato A.A., et al, 2003, “Characterization of corrosion products
formed on steels in the first months of atmospheric exposure”, Materials
research, 6, pp. 403-408.
Yuantai M., et al, 2009, “Corrosion of low carbon steel inatmospheric environments of different chloride content”, Corrosion science,
51, pp. 997-1006.
Zhang Q.C., et al, 2002, “Corrosion behavior of weathering steel inmarine atmosphere”, Materials Chemistry and physics, 77, pp. 603-608.
Zise W. et al., 2007. “The Morphology, Phase Composition and Effectof Corrosion ProductonSimulated Archaeological Iron”. Chin. J. Chrm. Eng,
15(3),pp.433-438

180

4th Joint Student Seminar on Civil Infrastructures

APPLICATION OF ELASTIC WAVE
MEASUREMENT TO MODEL TESTSUSING BENDER ELEMENTS
1

Sho OH1and Reiko KUWANO2
Graduate Student, School of Eng., The University of Tokyo, Japansoh@ iis.u-tokyo.ac.jp
2
Associate Professor, ICUS, IIS, The University of Tokyo, Japankuwano@iis.u-tokyo.ac.jp

ABSTRACT
Elastic wave measurement using Bender Elements have beendeveloped as a dynamic measurement method for element test in
geotechnical field. Now this method is expected to apply also for model tests
in order to obtain the pressure distribution inside soil chamber. This
research aims to obtain the basic performance information of benderelements in model tests. First, in order to detect the wave arrival clearer,signal analysis is conducted. Low frequency noise is reduced by polynomial
approximation, and high frequency noise is dealt with FFT filter. Next, theeffect of attaching aluminum block to bender element is discussed. Somechange in the received wave form is found, but wave velocity was almost
same. Also, property of wave propagation is checked using plural receiverfor one transmitter. Finally, shear modulus is calculated for obtained datafor validation. Results showed good agreement with those of element test.
Keywords: elastic wave measurement, bender elements, model test,
aluminum block, increment of earth pressure.

1. INTRODUCTION
Box culvers are structures buried inside high embankment as tunnels forwater, electric line, communicate line and so on. In designing of these box
culverts, increase in vertical earth pressure must be considered, sincedifferential settlement often occurs around a buried structure, as
schematically shown in Figure 1. However, in practice, the increment ofvertical earth pressures on underground structures is estimated in theempirical manner, mainly based on the information of past earth pressure
measurements in limited of sites (General guidelines for road earthworks,
1999). In this method, degree of settlement and mechanical properties ofbackfill materials are not considered. Some researches have been conductedto solve this problem. However, due to the limited performance of earthpressure transducers used in experiment, details of the earth pressure
distribution were not clearly understood.

181

August 2011, Thailand

Recently, a sophisticated trap door apparatus is developed by Ebizuka
and Kuwano (2010), in order to improve the problems in previous studies. A
soil chamber for trap door testing is constructed, the inside of which isshown in Figure 2. It can accommodate model ground of 700mm wide,
294mm long and 555mm high. The base of the chamber consists of five
separated movable blocks whose size is 99.8mm wide, 293.6mm long and
105mm high, and fixed parts in both sides, in order to create unevensettlement in the model ground, as schematically shown in Figure 3.
The final goal of this research is to obtain pressure distribution insidemodel test using this soil chamber to quantitatively evaluate the verticalearth pressure acting on buried structures. For the method of measurement,
elastic wave measurement using bender element is chosen.
Elastic wave measurement using bender elements have beendeveloped as a dynamic measurement method for element test. Measuring
propagation time of S wave inside soil gives shear modulus of soil. And by
conducting shear wave tomography, distribution of pressure can be obtained.
Although this is a useful method, no application for model test is conducted
before. In this paper, basic performance information of bender elements isstudied for model tests.

Figure 1: Increment of the earth pressure due to the uneven settlement

5 movable parts

Figure 2: Detail of soil chamber

182

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2. ELASTIC WAVE MEASUREMENT
2.1 Bender Elements
Bender elements are small measurement devise that transfer electric
voltage to physical deformation and vice versa. There are two types of
elements as shown in Figure3. Series type is more suitable for receiver andparallel type is better used as transmitter. Measurement is done by burying
these two types of bender elements in soil.

(a)

(b)

Figure 3: Bender Elements of (a) Series and (b) Parallel type
2.2 Measurement Apparatus
Measurement apparatus used in this study is shown in Figure4, and thedetails of setting are shown in Table 1.
Table 1: Setting of Aparatus
Output Voltage of Function Generator
Trigger Interval
Wave form
Amplifier rate
Averaging times by Oscilloscope

(a)

8Vpp
1s
Sin wave
100 time
20(b)

Figure 4: Aparatus used in this study(a) Function Generator (b) Oscilloscope

3. DETECTION OF WAVE ARRIVAL
The biggest problem of bender element application in model test so far isthe difficulty of detection of wave arrival. In order to make the detection
more accurate, two methods are taken. One is signal analysis and another is
attaching an aluminum block on the top of bender element. The effect ofeach method is discussed in this chapter.

Application of elastic wave measurement to model tests using bender elements

183

August 2011, Thailand

3.1 Signal Analysis
Figure 5 (a) shows original obtained wave signal. To reduce the large
low noise in this signal, polynomial approximation using quadratic curve isconducted for 2000 points out of 5000. Subtracting the fitted curve from theoriginal signal gives more clear view of response but still some highfrequency noise as shown in Figure 5 (b).
(a)

Wave Response (Vpp)

0.000-0.002-0.004-0.006-0.004-0.002

0.000

0.002

0.004

0.002

0.004

time (s)(b)
0.0004

Wave Response (Vpp)

0.00020.0000-0.0002-0.0004-0.004-0.0020.000

time (s)Figure 5: (a)Original obtained signal and (b) Low noise reduced
signalIn order to reduce the high frequency noise, the spectrum intensity ofsignal at -0.003 to -0.001s (before wave arrival) and 0.001 to 0.002 (after
wave arrival) is compared and shown in Figure 6. Three strong peaks seen
before wave arrival (a) are reduced after wave arrival (b), therefore thesefrequency can be regarded as noise.

184

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

(a)
0.005

0.004

Intensity
強度

0.003

0.002

0.001

0.000

-0.001
050000

100000150000200000250000

周波数 (Hz)
Frequency

(b)
0.06
0.05

強度
Intensity

0.04
0.03
0.02
0.01
0.00
-0.01
0

50000

100000150000200000250000Frequency
周波数(Hz)Figure 6: Spectrum intensity (a) before wave arrivaland (b) after wave arrival
Removing high frequency noise by FFT filter gives final version ofwave signal as shown in Figure 7. Method of wave detection is shown in
Figure 8.

Application of elastic wave measurement to model tests using bender elements

185

August 2011, Thailand

0.0004

Wave Respose (Vpp)

0.00020.0000-0.0002-0.0004-0.004-0.002

0.000

0.002

0.004

time (s)Figure 7: Wave signal after noise cutting

0.0004

Wave Respose (Vpp)

0.00020.0000-0.0002

Wave Arrival-0.0004-0.004-0.002

0.000

0.002

0.004

time (s)Figure 8: Wave arrival point

3.1.1 Effect of Aluminum Blocks
In element tests, attaching aluminum blocks on top of a benderelement reduced much noise and gave clear wave. This method is appliedalso for model tests. Size of aluminum blocks used in this study is
5mm/5mm/10mm.
There are several ways of attaching. Results are shown in Table 2.

186

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Transmitter

Table 2: Results of aluminum block
ReceiverObtained Signal0.0004Filtered Y1

0.00020.0000-0.0002-0.0004-0.004-0.002

0.000

0.002

0.004

Filtered X1

0.0004Filtered Y1

0.00020.0000-0.0002-0.0004-0.004-0.002

0.000

0.002

0.004

0.002

0.004

0.002

0.004

Filtered X1

0.0004Filtered Y1

0.00020.0000-0.0002-0.0004-0.004-0.0020.000

Filtered X1

0.0004Filtered Y1

0.00020.0000-0.0002-0.0004-0.004-0.0020.000

Filtered X1

Application of elastic wave measurement to model tests using bender elements

187

August 2011, Thailand

Following facts can be said from this result. Attaching aluminum
block to both transmitter and receiver make arrival of wave small.
Transmitter with aluminum block cause different shape of arrival wave, buttravel time is almost same as non-aluminum block. Attaching aluminum
block to receiver gives almost same wave shape and arrival time with nonaluminum blocks. As a result, attaching aluminum blocks does not haveeffect to clear arrival wave in model test.

4. VALIDATION OF OBTAINED DATA
4.1 Simitenous Receiving
Property of shear wave propagation should be checked. Three
receivers are set for one transmitter to see if wave is propagating in straight
way as shown in Figure 9.

S

S

S

P

Direction of Wave Propagation

S

S

S

P

Figure 9: Experiment for wave propagation checking
Results are shown in Figure 9. Divergence of result for last receiver israther large because of the long distance between bender elements. Wavevelocities are almost same, which suggest wave is propagating in straight
way.

188

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Wave Velocity (m/s)

200

150

100

4kHz
50

8kHz
10kHz
12kHz

0
0

5

10

15

20

25

Distance between Elements (cm)Figure 9: Results for wave propagation checking

4.2 Shear modulus
Shear modulus is calculated for obtained data using following
formulas.
G = ρVs 2(1)(2.17 − e) 2
f (e) =
1+ e
(2)where G is shear modulus, e is density, Vs is shear wave velocity ande is void ratio. Since model test is conducted under nearly uniform condition,next relationship is approximated for calculation. Results agreed favorablywith element test as shown in Figure 10.

12σh = σv(3)

Application of elastic wave measurement to model tests using bender elements

189

August 2011, Thailand

Normalized shear modulus (G/f(e)). MPa

1000

100

10
Element Test (DT)Element Test (TA)Model Test (BE)

1
1

2.5

5 7.5 1025

50 75100250

500 7501000

0.5(σ1.σ3) ,kPa

Figure 10: Normalized shear modulus versus earth pressure

5. CONCLUSION
1. In order to detect the wave arrival clearer, signal analysis is conducted.
Low frequency noise is reduced by polynomial approximation, and highfrequency noise is dealt with FFT filter.
2. Effect of attaching aluminum block to bender element is discussed.
Some change in the received wave form is found, but wave velocity wasalmost same.
3. Property of wave propagation is checked using plural receiver for one
transmitter and found to be in straight way.
4. Shear modulus is calculated for obtained data for validation. Results
showed good agreement with those of element test.

REFERENCES
General guidelines for road earthworks - culvert work -, 1999. Japan
road association, ISBN 978-4889504101, Maruzen Print Co. Ltd. (inJapanese).
Kuwano, R., Horii, T. and Kohashi, H., 2007, Increment of Earth
Pressure Acting on a Buried Box Structure due to Differential Settlements,
New Frontiers in Chinese and Japanese Geotechniques, Proceedings of theThird Sino-Japan Geotechnical Symposium, 2007, 309-314.
Suwal, L., Kuwano, R., Small strain stiffness measurement of sandand gravel using disk shaped piezo-electric transducer, 5th International
Conference on Earthquake Geotechnical Engineering, 2011.

190

4th Joint Student Seminar on Civil Infrastructures

WATER RESOURCES DEVELOPMENT
IN KONTO BASIN, INDONESIA:IS THAT ENVIRONMENTALLY SOUND?
Md. Reaz Akter MULLICK1 and Mukand S BABEL1
1
Water Engineering and Management,
Asian Institute of Technology, Thailand
reazmullick@gmail.com

ABSTRACTMultiple uses of water are inevitable to produce more with less water.
A sequential multiple use water systems in the Konto River, Indonesia is
examined to understand the existing operation of the system where water issequentially used in three hydropower plants and then in an irrigation
project from a upstream reservoir named Selorejo. Environmental flow
requirements for the river are estimated. It is found that the current
operation policy does not ensure environmental flow requirements
downstream to Selorejo reservoir which might cause environmental
degradation and loss of ecosystem goods and services for the basin. The
system is simulated for three different levels of environmental flow
provisioning at the downstream of Selorejo. Ensuring environmental flowcauses reduction in power production and higher level of environmental
flow provisioning results higher reduction in power production. Changes in
power production due to different environmental flow levels are examinedand presented, which will help the basin managers to decide and ensure
certain environmental flow level for the Konto basin.
Keywords: multiple use of water; water-use benefit; environmental
flow; tradeoff; Konto River, Indonesia.

1. INTRODUCTION
Growing water demands and limits over supply augmentation often entail
competition and conflicts among the water users. Situation aggravates whenenvironmental flow (EF) is considered for the rivers. Environmental flow is
the provision of certain amount of flow to maintain the river health. Whileencountering such challenges, society constantly seeks to maximize thevalue that the limited resources provide and efforts are therefore committed
to utilize the available water resources efficiently and effectively. Along thisline, multiple uses of water i.e. using the available water for more than one
uses or in production systems is inevitable to produce more with less water(Khan, 2010). However, intensification of multiple use of water in thecatchment may affect downstream flow both in terms of quality and quantity.
Hence, there is a need to revisit the multiple use water management

191

August 2011, Thailand

activities and environmental sustainability at a system or catchment level(Bakker and Matsuno, 2001).
Multiple use of water exists at different levels, namely: homestead
level (lowest level, where people use water for different uses around or near
the homestead); water system level (the level of much communal water
infrastructure, e.g. irrigation canal, domestic distribution network); andcatchment or river basin level (multiple uses of water occur from upper
catchments down to estuaries) (Van Koppen et al., 2006). Furthermore
multiple use of water can be classified into several types, namely: MPR(multipurpose reservoir), MPN (multipurpose network), MU+ (multiple use
domestic plus or productive plus, often productive plus is found as irrigationplus), MU-seq (sequential system), and MF (multi function, e.g. paddy fieldsystem) (Smits et al., 2008). MUS systems are usually found de facto,
meaning that systems were developed with a single use in mind, but are de
facto used for multiple purposes by users themselves or in a planned mannerby later-adopting specific management measures or infrastructural add-ons
to facilitate some other small-scale uses. This is probably still the mostcommon type of system (Van Koppen et al., 2006).
This paper aims to demonstrate an ‘MU-seq’ type of multiple use at
the Konto River, Indonesia, where water from an upstream reservoir issequentially used in three hydropower plants and then in an irrigation
project. The schematic of the system is presented in Figure 1. The usedwater is not redirected back to Konto main course. The paper further widens
the analysis by checking whether the system provides enough water as EF
downstream to reservoir and in case when EF demand is not satisfied
scenarios are run with EF provision and loss in power production is
examined.

Main course of river

HP-1

HP-2

HP=Hydropower

HP-3

To irrigation

Figure 1: Schematic diagram of the sequential multiple use water at Konto

192

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

2. STUDY SITE
The Konto River basin in east Java, Indonesia is actually a sub-basin of theBrantas river basin. Selorejo reservoir is at the upstream of the Konto river.
The Konto at the downstream of Selorejo flows towards north-west andfinally drains to the Brantas. The total basin area of the Konto is 687 km2
which can be divided into two parts, upper (above Selorejo reservoir) andlower (downstream of Selorejo). Catchment area for the upper part is about
236 km2 that comprises Konto river 148 km2, Kwayangan river 12.5 km2,
Pinjal river 44.3 km2 and Selorejo reservoir 31.3 km2 (Solihah, 2011).
Lower part of Konto basin has an area of 451 km2. Three small tributaries
are found meeting the Konto in this lower part, namely: Sambong(catchment area 3.13 km2), Nogo (catchment area 1.35 km2) and Nambaan(catchment area 3.69 km2) (Solihah, 2011). Average annual rainfall in thebasin is about 2,700 mm whereas annual average evaporation is about 1,470
mm. Two season are mainly observed, wet (November -April) and dry(May-October) in the region. Roughly 80% of the rainfall occurs in the wet
season. The plains and delta consist of alluvial soils (silt, clay loams) well
suited to paddy cultivation. The annual average temperature is 23.50C, withmaximum monthly temperature of 24.50C in January, and a minimum
temperature of 22.70C in July. Annual average relative humidity is 79.9%,with a minimum humidity of 75% in January, and maximum of 83% in
September (Solihah, 2011).
Selorejo reservoir commissioned on 1970 is situated at the upper partof Konto and is equipped with a hydropower plant, namely Selorejo power
plant. In addition to Konto river, the reservoir is feed by Pinjal andKwayangan river. The capacity of the reservoir is about 40 MCM with awater surface area of 4 km2. The Selorejo hydropower started running since1972 has capacity of 4.5 MW and the design discharge of 14.8 m3/s. The
release from Selorejo hydropower plant was redirected to the Konto before
the construction of Mendalan and Siman hydropower plants in 2003.
However, currently the release from Selorejo hydropower is subsequently
being used by another two power plants Mendalan and Siman respectively
and then in Konto irrigation project. Figure 2 shows the schematic of thelower part of the Konto River (the study site) including the water uses.
The topography of the region resonates well in terms of getting
considerable heads in building these two other hydropower plants, namely:
Mendalan and Siman after the Selorejo plant. The plants are run-off-river
type. The tail water elevation of the Selorejo power plant is 582.00 m
a.m.s.l. Part of the release from Selorejo plant is taken using a 3.25 km longtunnel to Sekuli daily retention pond which stabilizes the discharge from
Selorejo and supplies water to Mendalan hydropower plant having an
installed capacity of 7.0 MW and design discharge of 8.5 m3/s. The tunnel
capacity that feeds Sekuli retention pond is 9.25 m3/s. A 5.55 m3/s capacity
pipe sends back the extra discharge from Selorejo plant to Konto mainstream. Water Elevation of the Sekuli pondage is 573.17 m a.m.s.l. and thetail-water elevation of Mendalan power plant is 422.90 m a.m.s.l. that

Water resources development in Konto basin, Indonesia: is that environmentally sound?

193

August 2011, Thailand

creates an effective head of 150.27 m for Mendalan power plant (PJT-I,
2007).

Figure 2: Schematic of the Konto river study site
Note: Discharge values mentioned in the figure are the capacities of tunnel/pipes

The release from Mendalan power plant is carried by a 3.2 km longtunnel and sent to Siman retention pond where Siman Hydropower plant isinstalled with the capacity of 9.0 MW and with design discharge of 8.5 m3/s.
The effective head of Siman hydropower plant is 106.4 m (PJT-I, 2007). At
the point of Mendalan sabo-dam (Sabo-dam is a kind of silt arresting dam );facility to divert 2 – 3 m3/s flow to Siman pond is available to stabilize thepower production from Siman plant. Effective head for Siman power plantis 106.4 m (Solihah, 2011). The water used in Siman power plant goes toSiman reservoir from where the Left- and Right-Konto irrigation projects
get supply of irrigation water. The entire water resources is managed by the
public company named PJT-I.

194

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

3. METHODS AND DATA
The system is simulated for the current operation policy using a spread sheet
model and power production from each power plant is calculated. Based oncommunication with PJT-I, it is understood that the system is currently
running with the aim of maximizing power generation. PJT-I provided all
the required data for the analysis. The obtained data includes monthly
inflow to and release from Selorejo reservoir, monthly power production
from three power plants, monthly flow for the three tributaries of the Konto,
namely: Sambong, Nogo and Nambaan for last ten years (1999 - 2008).
Power production from Mendalan and Siman plants were available for last
six years (2003 – 2008) since both of the plants came into operation on
2003. Storage-area-elevation for the Selorejo reservoir was also collectedfrom PJT-I database.
Environmental flow requirements are estimated for the Mendalan
Sabo dam point and checked whether EF demands are meeting at this pointin the current operation. In case EF is not ensured at Mendalan sabo dam
point, the system is simulated with three different level of EF. For eachsimulation with different level of EF, power production is calculated.
Estimation of EF at Mendalan sabo-dam point of Konto
Information related to flow in the Konto at natural condition i.e. theflow before commissioning Selorejo reservoir on 1970 is not available.
However, inflow to Selorejo is recorded and obtained from PJT-I. This flow
can be used as proxy to the natural flow in the Konto if the Selorejo
reservoir would not been there. At the downstream of Selorejo dam, threevery small (in terms of discharge) tributaries met with the Konto (Figure 2).
Up to the point of Mendalan sabo-dam, the length of the reach is about 8 km
and after this point there is no major water abstraction from the Konto.
Environmental flow is considered immediately downstream of this point
after the diversion to Siman pond. Environmental flow is estimated based on
the inflow to Selorejo with the addition of Sambong, Nogo and Nambaan
flow. Only ten years monthly discharge data is in hand, hence Tennant
method (Tennant, 1976) is adopted, which deals with only mean annual
flow (MAF). Environmental flow requirements for wet and dry season arecertain percentages of MAF for different environmental status according toTennant.

4. RESULTS
4.1 Simulation of the system for existing operation
Inflow to Selorejo reservoir is known and acts as the upper boundaryof the model. Reservoir operation is constrained by minimum and maximum
storage i.e. 8.09 MCM and 39.6 MCM respectively. Hydropower
generations are constrained as maximum discharge limit of the penstock.
Based on this boundary and constraints, the simulation model is set up in a

Water resources development in Konto basin, Indonesia: is that environmentally sound?

195

August 2011, Thailand

spread sheet and run with six years (2003 - 2008) mean monthly dataset,
which represents the existing scenario. Downstream of Selorejo, only
observed information is the Selorejo release, which is compared with themodel output. The model output fits with the observed data with acorrelation coefficient (r) of 0.88, root mean square error (RMSE) of 1.18
m3/s and overall volume error (OVE) of 0.08 m3/s.
In this simulation of existing operation, release from Selorejo firstmeets Selorejo power plant’s demand. In case of higher release from thedam than the penstock capacity of Selorejo power plant, the extra flow spills
to Konto main stream. Outflow from Selorejo plant is then going to
Mendalan and Siman power plant subsequently. If it is necessary, small
amount of flow is diverted to Siman plant from Mendalan sabo dam point.
The observed and simulated power productions are compared for thisexisting operation and it shows a considerable well agreement. Performanceof the simulation is measured using two parameters; namely: RMSE andOVE and reported in Table 1. Figure 3 shows the simulated and observed
power for the Selorejo power plant. The simulated power productions from
Selorejo, Mendalan and Siman power plants are 25,951, 78,844 and 57,611
MWh respectively. The total power production is obtained to be 162,407
MWh from the simulation model.
Table 1: Simulation model performance in terms of energy generation forexisting condition
Power plant
Selorejo
Mendalan
Siman

RMSE (MWh)167.61474.64557.09OVE (MWh)
29.13
48.78
35.15

4,000

Observed Energy

Computed Energy

Energy (MWh)

3,000
2,000
1,000
2003

2004

2005

2006

2007

2008

Figure 3: Observed and simulated monthly power production from Selorejo
Power plant196

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

4.2 EF requirements at Mendalan sabo-dam point of Konto
Based on ten years mean monthly inflow to Selorejo reservoir in
addition to the flow of Nogo, Sambong and Nambaan river, MAF of Konto
at Mendalan sabo dam point is estimated to be 10.74 m3/s. Two seasons are
considered, namely: high flow season (November – April) and Low flow
season (May – October). Environmental flow requirements for both theseasons and for different environmental (habitat) status are calculated asprescribed by Tennant and reported in Table 2. Keeping in mind of
maximization of overall benefit from offstream water uses ‘fair or
degrading’ environmental condition is considered to be maintained which is
30% and 10% of MAF i.e. 3.22 and 1.07 m3/s for the high and low flow
season respectively.
Table 2: EF requirements for the Konto based on Tennant method
EF requirement (m3/s)
High flow season
Low flow season(November - April)(May - October)
Flushing flow
20.48 (200%)
20.48 (20%)
Optimum range
6.44 – 10.74(60 –6.44 – 10.74
(60 –
100%)
100%)
Outstanding
6.44 (60%)
4.30 (40%)
Excellent
5.37 (50%)
3.22 (30%)
Good
4.30 (40%)
2.15 (20%)
Fair or degrading
3.22 (30%)
1.07 (10%)
Poor
1.07 (10%)
1.07 (10%)
Severe degradation<1.07 (<10%)<1.07 (<10%)High flow season = November to April; Low flow season = May to October;
EF= environmental flow; MAF= mean annual flow
Environmental status asdefined by Tennant

4.3 Checking EF at Mendalan Sabo dam for existing operation
The monthly flow at Mendalan sabo dam is estimated from thesimulation model for the existing operation system and compared with the
EF requirements for ‘Fair or degrading’ level as estimated and presented inTable 2. It is observed that in dry season the actual flow does not meet the
EF requirements. Comparison of the actual flow and EF requirements areplotted in Figure 4.

Water resources development in Konto basin, Indonesia: is that environmentally sound?

197

August 2011, Thailand

12

Actual flow
EFR

10

Flow (m3/s)

8
6
4
2
0
Jan-03Jan-04Jan-05Jan-06Year

Jan-07

Jan-08Figure 4: Existing estimated flow and required environmental flow at
Mendalan sabo dam, Konto
4.4 Power production after ensuring EF
Environmental flows can be regarded as not exactly empiricallydetermined figures, but they are more value judgments depending on theaim of river management. Specific physical situation and the expected stateof the ecosystem should control the EF decision making. Without limiting
the economic growth, achieving environmental sustainability is the main
challenge along this line and that can be achieved within the context ofwider EF assessment framework incorporating in the river basin planning.
Considering such critical issues, three different values of EF are used andpower production in each case is estimated.
Three different level of EF is tested, namely: (1) ‘Poor’ condition forthe high flow season with a flow of 1.07 m3/s and ‘Severe degradation’ forthe low flow season with a flow of 0.75 m3/s labeled as ‘Low’ level of EF,(2) ‘Fair or degrading’ for both the season with a flow of 3.22 m3/s for thehigh flow season and 1.07 m3/s for the low flow season labeled as ‘Medium’level of EF, (3) ‘Good’ status for both high and low flow season isconsidered with the flow of 4.3 and 2.15 m3/s respectively labeled as ‘High’level of EF. All these ‘poor’, ‘fair’ and ‘good’ environmental status is as
defined by Tennant.
Power production in the three levels of EF provisioning is estimatedusing the simulation model. The power production values are presented inTable 3. Figure 5 shows the power production relative to existing condition
for various EF levels.

198

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Table 3: Power production while ensuring EF at Mendalan Sabo dam
EF levelPower production (MWh)Selorejo

No EF

Mendalan

Siman

Total

Low

25,951
23,55678,84473,354

57,61153,723

162,407
150,633 (7%)

Medium

20,344

66,98749,511

136,843 (16%)

High

17,492

59,61544,854

121,962 (25%)

Note: value in parenthesis indicates % change with respect to no EF condition

Power production relative to existing
condition (%)

100
80
60
40
20
0
No EF

Low

MediumHigh

EF level

Figure 5: Power production relative to existing operation while EF is
ensuredIt is observed from Table 3 that higher the level of EF provisioning
results higher reduction in power production. Higher reduction is observedin Selorejo plant. Total power production is reduces by 7, 16 and 25% fromthe existing condition for ensuring low, medium and high level of EF
respectively.

6. CONCLUSION
In the present study, the sequential multiple water use system in the Konto
river basin in Indonesia is thoroughly analyzed. The system is simulated forthe existing operation policy which does not ensure EF downstream to
Selorejo reservoir. Such operation of the reservoir might cause
environmental degradation to the downstream part of Konto. Environmental
flow is therefore necessary to maintain, however, provisioning EF results
reduction in power production. The system is then simulated for threedifferent levels of EF namely, low, medium and high. Ensuring EF withhigh level results higher reduction in power production from the system.
Nevertheless, at least low level of EF is strongly suggested to be ensured.

Water resources development in Konto basin, Indonesia: is that environmentally sound?

199

August 2011, Thailand

The results from this analysis will help authorities to realize the cost of EF
provisioning for the Konto river basin.

ACKNOWLEDGEMENT
This research is part of the doctoral study by the first author at Water
Engineering and Management (WEM), Asian Institute of Technology (AIT)
which has been supported by the scholarship provided under the AITNorwegian Ministry of Foreign Affairs (AIT-NMFA) Cooperation Program.
Authors also acknowledge DUPC, UPaRF and its donors (DGIS, UNESCOIHE and AIT) for partially funding the research. Thanks are also extended to
PJT-I and UNPAR for providing data and information related to theresearch reported here.

REFERENCES
Bakker M. and Matsuno Y. (2001). A framework for valuing
ecological services of irrigation water. Irrigation and Drainage Systems,
vol. 15, pp. 99-115.
Khan, M. A. (2010). Enhancing water productivity through multiple
uses of water in Indo-Gangetic basin. Paper presented ISDA (Innovationand sustainable development) 2010, Montpellier, June 28-30, 2010.
Available
onlinehttp://www.isda2010.net/var/isda2010/storage/original/application/9dc633a
9f77b8f381062d94ffdfb43a0.pdf accessed on 05/06/2011
Perum Jasa Tirta (PJT) – I. (2007). Manual Operasi Dan
Pemeliharaan Bendungan Selorejo (Dam Operation and Maintenance
Manual Selorejo). PJT - I, Malang.
Smits, S., Renwick, M., Renault, D., Butterworth, J. and Van koppen,
B. (2008). From practice to policy: background paper for the symposium on
multiple-use services. International Symposium on Multiple-Use Water
Services Addis Ababa, Ethiopia, 4-6 November 2008.
Solihah, R. (2011). Analisis pemanfaatan potensi air waduk Selorejo(Analysis of the potential use of water from reservoir Selorejo) B.Sc. Thesis
at Universitas Katolik Parahyangan (UNPAR), Indonesia.
Tennant, D. L. (1976). Instream flow regimes for fish, Wildlife,
Recreation and Related Environment Resource, Fisheries, vol. 1, 1976, pp.
6-10.
Van Koppen, B.; Moriarty, P.; Boelee, E. (2006). Multiple-use water
services to advance the millennium development goals. Research Report 98.
International Water Management Institute, Colombo, Sri Lanka.

200

4th Joint Student Seminar on Civil Infrastructures

A STUDY ON LONG-DISTANCEEVACUATION MEASURE TO
LARGE-SCALE FLOOD DISASTERS
IN JAPANESE THREE MAJOR
METROPOLITAN AREAS
Hiroyuki NAKAI1 and Takaaki KATO2
1
Graduate Student School of Eng., The University of Tokyo, Japanh-nakai@iis.u-tokyo.ac.jp
2
Associate Professor, ICUS, IIS, The University of Tokyo, Japan

ABSTRACTThe purpose of this study is to examine what should be of evacuation
measures by the municipality which is exposed to the danger of a largescale flood disaster through the analysis of the flood hazard map. The plainsof Japanese three major metropolitan areas holds an extensive risk to theflood disaster such as broadness of flooded area, flooded length andaccumulation of population. In this study, contents of the flood hazard map
which is distributed to the citizens were analyzed to understand the current
state of evacuation measures at a large-scale flood disaster in eachmunicipality. Most of municipalities assign facilities where the flood wasassumed to the refuge. It is necessary to take evacuation measures inconsideration of characteristics of the region such as broadness of flooded
area and flooded length. Moreover, when long distance evacuation isprescribed, it becomes very important to use the transportation such as
public traffic and bicycles and how to evacuate the senior citizen and thehandicapped person in priority. The assessment model is to be establishedat two points; the time to complete evacuation by behavioral traits like useof transportation, and the scale and the arrangement of the refuge.
Keywords: flood, evacuation, hazard map, contents, below sea levelarea

1. INTRODUCTION
1.1 Background of this study
Recently, an increase in the flood damage risk originating in the climaticchange is pointed out. Global warming progresses because of increase of aheat-trapping gas such as anthropogenic carbon dioxide, and the normal
temperature rise of 0.74℃ every 100 years is confirmed according to thefourth evaluation report issued by Intergovernmental Panel on Climate
Change (IPCC). A further normal temperature rise is expected, and the riseof the sea level, an increase of the downpour frequency and enlargement of

201

August 2011, Thailand

typhoons are assumed. About 50% of the population and about 75% of theproperty have concentrated on the flood assumption district in the waste
plain where it accounts only for about 10% of the area of the country inJapan. The incidence of a large-scale flood damage by the flood and theclimax can be said that it decreases in recent years as the maintenance of theflood control facilities such as the embankment advances, andconsciousness of crisis to the flood damage is weakening at the same time aspeople's reliance and relief to the flood control business rise. However,danger over the maintenance target such as the flood control facilities rises
by a great climatic variation, and the possibility of the occurrence of a largescale flood damage due to the flooding and the climax flood cannot bedisregarded in the metropolitan area located in the littoral region and the
low-lying area in Japan. It is thought that not only a lot of lives and theproperties are lost as confirmed by the climax disaster caused by Hurricane
Katrina in New Orleans, but huge damage and confusion are caused sociallyand economically if a large-scale flood damage occurs by any chance.
1.2 Flood disaster risk in the large-scale below sea level areas
The district below the average flood tide level is called a below-sea-levelarea. Tokyo metropolitan district, Chukyo metropolitan district and Kinki
metropolitan district are generically called three major metropolitan areas,
and the population, undertaking activities and the educational institution
concentrate there. The more than half of the total population of Japan
concentrates on three major metropolitan areas at the end of March in 2007,
and it can be said that it forms the center part of the country.
In three major metropolitan areas, wide plains exist, and especially on
gulf coast, there are regions below the average flood tide level called the
below-sea-level area. The below-sea-level area in Japan is assumed to be
chiefly formed by the subsidence by an excessive drawing up undergroundwater. The total size of the below-sea-level area is about 580 km2 in January,
2006; 116 km2 in Tokyo metropolitan district, 336 km2 in Chukyo
metropolitan district and 124 km2 in Osaka metropolitan district. Moreover,
over four million people reside there in total; 1.76 million people in Tokyo
metropolitan district, 900,000 people in Chukyo metropolitan district and1.38 million people in Osaka metropolitan district.
The ground level is low in the below-sea-level area, and it exists on
gulf coast, it is located in the lower part of large rivers. Therefore, it isassumed that various flood damage such as inundation inside the levee, theflood and the climax occurs. Large area is continuously flooded once theflood damage occurs. It is expected that the flood continues for a long termof two weeks or more because natural drain is not done if the drain facilities
are not used in the below-sea-level area.
Therefore, the victims are pressed long-term refuge life. A large
difficulty is attended to the rescue supply to people who are isolated in theflooded district assumed to reach several hundred thousand people in themetropolitan area. The efficiency improvement of the goods delivery is

202

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

attempted by bringing the evacuee together in the refuge. Furthermore, it ispreferable to move outside the flooded district and to make them do a longterm relocation. However, it is thought that a large difficulty is attended also
by the safe inducement of the evacuee outside the flooded district. It isthought that the seriousness of the flood damage risk is exposed in the largescale below-sea-level area when a large-scale flood damage.

2. CONTENTS ANALYSIS OF FLOOD HAZARD MAPS
2.1 Meaning of flood hazard maps to take measures to large-scale flood
disasters
It is necessary to provide the measures for smooth, prompt evacuationsuch as the means of information transmission and the refuge areas for thedamage reduction at a large-scale flood damage. The flood hazard map islocated as a well-known means to the resident of these measures. The local
government was obligated to make public of the flood hazard map in Japan
along with the flood control law revision in June, 2001. As a result, the rateof information disclosure that was 23.1% at the end of 2003 rose up to
85.2% in January, 2011. Therefore, it is thought that the current state of alarge-scale flood damage measures in each municipality can be evaluated byanalyzing the flood hazard map.
2.2 Objective and item of analysis
In this chapter, contents of the flood hazard map were analyzed for the
local government where the flood district extended to the width of about
1km or more in the municipality including the flood assumption district in
the river that flowed to the below-sea-level area in Japan's three major
megalopolises. It is because of the idea it is expected that it takes some timeto evacuate in such a place, and giving information about disasterprevention to the resident by the flood hazard map becomes important forsmooth, prompt evacuation in the idea of evacuation measures.
Municipalities for the analysis were the following 116 municipalities,
and 105 municipalities to which the flood hazard map was open to the
public on the Internet were analyzed among these. (Table 1)
Table 1: Municipalities for flood hazard map analysis
Major metropolitan areas Prefecture Municipality
Tokyo
Edogawa, Katsushika, Adachi, Koto, Sumida, Taito, Arakawa, Kita, Itabashi (9 municipalities)
Wako, Asaka, Shiki, Fujimi, Kawagoe, Kawashima, Yoshimi, Kawaguchi, Hatogaya, Toda,
Tokyo
Saitama, Ageo, Okegawa, Konosu, Gyoda, Kumagaya, Kazo, Ina, Kuki, Hasuda, Shiraoka,
metropolitan
SaitamaMiyashiro, Sakado, Higashimatsuyama, Soka, Misato, Yashio, Yoshikawa, Koshigaya,
districtMatsubushi, Kasukabe, Sugito, Satte (33 municipalities)(48 municipalities)
Chiba
Urayasu, Ichikawa, Matsudo, Nagareyama, Noda (5 municipalities)
IbarakiGoka (1 municipality)
Nagoya, Ama, Oharu, Kiyosu, Kitanagoya, Toyoyama, Ksugai, Yatomi, Aisai, Tsushima,
ChukyoAichi
Inazawa, Ichinomiya, Kanie, Tobishima (14 municipalities)
metropolitan
Gifu, Hashima, Kakamigahara, Kasamatsu, Ginan, Kaizu, Wanouchi, Anpachi, Mizuho,
district
Gifu
Kitagata, Ogaki, Yoro, Godo, Ono, Ibigawa (15 municipalities)(34 municipalities)
Mie
Kuwana, Kisosaki, Kawagoe, Asahi, Toin (5 municipalities)
Osaka, Moriguchi, Neyagawa, Hirakata, Kadoma, Daito, Higashiosaka, Settsu, Suita, Ibaraki,
Osaka
Osaka
Takatsuki, Toyonaka, Ikeda, Yao, Sakai, Matsubara, Fujiidera, Kashiwara (18 municipalities)
metropolitan
Oyamazaki, Nagaokakyo, Muko, Kyoto, Uji, Kuiyama, Joyo, Ide, Yawata, Kyotanabe,
district
Kyoto
Seika, Kizugawa (12 municipalities)(34 municipalities)
Hyogo
Itami, Kawanishi, Amagasaki, Nishinomiya (4 municipalities)

A study on long-distance evacuation measure to large-scale flood disasters in Japanese three major
metropolitan areas203

August 2011, Thailand

I arranged the analysis item based on the description item provided in"Guidance of flood hazard map edit" (Table 2). The description item can beclassified into three; "Contents of the map" including information that is thenecessity of the description on the map, "Information about evacuation"
which is necessary to evacuate, and "Shelter use information" which
enlighten the disaster awareness in daily life. "Contents of the map" and"Information about evacuation" are thought to be more important weretotaled and analyzed in the present study.
Table 2: Items for flood hazard map analysis
Contents of the map
Flood-assumed area
Refuge area
Designation to evacuate from flood-assumed area
Zone-by-zone refuge areas
District
The number of districted areas
Refuges for who need help on evacuation
Avairable floors of refuges
RefugeContact information of refuges
Type of refuges
Danger area on evacuation
Route
Underground facility and flooded road
Designation of evacuation route
Hospital
Facilities
Public institution
Note about flow rate

Information about evacuation
Where to get information
Getting
How to get information
information Contact information of government
Contact information of disaster-related facilities
Acquaintanceship at normal times
A series of Acquaintanceship on evacuation
evacuation Taking out list
Information about refuge life
Action on evacuation order
When to start Earlier evacuation order to who need help
evacuation Distinction of evacuation order by floor of home
Note about time change
Designation of transportation device
Transportation
Special note about using cars
Difficulty and speed of walking at flooded condition
Note about those who need help
Figure about depth of flood

2.3 Result of analysis
When the description rate to the flood hazard map was arranged, it became
the following. (Figures 1 and 2) Especially important items for thinking
about the evacuation activity were shown in red. The item described by all
the hazard maps is only "Position of the refuge area". The difference of thedescription rate of “contents of the map” by the item is large, and“information about evacuation” has understood the description rate is
generally high compared with “contents of the map”. It is thought thatcharacteristics of the region are reflected on “contents of the map” better
than “information about evacuation. Moreover, there is a municipality
where necessary information of escape route is insufficient when theresidents evacuate, and the improvement is hoped for.
Next, the flood hazard map of each item was arranged. (Figure 3) As aresult, the item with an especially remarkable difference by the area is"Designation of long-distance evacuation", "Notification of evacuationroute", and "Available floors of refuges".

204

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

Figure 1: Contents of the map

Figure 2: Information about evacuation

Figure3: Item with remarkable difference by municipality
2.4 Sort flood hazard maps by type
It has been understood to be able to classify evacuation measures ofeach municipality at a large-scale flood damage into the following three
types by the character in the specified refuge area through the contents
analysis of the flood hazard map.

A study on long-distance evacuation measure to large-scale flood disasters in Japanese three major
metropolitan areas

205

August 2011, Thailand(1) Municipality where description exists for long-distance evacuation
outside municipality (13 municipalities) In the municipality whose floodarea is wide and whose whole area is almost flood assumption area, it isthought that long-distance evacuation (Evacuate over the municipality field)
is necessary, and the municipality enumerated in this item has included
long-distance evacuation in some shape in the flood hazard map.
(2) Municipality where design refuge out of the flood-assumed area inthe municipality or refuge at less flood-assumed area (23 municipalities)
When the district by the height's there in the municipality it that is not
flooded in the region exists in the municipality, the necessity of large area
shelter can evade the few isolation of the refuge at the ponding.
(3) Municipality that specifies refuge to the nearest refuge in flood
assumption district (70 municipalities) In the other than the abovementioned two types, it is specified to evacuate to the nearest refuge in theflood district, and 66.0% of the whole (70 municipalities) is the hazard mapsof this type.

3. CONSIDERATION ABOUT EVACUATION MEASURES TO
LARGE-SCALE FLOOD DISASTERS
In this chapter, the indicator of the matter that should be going to advancethe examination to the problem exposed by the hazard map contents analysis
in Chapter 2 is shown.
The first point is improvement of flood hazard maps corresponding tocharacteristics of the region. The refuge that conforms by the struck
situation can be set, and the flood hazard map be improved for evacuation
measures chiefly provided based on spatial characteristics by understanding
regional characteristics in two axes (the time axis and the space axis).
The second point is making and giving public notice of the flood
assumption district map including a change with the lapse of time. In
including "Time axis" in evacuation measures, there is a part notameliorable in each municipality. About this, it is necessary to make publicin the form of that each municipality can use "Flood water arrival time map"and "Distribution map of the ponding time" besides the flood assumption
district map in the flood hazard map as an output of the flood simulation
done in each river office.
The third point is a review of the refuge capacity. It is necessary toreview the refuge capacity from the viewpoint of depth of flood andponding time and availability of the stockpile. Whether capacity of specified
refuges is enough is considered, if it is assumed to evacuate to the refuge inthe flood district at a large-scale flood damage. If it is not enough, it isnecessary to review the specification of the refuge considering thepossibility of long-distance evacuation.

206

4th Joint Student Seminar on Civil Infrastructures

ICUS Report 57

The fourth point is an examination about the transportation method on
long-distance evacuation. Though long-distance evacuation on foot isbasically specified, it is not easy to think that this policy is the best when
thinking about prompt and adequate evacuation. It is necessary for groping
for the possibility of evacuation using the public transportation facility andthe bicycle. In addition, it is necessary to decide a main route and the
direction of evacuation to achieve the prompt evacuation conduct, because
an indefinite part is a lot about route of evacuation.
Finally, there are problems of who need help on evacuation. First ofall, it is important to list who need help on evacuation. And then, it isimportant to secure cooperators, rapid transportation to refuges, and refuges
for those who need help.
In considering of evacuation measures at a large-scale flood damage,it had turned out not to be examined enough, so the measures ideas about
the above-mentioned five items was presented with the importance of the
problem. It is necessary to improve in evacuation measures at a large-scale
flood damage, and to be held off to the minimum of damage for the assumed
risk in each municipality.

4. CONCLUSION
4.1 Conclusion of this study
The rate of making public of the flood hazard map went up greatly byrevising the flood prevention law in 10 years. "Guidance of flood hazard
map edit" is made public along with the flood control law revision, and theflood hazard map is made as a part that takes measures against the flood of
the river in each municipality including the flood assumption district basedon the guidance. However, it was clearly shown that there was a big
difference between a municipality thought for the examination situation ofevacuation measures to be examined enough and municipality not so in thecurrent state. Moreover, becoming what suits a more actual water disastercondition has understood evacuation measures from introducing "Time axis"
when thinking about the flood assumption. It is thought that the present
study is meaningful each municipality examines a large-scale flood damage
measures in these two points.
4.2 Agenda and next vision
The study on the public assistance in evacuation measures at a largescale flood damage was advanced in this thesis. However, there is a limit
within the range where the public assistance can be done. Therefore, the roleof the self-help and the mutual assistance is very important. The self-help isto think about and provide for the disaster routinely. And, the mutual
assistance is the activity of the voluntary organization for disasterprevention. Therefore, it is necessary to hold an enough conference between
each subject routinely so that the self-help, the mutual assistance, and the
A study on long-distance evacuation measure to large-scale flood disasters in Japanese three major
metropolitan areas

207

August 2011, Thailand

public assistance are interlocked and synchronized well.
Moreover, it is necessary to improve not only such soft measures but
also continuous hard ones such as the reliabilities of the safeguard facility,
construction of the piloti, and the maintenance of the pedestrian deckconnected with the station. However, because the memories of a past flood
damage are weakened by maintenance in hard respect, and resident's
disaster awareness is decreasing, it is necessary to continue to hold awarning for the danger of a large-scale flood damage, to maintain andimprove resident's disaster awareness and self-reliant efforts.
I want to keep on studying about improvement of the evacuation
completion rate and achievement of rapid evacuation by comparative study
using valuation modeling of evacuation method, and edit of real-time hazard
map which express in the shape of fixed to one by one risk and make itpublic through the web.

REFERENCES
Kazuya, S., and Osamu, M., 2007. Comparison of Contents ofJapanese Hazard Map to the Earthquake Flood. Digest of Academic Lecture2007 F-1, Japanese Architecture Academy, 643-644
Fumio, N., 2006. Public Presentation of the Hazard Map using theInternet. Collection of Lecture Papers 2006, Japan Society ofGeoinfomatics, 91-92
MLIT
Japan,
PortalSiteofHazardMap:
http://disapotal.gsi.go.jp/index.html
MLIT Japan, Manual to Create Flood Hazard Map:
http://www.mlit.go.jp/river/basic_info/jigyo_keikaku/saigai/tisiki/hazardmap/index.html
MLIT Japan, Approach to the Climate Shift by River Bureau:
http://www.mlit.go.jp/river/basic_info/jigyo_keikaku/gaiyou/kikouhen
dou/index.html
MLIT Japan, Review Meeting of Storm Surge on Below Sea Level
Area:
http://www.mlit.go.jp/river/shinngikai_blog/past_shinngikai/shinngika
i/takashio/index.html
Cabinet Office, Government of Japan, Expert Study Meeting about
Countermeasure against the Mass Flood Disaster:
http://www.bousai.go.jp/jishin/chubou/suigai/index.html.

208

4th Joint Student Seminar on Civil Infrastructures