Numerical Simulation of Long-Runout Landslides
Ryoichi Michihata
1
, Wataru Sagara
1
, Ryosuke Tsunaki
1
1 SABO & LANDSLIDE Technical Center(4-8-21 Kudanminami, Chiyoda-ku, Tokyo 1020074, Japan)
* Corresponding author. E-mail: michihata@stc.or.jp
In the study, we collected example data from “landslides” that traveled a “long distance” (“long-runout landslide” hereinafter) in Japan, and ran a numerical simulation of these examples while considering their phenomena. In this simulation, the Zhang’s model was applied. At the start of the simulation, a sensitivity analysis was conducted to find the parameters that have to be carefully set in the model and then a landslide reproduction calculation was carried out with collected data from past long-runout landslides in order to investigate how to set the parameters for the simulation of a long-runout landslide.
Key words:Landslide, sediment-related disasters, mitigation, long-runout landslide
1. OBJECTIVE
In order to ensure people are safe, it is effective to simulate the traveled area of a landslide as soon as signs are observed on a slope, such as the expansion of cracks. And, many numerical simulation models have been proposed. In Japan, some long-runout landslides have occurred recently. Various mechanisms of these long-runout landslides have been suggested, such as excess pore pressure by [Iverson et al., 2000] and grain collision by [Hsü, 1975]. But there are few comprehensive simulation models involving such mechanisms. So we simulated a long-runout landslide by using one of the common simulation models, Zhang’s model [Zhang et al., 2004]. And we considered how to set parameters for simulating of long-runout landslides, and the issues.
2. Collection of Examples of Long-Runout Landslides
2.1 The landslide examples that were the subjects of this study
We collected data on landslides caused by rainfall in Japan and selected the “long-runout landslides”
from among the collected data. The data on
“long-runout landslides” were examined in terms of the traveled distance.
2.1.1 The phenomena that were the subjects of this study
The “landslide” phenomena that were the subjects of this study can be classified in accordance with the
㼀㼥㼜㼑㻌㼛㼒
㼙㼛㼢㼑㼙㼑㼚㼠 㼜㼔㼑㼚㼛㼙㼑㼚㼛㼚 㼠㼔㼑㻌㼟㼡㼎㼖㼑㼏㼠㻌㼛㼒㻌㼠㼔㼕㼟㻌㼟㼠㼡㼐㼥
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㼠㼔㼑㻌㼟㼡㼎㼖㼑㼏㼠㻌㼛㼒㻌㼠㼔㼕㼟㻌㼟㼠㼡㼐㼥
㻲㼘㼛㼣
㻵㼠㻌㼕㼟㻌㼍㻌㼟㼜㼍㼠㼕㼍㼘㼘㼥㻌㼏㼛㼚㼠㼕㼚㼡㼛㼡㼟㻌㼙㼛㼢㼑㼙㼑㼚㼠㻌㼛㼚㻌㼣㼔㼕㼏㼔 㼟㼡㼞㼒㼍㼏㼑㼟㻌㼛㼒㻌㼟㼔㼑㼍㼞㻌㼍㼞㼑㻌㼟㼔㼛㼞㼠㻙㼘㼕㼢㼑㼐㻘㻌㼏㼘㼛㼟㼑㼘㼥㻌㼟㼜㼍㼏㼑㼐㻘㻌㼍㼚㼐 㼡㼟㼡㼍㼘㼘㼥㻌㼚㼛㼠㻌㼜㼞㼑㼟㼑㼞㼢㼑㼐㻚㻌㼀㼔㼑㻌㼐㼕㼟㼠㼞㼕㼎㼡㼠㼕㼛㼚㻌㼛㼒㻌㼢㼑㼘㼛㼏㼕㼠㼕㼑㼟㻌㼕㼚 㼠㼔㼑㻌㼐㼕㼟㼜㼘㼍㼏㼕㼚㼓㻌㼙㼍㼟㼟㻌㼞㼑㼟㼑㼙㼎㼘㼑㼟㻌㼠㼔㼍㼠㻌㼕㼚㻌㼍㻌㼢㼕㼟㼏㼛㼡㼟㻌㼘㼕㼝㼡㼕㼐㻚
㼎㼍㼟㼕㼏㼍㼘㼘㼥㻌㼛㼡㼠㻌㼛㼒㻌㼠㼔㼑㻌㼟㼡㼎㼖㼑㼏㼠㻘㻌㼎㼡㼠㻌㼕㼠㻌㼕㼟 㼟㼡㼎㼖㼑㼏㼠㼑㼐㻌㼠㼔㼑㻌㼜㼔㼑㼚㼛㼙㼑㼚㼛㼚㻌㼛㼒㻌㼟㼘㼕㼐㼑 㼣㼕㼠㼔㻌㼜㼍㼞㼠㼕㼍㼘㼘㼥㻌㼒㼘㼛㼣㻌㼍㼞㼛㼡㼚㼐㻌㼠㼔㼑㻌㼒㼞㼛㼚㼠 㼛㼒㻌㼙㼛㼢㼕㼚㼚㼓㻌㼙㼍㼟㼟㻚
definitions shown in Table 1, which was proposed by [Cruden et al. in 1996]. The phenomena we studied were classified as the “slide” in this table, and did not include such phenomena as the “fall”,
“topple”, and “flow” in this table. “Fall” and
“topple” are phenomena that do not have the slide plane, and “flow” is a phenomenon such as “debris flow” and “debris avalanches” and so on. The phenomenon classified as “debris flow” includes the phenomenon in which a part of the mass of soil and rock in the “landslide” slides down to reach the torrent and turns into debris flow, which may flow down a long distance. Sometimes it may flow down as long as several kilometers. In Japan, the Sumikawa landslide [Sasaki, et al., 1998] is an example of this kind of debris flow. However, the
“slide” and “flow” may sometimes occur in a transitive state in the “slide” phenomenon, making it difficult to discern exactly between the “slide” and
“flow” in many “slide” phenomena. Especially at the front edge section of the landslide deposit there are severe disturbances making a “slide” into a
“flow” locally. Thus in this study, when the major
Table 1 The phenomenon of the subject of this study
phenomenon in the landslide spread (i.e., the area the slid soil and rock can reach) is the “slide”, it is treated as a “landslide” phenomenon that can be an example of this study even though a “flow”
phenomena locally contains.
2.1.2 Definition of “long distance” travelling phenomenon
A “long-runout landslide” is one whose traveled distance (L2) as shown in Fig.1 is 250 m or longer measured from the lower end of the slid mass, or one whose traveled distance (L2) was shorter than 250m but was longer than the length of the slid mass (L1). You can see the illustrated definitions of L1 and L2 in Fig. 1. The above threshold (250 m) for the traveled distance and the values L1 and L2 are the figures used to set the “sediment-related disaster warning area (Yellow zone)”. This zone is defined in the “Law related to promotion of measures for sediment-related disaster prevention in a restricted area etc. due to sediment-related disaster”, which is the Japanese law controlling land-use and establishing warning and evacuation systems in cases of “landslide” disasters.
In the promotion of the act above, the Yellow zone is defined as the area in which L2 is smaller than 250 m (L2<250m) or L1 (L1>L2) and almost all (99%) of the examples are covered with this zone.
2.1.2 Period of time to collect examples
Most of the examples used in this study occurred in or after 2001, whose traveled distances were identified. And the rest of the examples were in or before 2000, reported in the Journal of the Japan Society of Erosion Control Engineering or the Journal of the Japan Landslide Society, etc.
2.2 Observation of collected landslide examples 2.1.1 Size of collected examples
The number of long-runout landslide examples collected was 23. These examples are listed in
Table 2, and the L1 and L2 in these examples are plotted in Fig. 2.
㻸㻝㻔㼙㻕 㻸㻞㻔㼙㻕 㼃㼕㼐㼠㼔㻔㼙㻕 㻭㼞㼑㼍㻔㼙㻞㻕 㻔㻸㻝㽢㼃㼕㼐㼠㼔㻕
㼛㼛㼠㼍㼗㼕 㻲㼡㼗㼡㼕 㻠㻢 㻠㻣 㻠㻣 㻞㻘㻝㻢㻞 㻙
㼙㼕㼦㼡㼟㼍㼣㼍㼟㼔㼕㼚㼐㼑㼚㻺㼕㼕㼓㼍㼠㼍 㻝㻡㻜 㻠㻡㻜 㻞㻡㻜 㻟㻣㻘㻡㻜㻜 㼒㼘㼛㼣
㼟㼔㼕㼓㼑㼠㼛㼡 㻷㼛㼡㼏㼔㼕 㻝㻡㻡 㻞㻜㻣 㻝㻥㻜 㻞㻥㻘㻠㻡㻜 㼒㼘㼛㼣
㼍㼗㼍㼙㼍㼠㼟㼡 㼅㼍㼙㼍㼓㼍㼠㼍 㻝㻜㻜 㻞㻡㻡 㻝㻜㻜 㻝㻜㻘㻜㻜㻜 㼒㼘㼛㼣
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㼚㼍㼗㼍㼎㼍 㻿㼔㼕㼙㼍㼚㼑 㻥㻡 㻝㻣㻜 㻠㻞 㻟㻘㻥㻥㻜 㼒㼘㼛㼣
㼗㼕㼚㼛㼟㼔㼕㼠㼍 㼅㼍㼙㼍㼓㼍㼠㼍 㻟㻡 㻠㻜 㻝㻡 㻡㻞㻡 㻙
㼕㼟㼔㼕㼗㼡㼞㼍 㻺㼍㼓㼍㼟㼍㼗㼕 㻞㻡㻜 㻞㻡㻜 㻡㻜 㻝㻞㻘㻡㻜㻜 㻙
㼎㼕㼟㼔㼍㼙㼛㼚 㻷㼍㼓㼛㼟㼔㼕㼙㼍 㻝㻣㻜 㻝㻥㻜 㻝㻜㻜 㻝㻣㻘㻜㻜㻜 㼒㼘㼛㼣
㼍㼓㼍㼚㼛 㻿㼍㼕㼠㼍㼙㼍 㻝㻜㻜 㻝㻞㻡 㻢㻡 㻢㻘㻡㻜㻜 㼒㼘㼛㼣
㼗㼛㼐㼛㼙㼍㼞㼕 㻺㼕㼕㼓㼍㼠㼍 㻟㻡 㻡㻡 㻠㻜 㻝㻘㻠㻜㻜 㼒㼘㼛㼣
㼟㼕㼙㼕㼦㼡㼥㼍㼙㼍 㻺㼍㼓㼍㼚㼛 㻝㻝㻝 㻝㻟㻟 㻥㻜 㻥㻘㻥㻥㻜 㼒㼘㼛㼣
㼖㼕㼚㼓㼍㼙㼕㼚㼑 㻺㼕㼕㼓㼍㼠㼍 㻠㻜㻜 㻤㻜㻜 㻞㻡㻜 㻝㻜㻜㻘㻜㻜㻜 㼒㼘㼛㼣
㼛㼓㼡㼞㼕㼥㼍㼙㼍 㻲㼡㼗㼡㼟㼔㼕㼙㼍 㻝㻟㻜 㻞㻜㻜 㻤㻜 㻝㻜㻘㻠㻜㻜 㼒㼘㼛㼣
㼚㼕㼓㼛㼞㼕㼟㼍㼣㼍 㼅㼍㼙㼍㼓㼍㼠㼍 㻠㻜㻜 㻟㻢㻜 㻟㻜㻜 㻝㻞㻜㻘㻜㻜㻜 㼒㼘㼛㼣
㼥㼍㼙㼍㼟㼔㼕㼚㼍 㻵㼟㼔㼕㼗㼍㼣㼍 㻝㻜㻜 㻝㻟㻜 㻢㻜 㻢㻘㻜㻜㻜 㼟㼘㼕㼐㼑
㼔㼕㼞㼛㼟㼍㼗㼕㼟㼕㼗㼛㼡㼓㼍㼕 㻭㼛㼙㼛㼞㼕 㻡㻡 㻝㻢㻡 㻢㻜 㻟㻘㻟㻜㻜 㼒㼘㼛㼣
㼕㼏㼔㼕㼚㼛㼟㼑 㼀㼛㼠㼠㼛㼞㼕 㻞㻜㻜 㻟㻢㻜 㻞㻝㻜 㻠㻞㻘㻜㻜㻜 㼒㼘㼛㼣
㼠㼛㼙㼕㼗㼡㼞㼍 㻺㼍㼓㼍㼚㼛 㻡㻜 㻣㻜 㻟㻜 㻝㻘㻡㻜㻜 㼒㼘㼛㼣
㼔㼛㼡㼗㼛㼡㼕㼚㼡㼞㼍 㻺㼕㼕㼓㼍㼠㼍 㻢㻜 㻥㻜 㻠㻜 㻞㻘㻠㻜㻜 㼒㼘㼛㼣
㼠㼍㼗㼕㼥㼍 㻹㼕㼑 㻣㻜 㻝㻟㻜 㻠㻜 㻞㻘㻤㻜㻜 㼒㼘㼛㼣
㼗㼛㼗㼡㼓㼍㼣㼍 㻺㼕㼕㼓㼍㼠㼍 㻡㻜㻜 㻞㻡㻜 㻝㻡㻜 㻣㻡㻘㻜㻜㻜 㼒㼘㼛㼣
㻸㼍㼚㼐㼟㼘㼕㼐㼑 㼚㼍㼙㼑
㼠㼛㼜㼛㼓㼞㼍㼜㼔㼥 㼜㼔㼑㼚㼛㼙㼑㼚㼛㼚
㼍㼠㻌㼠㼔㼑㻌㼒㼞㼛㼚㼠 㼛㼒㻌㼐㼑㼜㼛㼟㼕㼠 㼘㼛㼏㼍㼠㼕㼛㼚
㻔㻼㼞㼑㼒㼑㼏㼠㼡㼞㼑㻕
0 100 200 300 400 500 600 700 800 900
㻜㻌 㻝㻜㻜㻌 㻞㻜㻜㻌 㻟㻜㻜㻌 㻠㻜㻜㻌 㻡㻜㻜㻌 㻢㻜㻜㻌 㻣㻜㻜㻌 㻤㻜㻜㻌 㻥㻜㻜㻌 㻝㻘㻜㻜㻜㻌 㻝㻘㻝㻜㻜㻌
L2䠄m䠅
L1䠄m䠅 tomikura
kodomari kinoshita
jingamine
ootaki shimizuyama
yamashina agano houkouinura nakaba
hirosakishi kougai
oguriyamashigetou bishamon akamatsu
itchinose mizusawashinden
mushigame
nigorisawa fukuchi
ishikura kokugawa
In all of the examples, the area of the slid block is between 525 and 200,000 m2. The sediment volume was several million m3 or less. The ratio of L2 to L1 (L2/L1) was between 0.5 and 3.0, and this ratio exceeded 2.0 in only four examples, which were the landslides at Hirosakishikougai, Mizusawashinden, Akamatsu, and Jingamine. In seven examples, the
Fig. 1 Definition of L1 and L2
Fig. 2 Relation of L1 to L2
Table 2 List of long-runout landslides in Japan
distance L2 exceeded 250 m, and their sediment area distributed between 10,000 and 200,000 m2. 2.1.2 Features of the phenomena
The collected examples were examined if the front of the deposit was transferred by the “flow”
type phenomenon or was deposited by the “slide”
type phenomenon, and the results of this examination are listed in Table 2. Photos or other data on the collected examples were used in this examination. Fig. 3 is a photo of the Bishamon landslide, whose type was decided to be “flow”
based on this photo.
In 19 of the 23 examples, the front of the deposit was confirmed to be of the “flow” type; in 1 example, the front of the deposit was confirmed to be the “slide” type; and in the 3 remaining examples, the type could not be confirmed (i.e., it was confirmed to be “unknown”). In eight examples, the ratio L2/L1 exceeded 2.0 or the distance L2 was 250 m or longer. In all of these eight examples, the front of the deposit was confirmed to be of the “flow” type. It is believed that a long-runout landslide is caused or influenced by the weathering of and/or water content in the sliding mass of soil and rock.
In Ichinose, the extensometer showed variations in its measurements on the day of the landslide, and in Akamatsu, the generation of cracks was observed a few days before the landslide, which could be deemed as the warning signals.
3. Simulation Procedure
3.1 Selection of the simulation model
In this study, the reproduction of the above
long-runout landslide examples was attempted with numerical simulation and we examined the applicable parameter sets to the prediction of long-runout landslides. In this simulation, the Zhang’s model [Zhang et al., 2004] was used in consideration of the above mentioned mechanism of the long-runout landslide. The Zhang’s model was selected in this study because of the following features:
(1) Suitability for continuously simulating the state covering the “slide” as well as “flow”
conditions because it is a continuum model.
(2) Ability to take water content and internal dissipation stress into account in the simulation.
(3) Ability to run planar two-dimensional analysis.
Note that the Zhang’s model is one originally used to simulate “slide” type “landslides”.
Therefore, the present evaluation of internal dissipation stress is limited to evaluation of the stress with shear resistance that is normal to the planar direction motion. In general, in the models that express the “flow” type of landslide like debris flow [e.g., Takahashi, et al., 2002], internal dissipation stress is evaluated with shear resistance, which is a function of the velocity gradient in the vertical direction.
On the other hand, models such as the CIVA model [Yoshimatsu, et al., 2005] have been proposed as landslide simulation models that can simulate states covering “slide” as well as “flow”
conditions, but at present they are rarely used. It should be noted that there are few models that can comprehensively cover the “slide” conditions as well as the “flow” conditions of a landslide in a continuous and strictly theoretical manner.
3.2 Outline of the simulation model
The outline and constitutive equations are described in this Section.
Zhang’s model applies the Coulomb mixture model, which assumes that grain-fluid flows behave as mixtures of interacting Newtonian fluids and Coulomb solids. In order to exactly predict the range of sediment, the third-order upwind scheme and the preserving mass conservation method are applied to the model [Zhang et al., 2004]. And we additionally applied the model to Voellmy’s flow resistance which depends on velocity.
There are some important parameters to apply with this model, and they are the friction angle of soil (int), friction angle of the boundary between the
Fig. 3 Deposit by flow mechanism in the Bishamon landslide (http://www.nan9.co.jp/)
ground and soil (bed), and the coefficient of pore water pressure ().
The equations of continuity and momentum conservation are as follows:
w 0 w w w w w
y N x M t
h (1)
> @
ࠉ
[ P
X
M O U
P X
M O U
OU U
O U
U
2 2 2 2 2 2
int /
/
3
tan ) 1 ( ) sgn(
) (
sin ) 1 ( )
sgn(
) 1 (
) ) (
(
y x z x f
bed z
x x
x f
z pass act x
z pass act z x
x y
v v M h g v
h g y v
v x
h v
h y g y hk
v
x h h g hk
g h
g
y M v x
M v t M
w
w w w
w w w
w
w w
»¼
« º
¬ ª
w w w w w w
> @
ࠉ
[ P
X
M O U
P X
M O U
OU U
O U
U
2 2 2 2 2 2
int /
/
3
tan ) 1 ( ) sgn(
) (
sin ) 1 ( )
sgn(
) 1 (
) ) (
(
y x z y f
bed z
y y
y f
z pass act y
z pass
act z y
x y
v v N h g v
h g y v
v x
h v
h x g x hk
v
y h h g hk
g h
g
y N v x
N v t N
w
w w w
w w w
w
w w
»¼
« º
¬ ª
w w w w w w
On the right side of Equations (2) and (3), the first term represents gravity; the second term pressure; the third term shear force normal to the direction of motion; the fourth term viscosity of pore water; the fifth term shear force on the boundary between the ground and soil; the sixth term shear stress of pore water; and the seventh term turbulence.
Where; gx, gy, gz are gravitational acceleration in the X, Y, and Z directions, M vxh,N vyh: flow quantity flux, : Voellmy's turbulent coefficient, : density of mixed fluid, : pore water pressure coefficient, vx㸪v㹷㸪v㹸ࠉ: velocity in the X, Y, and Z directions, h: flow depth, int: internal friction angle, bed: friction angle of the boundary between the ground and soil (bed friction angle, hereinafter), f: 1-porosity, : viscosity coefficient of pore water, kact/pass: active/passive earth pressure coefficient.
4. Simulation Process 4.1 Selection of simulation site
The following three examples of landslides were selected for simulation out of the 23 collected examples because the topography of these three sites was clearly identifiable before and after the landslide and the topography before the landslide was reproducible.
- Bishamon - Akamatsu - Ichinose
4.2 Outline of the simulation site
The three selected simulation sites are outlined in this Section. Parameters like size of landslide are listed in Table 2.
4.2.1 Bishamon
The Bishamon landslide in Kagoshima Prefecture occurred on September 20, 1993 due to heavy rainfall. That year, there was as much as 2,000 mm or more of precipitation in the two months from August. Since it was estimated that the average failure depth was 10 m [Nakagawa, et al., 2007], the sediment volume can be estimated to have been about 150,000 m3. Even though it was not raining on that day, the landslide occurred leaving two dead, three injured and two houses totally destroyed.
4.2.2 Akamatsu
The Akamatsu landslide occurred in Yamagata Prefecture, an area of deep winter snow, on April 26, 1974 during the snow melting season. The sediment volume is estimated to have been 100,000 m3. According to the data at Shinjo weather station, the maximum temperatures were 20.8qC, 21.2qC, and 16.8 qC respectively on the 24th, 25th, and 26th of the month, and it is believed that a lot of snowmelt runoff infiltrated underground. Precipitation on the day was 3.5 mm. A mountain called Matsuyama whose elevation was 181 m (or about 100 m in relative elevation) broke and fell in a very short time and the soil and rock crushed houses at the foot of the mountain. The huge disaster caused by this landslide left 15 people dead, two missing, 11 people with minor or serious injuries, 18 houses collapsed or buried, and two houses partially destroyed [Minagawa, et al., 1975].
4.2.3 Ichinose
The Ichinose landslide in Tottori Prefecture occurred on September 29, 2004 due to heavy rainfall. Precipitation on the day was recorded at 204 mm. The sediment volume was estimated to be 330,000 m3, part of which flew into the river (2)
(3)
running at the bottom of the slope and formed a landslide dam filled with about 410,000 m3 of water.
There were no human casualties but 12 houses were flooded due to the raised river water level because the flow of the river was blocked by the landslide dam.
4.3 Parameter sensitivity analysis
We simulated the Akamatsu landslide as an example of a long-runout landslide that occurred in Yamagata Prefecture in Japan in the snow-melting season. The deposit range of sediment in the Akamatsu landslide had topographic characteristics such as plane surface and little influence of a river.
The sediment volume was about 100,000m3. The relative height between the top of the landslide and the front of the deposit was about 100 m and the horizontal distance (L1) was about 400 m (the horizontal distance except the landslide area (L2) was about 300 m). The simulation cases and results are shown below (Fig. 4).
The most important parameter is bed where about 5° change in the parameter causes about 50 m change in reach distance. Also parameter is important, where a change of about 10% in the parameter causes a change of about 10 m. And parameter int has little influence. Considering the actual condition, we assumed that parameter bed was about 20°, int was about 25° and was about 20–30%, and then the difference between the actual and simulated results of the front deposit position is about -150 m (Fig. 4). Parameter is fixed in a relatively high range considering infiltration of melting snow into weathering mudstone. With rich water content, the runout mechanism of a landslide tends to change to a flow, and then the Coulomb solids resistance decreases. Thus we suppose we need lower bed value than actual condision to assure the reproducibility.
4.4 Analysis process
After creating the topography model of the area before the landslide respectively for the three simulation sites, we tried to assure the reproducibility of the landslide phenomenon by adjusting the most dominant parameter (bed) with the both determined parameter; the pore water pressure coefficient () and internal friction angle (int). The pore water pressure coefficient () used in the analysis was set at 0.2 for all the sites. This coefficient of 0.2 was selected based on the coefficient generally observed in weathered rock and unconsolidated ground. The internal friction
200 150 100 50 0 50 100
0.1 0.2 0.3 0.4 0.5
differencebetweentheactualand calculationofthefrontdeposit position;D
coefficientofporewaterpressure;
bed=5㼻int=25㼻 bed=10㼻int=25㼻 bed=20㼻int=25㼻 bed=10㼻int=15㼻
㻸㼍㼚㼐㼟㼘㼕㼐㼑 㼚㼍㼙㼑
㼏㼛㼑㼒㼒㼕㼏㼕㼑㼚㼠㻌㼛㼒㻌㼜㼛㼞㼑 㼣㼍㼠㼑㼞㻌㼜㼞㼑㼟㼟㼡㼞㼑㻧䃚
㼕㼚㼠㼑㼞㼚㼍㼘㻌㼒㼞㼕㼏㼠㼕㼛㼚 㼍㼚㼓㼘㼑㻧䃥㼕㼚㼠
㼎㼑㼐㻌㼒㼞㼕㼏㼠㼕㼛㼚㻌㼍㼚㼓㼘㼑㻧䃥㼎㼑㼐 㻮㼕㼟㼔㼍㼙㼛㼚 㻜㻚㻞 㻞㻡㼻 㻭㼚㼍㼘㼥㼕㼚㼓㻌㼜㼞㼛㼜㼑㼞㻌㼢㼍㼘㼡㼑㻌㼎㼥㻌㼟㼕㼙㼡㼘㼍㼠㼕㼛㼚 㻭㼗㼍㼙㼍㼠㼟㼡 㻜㻚㻞 㻞㻡㼻 㻭㼚㼍㼘㼥㼕㼚㼓㻌㼜㼞㼛㼜㼑㼞㻌㼢㼍㼘㼡㼑㻌㼎㼥㻌㼟㼕㼙㼡㼘㼍㼠㼕㼛㼚 㻵㼏㼔㼕㼚㼛㼟㼑 㻜㻚㻞 㻞㻡㼻 㻭㼚㼍㼘㼥㼕㼚㼓㻌㼜㼞㼛㼜㼑㼞㻌㼢㼍㼘㼡㼑㻌㼎㼥㻌㼟㼕㼙㼡㼘㼍㼠㼕㼛㼚
angle (int) was set at 25° for all the sites. This angle of 25° was selected based on the slope gradient of the landslide area (Table 3.)
5. Results of Numerical Simulation
The results of the simulation and their differences from the actual data are summarized in Table 4 Results of numerical simulation. The simulated and actual cross sections of the landside profile are shown in Fig. 5 to Fig. 7 respectively for the three simulation cases, where the details at the sediment front are shown in the inset of each figure. The results of the simulation indicate that the optimum solution is obtained when the bed friction angle (bed) is 15°, 5°, and 17° (bed=15°, 5°, 17°) respectively at Bishamon, Akamatsu, and Ichinose landslides.
6. Consideration
6.1 Consideration of simulation results
In this section, the simulation results at two of the simulation sites, Akamatsu and Bishamon, are reviewed because, unlike the simulation site at Ichinose that formed the landslide dam profile, the fronts of the deposits of these two sites were not blocked and they have similar slope gradients on their landslide areas and on the slopes of their lower diposit areas.
The phenomena in both sites feature rich water content in the mass of slid soil and rock and weathering soil and rock, resulting in it being
Table 3 Parameters set for simulation Fig. 4 Parameter study cases and results
0 50 100 150
0 100 200 300 400 500
beforethelandslide afterthelandslide
f bed=10㼻f int=25㼻 ?=0.2 f bed=15㼻f int=25㼻 ?=0.2 f bed=20㼻f int=25㼻 ?=0.2
Fig. 6 Cross section of simulation results, Akamatsu Fig. 5 Cross section of simulation results, Bishamon
Fig. 7 Cross section of simulation results, Ichinose
50 100 150 200
0 100 200 300 400 500 600
beforethelandslide landslidesurface afterthelandslide f bed=5㼻f int=25㼻?=0.2
100 200 300 400 500
0 100 200 300 400 500 600 700 800
beforethelandslide afterthelandslide
f bed=12㼻f int=25㼻 ?=0.2 f bed=17㼻f int=25㼻 ?=0.2 f bed=22㼻f int=25㼻 ?=0.2
35 40 45 50 55
250 300 350 400 450 500
㻸㼍㼚㼐㼟㼘㼕㼐㼑 㼚㼍㼙㼑
㼎㼑㼐㻌㼒㼞㼕㼏㼠㼕㼛㼚 㼍㼚㼓㼘㼑 㻧䃥㼎㼑㼐
㼐㼕㼒㼒㼑㼞㼚㼏㼑㻌㼎㼑㼠㼣㼑㼑㼚㻌㼠㼔㼑 㼍㼏㼠㼡㼍㼘㻌㼍㼚㼐㻌㼏㼡㼘㼡㼘㼍㼠㼕㼛㼚
㻌㻔㼙㻕
㼟㼘㼛㼜㼑㻌㼍㼚㼓㼘㼑㻌㼛㼒㻌㼐㼑㼜㼛㼟㼕㼠㻌㼍㼞㼑㼍
㻝㻜㼻 㻣㻤
㻝㻡㼻 㻜
㻞㻜㼻 㻙㻡㻟
㻡㼻 㻤
㻝㻜㼻 㻙㻢㻡
㻝㻞㼻 㻝㻟
㻝㻣㼻 㻜
㻞㻞㼻 㻙㻝㻡
㻮㼕㼟㼔㼍㼙㼛㼚 㻭㼗㼍㼙㼍㼠㼟㼡 㻵㼏㼔㼕㼚㼛㼟㼑
㻠㼻 㻠㼻
㼎㼘㼛㼏㼗㼑㼐㻌㼎㼥㻌㼠㼔㼑㻌㼛㼜㼜㼛㼟㼕㼠㼑㻌㼟㼘㼛㼜㼑
susceptible to the crushing of soil and rock. It is believed that the conditions to transfer from “slide”
to “flow” were met. However these two sites exhibited a stark difference in the highly reproducible bed friction angle (bed), which was 5°
and 15° respectively at Akamatsu and Bishamon.
Also these two sites are different in the location on the slope where the landslide started (Fig. 5 and Fig.
6).
Since the Zhang model is one that handles momentum, the bed friction angle (bed) need not be varied based on the location on the slope where the landslide started. As has already been described, this model cannot fully evaluate the internal dissipation stress of the “flow” type of landslide due to the internal friction angle (int). Thus it is understood that the influence of such parameters like internal friction angle (int) that control the intrinsic behaviors of landslide motion is exhibited in the form of the difference in bed friction angle (bed).
6.2 Consideration of the use of this simulation to damaged area prediction
In applying this simulation process to predict expected damaged area, it is necessary to define the policy for setting the internal friction angle (int).
In an urgent prediction of damaged area involving human life, the parameters used in the simulation have to be set without the results of time-consuming investigations. In uncertain and urgent situations, it is important not to underestimate damage. As described above, this simulation model does not always fully express the “flow” type phenomenon.
Also note that with the present knowledge it is impractical to use different parameters for the different conditions (e.g., materials and weathering status) of the base rock in urgent and critical evaluations.
Therefore, it is desirable as a tentative approach to set the bed friction angle (bed) at 5° in order to make sufficiently safe predictions. For such parameters as pore water pressure coefficient () and bed friction angle (bed), it is also desirable to
set appropriate values based on estimation using precipitation and the gradient of the ground surface at the landslide site because the results of detailed investigations will not be available.
7. Issues
7.1 Issues related to the application of this simulation to urgent events
In Japan, some of the phenomena that have been classified as “landslides” as described above are controlled under the disaster prevention activities set forth in the “Landslide Prevention Act” depending on the state of activity and the property of the phenomenon. In some of the landslide areas controlled under disaster prevention activities, we can be observed slow topographical behaviors. And if sudden topographical activity is observed, it comes with warning signals like the expansion of cracks in most cases. In order to detect these phenomena and to prevent disasters, some of these sites are heavily monitored.
Among the “landslide” sites whose data was collected in this study, the warning signals were observed by extensometer at Ichinose, which had experienced a landslide. With the proper distribution of measurement equipment, it is expected that the zone of sliding blocks and the start of sliding could be identified by the warning signals, allowing this simulation model to be practically applicable.
On the other hand, some of the collected
“landslide” examples include “primary landslide”
examples. Akamatsu did not have landslide topography, but several days before the day of the landslide, cracks were observed. It can be said that when a phenomenon is primary, the zone of sliding blocks and the time when the phenomenon would occur will be difficult to predict.
7.2 Issues related to the phenomenon to be simulated
In Japan, long-runout landslides rarely occur.
Thus, it should be remembered that, except for the simulation of urgent situations, this simulation of the long-runout landslide may result in overestimation. And in cases of urgent prediction, the possibility of a long-runout landslide occurrence should be investigated seriously. In this study, it is shown that in a lot of the collected landslide examples, the landslide travels a “long distance”
when the landslide transfers from the “slide” to the
“flow” type. It was reported in a past study [NILIM., 2011] that a landslide transfers from the
Table 4 Results of numerical simulation
“slide” to the “flow” type when the sediment volume is more than 10,000m3; the bedrock has a small grain diameter; the water content of the bedrock is more than 16%; and the slope gradient is more than 20°. It is necessary to investigate the applicability of the simulation process by using the outcomes of this study considering past studies as referred above. As for the phenomenon classified as
“debris flow” not supported by this simulation process, it will be necessary to study this phenomenon taking torrent into account. That is, it will be necessary to consider whether there is a torrent in the landslide deposit area or not.
7.3 Issues related to improvement of the model This simulation process does not fully evaluate the internal dissipation stress of the “flow” type of landslide. Therefore, it is important to make a model that can properly evaluate internal dissipation stress in the “flow” type of landslide in order to improve prediction accuracy and avoid overestimation. It is also important to accumulate reproduction calculation examples.
8. Conclusion
The results and issues of this study are summarized as follows:
(1) Even though long-runout landslides rarely occur in Japan, 23 long-runout landslide examples were confirmed in past literature.
(2) It was confirmed that, in most of these examples, the soil and rock changed their type of movement from the “slide” type to the “flow” type.
(3) The Zhang’s simulation model was used to predict damaged area of the long-runout landslide and from the sensitivity analysis of parameters in the model, it was understood that the bed friction angle (bed) is an important parameter which has a large influence on simulation results.
(4) The process of setting the bed friction angle (bed) in predicting landslide spread was studied with the reproduction calculation. This reproduction calculation revealed that it was desirable as a tentative approach to set this angle to 5° (bed=5°) in order to make sufficiently safe predictions.
(5) The issues in applying the simulation process to landslide dameged area prediction were shown to be that serious consideration is required to simulate
phenomena like debris flow and so on, and that proper evaluation of internal dissipation stress is required to improve prediction accuracy.
ACKNOWLEDGMENTS: In the course of this study, we have received valuable advice about how to set up the constitutive equations for the simulation model from Dr. Zhang and Moriya of Okuyama Boring Co., Ltd. Also Dr. Yoshimatsu of the Japan Association for Slope Disaster Management (General Incorporated Association) kindly provided us with valuable advice and counsel throughout the study. We sincerely appreciate their cooperation.
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㸫
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