10th Asian Regional Conference of IAEG (2015)

3D Slope Stability Analysis for Slope Failure Probability in Sangun
Fukuoka Prefecture, Japan

mountainous,

Hendra PACHRI(1), Yasuhiro MITANI(1),Hiro IKEMI(1), and Wenxiao JIANG (1)(1)

Graduate School of Engineering, Kyushu University, Japan
E-mail:hendra@doc.kyushu-u.ac.jp

Abstract
On July, 2003, following a short duration of heavy rainfall, a slope failure disasteroccurred in the Sangun Mountain area, Fukuoka Prefecture, Japan. This disaster was
triggered by a landslide. In order to assess the slope failure hazard potential in the future,
the study of probability of slope failure is critical.
This paper presents the integrating of deterministic and statistical approach to assess
the performance of the predicted results based on a factor of safety (FS) of whole slope
units. Therefore, the spatial probabilistic modeling of slope failures using a combined
Geographic Information System (GIS), 3D slope stability analysis, and a Monte Carlosimulation approach is applied in the slope failures-prone area of Sangun mountain area,
Fukuoka Prefecture. Morphological aspects (elevation, slope aspect, slope gradient) are
extracted from airborne laser survey data (LiDAR data) in each 5 m-grid that provided by
local governments in Japan were assumed ground elevation to create a digital terrain model(DTM); a range of soil properties has been reported in adjacent basins with similargeological and geomorphologic condition.
The overall performance achieved by the Frequency Ratio (FR) analysis wasassessed by comparison between the expected results of the 3D slope stability analysis andan independent dataset extrapolated from the slope failure inventory. As result, thevalidation of this model has shown enough, which the samples classified to slope failure
occurrence, 70% corresponds to area where slope failure phenomena have actually occurred.
In addition, the consideration of FR analysis is presented that the hydrologicalcharacteristics as a causal factor in determining the occurrence and distribution of slope
failure probability in Sangun mountain area.
Keywords: 3D Slope stability, Frequency Ratio, GIS, Slope failure
1.

Introduction

geomorphological, hydrogeological and climaticconditions as in the past. On the other hand, theproperties and types of landslides will also be thesame. Therefore, the study of the mechanisms andproperties of past landslide is a valuable reference
for assessing the future landslide hazard in its
adjacent or geotechnically similar area.
Here, we demonstrated the implementation of the3D slope stability modelling to assess the factor ofsafety (FS) of the whole slope units based on sub
catchment and produce a reasonable cohesion values.
The critical 3D slope stability simulation gave theresults about the relationship on between
morphological aspect and Geotechnical parameters.
The Factor of Safety (FS) values has become
important to explain the susceptibility of landslide. In

The prediction of the location, intensity and timeor periodicity of landslides is the primary goal of thelandslide hazard assessment (Ondrasik. R., 2002).
The prediction of localities with landslides potential
or probability is the simpler case. Analyze therelationship between landslides and the various
factors causing landslides not only provides aninsight to understand landslide mechanisms, but also
can form a basis for predicting future landslides andassessing the landslide hazard. Chunxiang.W.et.al(2006), summarized that in areas with the similar
Geotechnical conditions, researchers generally maketwo fundamental assumptions. Such as, the one
hand, landslides will occur in the same geological,

110th Asian Regional Conference of IAEG (2015)

this analysis is concern for two parts of FS value
ranges, such as FS≤1.0, and FS≤1.2.
Furthermore, the observation of relationships
between the distribution of slope failures in the pastand probability of the occurrence slope failure basedon 3D slope stability analysis is carried out by
determining the best-fit model for each Geotechnicalparameter value. In this study, the frequency ratio(FR) model is used, furthermore, the reasonable value
obtained. Moreover, the effect of cohesion values wasanalyzed to obtain more knowledge about hydrology
control in slope failure susceptibility.

instability factors were chosen based on theabove-mentioned studies which were carried out fromliteratures and field investigations.

2. Study Area and Slope Failure Historical
The catchment is located in the mountain region
in northern Kyushu, Japan, the Sangun mountainmassif (Fig.1). The geology consists mainly of
Mesozoic granitic rock. Paleozoic and Mesozoic
metamorphic rocks can be also observed on somevalley slopes as roof pendants, but they are limited.
The mountainous catchment, especially in the
Sangun region is hazardous areas for sedimentdisasters. On July 18-19, 2003, a short duration,
high-intensity rainfall event impacted the city of
Sangun area in Fukuoka Prefecture, Japan (Fig.1).
The rainfall triggered many landslides and debris
flows (Hanamura et.al., 2004). The slope failure
and resultant debris flow in a Sangun area in Fukuoka
was the largest and most damaging of these disasters.
A moderate-size, 1-8 m deep debris avalanche
triggered the debris flow about 0.5 km at the upslopeof where the casualties occurred. The rainfall
intensity that plunging into the village of Sangun area
was estimated about 315 mm / day, and the density ofslope failure were estimated about 48 points/km2.
Based on the aerial map and field investigation, thenumber of slope failure in the study area was about
278 points.

Fig.1 Study area map and distribution of slope
failures
3.2 Methods
The availability of the geographical information
system (GIS) can facilitate the use of a deterministic
or a probabilistic Geotechnical approach as part of amethod of assessment of slope failure hazard.
Regarding the attributing factors, the assessment ofslope failure susceptibility is carried out by using the
Slope Failure Analysis System (SFAS) GIS. Based on
the model of slope stability analysis and Monte Carlosimulation, the critical slip surface and minimum 3D
safety factor can be obtained for each slope unit (Xie.
M, et al., 2003).
(1) Slope units
For the landslide-hazard assessment of a large
mountainous area with complex geometry andgeological conditions, a key problem is how to
extract the appropriate slope unit for potential
sliding-surface identification and minimum 3D
safety-factor calculation. Slope unit, namely, theportion of the land surface delimited by watersheddivides and the channel has similar topographic andgeological characteristics. The suitability of the slope
units for landslide-hazard assessment and otherlandrated study has been recognized by several
authors (Hansen, 1984; Xie. M et al., 2003; Dymond
et al., 1995). To improve the results related to the
distribution of slope unit, the eliminate tool has beenused in the feature of GIS data.
(2) Analysis of mechanical parameters
In this study, we assume the flat area is the slope
value at less than 40. Based on the field slope failure
survey in July, 2013, the depth of slope failure wasdescribed, such as 3 to 30 meters. The geological map
was described that the Sangun mountainous is mostof granite rock. The engineering geological report ofthis area provides the initial value of mechanicalparameters, such as cohesion (c) 12,3 kN/m2; friction
angle (φ) 200; unit weight (γ) 40 kN/m3.

3. Data sets and Methods
3.1 Data sets
The following Geo-spatial data were prepared forthe slope stability analysis: (1) geology map, scale 1:200.000 obtained from the Geological Survey ofJapan, AIST 2005 and (2) airborne laser survey data
(LiDAR data), provided by local government in Japan.
The LiDAR data were a point cloud with only
attributes of x, y and z values. In this study,
minimum-z-values in each 5 m-grid were assumed as
ground elevations to create a DTM. Geotechnical data,
including cohesion, friction angle, and unit weight,
were derived from the laboratory test of previous
studies conducted in the area and literature review. In
this study, slope failures location map is preparedbased on the previous researches conducted in thearea and aerial photographs. In addition, field work
has been carried out to map recent landslides. The2

10th Asian Regional Conference of IAEG (2015)

Table.1. Mechanical parameters values on 3D Slope Stability simulation
No

Cohesion(kN/m2)Friction angle(0)

Unit weight(kN/m3)

1
2
3

10
12.3
15

20
20
20

40
40
40

4

20

20

40

5

25

20

40

6

30

20

40
slope unit in the study area, and also, is the ratio of
probability of slope failure occurrence of anon-occurrence for a given factor’s attribute.

Based on the history of slope failure, the occurredslope failure was due to rainfall. Therefore, in thisstudy the assessment is concerned the influence ofcohesion value in the simulation. Furthermore, theseveral pairs of mechanical parameters with different
values of unit weight are calculated (Table 1).
In order to determine of the 3D safety factor of
the sliding surface, using the slope units feature,
topographic information, and the GIS-based 3D
limit-equilibrium models (Hovland model) (Xie et al.
2003; Xie and Esaki, 2005) are being used in thesimulation. By inputting these parameters into themodel of slope stability, the value of a safety factor or
failure can be obtained. Based on Hovland’s (1977)
model, the 3D safety factor can be expressed asfollows equation:(1)

4. Results
4.1 Slope failures characteristic in the catchment

Where, F3D: the 3D slope safety factor; W: the weightof one column; A : the area of the slip surface; c :the cohesion: φ: the friction angle; θ : the normal
angle of slip surface: and J,I: the amount of row andcolumn of the pixel in the range of slope failure.
To quantitatively assess the stability of a slope inengineering geology, a parameter F3D known as factorof safety is introduced. A value F3D >1 indicatesstability, whereas F3D <1 implies instability. Thus, thetransition between stability to collapse may beenvisaged mathematically as a decrease in the factorof safety to values below unity.
(3) Performance of the 3D Slope Stability analysis
Spatial effectiveness of the susceptibility map will
be checked by the highest percentage of the actual
slope failure by comparing the predicted unstable site.
The relationship between number of slope failure
occurrence and number of slope unit by 3D slope
stability analysis based on Factor of Safety (FS)criteria are the assumption that be used in theperformance task. The frequency ratio is the ratio of
the slope unit where slope failure occurred in the total

Fig.2 Slope failure characteristic in the Sangun
basin

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10th Asian Regional Conference of IAEG (2015)

Table 2 Results of the 3D Slope stability simulation by using the varying of cohesion values

Cohesion(KN/m2)

FS
Criteria

Number of slope
unit by 3D slope
stability calculation
(A)

10

FS≤1
FS≤1.2
FS≤1
FS≤1.2
FS≤1
FS≤1.2
FS≤1
FS≤1.2
FS≤1
FS≤1.2
FS≤1
FS≤1.2

394
515
386
507
386
495
352
496
325
473
285
456

12.3
15
20
25
30

D (%)

Number of slope
failure occurrence
(B)

E (%)

C=A-B

31.88
41.67
31.23
41.02
31.23
40.05
28.48
40.13
26.29
38.27
23.06
36.89

200
239
198
236
195
235
182
234
162
233
144
228

71.94
85.97
71.22
84.89
70.14
84.53
65.47
84.17
58.27
83.81
51.80
82.01

194
276
188
271
191
260
170
262
163
240
141
228

FS: Factor of safety; Total number of slope unit (ΣnSU)=1,236 units; Total number of slope failure occurrence (ΣnSF) = 278 points; D: A /ΣnSU x 100%; E: B / ΣnSF x 100%; C: the difference of the number of slope unit by 3D Slope stability analysis and number of slope
failure occurrence, D: the percentages of the number of slope unit by 3D Slope stability calculation (A) and total of number of slope unit(ΣnSU); E: The percentages of the number of slope failure occurrence by intersection with 3D slope stability calculation (B) and total of
number of slope unit (ΣnSF).

A slope failure inventory map shows the location,
morphology characteristic of slope failures (Fig.1).
However, no detailed slope failure inventory map
existed in the study area. The locations of landslide
scars are determined and mapped by a field survey in
2003. The relationships between morphology aspectand slope failure distribution is presented in this study(Fig.2). The distributions of slope failure are used inthis analysis. The morphology aspect are elevation,slope angle, and slope aspect.
Elevation is classified into five categories such as.,<150 m, 150-300 m, 300-450 m, 450-600 m, and>600 m. It is found that the majority of the area
failures in the category of 300-450 m followed by
150-300 m, >600 m, 150 -300 m, and <150 m. Fromthe calculation of frequency ratio, class interval of300-450 m is the highest with 92 slope failures eachout of 278 failures which are 33.09 %.
Slope angle is classified into five categories suchas, <100, 10-200, 20-300, 30-400, and >400. Majorityof slope failures (172 units) in the area have been
occurring within a slope angle of 30 - 400 followed by<400, 20-300, <100, and 10-200. The amount of slope
failure show increase with the increasing of slope
angle values in the range 10 -400 (Fig.2b).
Slope aspect is classified into eight categories
such as., North (0-22.50, 337.5-3600), Northeast
East(67.5-112.50),
Southeast(22.5-67.50),
0(112.5-157.5 ), South (157.5-202.50), Southwest(202.5- 247.50), West (247.5-292.50), and Northwest(292.5-337.50). Majority of slope failure in the area

have been occurring within the slope aspect of theSouthwest and West which are 59 units. In the Figure
2c, the graph shows the direction of slope aspect
which
N=North,
NE=Northeast,
E=East,
SE=Southeast, S=South, SW=Southwest, W=West,and NW=Northwest. Figure 2c shows the slope
failure increase with the direction of slope aspect ofEast to West.
4.2 Slope failure probability analysis by using a 3D
Slope stability tool on Sangun mountainous
The calculations of Slope failure probability were
performed by using 3DFAS and GIS tools.
Geotechnical values were used to obtain the highest
prediction accuracy for study area based oncomparing the slope failure distribution. Table 1
shows the Geotechnical values based on the results ofthe small catchment analysis in a Sangun mountainarea. The detail of a slip surface is uncertain because
the lack information that obtained from a previous
report.
To detect the 3D critical slip, the search is
performed by means of a minimization of the 3D
safety factor using the Monte Carlo simulation. Partsof study area (weathered granite zone) for the slope
failure hazard assessment have been selected forpriority assessment of safety factors of the slope
where the most dangerous slope has also been
identified by field investigation in the past.

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10th Asian Regional Conference of IAEG (2015)

Number of slope failures (%)

calculation (B) and total of number of slope unit
(ΣnSF), % (E), have been calculated, too. The % (E)values were employed in this study to perform thehighest percentage of the actual slope failure bycomparing the predicted unstable site. On the other
hand, the difference number of slope unit ofcalculation and intersection with slope failure,
inventory, C value, which can be calculated from C =A-B, cautiously indicate how the good perform fromsimulation. The minimum values of C indicate aperfect result. In this study, C value shows decrease
when the cohesion values increases, except thecohesion, 15, value.

Cohesion values (kN/m2

Fig.3. Relationships of cohesion values (c) andnumber of slope failures (%) based on the factor ofsafety (FS) of slope stability simulation.

5. Discussions
5.1 Evaluation the Slope failure probability map
New advanced technology has been used in thisstudy, and results based on this simulation are
obtained. The use of LiDAR in this study shows thatthe technique is very useful in the simulation process.
With the help of the LiDAR was extracted themorphology of the study area.
For Sangun mountain area the largest uncertainty
is the Geotechnical parameters, since these are notmeasures in-situ. It is also a limited availability on
representative literature data and knowledge of theactual slope conditions. Therefore the input
parameters are assumed to have as good quality as
possible. The input parameters are based on literature,
this does clearly introducing some uncertainty. For all
the figures that have been modeled in the 3D slope
stability analysis has been measured with the
influence of cohesion parameter.
Spatial effectiveness of the susceptibility map will
be checked by the highest percentage of the actual
slope failure by comparing the predicted unstable site.
The rate graph will be created for variance ofGeotechnical values, and their percentage will becalculated. The rate explains how well the model andcontrolling factors predict the landslide.
The model with the highest percentage isconsidered to be the best. Fig. 3 shows a
representative the evaluation of slope failure
predicted that produced by 3D slope stability analysis
of variance of Geotechnical values for each factor ofsafety glasses. Therefore, the minimum of the numberof cohesion values in each factor of safety classes
gave a consideration performance in this evaluation.
In fact, the slope failure occurred in the FS≤1.0
value was higher than other values, where thecalculated probability can be over 70 %. In order, thegraph shows a highest of the actual slope failure
occurred in cohesion values 10 KN/m2 for FS≤1.2
about 85.97%. The total number of slope failure isincreasing, when the value of a factor of the safety
increases. Due to the C values gave the similar values,
therefore the cohesion value equal 10 KN/m2 gave avery reasonable performance compare to similar C

For surface geological layer, the weathered
granite parameters of cohesion (12.3 kN/m2), friction
angle (200) and unit of weight (40 kN/m3). General
geometrical parameters of the slopes have been
investigated for assessing their probability
geometrical calculation range value using Monte
Carlo simulation. In this study, the geometricalcalculation parameter range of depth (c) is 3 to 30 m,
to simulate the critical sliding surface by Monte Carlosimulation.
Slope unit can be considered as the left or rightpart of a catchment. In this study, the total slope units
are 1,236 units. Figure 3 shows the distribution of
3D safety factor calculated by GIS-based 3D slope
stability analysis for specific dangerous slopes unit in
the weathered granite area. Figure 3 also gives thedetail comparative results in which dangerous slope
was detected by field investigation of slope failure in
the past. In the simulation, the cohesive values are
considered to change and the range values ofcohesion are 10 to 30 KN/m2. The factor of safety
results has been divided by fourth category, such as
FS<1.00; 1.00<FS≤1.20; 1.20<FS≤1.50; and FS≥1.50.
Table 2 presents the results of 3D Slope stability
simulation based on the varying of cohesion values.
The criteria of safety factor were determined in twoclasses, such as FS≤1 and FS≤1.2.Number of slope
unit by 3D slope stability calculation is decreased
when the cohesion value increases. For the both offactor of safety classes, the percentages of the numberof slope unit by 3D Slope stability calculation (A)and total of number of slope unit (ΣnSU), % (D), were
calculated. Furthermore, intersection of slope unit
distribution by 3D Slope stability calculation andinventory slope is in the boundary area provided
detailing the variation of number of slope failure
occurrence that based on the varying of cohesion
values. In the simulation results, the number of slope
failure occurrence has decreased when the cohesive
value increases.
The percentages of the number of slope failure
occurrence by intersection with 3D slope stability

510th Asian Regional Conference of IAEG (2015)

values performed by other cohesion values.

this tool.
Furthermore, the comparison results of the 3D
slope stability simulation and slope failure evidence
has better prediction accuracy for the study area.
Therefore, in the future, the parameter values for unit
weight (γ) and friction angle (ϕ) will be changed tosimulate of the probability of critical failure in the
Sangun catchment area.

5.2 Effect of cohesion of granite in the Slope
failure Probability analysis
Landslides tend to occur in topography hollow
due to convergence of water and accumulation ofcolluvial soils in steep soil-mantled landscape that
leads to a cycle of periodic filling and excavation bylandsliding (Dietrich and Dunne, 1978).
If it increases weight and water pore pressure in
granular granite, the rate at which water seeps into to
the slope may also critical. When adding the cohesionin the 3D Slope stability tool does not allow us tomodel the water table to follow the infiltration
networks, since normally this is the reality. This isalso a weakness.
When the water is assumed to fill the pores within
the rock, this will not be the case when modeling andthe water will only affect the effective stresses within
the slope as a whole. In our analysis of slope failure
probability in Sangun mountainous area, we find thatparameter combinations emphasizing topographic
through low cohesion values provide the best results.
In this study, the effect of water surface is equal thecohesion of soil value that used in the simulation of
3D Slope stability.
It is found that the number of slope failure basedon FS≤1.00 is increasing with the cohesion value
increase. Therefore, a ratio of slope failure in the pastand slope failure probability shows more than 50% of
all cohesion values for FS ≤ 1.00 class. And thecohesion value 10 KN/m2 is the best results in thesimulation process, which the ratio shows more than
79%. Moreover, the ratios are increasing with thefactor of safety classes is increasing, such as 28 –50 % for FS≤1.2.
The relationship between slope failure probability
and morphology aspect has been conducted that show
the variety of distribution slope failure probability
with slope angle aspect. The analysis showed that theslope angle within the greater than 300 indicates ahigh probability of slope failure occurrence. The ratioof slope failure occurrence was 0.89 and 0.9 in therange of 30 - 400 and greater than 400. During thefieldwork, few slope failures were observed in theareas presented by groundwater surface.
The occurrence of groundwater on the slope
failure surface is indicated that the influence ofhydrology aspect may consider the related cohesion
value. Weathered granite shows a behavior as soilmaterial.

Acknowledgements
The authors would like to thank the Indonesian
Government, Directorate General of Higher
Education for its DIKTI Scholarship. This work was
supported by JSPS KAKENHI Grant Number
25350429.
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6. Conclusions
The 3D Slope Stability tool has been provided theprediction of slope failure in the future in Sangun
Mountainous. However, the performed of the results
will be needed to get better results and the function of6