10th Asian Regional Conference of IAEG (2015)

Various Models of Slope Stability due to Earthquake Activity in Gunung Tigo,
Padang Pariaman, West Sumatera, Indonesia
Arif R. DARANA.(1,3),SUMARYONO(2), Dicky MUSLIM(1), Zufialdi ZAKARIA(1),Donny R. WAHYUDI (1,3), and Twin H.W. KRISTYANTO (1,3).
(1) Faculty Of Geological Engineering, Padjadjaran University, Bandung, Indonesia
E-mail: arif.rahmansyahd@gmail.com(2) Center of Volcanology and Geological Hazard Mitigation – Geological Agency (PVMBG), Indonesia,(3) Geotetra Research Group, Indonesia
AbstractIndonesia is placed on the subduction zone. It makes Indonesia becomes prone ofearthquake activity. Earthquake can trigger another geo-hazard such as landslide. This kindof landslide was happened in Gn. Tigo, Padang Pariaman, West Sumatra in last September
2009. The aims of this study are to make sesismic slope stability models and to analyze thedifferences of slope failures which have various strength of earthquake (PGA). This
research used three methods, i.e.: (1) field method: engineering geological mapping; (2)laboratory method: sample analysis; (3) studio method: model analysis. The research area
has three soil layers, they are pumiceous tuff on top, mixed layer, and at the bottom ispaleosol. Based on Probabilistic Seismic Hazard Analysis (PSHA) data, the model of slope
LB 1 had the Fs results, i.e.: 1.083; 0.920; 0.790; 0.683; 0.594; 0.519. Furthermore themodel of slope LB 9 had the Fs results, i.e.: 1.566; 1.27; 1.064; 0.902; 0.775; 0.674. The
results show that earthquake activity have correlation with landslide occurrence. It showsthat the higher of PGA the lower value of Fs.
Keywords: Earthquake, landslide, slope stability, soil, peak ground acceleration (PGA), West
Sumatera, Indonesia
1.

Introduction

gravity (Anonymous, 2007).
To find out the relationship between theearthquake and slope stability, it is needed to makeseismic slope stability model and to analyze thecorrelation of them.

At September 30th 2009, tectonic earthquake
occurred in West Sumatera with magnitude 7.6 SR. It
also occurred in Gunung Tigo Region, Padang
Pariaman. The earthquake triggered several slopes in
Gunung Tigo to fail. They harmed three districts inthat area. The landslides killed about 289 people anddestroyed a lot of houses and public facilities (Saidi,2011).
Bromhead (1992) said that one of the causes ofthe landslides is the earthquake. When the earthquake
is happened, the materials on the earth will be moved
back and forth irregularly. This movement can beinterpreted as the changed of wave velocity which
spread through the materials. When the accelerationpassing through the materials, it produces either
horizontal or vertical forces. It is also connected withground acceleration.
The Peak Ground Acceleration (PGA) is a
maximum acceleration which is felt by the materials
during the earthquake. PGA induces the change ofgravity acceleration, so the unit PGA is the percent of2.

Methods

This research consisted of three methods. They
were field methods, laboratory methods, and studiomethods. Field methods consisted of lithology andlandslide area distribution mapping. It also measured
the failed slopes geometry and took undisturbed soil
samples.
Laboratory methods consisted of samples
analysis. It was conducted to know the physicalproperties and mechanical properties. Direct shear
test had been done to get cohesion and angle offriction value. Density test was also conducted togenerate the unit weight value of soil samples.
The last method was studio analysis. This
analysis consisted of seismic slope stability
simulations. It generated the value factor of safety1

10th Asian Regional Conference of IAEG (2015)(Fs) when the earthquake occurred on different PGA.

approximately 3-5m, grain size is fine sand to roughgravel, and it also dominated by fine gravel.
This layer was the collapsed layer. The physicalproperties and mechanical properties of this material
are: γ: 14.86 kN/m3; c: 7.84 kPa; and φ: 37.280. Basedon USCS, this material is classified as SP.

This simulations were run by Geostudiosoftware using Slope W/Analysis by adding seismic
load parameter. The seismic load parameter wasgenerated from PGA data. These simulations usedpseudo-static analysis with horizontal PGA. The
vertical PGA was not used, due to its insignificant
effect to Fs value (Jibson, 2011 and Kim, 2012).
Therefore, the formula in this analysis is:
kh = ah / g

3.1.2 Mixed layerThis layer is the second layer which placed
between pumiceous tuff and paleosol. This layer
existed due to the mixed of pumiceous tuff andpaleosol underneath. This layer has characteristics:
greyish white for fresh color, greyish brown for theweathered one, grain size is clay to pebble which
dominated by silt. The thickness of this layer
approximately 10-40 cm.

(1)

kh = horizontal pseudo-static coefficient (g),ah = horizontal acceleration (m/s2), andg = gravitational acceleration (m/s2).
So that the formula for Fs become:Fs =.

φαααα(2)This unique layer was the sliding surface of thelandslides. It occurred because this layer is dominatedby silt and halloysite. In natural condition this layer
has more water than others. It was proofed bysaturated degree more than 80%. Mechanicalproperties of this layer are: γ: 15.575 kN/m3; c:
18.326 kPa; and φ: 29 110. Based on USCS, thismaterial classified as ML.

2c = cohesion (kN/m ),φ = angle of internal friction (Mohr-Coulomb
strength parameter) (0),W = wide multiple by density (kN/m3), slope angle (0), andkh = pseudo-static coefficient (g).
3.

Results and Discussion

3.1.2 Paleosol
This layer is the third layer or the bottom of soil
profile in research area. This layer had been buried by
pumiceous tuff, and became paleosol. It is a depositof debris flow which became a part of topography
before it was covered by pumiceous tuff
(paleotopography). This material has characteristics:
fresh color reddish brown, weathered color yellowish
light brown, grain size is clay to pebble which
dominated by medium sand to silt.
This layer was interpreted as debris flow
deposition which did not fail. The mechanicalproperties of this layer are: γ: 13.073 kN/m3; c: 4.998
kPa; and φ: 30.1850. Based on USCS, this material
classified as SM.

3.1 Materials
Based on engineering geological map, which wasgenerated by field mapping (Fig. 6), research area
consists of three materials.

Fig. 1 Soil profile in research area.

3.2 Geomorphology
Based on field observation, geomorphologicalunit in research area is classified as rough hills. This
research area is dominated by hills with slopes value
15-70%. It is dominated by V shape valley. It
represents that research area is a young valley andlied on hard rock formation. It also indicates that
research area has high value of angle of friction,

3.1.1 Pumiceous tuff
This material was placed on the top of the soillayer. This materials came from the tephra of Tandikat
or Singgalang Mountain. When that volcanoes erupted,
the tephra felt down then covered the surface. It
becomes recent topography.
The characteristics of pumiceous tuff are: freshcolor greyish-white, weathered color brownish-yellow,
size is fine-gravel (gravel dominated), the thickness

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10th Asian Regional Conference of IAEG (2015)

3.3 Peak Ground Acceleration (PGA) of ResearchArea
The PGA was derived from Probabilistic
seismic hazard analysis (PSHA). It was based on
PGA Probabilistic Zonation Map period of 50 years(Ministry of Public Works, 2010). The research area
had PGA from 0.1 – 0.5 g. Meanwhile, the PGA ofSeptember 30th 2009 earthquake could be estimatedby deterministic seismic hazard analysis (DSHA).
Furthermore the PGA at that moment were 0.24 g atslope LB 1 and 0.25 g at slope LB 9 (Darana, 2015).

rough materials such as tuff, and low grade oferosions.

Photo. 1 Rough hills geomorphological unit.

Fig. 2 PGA Probabilistic Map of research area Period 50 years (not to scale) (modified from Ministry of PubliWorks).
3.4 The Effect of PGA to the Slope Stabilities (Fs)
Table 1 Slopes Geometry.
CoordinateSlopes
LB 1LB 9Latitude

Longitudea(m)b(m)S 00 29’ 06.4”
S 00 29’ 03.3”

1000 14’ 28.3” BT
1000 13’ 48.5” BT

57
76.5

67.5
41

Explanation

aLandslidearea(m2)54.48042.550Explanation:
Pumiceous tuff

cbSlopes Geometry
Crownc
length(m)(m)
88.34
37
225
86.8 30.4
413.7

Mixed Layer
Paleosol3

10th Asian Regional Conference of IAEG (2015)

Fig. 3 Research Slope, LB1 Left and LB 9 Right by Software Geostudio (not to scale).
(DEM) was used to generate the topography ofTwo slopes from research area had been
research area before landslide occurred. After thechosen as models to analyze the effect of seismic todata had been collected, the slopes were modeled
the Fs value. They were LB 1 and LB 9 (Fig. 6).
and analyzed in Geostudio Software (Fig. 4).
The geometry of slopes were measured in the fieldand shown in table1. Digital elevation model
3.4.1 Slope LB 1
Table 2 Result of Fs Simulation in LB1.
PGA (g)

Fs

0
0.1
0.2
03
0.4
0.5

Ordinary
Moment
1.128
0.965
0.831
0.719
0.625
0.544

Force-

BishopMoment
1.185
1.022
0.890
0.782
0.690
0.611

Janbu

Force-

Moment-M.P

Force
1.083
0.920
0.790
0.683
0.594
0.519

1.2
1.15
1.1
1.05
10.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5

Moment
1.171
1.015
0.890
0.789
0.706
0.640

Force
1.180
1.013
0.887
0.786
0.697
0.636

Ordinary Moment
Bishop Moment
Janbu Force
M-P Moment
M-P Force
00.1

0.2

0.3

0.4

0.5

PGA
Fig. 4 Graphic of Fs Simulation in LB 1.

Photo. 2 Slope LB 1 slope that occurred landslide when earthquake happened at 30th September 2009.

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10th Asian Regional Conference of IAEG (2015)

which is based on Fs classification (Bowles, 1989).
This analyzes show that the higher PGA the lower
Fs value. The data of PGA and Fs value wereplotted to the graphic Fs simulation (Fig. 4) then.
The graphic shows the declining trend of Fs.

The natural slope stability (PGA=0) was 1.083
calculated used Janbu Method (the smallest value
around PGA=0). Based on Fs classification byBowles (1989), the slope is in critical condition.
When the earthquake occurred with PGA 0.1 g, theFs value became 0.920. It means the slope is failed,
3.4.2 Slope LB 9

Table 3 Result of Fs Simulation in LB9.
PGA (g)
00.1
0.2
0.3
0.4
0.5

Ordinary
Moment
1.574
1.293
1.085
0.923
0.795
0.690

Force
-

BishopMoment
1.732
1.423
1.199
1.026
0.889
0.779

Janbu

Force
-

Moment
-

M.P

Force
1.566
1.276
1.064
0.902
0.775
0.674

Moment
1.614
1.318
1.104
0.939
0.807
0.705

Force
1.605
1.312
1.097
0.936
0.811
0.708

Fs

Photo. 3 Slope LB 9 slope that occurred landslide when earthquake happened at 30th September 2009.
1.75
1.65
1.55
1.45
1.35
1.25
1.15
1.05
0.95
0.85
0.75
0.65

Ordinary Moment
Bishop Moment
Janbu Force
M-P Moment
M-P Force

00.1

0.2

0.3

0.4

0.5

PGA
Fig. 5 Graphic of Fs Simulation in LB 9.
The natural slope stability (PGA=0) was 1.566
calculated used Janbu Method. This value meansthat slope is stable (Bowles, 1989). It remained
stable even the PGA is 0.1 g. However, when thePGA became 0.2 g, the Fs value became 1,064
which means the slope is critical (Bowles, 1998).
Moreover when the PGA became 0.3 g, the slope
became unstable. It is also confirmed that the5

10th Asian Regional Conference of IAEG (2015)

the land use such as infrastructure, houses,farms, etc.

higher PGA the lower Fs Value. Likes the LB 1, thisslope generated the graphic which showed thedeclining trend.
Those slope stability simulations show thesmallest Fs value was generated from Janbu
Method. It could be happened because the materialof these mass movement are soil and the type ofthese landslides are rotational not circular (notproperly circular). Janbu method is more reliable
than others for this case. It because Janbu method
determines the failure plane based on the weak zonethat is existed in soil mass (Bromhead, 1992;Khrahn, 2004).
Slope LB 1 Generated lower Fs value thanslope LB 9. It is because slope LB 1 had higher
slope angle than slope LB 9. It means LB 1 steeper
than slope LB 9. Therefore, in this case, the steeper
slope angle the lower Fs value.
4.

Conclusions

Soil profile shows three materials: (1)pumiceous tuff (failure layer), (2) mixedlayer (sliding surface), and (3) paleosol(stable layer).
Research area has the rough hills
geomorphological unit with slopes value
15-70%.
Natural slope stability shows the Fs Value of
LB1 is 1.083 which means critical and LB 9
is stable due to its Fs value is 1,566.
Slope LB 1 and LB 9 were failure when Fs
simulations was run with PGA 0.1-0.5. It
shows that the higher PGA the lower Fsvalue.
The Fs value of slope LB 1 were lower thanslope LB 9. It is because slope LB 1 higherangle than slope LB 9. Therefore, in this case,the steeper slope the lower Fs value.



Acknowledgements
The authors would like to say thanks to Centerof Volcanology and Geological Hazard Mitigation– Geological Agency who helped this research
could reached the goals. Miss Ervin, who helped
the authors revising the grammatical errors.
References
Bromhead, E.N. (1992): The stability of Slope.
2nd_ Edition. UK: Blackie Academic &Professionalan Imprint of Chapman & Hall.
Bowles, Joseph E. (1989): Sifat-sifat Fisik dan
Geoteknis Tanah (mekanika tanah). Jakarta:
Erlangga.
Darana, Arif R. (2015): Perubahan Kestabilan
Lereng Akibat Gempa Di Daerah Gunung Tigo
Kabupaten Padang Pariaman Sumatera Barat.
Bandung: Padjadjaran University. Unpublished.
Jibson, R.W., (2011): Methods For Assessing The
Stability of Slopes During Earthquakes—A
Retrospective: Engineering Geology, v. 122, pp.
43-50.
Kim, Byungmin. (2012): Seismic Hazard Analysis
And Seismic Slope Stability Evaluation Using
Discrete Faults In Northwestern Pakistan;
Disertation. Illinois: University of Illonois at
Urbana-Champaign. Unpublished.
Krahn, John. (2004): Stability Modeling With
Slope/w Ebook. Canada
Saidi, A; Berd, I., Har, R. (2011): Kajian Longsor
Akibat Gempa di Nagari Tandikek Kecamatan
Partamuan Kabupaten Padang Pariaman
Sumatera Barat, Indonesia. J. Solum Vol. VIII
No. 2 Juli 2011:78-91.
Tim Peta Gempa Indonesia. (2010): Peta Hazard
Gempa Indonesia.Ministry of Public works.

4.1 Future Plans of This Study This landslide study only used earthquake(PGA), the mechanical and physicalproperties of its materials, and the geometryof the slopes as parameters. It could be moreaccurate if this study use another parameterssuch as rainfall intensity, groundwatercondition, the extra load at the surface (e.g.
houses), vegetation condition, etc.
 It is also suggested to analyze more slopes, sothat the Fs data could be shown as spatial
information. It also could be made thezonation of areas prone to this hazard. It
could be useful for planning and managing

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10th Asian Regional Conference of IAEG (2015)

Fig. 6 Engineering Geological Map of research area.

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