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ISSN 0916-3891
自 然 災 害 研 究 協 議 会
西部地区部会報
第36号
研 究 論 文 集
平成24年 2月
平成 23 年度西部地区部会
研 究 発 表 会
平成 24 年 2月 24 日 福 岡 市
自然災害研究協議会西部地区部会
自然災害研究協議会西部地区部会報第36号
二〇一二年二月
Western Regional Division Report of Natural Disaster Research Council
No.36
Contents
ARTICLES
1. Developing GIS-based Method to Find Dangerous Slope Forming Dam
Fan Yanan, Guoyun Zhou, Guangqi Chen and Kiyonobu Kasama 1
2. Design Strength of Ground Improvement for Liquefaction
Yutaro Inatomi, Kiyonobu Kasama, Kouki Zen and Guangqi Chen 5
3. An Estimation of Runout Distance of Debris with Discontinuous Deformation Analysis
Taisuke Koga, Kouki Zen, Guangqi Chen and Kiyonobu Kasama 9
4. An Approach Which Forecast the Slope Failure that Happens Because of the Rainfall using
Geographical Information System
Taiki Hiraoka, Kouki Zen, Guanqi Chen and Kiyonobu Kasama 13
5. GIS-based Two-dimensional Numerical Simulation of Debris Flow in Mobile-bed Gully
Jian Wu, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Lu Zheng and Yingbin Zhang 17
6. Seismic Slope Stability Analysis Subjected to Tension Failure
Yingbin Zhang, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Jian Wu and Lu Zheng 21
7. Comparison of Weight of Evidence and Logistic Regression Model for Medium Scale Landslide
Susceptibility Mapping in Yogyakarta Region Indonesia
Guruh Samodra, Guangqi Chen, Junun Sartohadi, Kouki Zen and Kiyonobu Kasama 25
8. Estimate of Debris Flow Hydrograph and Modeling of the Depositional Process :the Debris Flow�
Event in Hachimandani River, Hofu City in Japan on July 21, 2009
Tomohiro Miyoshi, Haruyuki Hashimoto, Shinya Ikematsu, Farouk Maricar, Kyosuke
Hashimura and Kensuke Sakada 29
9. Experiments on Open Check Dam as a Countermeasure against Debris Flows with Driftwood
Kyosuke Hashimura, Tomohiro Miyoshi, Haruyuki Hashimoto, Shinya Ikematsu, Tadahiko
Hasuo, Farouk Maricar and Kensuke Sakada 33
10. Analysis on the Landslide Traveling Distance from Forest Slopes
Souhei Otani and Tetsuya Kubota 37
11. The Characteristics of the Landslide Disaster and Sediment Runoff in Sierra Madre Oriental
Mountain range, Mexico
Tetsuya Kubota, Israel Cantu Silva and Laura Sanchez Castillo 41
12. Geo-Simulator-3 (Utilization of Various Geo-Information)
Ryosuke Kitamura, Fumio Nakata, Yoshito Tanaka, Hisashi Kawakami, Ryoji Tanaka
and Kazuyoshi Jomoto 45
13. Characteristics and Changes of Tephra Layer Deposited by 2011 Shinmoe-dake Eruption in
Kirishima Volcanoes
Takahito Kuroki, Nozomi Iso, Kensuke Goto, Tatsuroh Soh and Keisuke Kuroda 49
14. Some Disasters in Historical Novels Dealt with Edo Period
Keinosuke Gotoh 53
15. Relationship between Natural Disasters and Place Names in Case of 1982 Nagasaki Disasters and
2011 Higashi-Nippon Earthquake Disasters
Kensuke Goto and Keinosuke Gotoh 57
16. Changes in Land Use and Flood Disasters in Asa Area of Sanyo-Onoda City, Yamaguchi Prefecture
Haruhiko Yamamoto, Minori Yamamoto, Toshiaki Yamasaki, Kiyoshi Iwaya and Hisashi
Yoshikoshi 61
17. Development of Visualization Tool for Safe Evacuation Route During Flood Using Integration of
3D Images and GIS
Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori Yamamoto, Toshiaki
Yamasaki, Shigetoshi Itou and Junichi Hironaka 65
18. WEB Rainfall and Water-Level Information in Western Part of Japan and Problem of Disaster
Prevention Information during Disaster
Nozomi Kanamoto, Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori
Yamamoto, Toshiaki Yamasaki and Taiju Miwa 69
19. Disaster Damages and Missing Values at Rainfall and Gauging Stations during Heavy Rainfall
Disasters
Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori Yamamoto and Toshiaki
Yamasaki 73
20. The Latest Progress of the Erosion Control Area Utilization in Unzen
Kazuo Takahashi and Shinichi Sugimoto 77
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6. Seismic Slope Stability Analysis Subjected to Tension Failure��������������21
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自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
FLIP 7) -
20m 20m1.0m×1.0m 400 441
Fig. 1 Table 1uq
uqCOV uq
8) Fig. 2
R() P
C (1)
CPR �� (1)
Fig. 3
Pr[FL<1.0 �]Number[element]
Number[FL<1.0] Pr[FL< �]
(%)100][]0.1[
]|0.1[ ��
��elementNumberFNumber
FP LLr � (6)
Co K C (8)
0]]|0.1[[ CFPKC Lr ��� � (8)
Pr[FL<1.0 �] NB
9)
(9)
BLr NFPFK ��� ])|0.1[( � (9) 0.1])|0.1[( �� �Lr FPF (10)
20m
20m(=1.0 20)
zx
Target zone Buffer zoneBuffer zone
Fig. 1 Finite element mesh Table.1 Input parameters
Input parameter Symbol Unit ValueUnconfined
compressive strength�qu kPa 200
COVqu 0.2-1.0Correlation length � m random
Poison ratio 0.33Density � t/m3 1.89
Damping coefficient h 0.15Internal friction angle degree 30
Unit weight kN/m3 18.5Effective unit weight ’ kN/m3 8.5Monte Carlo iteration 1000
FL1.61.51.41.31.21.110.90.8
FL
�max=2m/sec2
COVqu=0.8
�qu=100kPa
0
-5
-15
-10
-205 1510 200
Horizontal direction X (m)
Dep
thZ
(m)
a) qu
─ 6 ─
F(Pr) K NB
F(Pr[FL<1.0 �]) = 2m/sec2
K(Pr[FL<1.0|�])=100% (11)R
� � �]]|0.1[[|1 0 �� ����� LrLr FPCKFPR (11)
Pr[FL<1.0|�]
Fig. 4
1.0
1.0
100kPa 4
Fig. 5Pr[FL<1.0 �]
Pr[FL<1.0 �]
Fig. 6 K K� K
� K��
Fig. 9 R
R 30 Fig.10
Probability Analysis
Earthquake hazardcurve, Pa
Liquefaction potential
Damage Analysis
Liquefaction Risk, RCPR ��
Monte Carlo simulation
Mean,�qu,Coefficient of Variation,COVqu,and correlation length,�
2D seismic response analysis using FLIP
Liquefaction safety factor ,FL
Liquefaction probability,PL
Damage ratio,KAssumed earthquake damage,C
0CKC ��
Probability numerical limit analysis
0.1
1
10
100
0.5 1 1.5 2 2.5 3
Stre
ngth
ratio
qu
site
/quo
Overdesign factor
Percent defective = 2.3%19.0%
32.8% 4.4%
Fig. 4 Strength ratio and overdesign factor
Fig. 3 Liquefaction risk analysis flow
─ 7 ─
5
(2)
�
(3) 0.1/ 10 R
0.5C0 55%
2.0 5
(4)
1.0
2.0 2.0
1) Zen, K., Yamazaki, H. & Mori, K.: Development of Premixing Method against Liquefaction, Proceedings of the 9th Asian Regional Conference on Soil Mechanics and Foundation Engineering, Vol.1, pp.461-464, 1992.
2) Larsson, S., Stille, H. & Ollson, L.: On horizontal variability in limecement columns in deep mixing, Geotechnique, 55(1), pp.33-44, 2005
4)
5)
6) 9
7)
9)
8
Fig. 5 Liquefaction potential
Fig. 6 Damage ratio induced
Fig. 7 Liquefaction risk curve (Fukuoka)
Fig.8 Total cost for liquefaction
0
20
40
60
80
100
0 0.5 1 1.5 2 2.5
11.522.53
Liqu
efac
tion
pote
ntia
l PL[F
L<1|��
Overdesign factor
Maximum horizontal accelaration � (m/sec2)
0
0.2
0.4
0.6
0.8
1
0 0.5 1 1.5 2 2.5
Dam
age
ratio
KC 0
Overdesign factor
Maximum horizontal accelaration � (m/sec2)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0 0.2 0.4 0.6 0.8 1
0.512
Ann
ual e
xcee
danc
e pr
obab
ility
(10
2 time/
year
)
Liquefaction loss R ( C0)
Overdesign factor
0.5
1
1.5
2
2.5
3
0 0.5 1 1.5 2 2.5
0.10.20.30.40.5
0.60.70.80.91
Tota
l cos
t(C 0)
Overdesign factor
Ratio of initial cost
─ 8 ─
1 2 2 2
1 2
13 4
2 (50m 50m)
DDA
6
1)
1
1)
2) 3)
4) 3)DDA
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2
1
1 32 32
20m
─ 9 ─
自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
[MN/m2] 30
0.3 [kN/m3] 18.6
[kN/m3] 19.6
( )[kN/m2] 20 ( )[kN/m2] 0( )
H
1
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( )
c=20kN/m2 =34°
DDA
c=0kN/m2
=k
=
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─ 10 ─
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27 7.7m DDA
k=70% 34.4m( 6) k=80%32.3m( 15) k=90% 32.6m( 16)
k=70%
k=70%3)
DDAk=70%
k=70%
6
─ 11 ─
DDA( 70%)
H=21.48mL=8.53m(k=70% )
27H=5.73m L=10.62m
4)
H’ L’(H’/L’) k=70%
6 H’/L’=0.54�=44 11° 27 H’/L’=0.32�= 4 ° H’/L’( 0.5) 31H’/L’=0.50 �= ° 0.81
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─ 13 ─
自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
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─ 15 ─
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L. Montrasio R. Valentino A model for triggering mechanisms of shallow landslides, Natural Hazards and Earth System Sciences, Vol.8, pp.1149 -1159, 2008.
530 pp.72 2009
20032004
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─ 16 ─
GIS-based two-dimensional numerical simulation of
debris flow in mobile-bed gully
Jian Wu, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Lu Zheng, Yingbin Zhang
Division of Civil and Structure Engineering, Kyushu University, Fukuoka, 819-0395, Japan
1 INTRODUCTION
Broadly speaking, a debris flow represents the gravity-driven flow of a mixture of various sizes
of sediment(from clay to boulders), water and air, down a steep slope, often initiated by heavy rainfall
and/or landslides[1]
. In fact, There is often significant erosion and deposition that can dramatically
change the gully bed. Where in the end of an alluvial fan, debris flows slow significantly, depositing
large amounts of sediments[2]
. Debris flows could lead to severe flooding with catastrophic
consequences, such as damage to properties and loss of human life. Therefore, debris flows have been
the subject of scientific and technical research for many civil engineers. At present, how to modelling
and forecasting of debris flows is a key issue for the safety of mountain regions.
In the last few decades, debris flows has been studied by several authors from different points of
view[3] [4] [5]
. According the different type (debris torrents, debris floods, mudflows, mudslides,
hyperconcentrated flows. classified by Iverson in 1997[3]
.) the forces that support the movement of
debris flows due to two actions: dispersive pressure resulting from collisions among the particles[6]
and
plastic strength of the interstitial fluid when this is composed of a clay or mud slurry[7]
. Therefore,
different rheological schemes using for each specific type of flow. For examples, Granular flow in
which fluid effects are negligible, mud flow in which fluid effects are dominant and particle collisions
are negligible, and stony debris flow in which particle interactions are important as well as the fluid
effects[8]
. In this work we will refer to a stony debris flow that is a flow made of mixtures of water and
non-cohesive and relatively large particles. Therefore, the rheology of debris flows used Takahashi 's
dilatant model in this study.
The aim of this paper is to study the propagation of debris flows with erosion and sedimentation
processes in mobile-bed gully by use of finite difference method combine with GIS. Therefore the
propagation in real time and the morphological variations can be obtained. According these results we
can give some reasonable references of prevention for debris flows.
2 THE GOVERN EQUATIONS AND NUMERICAL SOLUTION
In this paper, debris flows is treated as the solid-fluid flow of a continuum and incompressible.
Because debris flows depth is small compared to the length or width of the channel. So that debris
flows can be considered as shallow water flows and the 2D model is shown in Fig.1 . Consequently,
following the De Saint-Venant approach, local variables can be integrated along the vertical direction
obtaining a two-dimensional model which may be described as following:
The continuity equation of the solid-fluid mixture:
i
y
vh
x
uh
t
h
=
∂
∂
+
∂
∂
+
∂
∂ � ����
The continuity equation of the solid particle fraction:
biC
y
Cvh
x
Cuh
t
Ch
=
∂
∂
+
∂
∂
+
∂
∂
� ����
The depth averaged momentum conservation
equation in −x and −y
direction:����������������������Fig.1.The model for 2D debris flows�
x
H
gh
y
uvh
x
hu
t
uh
m
bx
∂
∂
−−=
∂
∂
+
∂
∂
+
∂
∂
ρ
τ
ββ
)()(2
���
─ 17 ─
自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
y
H
gh
y
hv
x
uvh
t
vh
m
by
∂
∂
−−=
∂
∂
+
∂
∂
+
∂
∂
ρ
τ
ββ
)()(2
���
The river bed changing equation:
0=+
∂
∂
i
t
Z
� ���
Where u and v are the −x and−y
components of the depth-average velocity;H is the elevation of
the free surface; Z is the elevation of the bottom surface;h is the flow depth; β is the momentum
correction factors which is associated with the shape of vertical velocity distribution of flow. β
equals 1.25 for uniform flow of dilatant fluid in which the shear stress is proportional to the square of
the shear rate.; m
ρ is the apparent density of the debris mixture, and ffsmC ρρρρ +−= )( ,
s
ρ and fρ are
the densities of solid grains and water; C and b
C are the volume concentrations of solids fraction in
the flow and on the bed; bx
τ and byτ are the −x and
−y components of flow resistance on the bed;
g is the gravitational acceleration.
The erosion and deposition velocity respectively written as :
when erosion ( ):
d
NM
CC
CC
i
eb
e
e
22
+
−
−
= δ
(6)
when deposition ( ):
h
NM
C
CC
i
b
e
d
22
+−
= δ
(7)
where e
δ and d
δ is the erosion and deposition coefficient respectively; d is the represent diameter.
The x and y components of flow resistant stresses on the bed, bx
τ and byτ , could be expressed as
following:
2
3/1
3
222
1)1(8
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡
−⎟
⎠
⎞
⎜
⎝
⎛
⎥
⎦
⎤
⎢
⎣
⎡
−+
+
=
C
C
CCh
dNMM
b
s
m
m
bx
ρ
ρ
ρ
τ
�
(8)
2
3/1
3
222
1)1(8
⎥
⎥
⎦
⎤
⎢
⎢
⎣
⎡
−⎟
⎠
⎞
⎜
⎝
⎛
⎥
⎦
⎤
⎢
⎣
⎡
−+
+
=
C
C
CCh
dNMN
b
s
m
m
by
ρ
ρ
ρ
τ �
(9)
The equilibrium concentration:
( )( )θφρρ
θρ
tantan
tan
−−
=
fs
f
eC (10)
where θ is the inclined angle of the channel bed; φ is the internal friction of debris flows.
Finite-difference method on rectangular grids is widely used in numerical models of
environmental flows. Therefore, we used grid networks from GIS as the rectangular grids of
finite-difference methods.
3 APPLICATION TO A DEBRIS FLOW IN AMAMI CITY
The above approach is used to simulate the real debris flow that occurred in Amami city, in this
simulation, the depth of the landslide mass is assumed as the initial thickness of flow and the
CCe
≥
CCe
<
─ 18 ─
rheological parameters are set co
the propagation and flow depth
Fig.3 which shows the morpholo
Table 1. materia
( )3
/mkgs
ρ ( )3
/mkgf
ρb
C
2550 1180 0.60
Fig 2. The simulation of
Fig 3. The mo
onstant throughout the duration of the flow event(
in each real time. Finally, we can get the erosi
gical variations in this debris flow.
al properties and rheological parameters for simulat
( )md ( )2
/ smge
δ d
δ φtan tΔ
0.10 9.8 0.0007 0.05 0.60 0.0
f debris flow propagation in Amami city (2s, 68s, 1
orphological variations of debris flow in Amami city
�
(table 1). Fig.2 shows
ion and deposition in
tion
( )st
( )mxΔ
( )myΔ
001 2.5 2.5
52s, 200s)��
y
─ 19 ─
4 CONCLUSIONS
In this paper assumed debris flows as the solid-fluid flow of a continuum and incompressible in
flow routing. The govern equations was build to describe debris flow including erosion and
sedimentation processes. The govern equations were then solved numerically with finite difference
method use GIS grid networks. Finally, The numerical model was applied to simulate the landslide
type debris flow in Amami city on 20 October 2010. The simulated results was good reproduced the
propagation, erosion and deposition processes in the actual debris flows.
�
5.REFERENCES
[1] K. Huttcr, B. Svendsen , D. Rickenmann., Debris flow modeling: A review, Continuum mech.
Thermodyn. 8 (1995) 1-35.
[2] A. Armanini, L. Fraccarollo, G. Rosatti., Two-dimensional simulation of debris flows in erodible
channels. Computers & Geosciences 35 (2009) 993-1006.
[3]Iverson,R.M.,1997.The physics of debris flows. Reviews of Geophysics 35,245–296.
[4]Takahashi,T.,1978.Mechanical characteristics of debris flow. Journal of Hydraulic Division—ASCE
104(8),1153–1169.
[5]Johnson,A.M.,1984.Debris flow. In: Brundsen, D., Prior, D.B. (Eds.),Slope Instability. Wiley, New
York,pp.257–311.
[6]Bagnold,R.A.,1954.Experiments on a gravity-free dispersion of large solid spheres in a Newtonian fluid
under shear.� Proceedings of the Royal Society of London A 225,49–63.
[7]Coussot,P.,Ancey,C.,1999.Rheophysical classification of concentrated suspension and granular pastes.
Physical Review E 59(4),4445–4457.
[8]Cheng-Lun Shieh, Chyan-Deng Jan and Yuan-Fan Tsai. A Numerical Simulation of Debris Flow and Its
Application. Natural Hazards 13:39-54,1996.
─ 20 ─
Seismic Slope Stability Analysis Subjected to Tension
Failure
�
Yingbin Zhang, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Jian Wu� Lu Zheng
Division of Civil and Structure Engineering, Kyushu University, Fukuoka, 819-0395, Japan
1. INTRODUCTION
Landslide is one of the worst natural disasters. As one of the main trigger, the earthquake can easily
induce collapse of slopes and produce landslides that can result in serious damage to life and property.
The evaluation of stability of seismic slope is the most important aspect of geotechnical earthquake
engineering, especially when the slopes are situated close to residential areas.
Several methods can be used in evaluating the stability of slopes subject to earthquake loading. These
methods can fall into three general categories: (1) pseudo-static method, (2) permanent displacement
method, and (3) stress-strain method. Each of these types of analysis has strengths and weaknesses, and
each can be appropriately applied in different situations.
However, almost all of the above methods consider the failure mechanism of seismic slope as a
completely shear failure. The frequently occurring earthquake events in recent years in New Zealand,
Japan and China have led to a renewed knowledge in mechanism of instability of slopes. A large number
of investigations of earthquake induced landslide show that tension failures appear in top of almost all
landslides or potential sliding slopes. The existing methods what usually only based on shear failure
mechanism while ignoring the tension failure may lead to inaccuracy especially when the slope shook by a
strong earthquake load. There are a large number of described, analytical and numerical studies which
have provided supporting evidence of the existence of tension failure in slope stability analysis. Huang et
al.[1]
and Yin et al.[2]
have gave much detailed description for tension segment of slope failure surface
based on post-earthquake investigations. Yan et al.[3]
have certificated the existing of tension failure zone
by analyzing the mechanism of seismic slope using numerical simulation. Zhang et al.[4]
showed that
significant effect of tension failure on slope stability analysis by upper bound limit analysis. These studies,
however, most focus on the description or explanation of phenomenon, but few on deep research of
stability analysis subjected to the tension failure.
Hence, to investigate how the tension failure effect on seismic stability analysis, a full dynamic
analysis is carried out in this paper with an emphasis on seismic slope stability using finite difference
method through a homogeneity slope.
�
2. FAILURE CRITERION AND STRENGTH REDUCTION TECHNICAL
2.1 Failure mechanism of seismic slope subjected to tension failure
At present, the failure mechanism of seismic slope follows the static slope failure mechanism, i.e. the
main reason that caused seismic slope instable is shear failure while ignoring the influence of tension
failure. In fact, with the reason of small tensile strength and the action of earthquake loading, the slope in
reciprocating motion is more easily to be tensioned.
Many failure criterions have been presented for modeling the strength of soil, the Mohr-Coulomb
criterion remains the one most widely used in geotechnical practice. A modified Mohr-Coulomb failure
criterion is used in this study. The representation of the failure criterion in the (σ, τ) plane is sketched in
figure 3. The failure envelope is defined from point A to B by the Mohr-Coulomb yield function
tanshear
f cτ σ ϕ= − − (5)
and from B to C by the tension yield function
3 t
'tension
f σ σ= − (6)
Where c is cohesion, φ is friction angle of soil material, τ is shear stress, σ represents normal stress on the
─ 21 ─
自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
plane of failure, and σt is tension strength.
�
Fig. 1. Failure criterion combined tension-shear failure mechanism.
2.2 Factor of safety F and strength reduction factor SRF
Factor of safety F is a value that is used to examine the stability state of slopes and of great interest
for engineering practice. A generally accepted definition is the ratio of the available strength of soil
material to that required to maintain equilibrium. For c-φ-σt material studied in this paper, factor of safety
F against slope is simply calculated as
t
t
tan
' tan ' '
c
F
c
ϕ σ
ϕ σ
= = =
(8)
Where c, φ and σt are the actual strength of soil material, and c', φ' and σt' are the parameters required to
maintain the limit equilibrium. To achieve the correct factor of safety F, it is essential to trace the strength
parameters by a coefficient called strength reduction factor SRF until the limit state of slope is achieved,
i.e. factor of safety F equal 1. Strength reduction factor SRF defined as
t
t
tan
tanm m m
c
SRF
c
ϕ σ
ϕ σ
= = = (9)
Where cm, φ
m and σtm are the calculated values of strength.
3. DYNAMIC FORMULATION
3.1 Modeling with FLAC3D [5]
A schematic illustration of the 2D analyzed mesh, properties parameters and the boundary conditions
is provided in figure 2. In order to study the development process of slope failure surface, one square
meters of grid size is meshed. Both x and y displacements are fixed at the base of the model. And x
displacements are fixed on either side of the model along the y-axis. The slope is allowed free to move in
both the directions. Free field boundary is used in the present model to minimize the wave reflection.
Local damping of 0.157 is used in the model as suggested by other studies for these kinds of problems.
�
Fig. 2. Mesh generation and boundary conditions of finite difference model for dynamic slope stability analysis
3.2 Earthquake loading
The dynamic load applied in here is the transverse component of the acceleration time history
─ 22 ─
modified from the Kobe earthquake, occurred in Japan, 1997 as shown in figure 3. The total duration of
the earthquake loading is 15s with a time step of 0.02s. The amax
value of the recorded earthquake is
0.2046g at time of 3.52s. The earthquake loading is applied at the base of slope. From the acceleration
time history record, velocity and displacement time histories can be computed by once and twice
integration respectively.
�
Fig. 3. Acceleration time history of earthquake loading applied in study.
4. RESULTS
4.1 Factor of safety F
If only consider the shear failure mechanism, failure surface of slope is just induced by the shear
failure. After a series of trying calculation, it can be obtained that cut-through of shear plastic zone
occurred in the smallest SRF of 1.12. The figure 4(a) and figure 4(b) show the contours of shear strain
increment at SRF=1.11 and SRF=1.12, respectively. The maximum values of shear strain increment
(SSImax
) are 7.264cm and 7.963cm at SRF=1.11 and SRF=1.12, respectively. These results show that the
slope is in the limit state at the situation of SRF=1.11. As the previous definition of factor of safety and the
cut-through definition of slope failure, we can come to the conclusion that factor of safety F is 1.11 based
on shear failure mechanism.
The figure 5 shows the contours of tension plastic zone and shear strain increment at SRF=0.98 and
SRF=0.99. The maximum values of shear strain increment (SSImax
) are 3.219cm and 3.593cm, respectively.
As the previous definition of factor of safety, the factor of safety is 0.98 based on the tension-shear failure
mechanism.
Fig. 4. Contours of shear strain increment at (a) SRF=1.11 and (b) SRF=1.12.
4.2 Shape of slip surface
The figure 5 shows the failure surfaces of slope in different cases: static case, dynamic cases based
on shear mechanism and tension-shear mechanism.
─ 23 ─
�
Fig. 5. Block tension state and contour of shear strain increment at SRF=0.98 and SRF=0.99.
�
Fig. 5.Failure surfaces under different failure mechanisms.
5. CONCLUSIONS
Tension failure has a significant influence on seismic slope stability analysis. The finite difference
program can take the influence of tension failure into consideration, so as to make the analysis technique
more reasonable for practical application.
The shapes of failure surface of seismic slope under traditional single shear failure mechanism and
tension-shear failure mechanism have an obvious difference. Failure surface of seismic slope considering
tension-shear failure mechanism contains two segments and shallower than that just considering the shear
failure.
6. ACKNOWLEDGMENTS
This study has received financial support from the Global Environment Research Found of Japan(S-
8), and from Grants-in-Aid for Scientific Research(Scientific Research(B), 22310113, G. Chen) from
Japan Society for the Promotion of Science. These financial supports are gratefully acknowledged.
7. REFERENCES
[1] Huang, R.Q., Xu, Q., Huo, J., Mechanism and geo-mechanics models of landslides triggered by 5.12
Wenchuan earthquake, J Mountain Sci, 8 (2011) 200-210.
[2] Yin, Y.P., Wang, F.W., Sun, P., Landslide hazards triggered by the 2008 Wenchuan earthquake, Sichuan,
China, Landslides, 6 (2009) 139-152.
[3] Yan, Z.X., Zhang, S., Zhang, X.D., Duan, J., Failure mechanism and stability analysis of slope under
earthquake, J Eng Geol, 18 (2010) 844-849. (in Chinese)
[4] Zhang, Y.B., Chen, G.Q., Zen, K., et al., Limit analysis of seismic slope stability based on tension-shear
failure mechanism, Proceedings of International Symposium on Rock Slope Stability in Open Pit Mining
and Civil Engineering, Vancouver, Canada, 2011.
[5] Itasca, FLAC3D fast Lagrangian analysis of continua in 3 dimensions V.2.0, Itasca Consulting Group, Inc.,
Minneapolis.
─ 24 ─
Comparison of Weight of Evidence and Logistic Regression Model for Medium Scale Landslide Susceptibility Mapping in Yogyakarta Region Indonesia
Guruh Samodra1,2, Guangqi Chen2, Junun Sartohadi1, Zen Kouki2, Kiyonobu Kasama2
1Environmental Geography Department, Universitas Gadjah Mada, Indonesia.2Geo-Disaster Prevention Laboratory, Kyushu University, Japan.
1. IntroductionRecently, landslides have created several damages in Kayangan Catchment, Kulon Progo Regency Yogyakarta Indonesia. It usually occurs in the month of November to April during rainy season. During November 2008 toApril 2009, 46 landslides occurred in Kebonharjo village with various extents. Even though there was no loss of life caused by landslide at this period, several damages on the building were found and various extent of material were deposited in the road body. It was estimated that material loss up to 1 billion IDR per year in Kulon Progo Regency affected by landslides. In addition, an inexistence of regulation for building code and landuse planning based on the landslide disaster risk reduction caused the development of settlement area in the hilly area increased year by year without any regulation control. It may cause the increasing landslides victims in the near future. Thus, landuse planning is needed in order to maintain the area which is susceptible to landsliding related to the new development of settlement area.
Landslide map can be one of tool to figure out the potential location of landslide and can be applied for effective measures for landslide hazard mitigation as an input of landuse planning. It can be produced by several methods and techniques depend on the purpose of mapping, the area, the availability of data and the financial budget available. Geographic Information Systems (GIS) offers a technological framework for supporting effective and efficient data capture, storage, management, retrieval, analysis, integration and display, and have shown great advantages for landslide distributions and susceptibility mapping [2]. A method that widely used due to the development of GIS technology is statistically based landslide susceptibility mapping.
Statistically based landslide susceptibility mapping is usually applied either in multivariate or bivariate model. Multivariate analysis assumes that the presumed controlling factors of landslide are interrelated each other. In the other hand, bivariate analysis assumes that the presumed controlling factors of landslide are not interrelated each other [5]. Logistic Regression Model (LRM) which was formerly invented as a description of population growth and autocatalytic chemical reaction [3] can be classified as a multivariate statistical model. It was applied successfully in landslide susceptibility mapping in many regions. In the other hand, the weight of evidence method (WoE) which is based on bivariate statistic is also widely used to analyze landslide susceptibility. It was formerly applied to map mineral potential [1]. WoE calculates the weight of predictive factor based on the absence or presence of landslide in the study area. Both models are simple and less time-consuming. However, each method capability of future spatial prediction remains a trending topic on statistically based landslide susceptibility mapping. Thus, WoE and LRM were compared in this research in order to show the spatial predictive capability of both methodologies to produce landslide susceptibility map.Thus, both methodologies are going to be employed in order to map landslide susceptibility in tropical region Yogyakarta Indonesia.
2. LocationKayangan Catchment is located in the middle part of Java Island. It is dominated by hilly area with large open valley striking mostly NW-SE (Figure 1). Elevations of the hills range from 49 m (Nanggulan) to 825 m (Jonggrangan). The average annual rainfall in Kayangan Catchment is 2478 mm. The highest rainfall intensity usually occurs from February to March with average monthly rainfall 426 mm. Landslide usually occurs in the month of November to April during wet season. Kayangan Catchment is determined as a part of south zone of geomorphology of middle Java embodied by bedded limestone and coralline limestone. Kayangan Catchment consists of Young Volcanic Deposit of Merapi; Colluviums; Sentolo Formation (limestone and marly sandstone); Jonggrangan Formation (conglomerate, tuffaceous marl and calcareous sandstone, limestone
Kouki Zen
─ 25 ─
自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
and coralline limestone); van Bemmelen Formation (old andesit breccia); and Nanggulan Formation (sandstone with intercalated of lignite, sandy marl, claystone with limonite concretion, intercalations of marl and limestone, sandstone, and tuff) [4].
= Research Area (Kayangan Catchment)
Figure 1. Location of Kayangan Catchment
3. MethodologyThematic maps representing various factors of landslide were generated by GIS technique to obtain the probability to landsliding by weights of evidence and logistic regression model. Those are distance to road, landform, elevation, slope, distance to river and landuse. Each controlling factor of landslide were mapped and converted to raster map. Cross map analysis was applied to evaluate each controlling factor of landslide toward landslide occurrence. All of controlling factors of landslide were overlaid with landslide inventory map based on the equation (1) and (2).In WoE, the existing landslide distribution was compared to various factors of landslide separately. It was applied to evaluate the relationship of each predictable variable controlling factor of landslide towards landslideevents. In ILWIS 3.7 open source software package, the analysis can be written on a formula based on the equation (1):
ln � �� � �
���)(
)(/
)()(lnln
NiNpix
SiNpix
NiNpixSiNpix
DensmapDensclasWi (1)
Where Wi = the weight given to a certain factor of landslide, Densclas = the landslide density within the factor of landslide, Densmap = the landslide density within the entire map, Npix(Si) = number of pixels which contain landslides in a certain factor of landslide, and Npix(Ni) = total number of pixels in a certain factor of landslide [5].
In the other hand, multivariate logistic regression model compared the existing landslide distribution with various factors of landslide simultaneously. Therefore the relationship of environmental factors of landslide with the existing landslides was evaluated by logistic regression equation which is written as follow:
(2)
z = (3)
Where P(S=1|X1,X2,…Xn) is a pixel affected by slope failure which is given the presence of independent variable from X1 to Xn,�0 ���� ��� �������� � � ��� ��������� �����1�� �2����n are the coefficient of variables X1,X2,…Xn���0����n are the unknown coefficients which has to be estimated based on the data of independent variables of landslides by using maximum likelihood [3].
─ 26 ─
Technically, 177 landslide events dataset were used to build landslide susceptibility map in Kayangan Catchment. It was divided into 2 year dataset. The first year dataset is 131 landslides events occurred before 2008. It was employed for landslide susceptibility mapping (spatial prediction). The second one was 46 landslide events occurred after 2008 (November 2008 – April 2009). It was used as a validation of the prediction. Therefore, the cross validation between the landslide map as a spatial prediction and the landslide events in the second year survey was employed in order to show the spatial prediction capability of each method.
4. Result and DiscussionIn this study, bivariate WoE and multivariate LRM were employed to generate landslide susceptibility map in tropical region Yogyakarta Indonesia. Several factors relatively significance to landsliding such as distance to road, landform, relief, slope, distance to river and landuse were converted into raster GIS data and employed as landslide factors map. WoE computes an existence and inexistence of landslide events towards each input factor separately. It was based on the pixel numbers of each factor map as a relative significance causing landslide. The result was the weight values of each class of landslide factors map (Table 1). Positive and negative weights would indicate the contribution of each controlling factor toward landslide. Positive meant high contribution and negative meant low contribution. Final susceptibility was simple combination of the weight of each class of factors map processed in GIS platform. The final value of WoE ranged from 0.2 to 2.74. Therefore, the final value map was normalized into 0-1 in order to interpret the spatial probability of landslide. The higher probability to landslide was indicated by the number closer to 1.
Table 1. Summarize of Si, Ni and Wi in WoE
DRoad Landform Relief Slope DRiver Landuse
Si min 29 23 64 64 296 350max 2576 2143 1816 2084 2268 3359
Ni min 215867 26535 102769 64 76830 89969max 378395 357998 236524 2084 773768 708945
max min max min max min max min max min max minWi 1.2 -3.8 0.51 -5.89 0.76 -5.89 0.58 -1.19 0.25 -0.22 0.29 -5.88
In the other hand, multivariate LRM computes the factors of landslide simultaneously. All factors of landslides as independent variables and landslide events as dependent variable were treated together. Landslide factors were coded as categorical data and landslide events was coded as binary data. Binary landslide event was coded 0 for absence of landslide and 1 for presence of landslide in nature. LRM was performed to analyze the contribution of each factor to landsliding. It �������������������������� ���������� �������������f each independent variable which is shown in the regression model:
z = 11.120 – 0.07 distance to road + 3.489 landform – 3.538 elevation – 0.709 slope + 0.005 distance to river – 0.129 landuse
Thus, the sequent of predominant factors of landslide based on the LRM were landform, distance to river, distance to road, landuse, slope, and elevation. In addition, LRM provides information of the quality of datasets and how the model fits the dataset. The statistical parameters resulted on the LRM was written as follows: Total number of pixels: 5700; -2ln L (L=likelihood): 2231.43; Model chi-square: 5670.441; Cox & Snell R Square: 0.63; Nagelkerke R Square: 0.84; RoC: 0.96. RoC 0.96 indicated that the model was useless for discrimination that meant resulted higher probability to predict the landslide events successfully. Nagelkerke R Square indicated that 84% of the variation of the result was explained by the logistic model. Cox & Snell R Square was also indicated the better fit of the landslide factors with the model.
Thus, LRM computed the final susceptibility map (each pixel) based on the equation 2 in GIS platform. The result ranged from 0.07 to 0.73 for the minimum to maximum probability. Therefore, both models result wereclassified into 0-0.18 (very low), 0.19-0.36 (low), 0.37-0.49 (medium), 0.50-0.70 (high), 0.71-1 (very high) to layout final susceptibility map (Figure 2).
─ 27 ─
Figure 2. Landslide Susceptibility Map with WoE (left) LRM (right)
Table 2. Cross Validation Map of WoE (Left) and LRM (Right) based on Number of Pixels
Cross validation based on the number of pixel was employed in order to analyze the capability of both models to predict landslides events spatially. For this purpose, the classification of landslide susceptibility was divided into two category i.e. high probability (high and very high) and low probability (very low, low and medium). The susceptibility map of Kayangan Catchment was overlaid with the second year landslide events dataset in order to show the percentage accuracy of landslide prediction (table 2). WoE model attained a bit more accurate than LRM with the accuracy of 69.54% and 69.38% respectively. WoE successfully predicted 1205 pixels to be landsliding whereas LRM was only 845 pixels. In addition, WoE also successfully predicted 968959 pixels to be no landsliding, whereas LRM was only 967065.
5. ConclusionIt was generally accepted that landslide susceptibility is an essential tool for landslide mitigation. GIS based WoE and LRM was successfully applied and offered great advantages for landslide susceptibility mapping in tropical region Yogyakarta Indonesia. In this case, WoE seemed a bit to be more accurate than LRM with the mapping accuracy 69.54% and 69.38% respectively.
6. References1) Bonham-Carter, G.F., Agterberg, F.P. and Wright, D.F., 1990 , Weights of Evidence Modelling: A New
Approach to Mapping Mineral Potential, in Agterberg, F .P. and Bonham-Carter, G.F., (eds.), Statistical Applications in the Earth Sciences: Geol. Survey of Canada, Paper 89-9 , p. 171 -183.
2) Carrara, A., Cardinali, M., Guzzetti, F., and Reichenbach, P. 1995. GIS Technology in Mapping Landslide Hazard. In: Carrara, A. and Guzzetti, F. (eds.), Geographical Information Systems in Assessing Natural Hazards page: 135–175. Kluwer Academic Publisher, Dordrecht, the Netherlands.
3) Cramer, 2002. The Origin of Logistic Regression. Tinbergen Institute Discussion Paper. Accessed in 29 December 2011 from http://dare.uva.nl/document/204.
4) Rahardjo, W., Sukandarrumidi, dan Rosidi, H. M. D. 1995. Peta Geologi Lembar Yogyakarta, Jawa.Pusat Penelitian dan Pengembangan Geologi, Bandung.
5) Westen van, C. J., Rengers. N., Soeters. R. 2003. Use of Geomorphology Information in Indirect Landslide Susceptibility Assessment. Natural Hazard 30: 399-419. Kluwer Academic Publishers. Netherlands.
Pred. vs Events Landslide Events (NrPix)WoE Yes No Total
Map Pred. High 1205 424505 425710Low 357 968959 969316
Total 1562 1393464 1395026
Pred. vs Events Landslide Events (NrPix)LRM Yes No Total
Map Pred. High 845 426399 427244Low 717 967065 967782
Total 1562 1393464 1395026
─ 28 ─
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自然災害研究協議会西部地区部会報・論文集 -36 号、2012 年 2月
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自然災害研究協議会西部地区部会報第 36 号
平成 24 年 2 月発行
自然災害科学研究協議会西部地区部会部会長 久保田 哲 也
(九州大学大学院農学研究院)九州大学西部地区自然災害資料センター
センター長 善 功 企(九州大学大学院工学研究院)
印刷所 社会福祉法人 福岡コロニー 福岡県糟屋郡新宮町緑ケ浜 1-11-1
電話 092 ‒ 962 ‒ 0764
15. Relationship between Natural Disasters and Place Names in Case of 1982 Nagasaki Disasters and
2011 Higashi-Nippon Earthquake Disasters
Kensuke Goto and Keinosuke Gotoh 57
16. Changes in Land Use and Flood Disasters in Asa Area of Sanyo-Onoda City, Yamaguchi Prefecture
Haruhiko Yamamoto, Minori Yamamoto, Toshiaki Yamasaki, Kiyoshi Iwaya and Hisashi
Yoshikoshi 61
17. Development of Visualization Tool for Safe Evacuation Route During Flood Using Integration of
3D Images and GIS
Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori Yamamoto, Toshiaki
Yamasaki, Shigetoshi Itou and Junichi Hironaka 65
18. WEB Rainfall and Water-Level Information in Western Part of Japan and Problem of Disaster
Prevention Information during Disaster
Nozomi Kanamoto, Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori
Yamamoto, Toshiaki Yamasaki and Taiju Miwa 69
19. Disaster Damages and Missing Values at Rainfall and Gauging Stations during Heavy Rainfall
Disasters
Haruhiko Yamamoto, Kiyoshi Iwaya, Hisashi Yoshikoshi, Minori Yamamoto and Toshiaki
Yamasaki 73
20. The Latest Progress of the Erosion Control Area Utilization in Unzen
Kazuo Takahashi and Shinichi Sugimoto 77
ISSN 0916-3891
自 然 災 害 研 究 協 議 会
西部地区部会報
第36号
研 究 論 文 集
平成24年 2月
平成 23 年度西部地区部会
研 究 発 表 会
平成 24 年 2月 24 日 福 岡 市
自然災害研究協議会西部地区部会
自然災害研究協議会西部地区部会報第36号
二〇一二年二月
Western Regional Division Report of Natural Disaster Research Council
No.36
Contents
ARTICLES
1. Developing GIS-based Method to Find Dangerous Slope Forming Dam
Fan Yanan, Guoyun Zhou, Guangqi Chen and Kiyonobu Kasama 1
2. Design Strength of Ground Improvement for Liquefaction
Yutaro Inatomi, Kiyonobu Kasama, Kouki Zen and Guangqi Chen 5
3. An Estimation of Runout Distance of Debris with Discontinuous Deformation Analysis
Taisuke Koga, Kouki Zen, Guangqi Chen and Kiyonobu Kasama 9
4. An Approach Which Forecast the Slope Failure that Happens Because of the Rainfall using
Geographical Information System
Taiki Hiraoka, Kouki Zen, Guanqi Chen and Kiyonobu Kasama 13
5. GIS-based Two-dimensional Numerical Simulation of Debris Flow in Mobile-bed Gully
Jian Wu, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Lu Zheng and Yingbin Zhang 17
6. Seismic Slope Stability Analysis Subjected to Tension Failure
Yingbin Zhang, Guangqi Chen, Kouki Zen, Kiyonobu Kasama, Jian Wu and Lu Zheng 21
7. Comparison of Weight of Evidence and Logistic Regression Model for Medium Scale Landslide
Susceptibility Mapping in Yogyakarta Region Indonesia
Guruh Samodra, Guangqi Chen, Junun Sartohadi, Kouki Zen and Kiyonobu Kasama 25
8. Estimate of Debris Flow Hydrograph and Modeling of the Depositional Process :the Debris Flow�
Event in Hachimandani River, Hofu City in Japan on July 21, 2009
Tomohiro Miyoshi, Haruyuki Hashimoto, Shinya Ikematsu, Farouk Maricar, Kyosuke
Hashimura and Kensuke Sakada 29
9. Experiments on Open Check Dam as a Countermeasure against Debris Flows with Driftwood
Kyosuke Hashimura, Tomohiro Miyoshi, Haruyuki Hashimoto, Shinya Ikematsu, Tadahiko
Hasuo, Farouk Maricar and Kensuke Sakada 33
10. Analysis on the Landslide Traveling Distance from Forest Slopes
Souhei Otani and Tetsuya Kubota 37
11. The Characteristics of the Landslide Disaster and Sediment Runoff in Sierra Madre Oriental
Mountain range, Mexico
Tetsuya Kubota, Israel Cantu Silva and Laura Sanchez Castillo 41
12. Geo-Simulator-3 (Utilization of Various Geo-Information)
Ryosuke Kitamura, Fumio Nakata, Yoshito Tanaka, Hisashi Kawakami, Ryoji Tanaka
and Kazuyoshi Jomoto 45
13. Characteristics and Changes of Tephra Layer Deposited by 2011 Shinmoe-dake Eruption in
Kirishima Volcanoes
Takahito Kuroki, Nozomi Iso, Kensuke Goto, Tatsuroh Soh and Keisuke Kuroda 49
14. Some Disasters in Historical Novels Dealt with Edo Period
Keinosuke Gotoh 53