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Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes

  • Rong Zhou EMAIL logo
Published/Copyright: November 4, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

Earthquake-triggered landslides are one of the most significant hazards worldwide. These landslides, involving small or large volumes, can develop debris flows or avalanches with high mobility and long runout distances. The motion processes of this type of landslide are also quite complicated. This study reproduced the motion processes of two landslides using the LS-RAPID program. The Aso-Bridge landslide occurred on April 16, 2016, and it featured rapid movement and a long runout distance. The Kataragai landslide occurred on April 9, 2018, and it traveled with a flow-like behavior. This work presents the features and mechanical parameters of these two landslides by means of survey investigations and laboratory experiments. We established pre-failure models and reproduced motion processes of these two landslides using the LS-PAPID program. Two indicators, consisting of the sliding speed and traveling morphology at different intervals, were considered to describe the motion processes. Results indicated that the resultant maximum sliding speeds of these two landslides were approximately 297.1 and 43.6 m/s, respectively, and that post-failure morphologies were consistent with those observed in field investigations.

Keywords: earthquake-induced landslides; rapid and long runout; flow-like movement; computer simulation; motion process

1 Introduction

Earthquake-triggered landslides, characterized by their high velocity and long runout distances, are one of the most serious hazards occurring worldwide [1,2]. Landslides of this type not only cause catastrophic damage to nearby infrastructure and scenic areas but also result in a larger number of victims and more significant economic losses [3,4,5,6,7,8,9,10,11]. For example, the 2016 Kumamoto earthquakes contained two foreshocks and one main shock that occurred consecutively at 21:26 on April 14, 00:03 on April 15, and 01:25 on April 16, 2016 (Japan standard time, JST). This event had both direct and indirect impacts, resulting in 273 deaths and 2,809 injuries, along with complete or partial destruction of a significant number of houses, public buildings, roads, and bridges [12]. This main shock was the cause of all the co-seismic landslides, including the Aso-Bridge landslide [13], which was the largest of all the landslides, characterized by rapid velocity and long-runout distances [14], and was identified as a debris avalanche [15]. The 2018 Western Shimane earthquake, with a magnitude of M6.1 (JMA), struck the Oda City, Shimane Prefecture, Japan, at 01:32 on April 9, 2018 (JST). This earthquake caused nine injuries and completely or partially destroyed 74 houses [16]. Although this earthquake only triggered two landslides: the Kataragai and Shizumi landslides [17], the Kataragai landslide was notable for its flow-like movement and a sliding distance of 250 m [18].

A quantitative hazard assessment focused on rapid and long runout landslides is necessary to predict the characteristics and extent of their motion. Numerical simulation is commonly regarded as an effective method for conducting back analyses of post-failure motions of such landslides. For example, DAN and DAN3D, two computer-based dynamic simulation programs, were applied to perform back analyses of motions of landslides [19]. DAN3D was also used to simulate runout behaviors of catastrophic landslides induced by heavy rainfall [20]. LS-RAPID, a landslide simulation program, was designed to simulate initiations and motions of rapid and long runout landslides triggered by earthquakes, rainfall, and/or their interactive effects [21].

Although LS-RAPID and the DAN model enable the simulation of post-failure motion characteristics of landslides, a major difference between them lies in the determination of input parameters. LS-RAPID requires mechanical parameters from ring shear tests on samples to reflect the changes in both shear resistance and pore water pressure buildup along the sliding surface. In contrast, the DAN method uses the optimum parameters to run a batch of simulations to achieve results consistent with observed outcomes.

LS-RAPID has been widely applied to simulate the initiation and motion of earthquake-/rainfall-induced rapid landslides [21,22,23,24,25,26,27,28,29,30,31]. These studies have emphasized the crucial and fundamental role in determining appropriate soil parameters. Some soil physical parameters, such as the initial apparent friction, accumulation possibility, excess pore pressure, lateral earth pressure coefficient, and unit weight of sliding soil, depend on the results of field investigations and general laboratory experiments. Other parameters, including the effective friction coefficient of sliding soil, shear resistance of sliding soil in the steady state, and cohesion inside sliding soil, are determined by means of ring shear tests.

The purpose of this study was to investigate the motion processes of the Aso-Bridge and Kataragai landslides using the LS-RAPID program. Field investigations and general laboratory experiments on the Kataragai landslide were carried out to obtain partial physical parameters of sliding soil. This study did not perform field investigations and laboratory experiments on the Aso-Bridge landslide; however, to obtain mechanical parameters, results of field investigations and laboratory experiments were described from previous studies [26,32,33]. Other parameters were determined based on results of ring shear tests [26] and recommended values of mechanical parameters [21]. To establish the pre-failure morphologies of these landslides, DEM data were downloaded from the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php), and all of the pertinent soil parameters were applied in this program. During the simulation process, the sliding velocity and motion features of these two landslides were recorded at different time intervals. The validity of the parameters used in the program was confirmed by comparing the post-failure features of the simulations with those observed in the field investigations.

1.1 Geological setting

The 2016 Kumamoto earthquakes occurred beneath the Kumamoto city, Kumamoto Prefecture, Kyushu Island, Southern Japan (Figure 1), which contains Aso caldera, one of the largest volcanic calderas in the world [34]. This caldera was formed by four instances of volcanic activity, and its four pyroclastic flow deposits are known as Aso-1 (270 ka), Aso-2 (140 ka), Aso-3 (120 ka), and Aso-4 (90 ka), in the ascending order [34,35]. The main shock triggered all the co-seismic landslides, which accounted for at least 10 of the 69 fatalities associated with the 2016 Kumamoto earthquakes [36]. One person was reported missing immediately following the Aso-Bridge landslide [15], which occurred on the steep western wall of the Aso caldera, where the pyroclastic soil is prone to failure during seismic shaking and rainfall [33,37].

Figure 1 Locations of main shocks and fault zones in Kumamoto City, Japan (base map from the Geospatial Authority of Japan).
Figure 1

Locations of main shocks and fault zones in Kumamoto City, Japan (base map from the Geospatial Authority of Japan).

Figure 2 depicts the lithologies of the Minamiaso village and Aso city, based on a 1:200,000 seamless geological map published by the Geological Survey of Japan (AIST). The basement complex of the post-caldera central cones is mainly covered by Holocene mafic volcanic rocks and Late Pleistocene felsic volcanic rocks and mafic volcanic rocks, while the western wall of the Aso caldera is composed of Middle Pleistocene mafic volcanic rocks and pyroclastic flow volcanic rocks. Additionally, the basement rocks of the Aso-Bridge landslide area consist of Middle Pleistocene mafic volcanic rocks and Late Pleistocene mafic volcanic rocks. The soil layers overlain the complex on the Aso-Bridge landslide consisted of medium brown cohesive soil with fewer lapilli and blocks, black cohesive soil, medium brown lapilli and blocks, black cohesive soil, and medium brown cohesive soil with fewer lapilli and blocks in an ascending order [33]. These cohesive soils are derived from the Aso-3 and Aso-4 Formations [26] and have a porous structure which enables the absorption and infiltration of water into the lower layer, weakening the shear resistance of slip soil.

Figure 2 Lithologies of Minamiaso village and Aso city, based on a 1:200,000 seamless geological map published by the Geological Survey of Japan (AIST) Authority of Japan Earth.
Figure 2

Lithologies of Minamiaso village and Aso city, based on a 1:200,000 seamless geological map published by the Geological Survey of Japan (AIST) Authority of Japan Earth.

The 2018 Shimane earthquake occurred in Oda city, Shimane Prefecture, located in the Yamain area on the southeast coast of the Sea of Japan in Western Honshu (Figure 3). Figure 4 delineates the lithology of this area based on a 1:200,000 seamless geological map published by the AIST. It can be seen from this figure that the lithology of the landslide-affected area was successively covered by Miocene marine and non-marine sediments. Its neighboring areas consist of Pleistocene marine and non-marine sediments, Miocene mafic plutonic and volcanic rocks, and Middle Eocene granite.

Figure 3 Aerial view of the region indicating the location of the earthquake epicenter and the Kataragai landslide (base map from Google Earth).
Figure 3

Aerial view of the region indicating the location of the earthquake epicenter and the Kataragai landslide (base map from Google Earth).

Figure 4 Lithologies of Minamiaso village and Aso city, based on a 1:200,000 seamless geological map published by the Geological Survey of Japan (AIST) Authority of Japan Earth.
Figure 4

Lithologies of Minamiaso village and Aso city, based on a 1:200,000 seamless geological map published by the Geological Survey of Japan (AIST) Authority of Japan Earth.

1.2 Preceding rainfall

Rainfall preceding an earthquake is commonly associated with failure mechanisms of earthquake-induced landslides. This is because as rainfall infiltrates the soil, it increases the water content and reduces the shear resistance of the soil, potentially leading to soil failure. Landslide soils with high water content can move greater distances than those that are dry [38]. Figure 5 shows the daily and cumulative precipitation for Oda city from March 10, 2018, to April 11, 2018, and for Minamiaso village from March 14, 2016, to April 18, 2016. Prior to the earthquake, the 3-, 10-, and 30-day cumulative precipitation at the Oda city and Minamiaso village reached 19.5, 33, and 150.5 mm and 4, 63, and 100 mm, respectively. The cumulative precipitation prior to the Kataragai and Aso-Bridge landslides did not differ significantly; however, the hydrological conditions preceding the Kataragai landslide were much worse than those experienced at the Aso-Bridge landslide. This was because the annual precipitation in Oda city, based on precipitation data from 2008 to 2018, was approximately 1,752 mm.

Figure 5 Daily and cumulative precipitation at the Oda city from March 10, 2018, to April 11, 2018, and at the Minamiaso village from March 14, 2016, to April 18, 2016.
Figure 5

Daily and cumulative precipitation at the Oda city from March 10, 2018, to April 11, 2018, and at the Minamiaso village from March 14, 2016, to April 18, 2016.

2 Methods

2.1 Field investigation

Field investigations were carried out, including observations of the geological conditions, determination of the landslide boundary, measurement of the landslide’s longitudinal profile, portable dynamic cone penetration tests, hardness tests, and sampling. The locations of the portable dynamic cone penetration tests (1–13), hardness tests (14, 15), and sampling (16) are marked, and apparatuses of the portable dynamic cone penetration tests and hardness tests are shown in Figure 6. The longitudinal profile of the post-failure slope was measured using a laser range finder, a ranging rod, an inclinometer, and a GPS tracker. The portable dynamic cone penetration test was used to mark the locations of the water table and determine the sliding surface [33,37]. This test was performed in two steps: a 5 kg hammer was dropped freely from a height of 0.05 m into the soil, and the counts of the dropped hammer to each 0.01 m depth of the portable dynamic cone tip were recorded simultaneously [37,39]. The correlation between the depth and compressive strength was presented using the hardness test [40]. The formula for this test is expressed as follows:

(1) q u = 1 , 000 x / 0.7952 ( 40 − x ) ,

where q u is the uniaxial compressive strength (kPa) and x is the reading of the hardness tester inserted into the soil (mm).

Figure 6 (Left) Redrawn general aerial view of the pre-failure Kataragai landslide. (Right) Apparatus of the penetration test (a) and hardness test (b). (https://link.springer.com/article/10.1007/s10346-020-01401-x) [17].
Figure 6

(Left) Redrawn general aerial view of the pre-failure Kataragai landslide. (Right) Apparatus of the penetration test (a) and hardness test (b). (https://link.springer.com/article/10.1007/s10346-020-01401-x) [17].

2.2 Laboratory method

General laboratory experiments were performed according to the requirements of Standards of the Japanese Geotechnical Society [41] to obtain the physical properties and for classification of materials from the slip layer. According to the above conditions, tests on the water content, specific gravity, and grain size distribution were conducted. Additionally, equations of the uniformity coefficient ( C u ) , curvature coefficient ( C v ) , and permeability coefficient (k) of soil are expressed as follows:

(2) C u = D 60 D 10 ,

(3) C v = D 30 2 D 10 × D 60 ,

(4) k = 100 D 10 2 × 1 100 ,

where D 10 , D 30 , and D 60 represent the grain size associated with a grain content greater than 10, 30, or 60%, respectively.

2.3 LS-RAPID program

The LS-RAPID program is commonly used to simulate the initiation and motion of landslides caused by earthquakes, rainfall, and/or their interacting effects [21]. The principle of this program lies in the derivation of the motion equation from the equilibrium of forces acting on a soil column, and the continuum equation from fluid dynamics [42]. Before setting up the program, the critical mechanical parameters, consisting of the apparent friction angle ( φ a ) , shear resistance in the steady state ( τ ss ) , and accumulation possibility excess pore pressure ratio (B ss), are necessary to be determined.

The apparent friction angle (φ a) was introduced to describe the relationship between the friction angle of the sliding surface and the built-up pore water pressure during shearing [43]. This angle is related to the average coefficient of friction, which is the ratio between the total height (H) and the total travel distance (L) [44].

According to the scheme of Varnes [45], the sliding zone comprises two layers: the debris layer (upper layer) and the sliding layer (lower layer). During motion, the thickness of the debris layer gradually decreases until the sliding mass reaches a distance where the shear resistance (τ ss) is stable, which can be directly measured using the ring shear apparatus [46].

The B ss parameter is significantly related to the groundwater situation and the drainage conditions of the sliding zone. The quantification of this parameter is performed by three types (A-type, B-type, and C-type) to simplify the failure behaviors and changing pore water pressure during shearing. In the A-type, the sliding soil remains dry and cannot build up pore water pressure during shearing. The shear resistance and apparent friction angle are equivalent to the residual strength and residual friction angle at a steady state, respectively. In the B-type and C-type, the sliding soil is saturated and can build up pore water pressure. The significant difference between these two types is the speed of pore water pressure generation and dissipation. The former has a generating speed that is significantly greater than the dissipating speed, which can form an undrained state, while the latter has a generating speed lower than the dissipating speed, which is referred to as a partially drained state. Based on the three types, this parameter ranges from 0 to 0.1 if the unsaturated sliding mass moves on a fully dry surface under the A-type. If the saturated sliding mass flows on an impermeable surface under the C-type, its range is 0.9–1.0. If the saturated sliding mass moves on a permeable surface or the unsaturated sliding moves on a fully saturated surface under the B-type, its range is 0.1–0.9.

3 Results

3.1 Observations of field investigation

Figures 7 and 8 show the general aerial view (post-failure) and observations of the geological conditions of the Kataragai landslide, respectively. From Figure 7, it can be observed that three ponds on the crown were unchanged, and one pond in the source area was destroyed. Sandy gravel soil mixed with water was exposed near the destroyed pond (Figure 8a), making it easy to get stuck in the soil (Figure 8b). Traces of water seepage and flow were also observed (Figure 8c), and the deposition area shows soil mantled with water (Figure 8d). During the sampling process, a phenomenon was observed in which a spring gushed out of the sliding layer. These observations confirmed that the four ponds being full of water contributed to the sliding mass being in a long-term state of saturation before the earthquake. Furthermore, the saturated state of pre-failure slip layer was verified by means of the self-potential tests in the field [17]. Thus, the hydrological condition of pre-failure slope was quite poor.

Figure 7 General aerial view of the Kataragai landslide (post-failure).
Figure 7

General aerial view of the Kataragai landslide (post-failure).

Figure 8 Photographs of the Kataragai landslide: (a) exposed slide area, (b) stuck in the soil, (c) water flow track, and (d) deposition area.
Figure 8

Photographs of the Kataragai landslide: (a) exposed slide area, (b) stuck in the soil, (c) water flow track, and (d) deposition area.

Figures 9 and 10 show the general aerial view and contour map, as well as the soil layer and the gorge of the Kurokawa River, respectively. Figure 9 shows that the Aso-Bridge landslide destroyed National Route 57, the JR Hohi Railway, and the underground water supply channel, and collapsed the 200 m long Aso–Ohashi Bridge that spans an 80 m deep gorge of the Kurokawa River near the Aso caldera [32]. The water seeping out of the failure surface was also found within 1 day after the failure of the slope. Additionally, the sliding mass of the Aso-Bridge landslide had a volume of approximately 1,830,000 m3 [33], with most of it sliding into the Kurokawa River, preventing the formation of a landslide dam [47]. Moreover, the small mass of debris from the source area had travelled to the opposite riverbank of the Kurokawa River prior to the collapse of the Aso Bridge [48]. This suggests that the driving force and/or weight of the sliding mass directly contributed to the collapse of the Aso Bridge, and it could also be attributed to the high velocity of the sliding mass, resulting in the building up of pore water pressure during shearing that could not be immediately expelled. As noted previously, the bedrock of the affected landslide area is primarily composed of lava and volcanic rocks from eruptions that occurred approximately 90,000 years ago [33]. These rocks weathered to form cohesive soil with porous voids, which can absorb and infiltrate water into the lower layer of the soil, reducing its resistance strength.

Figure 9 The Aso-Bridge landslide: (a) general aerial view and (b) redrawn contour map [32].
Figure 9

The Aso-Bridge landslide: (a) general aerial view and (b) redrawn contour map [32].

Figure 10 Photographs of the Aso-Bridge landslide: (a) soil layers and (b) gorge of the Kurokawa River.
Figure 10

Photographs of the Aso-Bridge landslide: (a) soil layers and (b) gorge of the Kurokawa River.

3.2 Characteristics of landslides

Figure 11 presents the longitudinal pre-failure and post-failure profiles of the Kataragai landslide. As shown in the figure, the landslide had an apparent friction angle of 7°, indicating high mobility. Additionally, the landslide traveled approximately 250 m, and its sliding direction was altered due to changes in topography during the motion process. According to the field investigations of the Aso-Bridge landslide [33], the slope angle was 38.6°, and the apparent friction angle was estimated to be approximately 23°. The Aso-Bridge landslide moved approximately 705 m and did not change its sliding direction during the motion process.

Figure 11 Longitudinal profile of the Kataragai landslide [17].
Figure 11

Longitudinal profile of the Kataragai landslide [17].

3.3 N d value and uniaxial compressive depth

Figures 12 and 13 show the relationship between the N d value and depth. From Figure 12, it can be seen that except for location 3, all the N d values (locations 1–10) at depths of 0–2 m were less than 10, indicating that the sliding mass was very loose and had a relatively low shear resistance [40,49]. The N d value at location 3 at a depth of 1 m was approximately 16, which might have been related to the little sliding mass deposited there due to the change in the sliding direction. Notably, only location 9 showed the position of the sliding surface, which was at a depth of 1.8 m. From Figure 10, the results of locations 11–13 showed the position of the sliding surface at depths of approximately 1.65 and 1.6 m, respectively, where values of N d were 21 and 16, respectively. The N d values increased dramatically at depths greater than 1.5 m. Therefore, the position of the sliding surface of the Kataragai landslide is thought to be at a depth of 1.6–1.8 m. However, the sliding surface of the right flank of the Aso-Bridge landslide was at a depth of 6.5 m with the N d value of approximately 20 kPa [33]. Therefore, the Aso-Bridge landslide had more potential energy to transform into kinetic energy, stronger geological conditions, and higher shear resistance than the Kataragai landslide.

Figure 12 Results of the portable dynamic cone penetration test in the study area.
Figure 12

Results of the portable dynamic cone penetration test in the study area.

Figure 13 Results of the portable dynamic cone penetration test on the crown.
Figure 13

Results of the portable dynamic cone penetration test on the crown.

Figure 14 shows the diagram of uniaxial compressive strength versus depth. The uniaxial compressive strength values at location 14 were relatively small (less than 500 kPa) at different depths, and the uniaxial compressive strength values at depths of 1.65–1.85 m at location 15 reached the lowest ranges.

Figure 14 Uniaxial compressive strength versus depth in the main scarp.
Figure 14

Uniaxial compressive strength versus depth in the main scarp.

3.4 Physical properties and classification

Figure 15 delineates the grain size distribution for the sample taken from the sliding layer of the Kataragai landslide. From the figure, the sample with gravel, sand, and fine contents of about 15, 76.5, and 8.5%, respectively, according to the JGS [41], was classified as gravel sand with fines (SG-F). In contrast to the former, the sample from the sliding layer of the Aso landslide with sand and fine contents of about 96% and 4% [26], respectively, was classified as sand (S). According to equations (2)–(4), the uniformity coefficient, curvature coefficient, and permeability coefficient were calculated. Table 1 presents physical properties of samples from the Kataragai and Aso-Bridge landslides. From the table, the permeability coefficient of sample from the Kataragai landslide was 1.21 × 10−2 m/s, indicating that the sliding layer was highly permeable. Contrary to the former, the permeability coefficient of the samples from the Aso-Bridge landslide was 7.29 × 10−4 m/s, indicating that the sliding layer was less permeable.

Figure 15 Grain size distribution of the sample taken from the sliding layer.
Figure 15

Grain size distribution of the sample taken from the sliding layer.

Table 1

Physical parameters of samples taken from the sliding layer

Depth (m) D 10 (mm) D 30 (mm) D 50 (mm) D 60 (mm) C u C c
Kataragai landslide 0.110 0.418 0.677 0.807 7.34 1.97
Aso-Bridge landslide 0.027 0.069 0.230 0.106 8.52 0.767

Table 2 summarizes the different features of the Aso-Bridge and Kataragai landslides obtained utilizing field investigations and laboratory experiments. By comparison, the overall relief (highest lowest elevation) of the Aso-Bridge landslide was ten times that of the Kataragai landslide, and the unidirectional sliding of the Aso-Bridge landslide during its movement, which explained the reason for its more rapid motion speed. Besides, based on the difference in sliding-surface liquefaction and structure collapse of landslide [50], the sliding-surface liquefaction mechanism of the Aso-Bridge landslide suggested that it was able to travel longer distances than the Kataragai landslide. However, the Kataragai landslide, with its much lower apparent friction angle value of about 7°, exhibited high mobility, enabling it to travel 250 m.

Table 2

Selected ranges for the mechanical parameters of the Aso-Bridge and Kataragai landslides in the LS-RAPID models [21]

Key parameters Sassa’ value Estimated value
Lower Upper Probable Aso-Bridge landslide Kataragai landslide
Friction angle during motion at the sliding surface ( φ m ) and coefficient ( tan φ m ) 25° (0.466) 35° (0.700) 30° (0.577) 30° (0.577) 35° (0.700)
Cohesion during motion at the sliding surface ( c m ) (kPa) 0.1 0.5 0.2 0.0 0.0
Peak friction angle at the sliding surface ( φ p ) and coefficient ( tan φ p ) 33° (0.466) 38° (0.700) 35° (0.577) 30° (0.577) 35° (0.700)
Peak cohesion at the sliding surface ( c p ) (kPa) 2 200 10-100 0.0 0.0
Friction angle inside the landslide mass ( φ i ) and coefficient ( tan φ i ) 20° (0.364) 30° (0.577) 25° (0.466) 30° (0.577) 35° (0.700)
Cohesion inside the landslide mass ( c i ) (kPa) 0.1 0.5 0.2 0.0 0.0
Shear resistance of the sliding surface in the steady state ( τ ss ) (kPa) 5 50 20 50 20

3.5 Parameter settings

The LS-RAPID program was used to simulate the motion processes of the Aso Bridge and Kataragai earthquake-triggered landslides. The topographic data, including the elevations required to determine slopes and sliding surfaces, were essential for establishing the pre-failure models of these landslides. DEM data for both landslides were downloaded from the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php). Various data, such as point data, mesh data, axial and mesh settings of the calculation area, and the possible maximum number of control points, provided input for the program.

To establish the pre-failure morphologies of the Aso-Bridge and Kataragai landslides, DEMs with different resolutions were downloaded. A 10 m grid resolution DEM was utilized for the Aso-Bridge landslide, with the following axial settings: x-direction – min. = 0.000 m, max. = 1,010 m; y-direction – min. = 0.000 m, max. = 570 m. This resulted in a total of 102 (x-direction) × 58 (y-direction) grid cells. Conversely, a 5 m grid resolution DEM was downloaded for the Kataragai landslide, with the axial settings of x-direction – min. = 4,481 m, max. = 4,964 m; y-direction – min. = 3,140 m, max. = 3,599 m. This also resulted in 162 (x-direction) × 154 (y-direction) grid cells. The pre-failure models for both landslides were established and are shown in Figure 16, with the original sliding mass area represented by the open white polygon in the models.

Figure 16 Establishment of 2D and 3D models using LS-RAPID: (a) Aso-Bridge landslide; (b) Kataragai landslide.
Figure 16

Establishment of 2D and 3D models using LS-RAPID: (a) Aso-Bridge landslide; (b) Kataragai landslide.

The mechanical parameters of this program were obtained from field investigations and laboratory experiments. The range of the steady-state shear resistance ( τ ss ) has been obtained by means of a ring shear test [21]. The sliding soil of the Aso-Bridge landslide was mainly dominated by recent volcanic materials and weathered cohesive soil, with a steady-state shear resistance of 45 kPa [26]. Thus, the steady-state shear resistance of sliding soil was estimated to be 50 kPa in this study. Although the saturated sliding soil of the Kataragai landslide had a lower shear resistance due to N d < 10 with higher destabilization, according to this range of Sassa [21], its shear resistance of τ ss = 20 kPa was assigned.

The lateral pressure ratio K , an indicator, was used to describe the “softness” or “potential for lateral spreading” of a sliding mass [51] and was associated with the lateral pressure ratio at rest [52]. This value was quantified by the apparent friction angle within the soil mass [42], which is expressed as equation (5):

(5) K = 1 − sin φ ,

where φ is the apparent friction angle and K is the lateral pressure ratio.

The field survey measured the apparent friction angles of 23° and 7° for the Aso-Bridge and Kataragai landslides, respectively. Using equation (5), these values were used to obtain the respective apparent friction coefficients of 0.61 and 0.88. Typically, the lateral pressure ratio can be calculated based on the apparent friction coefficient, but due to the saturated state of the sliding mass in both landslides, a lateral pressure ratio range of 0.65–0.85 was assumed [21]. Thus, the Kataragai and Aso-Bridge landslides were assessed to have lateral pressure ratios of 0.7 and 0.85, respectively.

The parameter B ss is influenced by the groundwater situation and drainage conditions of the sliding zone. For the Aso-Bridge landslide, a spring seeping from the sliding surface was observed [26], indicating saturation before slope failure. The sliding zone soil was predominately composed of recent volcanic soil and weathered volcanic cohesive soil, which had low permeability and led to a high pore pressure generation rate ( B ss ) of 0.8. Initially, the estimated value of the parameter B ss for the Kataragai landslide was 0.2, which was because the saturated sliding mass flowed on a permeable surface composed of gravel with fine sand. However, as the saturated sliding mass traveled a certain distance, the permeable surface transitioned into an impermeable one that was covered by pitch [17]. As a result, the parameter B ss increased to 1.0 to account for the limited drainage and high pore pressure generation during the sliding process.

Laboratory experiments determined the natural total unit weights of the sliding soil to be 20 and 18 kN/m3 for the Aso-Bridge and Kataragai landslides, respectively.

Table 3 provides the suggested range for the friction angle for the sliding zone, which varied from 25° to 35°. The friction angle of the Aso-Bridge landslide was 25° due to sliding-surface liquefaction, while the Kataragai landslide had a friction angle of 35° due to collapsed-structure liquefaction. Consequently, the friction coefficients for the sliding soil of the Aso-Bridge and Kataragai landslides were 0.577 and 0.700, respectively. In this study, the initial apparent friction angle was assumed to be equal to the measured apparent friction angle in the field. The Aso-Bridge and Kataragai landslides had measured apparent friction angles of 23° and 7°, respectively, corresponding to apparent friction coefficients of 0.4 and 0.1, respectively. Based on the soil composition of the Aso-Bridge and the Kataragai landslides, the soil cohesion within the landslide mass was assumed to be 0 kPa. Table 4 summarizes all the mechanical parameters of the Aso-Bridge and Kataragai landslides used in the LS-RAPID program.

Table 3

Different features of the Aso-Bridge and Kataragai landslides

Features Aso-Bridge landslide Kataragai landslide
Scale Large Small
Topography Elevation 385–725 m 199–240 m
Apparent friction angle 23° 7°
Free space condition in front of the landslide foot Good moving space due to the deep gorge Good moving space due to the farmland and wasteland
Soil composition Recent volcanic and weathered volcanic cohesive soil Sand with gravel
Motion features Sliding distance More than 700 m 250 m
Sliding direction S61°E SW
Sliding speed Rapid Slow
Landslide classification Slide Flow-slide
Liquefied mechanism Sliding surface [33] Structure collapse [18]
Table 4

Required mechanical parameters of the LS-RAPID models used for the Aso-Bridge and Kataragai landslides

Parameters Value
Aso-Bridge landslide Kataragai landslide
Shear resistance in the steady state ( τ ss ) (kPa) 50 20
Lateral earth pressure ratio 0.70 0.85
Pore pressure generation rate B ss 0.8 0.2/1.0
Natural total unit weight of the soil mass ( γ t ) (kN/m3) 20 18
Friction coefficient in the sliding zone tan φ m 0.577 0.700
Initial apparent friction coefficient tan φ i 0.4 0.1
Cohesion inside the sliding mass (kPa) 0 0

4 Discussion

To understand the motion process of the Kataragai and Aso-Bridge landslides, the sliding speeds and landslide features of the total motion processes are discussed in this section. Figure 16 illustrates the establishment of the 2D and 3D pre-failure morphologies of the two landslides. The models showed that the total volumes of the sliding mass (red solid circles) were 588,681 and 5,726 m3, respectively. Mechanical parameters were utilized to reproduce the motion processes of the Aso-Bridge and Kataragai landslides. Subsequently, the simulation results, such as the velocities in the x- and y-directions and the motion characteristics of the sliding mass, were recorded at different time intervals. The resultant velocity, which combined with the velocities in the x - and y -directions, was defined as the comprehensive reflection of the whole landslide’s velocity. Figures 17 and 19 depict various velocities (U max, V max, and Resultant) of the Aso-Bridge landslide and the Kataragai landslide during its motion. From the figure, the entire motion process lasted for 47.8 s. As the motion time was 24.8 s, the maximum resultant velocity peaked at 297.1 m/s. Compared to the Aso-Bridge landslide, it had a total motion time of approximately 48.9 s, and the maximum resultant velocity peaked at 43.6 m/s. This was due to a lower driving force, smaller volume of sliding mass, and gentle slope, despite the geological and hydrological conditions being much worse than those of the Aso-Bridge landslide. Notably, before the motion time approached 11.5 s, the difference in velocities of the two landslides was not significant. When considering only the slope angle and the depth of the sliding layer, the difference in velocities of two landslides should be significant since the Aso-Bridge landslide occurred on the steep slope, with a sliding layer depth of approximately 6.5 m, while the Kataragai landslide occurred on the gentle slope, with a sliding layer depth of about 1.8 m. This indicated that the sliding mass was affected by the shear resistance ( τ ss ) of the saturated sliding layer and water. The shear resistance of the saturated sliding layer for the Kataragai landslide was 20 kPa, whereas it was 50 kPa for the Aso-Bridge landslide. Additionally, four ponds were located at the Kataragai landslide, contributing to its high mobility; thus, the Kataragai landslide exhibited flow-like characteristics.

Figure 17 Various velocities of the Aso-Bridge landslide after slope failure. U is the velocity in the x-direction and V is the velocity in the y-direction.
Figure 17

Various velocities of the Aso-Bridge landslide after slope failure. U is the velocity in the x-direction and V is the velocity in the y-direction.

For the Aso-Bridge landslide, the motion went through two phases: increasing and decreasing phases.

Figure 18 shows post-failure morphologies at 11.8, 24.8, 36.8, and 47.8 s. At the motion time of 11.8 s, the velocities in the x- and y-directions increased to U max = 22.4 m/s and V max = 15.0 m/s, respectively, when some of the sliding mass in the source area (red solid circle) moved down and buried the underground water supply channel (Figure 18a). As most potential energy was converted to kinetic energy, the resultant velocities reached the maximum of 297.1 m/s. The range of velocity fluctuation was the largest during this phrase, resulting in the sliding mass accelerating downslope, destroying the JR Hohi Railway and National Road 57, with some materials being carried to the foe of the slope (Figure 18b). After that, the motion of the sliding mass entered the decreasing phase, and the velocities from different directions began to decrease. However, the cluster of the sliding mass continued to move downward on the slope due to its high kinetic energy and rushed into the deep gorge of the Kukawa River, causing the Aso Bridge to collapse (Figure 18c). At t = 47.8 s, the motion of the sliding mass gradually stabilized, and most of it was deposited downslope (Figure 18d). This final morphology of the sliding mass closely matched the post-failure geographic deposition of the actual landslide (Figure 9). During the simulation, the slope collapsed, and most of the sliding mass moved away from the main scarp and rushed into the Kukawa River due to the unchanged direction of slope. Therefore, the simulated motion of this steep slope can be identified as a typical debris avalanche.

Figure 18 Motion characteristics of the sliding mass of the Aso-Bridge landslide: (a) t = 11.8 s; (b) t = 24.8 s; (c) t = 36.8 s; (d) t = 47.8 s. The red polygon outlines the source area, and the brown polygon shows the affected area.
Figure 18

Motion characteristics of the sliding mass of the Aso-Bridge landslide: (a) t = 11.8 s; (b) t = 24.8 s; (c) t = 36.8 s; (d) t = 47.8 s. The red polygon outlines the source area, and the brown polygon shows the affected area.

Compared to the Aso-Bridge landslide, the change in the sliding direction caused the motion of this landslide to undergo three stages in this simulation: increasing, intermediate, and decreasing stages. In Figure 19, the resultant velocity in the increasing stage ranged from 0 to 33.5 m/s, followed by a velocity range of 33.5 to 22.6 m/s and then 22.6 to 43.6 m/s in the intermediate stage. Finally, the velocity of the decreasing stage reduced to 0.5 m/s. Thus, four typical motion times were represented to depict the motion features of this landslide.

Figure 19 Various velocities of the Kataragai landslide after slope failure. U is the velocity in the x-direction and V is the velocity in the y-direction.
Figure 19

Various velocities of the Kataragai landslide after slope failure. U is the velocity in the x-direction and V is the velocity in the y-direction.

As shown in Figure 20a, at t = 11.5 s, the post-initiation sliding mass in the original area (outlined by the white polygon) had completely moved away from the main scarp and bunched up along the front edge of the changing direction. The sliding mass continued to accelerate slowly and reached the toe of the slope as the motion time reached 23.6 s. During this process, the affected area (outlined by the red polygon) expanded into a deposition area where the range of motion fluctuation was large, as shown in Figure 20b. At t = 32.6 s, most of the potential energy had converted to kinetic energy, causing the velocities to increase to their maximum values. Some of the sliding mass deposited on the slope, while others flowed directly into the farmland and wasteland (Figure 20c). Furthermore, the range of motion fluctuations on the farmland and wasteland was greater than that on the slope. The sliding mass on the flat farmland and wasteland continued to move forward at decreasing velocities and ultimately stopping when the velocities approached 0.5 m/s (Figure 20d). If the sliding mass remaining on the farmland and wasteland was removed, the final morphology of the post-failure slope in the model would resemble the results of the field investigations (Figure 7). Some sliding mass mixed with water left the source area and stopped at the deposition area, which explains why the N d values of the source and deposition areas were very low.

Figure 20 Motion characteristics of the sliding mass of the Kataragai landslide: (a) t = 11.5 s; (b) t = 23.6 s; (c) t = 32.6 s; (d) t = 48.9 s. The white polygon outlines the original area of the sliding mass, and the red polygon shows the area with the sliding mass.
Figure 20 Motion characteristics of the sliding mass of the Kataragai landslide: (a) t = 11.5 s; (b) t = 23.6 s; (c) t = 32.6 s; (d) t = 48.9 s. The white polygon outlines the original area of the sliding mass, and the red polygon shows the area with the sliding mass.
Figure 20

Motion characteristics of the sliding mass of the Kataragai landslide: (a) t = 11.5 s; (b) t = 23.6 s; (c) t = 32.6 s; (d) t = 48.9 s. The white polygon outlines the original area of the sliding mass, and the red polygon shows the area with the sliding mass.

5 Conclusion

This study investigated the field features of the Kataragai landslide, triggered by the 2018 Western Shimane earthquake, and compared it with field investigations of the Aso-Bridge landslide, induced by the main shock of the 2016 Kumamoto earthquakes. Mechanical parameters were obtained from field investigations and laboratory experiments and were used in the LS-RAPID program to simulate the motion processes of these two landslides. The conclusions drawn from this study are as follows:

  1. Controlling factors, including the apparent friction angle, slope angle, hydrological conditions, change in the sliding direction, and liquefaction mechanism, contributed to significant differences in failure destructiveness, the sliding speed, motion features, and the sliding distance between these two landslides.

  2. The simulation results demonstrate that the motion features of the entire movement path of these two landslides are consistent with observations from the field investigations.

Acknowledgements

The author sincerely appreciates Fawu Wang (Tongji University, Shanghai, China), Tetsuya Sakai (Shimane University, Matsue, Japan), Ran Li and Shuai Zhang (Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing, China), and Zili Dai (Shanghai University, Shanghai, China) for their kind assistance in the field work and during COVID-2019. The author also gratefully acknowledges the financial support from the China Scholarship Council (CSC) to study in Shimane University, Japan.

  1. Funding information: This work was financed by Fundamental Research (2017-2019) of Shimane University on “Development of prediction and mitigation technologies on natural disasters in subduction zone using San-in region as a research field.”

  2. Author contributions: The author confirms the sole responsibility for the conception of the study, presented results and manuscript preparation.

  3. Conflict of interest: The author declares no competing interest.

  4. Data availability statement: The datasets associated with the slope elevations and sliding surface elevations of the Kataragai landslide and Aso-Bridge landslide were freely downloaded from the Geospatial Information Authority of Japan (https://fgd.gsi.go.jp/download/menu.php).

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Received: 2024-06-18
Revised: 2024-09-22
Accepted: 2024-09-24
Published Online: 2024-11-04

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  66. Efficient development technology of Upper Paleozoic Lower Shihezi tight sandstone gas reservoir in northeastern Ordos Basin
  67. Assessment of soil resources of agricultural landscapes in Turkestan region of the Republic of Kazakhstan based on agrochemical indexes
  68. Evaluating the impact of DEM interpolation algorithms on relief index for soil resource management
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  76. Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin
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  78. Distribution law of Chang 7 Member tight oil in the western Ordos Basin based on geological, logging and numerical simulation techniques
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  87. Prediction of formation fracture pressure based on reinforcement learning and XGBoost
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  105. Deeply buried clastic rock diagenesis evolution mechanism of Dongdaohaizi sag in the center of Junggar fault basin, Northwest China
  106. Application of LS-RAPID to simulate the motion of two contrasting landslides triggered by earthquakes
  107. The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023
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  119. Modification of dolomitization on reservoir spaces in reef–shoal complex: A case study of Permian Changxing Formation, Sichuan Basin, SW China
  120. Geological characteristics of the Daduhe gold belt, western Sichuan, China: Implications for exploration
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  126. Detection of seepage zones in artificial levees: A case study at the Körös River, Hungary
  127. Tight sandstone fluid detection technology based on multi-wave seismic data
  128. Characteristics and control techniques of soft rock tunnel lining cracks in high geo-stress environments: Case study of Wushaoling tunnel group
  129. Influence of pore structure characteristics on the Permian Shan-1 reservoir in Longdong, Southwest Ordos Basin, China
  130. Study on sedimentary model of Shanxi Formation – Lower Shihezi Formation in Da 17 well area of Daniudi gas field, Ordos Basin
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  132. Review Articles
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  137. Erratum
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  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
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  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0713
Keywords for this article
earthquake-induced landslides; rapid and long runout; flow-like movement; computer simulation; motion process
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BY 4.0

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  97. Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin
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  108. The critical role of c and φ in ensuring stability: A study on rockfill dams
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  122. Enhancing the total-field magnetic anomaly using the normalized source strength
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  136. Essential questions in earth and geosciences according to large language models
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  138. Erratum to “Random forest and artificial neural network-based tsunami forests classification using data fusion of Sentinel-2 and Airbus Vision-1 satellites: A case study of Garhi Chandan, Pakistan”
  139. Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part I
  140. Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)
  141. Exploring environmental awareness, knowledge, and safety: A comparative study among students in Montenegro and North Macedonia
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  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
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