Landslides In Mountainous Areas Research Articles

How revegetation reinforces soil at early stage of restoration: A 6‐year field study in southwest China

AbstractBackground and aimsRestoring vegetation on hillslopes has been found to increase soil strength, thereby reducing the risk of soil erosion and shallow landslides. However, limited information is available on the temporal changes in root biomechanical traits and increased soil shear strength related to vegetation growth following restoration with different species.MethodsIn 2012, Symplocos setchuensis, Buxus megistophylla, and Cynodon dactylon were replanted in a forest gap in Jinyun Mountain, Beibei District, China, and studied over a 6‐year period. We measured root traits (root tensile strength, Young's modulus, cellulose content, and root density) and soil traits (cohesion and internal friction angle) at two soil depths (0–20 and 20–40 cm) for undisturbed and reconstituted samples.ResultsS. setchuensis was found to have the highest tensile strength and resistance to failure for root diameters <2 mm. With elapsed time, tensile strength and cellulose content decreased. Cohesion and root mechanical reinforcement of topsoil generally increased with time (+10% per year). Root chemical and mechanical effects contributed approximately 50% to soil reinforcement. C. dactylon had the fastest growth rate and reinforced the topsoil soil rapidly, whereas S. setchuensis exhibited a consistent increase in soil reinforcement during the study period and provided more deep roots that could reinforce subsoil.ConclusionChemical and mechanical effects almost equally contributed to soil reinforcement. Although the relative contributions varied for different species, the variation in each contribution sheds new light on the sustainable use of vegetation for mitigating shallow landslides in mountainous areas.

Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry

Mapping potential landslides is crucial to mitigating and preventing landslide disasters and understanding mountain landscape evolution. However, the existing methods to map and demonstrate potential landslides in mountainous regions are challenging to use and inefficient. Therefore, herein, we propose a method using hot spot analysis and convolutional neural networks to map potential landslides in mountainous areas at a regional scale based on ground deformation detection using multitemporal interferometry synthetic aperture radar. Ground deformations were detected by processing 76 images acquired from the descending and ascending orbits of the Sentinel-1A satellite. In total, 606 slopes with large ground deformations were automatically detected using hot spot analysis in the study area, and the extraction accuracy rate and the missing rate are 71.02% and 7.89%, respectively. Subsequently, based on the high-deformation areas and potential landslide conditioning factors, we compared the performance of convolutional neural networks with the random forest algorithm and constructed a classification model with the area under the curve (AUC), accuracy, recall, and precision for testing being 0.75, 0.75, 0.82, and 0.75, respectively. Our approach underpins the ability of interferometric synthetic aperture radar (InSAR) to map potential landslides regionally and provide a scientific foundation for landslide risk management. It also enables an accurate and efficient identification of potential landslides within a short period and under extremely hazardous conditions.

Assessing Global Landslide Casualty Risk Under Moderate Climate Change Based on Multiple GCM Projections

Extreme precipitation-induced landslide events are projected to increase under climate change, which poses a serious threat to human lives and property. In this study, a global-scale landslide risk assessment model was established using global landslide data, by considering landslide hazard, exposure, and vulnerability. The global climate model data were then employed to drive the established global landslide risk model to explore the spatial and temporal variations in future landslide risk across the globe as a result of extreme precipitation changes. The results show that compared to the 30-year period from 1971 to 2000, the average annual frequency of landslides triggered by extreme precipitation is projected to increase by 7% and 10%, respectively, in the future 30-year periods of 2031–2060 and 2066–2095. The global average annual casualty risk of landslides is projected to increase from about 3240 to 7670 and 8380, respectively (with growth rates of 140% and 160%), during the 2031–2060 and 2066–2095 periods under the SSP2-4.5 scenario. The top 10 countries with the highest casualty risk of landslides are China, Afghanistan, India, the Philippines, Indonesia, Rwanda, Turkey, Nepal, Guatemala, and Brazil, 60% of which are located in Asia. The frequency and intensity of extreme precipitation will increase under climate change, which will lead to an increase in casualties from landslides in mountainous areas globally, and this risk should be taken seriously. The present study was an attempt to investigate and quantify the impact of global landslide casualty risk under climate change, which still has uncertainty in terms of outcomes, and there remains a need for further understanding in the future of the propagation of uncertainty between the factors that affect the risk.

A pseudo-3D methodology for regional-scale back-analysis of earthquake-induced landslides

Major earthquakes can cause hundreds to thousands of landslides in mountainous areas, but the methods for regional earthquake-induced landslide analyses are currently not well developed. This paper proposes an algorithm for back-analyzing earthquake-induced landslide inventories. The result of the back-analysis (or “inversion”) is a modeled three-dimensional (3D) “best-matching” landslide for each mapped landslide in an inventory as well as an estimate of average shear strength along the landslide failure plane. The algorithm leverages seismic displacement models to detect the triggering location (i.e., triggering grid cell) of modeled landslides. The triggering grid cell is then projected to form a 3D cone-shape failure surface for each modeled landslide using geomorphic characteristics, i.e., flow direction and flow accumulation, and a geometric rule, a procedure termed here as pseudo-three-dimensional (pseudo-3D). The inversion algorithm uses the landslide location, area, and volume (if available) of each mapped landslide to find a modeled landslide that minimizes the “mismatch” between the two, expressed as total error, and outputs an associated triggering depth, landslide geometry, and strength. The estimate of strength for each individual landslide can then, if needed, be aggregated using a geospatial methodology (e.g., geologic unit information or a K-means clustering method) to derive regional-scale Mohr-Coulomb shear strength parameters. The potential bias of the proposed pseudo-3D methodology on the strength estimate is assessed against synthetic landslide inventories generated using a 3D limit equilibrium method. The back-calculated friction angles agree well with 3D results, while the cohesions are lower than the estimated cohesions from 3D slope stability analysis. In addition, the effect of different seismic displacement models on the results of the back-calculated strengths is investigated. The proposed methodology is implemented to the 2015 Lefkada earthquake event in Greece, which triggered more than 700 landslides, with the goal to demonstrate the efficiency in the inversion of many landslides and the generation of regional estimates of strength parameters. For this study region, the spatial distribution of the shear strength results indicates that areas with fewer landslides have higher shear strength while areas with more landslides have lower shear strength.

Retrieving the Kinematic Process of Repeated-Mining-Induced Landslides by Fusing SAR/InSAR Displacement, Logistic Model, and Probability Integral Method

The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the traditional InSAR technique, making it difficult to explore the relationship between landslides and subsurface excavations in both temporal and spatial domains. In this study, the Tengqing landslide in Shuicheng, Guizhou, China, was chosen as the study area. Firstly, the large-gradient surface displacement in the line of sight was obtained by the fusion of SAR offset tracking and interferometric phase. Subsequently, a multi-segment logistic model was proposed to simulate the temporal effect induced by repeated mining activities. Next, a simplified probability integral method (SPIM) was utilized to invert the geometry of the mining tunnel and separate the displacement of the mining subsidence and landslide. Finally, the subsurface mining parameters and in situ investigation were carried out to assess the impact of mining and precipitation on the kinematic process of Tengqing landslides. Results showed that the repeated mining activities in Tengqing can not only cause land subsidence and rock avalanches at the top of the mountain, but also accelerate the landslide displacement. The technical approach presented in this study can provide new insights for monitoring and modeling the effects of repeated mining-induced landslides in mountainous areas.

Early Identification and Influencing Factors Analysis of Active Landslides in Mountainous Areas of Southwest China Using SBAS−InSAR

Potential landslides in the mountainous areas of southwest China pose a serious threat to the lives and property of local residents. Synthetic aperture radar interferometry (InSAR) technology has the advantages of wide coverage, all weather applicability, and low cost and can quickly and accurately identify large range of active landslides, making it a useful geodetic tool for the early identification and prevention of landslides. This paper employed small baseline subset InSAR (SBAS−InSAR) technology and ascending and descending Sentinel−1 data from January 2019 to December 2021 to early identify active landslides in the Maoxian County to Li County National Highway (G317 and G213). The InSAR deformation results were verified by geometric distortion analysis, optical remote sensing interpretation, and field investigation, and 115 active landslides were successfully determined, among which 23 active landslides were identified by ascending and descending Sentinel−1 data together. In addition, InSAR deformation results show that fault, stratigraphic lithology, and rainfall are the three main factors that accelerate the deformation of active landslides and can trigger new active landslides. This study can provide an important reference for the early identification and prevention of landslides in mountainous areas.

A GIS-based probabilistic analysis model for rainfall-induced shallow landslides in mountainous areas

A GIS-based probabilistic analysis model for rainfall-induced shallow landslides in mountainous areas

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model.

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples’ lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.

Large-scale landslides around the reservoir area of Baihetan hydropower station in Southwest China: Analysis of the spatial distribution

Southwest China is an area where geological hazards occur frequently. To develop a basic understanding of geological hazards in this region, the Baihetan hydropower station section of the Jinsha River basin and its surrounding region are selected as the study area. We compile a detailed inventory including the information of all large-scale landslides (i.e., >104 ​m2 in area) and analyze their typical characteristics. In the study region that covers an area of 6372.9 ​km2, we identify 3757 large-scale landslides with a total area of 1033.5 ​km2 and an average area of 275,097 ​m2 for each individual landslide. Specifically, the Qiaojia mega-landslide has the largest area, reaching 7,717,000 ​m2. These landslides are clustered and unevenly distributed, and those along rivers are characterized by zonal distribution. The analysis of the influencing factors of landslide occurrence shows that elevation, river valley (valley), fault, slope angle, and slope aspect have obvious influences on landslide occurrence. This study is not only beneficial for further understanding the development characteristics of landslides in mountainous areas of southwest China, but also provides basic data for subsequent landslide susceptibility mapping in this region.

Landslide susceptibility assessment for warning of dangerous areas in Tan Uyen district, Lai Chau province, Vietnam

ABSTRACT This paper presents the results of applying GIS–MCA–AHP integrated model in landslide susceptibility assessment for warning of dangerous areas in the Tan Uyen district of Vietnam. The main research method is a Multi-criteria assessment that is based on a hierarchical analysis process and a combination of the GIS spatial analysis model. Research results have shown four main groups of factors causing landslides including hydrogeological factors, topographic and geomorphological factors, surface cover factors and human impact factors. The landslide susceptibility assessment map has identified 22.69% of the area (204.96 km2) with a high and very high risk of landslides. Based on the overlapping population distribution map and landslide susceptibility assessment map. It has been shown that residential areas, including 723 households located at a high risk of landslides need to be moved to a safe area. This is a very significant research direction in a warning and minimizing the risk of landslides in mountainous areas of Vietnam.

Generation and Subsequent Transport of Landslide-driven Large Woody Debris Induced by the 2018 Hokkaido Eastern Iburi Earthquake

The earthquake that occurred on 6 September 2018, in the eastern part of the Iburi region of Hokkaido, Japan (the Hokkaido Eastern Iburi Earthquake) caused thousands of shallow landslides in mountain areas. In areas where many landslides occurred, the trees on the slope became large woody debris (LWD) and were supplied to the catchment. Understanding the properties of LWD during the earthquake and its subsequent movement after the earthquake are important to manage the produced LWD and implement disaster prevention measures. This study evaluates the risk of future LWD disasters based on the sequence of LWD generation, its spatial distribution, and LWD relocation linked to temporal fluctuations in rainfall events. The study site is the upper Habiu River catchment (0.37 km2), where multiple shallow earthquake-related landslides occurred. Orthophotos and elevation data acquired before and after the earthquake were used to detect the properties of LWD. To evaluate the risk of an LWD disaster, we examined the correspondence between the hydraulic quantities, including the precipitation for 2 years after the earthquake and the water depth. It was estimated that approximately 7,000 LWD pieces (9,119 m3 km−2) were produced during the earthquake. Orthophoto interpretations indicate that over 80% of the LWD produced at the time of the landslide moved from the slope to the channel accompanied by the landslide debris; some of that then flowed down, accumulated, and formed logjams. In the river channel approximately two years after the earthquake, the destruction of logjams and the clear and drastic movement of LWD could not be confirmed. In this catchment, the uneven LWD distribution and the formation of logjams were fixed almost immediately after the landslide at the time of the earthquake; these characteristics are important when considering future actions. The water depth evaluation based on the difference in the excess return period indicate that the degree of risk differs depending on the deposition location in the channel. This suggests that not all LWD in the catchment are dangerous and that a risk assessment focusing on the LWD location can be effective. This study also makes it possible to determine high priority areas for LWD treatment.

ВЗАИМОСВЯЗЬ ЭМАНАЦИИ РАДОНА С УРОВНЕМ ВНЕШНЕГО ВОЗДЕЙСТВИЯ НА ОСНОВЕ МОНИТОРИНГА КРУПНЫХ ОПОЛЗНЕЙ В ГОРНЫХ РАЙОНАХ

The paper investigates the phenomenon of radon emanation in various geological conditions – rocks, thick boulderpebble strata and a powerful landslide. Instrumental measurements of micro-seismic vibrations, radon volumetric activity (RAR) were carried out, and long-term values of the gravitational movement of the soil strata were determined on the largest Loire landslide in North Ossetia. A low OAR level was obtained, which did not exceed during the experiment (0.6 Bq/m3). It has been established that the concentration of radon can reflect the degree of enrichment in underground cracks and, therefore, information about geological cracks and the stress state of a landslide body. It has been determined that the analysis of radon anomalies can be used as an independent method for assessing the state of landslide and providing an objective scientific basis for assessing the stability of mountain slopes. The purpose of this work was to study the relationship between radon emanation and the level of external impact on the basis of monitoring large landslides in mountainous areas. Research results. As a result of the research, a low OAR level was obtained, not exceeding 0.6 Bq/m3 during the experiment. It has been established that the concentration of radon can reflect the degree of enrichment in underground cracks and, therefore, information on geological fracturing, the stress-strain state of a dispersed landslide body. This phenomenon was expressed in the fact that an increase in the amplitudes of the micro-seismic background manifests itself with a delay in an increase in the RAR or the “aftereffect time tp of radon”, after which a targeted effect on the environment or pulse-vibration processes in the environment itself, characterized by intense fluctuations, cause an increase in the intensity of radon emanation on surface. It has been established that the analysis of radon anomalies can be used as an independent method for assessing the stress-strain state of a landslide by analyzing the statistical characteristics of microscale events and, thus, providing an objective scientific basis for operational assessments of the stability of mountain slopes.

Retraction Note to: Sensitivity of mountain landslide based on Markov model and aerobic training management

In this paper, we will use MATLAB to create a Markov model, determine the input and output membership functions of the study area, and import the data of the study area into the model. The ROC method is used to prove the accuracy of the sensitivity distribution map of mountain landslide disaster and the feasibility of the model in the sensitivity assessment of mountain landslide. It also shows the reliability and applicability of the selected factors. Based on GIS technology, the influencing factors of sensitivity coefficient of landslides in mountainous areas can be extracted. According to the spatial distribution characteristics of landslides, six influencing factors of landslides and their spatial distribution relationships are analyzed: height, slope, geological lithology, terrain curvature, terrain humidity index, and runoff intensity index. Training management combined with aerobic exercise is helpful to improve people’s body shape. The effect of “resistance training + aerobic exercise” training method adopted by experimental group 1 on body shape is the most significant. The arm circumference of the male experimental group increased, while that of the female experimental group decreased. In the training management, different combination methods of resistance training and aerobic exercise have no significant effect on the improvement of the muscle strength of the subjects after the experiment. By analyzing and comparing the changes of cardiopulmonary function indexes of the subjects before and after training, this paper expounds the training effect of blood flow restriction under different pressures combined with low-intensity aerobic exercise on cardiopulmonary function. In this paper, through the study of the Markov model and mountain landslide sensitivity, it is applied to aerobic training management, which promotes the better improvement of people’s body shape.

Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.

Assessment and Analysis of a Rainfall–Time-Lagging Water-Related Disaster in Mountainous Areas

Both global climate change and human activities are continuously impacting the abruptness and frequency of water-related natural disasters such as flash floods, debris flows, and landslides in mountainous areas, greatly threatening the safety of lives and properties. A recent rainfall-induced debris flow event happened on July 6, 2020 in the Chenghuangmiao Gully, in Sichuan Province, China, resulting in severe damage to buildings at the outlet. An integrated analysis of the consequence and triggering mechanism of this debris flow event was conducted with hydrologic information, topographic details, vegetation regimes, and drone aerial imagery. The result shows that the entire runout of the debris flow differs from that of common ones (debris flow and rainfall were highly related and synchronized), which happened 4 h after the stop of the rainfall. The hysteretic feature increases the difficulty of the prediction and warning of the debris flow due to lack of a responsible triggering mechanism. The hillslope surface is well covered by vegetation, hindering regular observation and cleaning up of long-term deposited wood and sediment debris. This effect increases the crypticity and abruptness of potential debris flows. With field evidence and analysis, it is speculated that long-term accumulative processes of dead wood sand sediment deposition formed a small-scale debris dam, and the continuous water release from the watershed led to dam breaching, subsequently triggering the initiation of the debris flow. Multiple steps distributing along the gully of an average slope of 15.65° contributed to the amplification of the debris flow once the breach of the upstream wood and sediment dam occurred. Along the gully, small-scale landslide scars can be observed, possibly amplifying the scale of the debris flow and disaster impact. This debris event gives a lesson of necessary demands of predicting and managing the risks of a low-frequency debris flow non-synchronized with rainfall events.

Risk-Based Warning Decision Making of Cascade Breaching of the Tangjiashan Landslide Dam and Two Smaller Downstream Landslide Dams

Mega earthquakes or serious rainfall storms often cause crowded landslides in mountainous areas. A large part of these landslides are very likely blocking rivers and forming landslide dams in series along rivers. The risks of cascading failure of landslide dams are significantly different from that of a single dam. This paper presented the work on risk-based warning decision making on cascading breaching of the 2008 Tangjiashan landslide dam and two small downstream landslide dams in a series along Tongkou River. The optimal decision was made by achieving minimal expected total loss. Cascade breaching of a series of landslide dams is more likely to produce a multi-peak flood. When the coming of the breaching flood from the upstream dam perfectly overlaps with the dam breaching flood of the downstream dam, a higher overlapped peak flood would occur. When overlapped peak flood occurs, the flood risk would be larger and evacuation warning needs to be issued earlier to avoid serious life loss and flood damages. When multi-peak flood occurs, people may be misled by the warning of the previous peak flood and suddenly attacked by the peak flood thereafter, incurring catastrophic loss. Systematical decision making needs to be conducted to sufficiently concern the risk caused by each peak of the breaching flood. The dam failure probabilityPflinearly influences the expected life loss and flood damage but does not influence the evacuation cost. The expected total loss significantly decreases withPfwhen the warning time was insufficient. However, it would not change much withPfwhen warning time is sufficient.

A Slippery Slope: Could Climate Change Lead to More Landslides?

Scientists investigate whether warming temperatures and changing rainfall patterns could be triggering more landslides in mountainous areas.

Vulnerability Assessment of Peoples Exposed to Landslides in Panchase of Nepal using Analytical Hierarchy Process

The occurrence of landslides in mountainous areas of Nepal is recurrent phenomena and this can be disastrous if occurred within human settlements. Proper research is required to manage and reduce the risks of the disaster in places where landslides had occurred frequently. In risk assessment estimation of vulnerability is one major component. This research was aimed to generate information on the vulnerability of people in the Panchase area of central Nepal. The method of analytical hierarchy process was used to define the weightage to be assigned for 4 factors and 26 indicators used in the research. 377 households were surveyed with a questionnaire designed to collect information on those indicators. The households sampled for the survey were made sure that they were near or around the landslide affected area. The response for each indicator was converted into scores which when summed up yielded the vulnerability score. This score for each household was used to categorize households into five levels of vulnerability from very low, low, moderate, high, to very high. The numbers of households in each category were 16, 92, 191, 75, and 3 respectively. Environmental and economic indicators were inflicting higher vulnerability in this research location. Remoteness and lesser number of facilities and/or services, lower numbers of governmental offices, absence of banking and financial institutions, lesser preferences for insurances and savings, degraded natural water sources were major determinants of higher vulnerability in Panchase. These indicators should be prioritized during disaster risk management in Panchase.

The mudflow disaster at Villa Santa Lucía in Chilean Patagonia: understandings and insights derived from numerical simulation and postevent field surveys

Abstract. The evaluation of potential landslides in mountain areas is a very complex process. Currently, event understanding is scarce due to information limitations. Identifying the whole chain of events is not a straightforward task, and the impacts of mass-wasting processes depend on the conditions downstream of the origin. In this paper, we present an example that illustrates the complexities in the evaluation of the chain of events that may lead to a natural disaster. On 16 December 2017, a landslide occurred in the Yelcho mountain range (southern Chile). In that event, 7 million m3 of rocks and soil fell on the Yelcho glacier, depositing 2 million m3 on the glacier terminal, and the rest continued downstream, triggering a mudflow that hit Villa Santa Lucía in Chilean Patagonia and killing 22 people. The complex event was anticipated in the region by the National Geological and Mining Survey (Sernageomin in Spanish). However, the effects of the terrain characteristics along the run-out area were more significant than anticipated. In this work, we evaluate the conditions that enabled the mudflow that hit Villa Santa Lucía. We used the information generated by Sernageomin's professionals after the mudflow. We carried out geotechnical tests to characterize the soil. We simulated the mudflow using two hydrodynamic programs (r.avaflow and Flo-2D) that can handle the rheology of the water–soil mixture. Our results indicate that the soil is classified as volcanic pumices. This type of soil can be susceptible to the collapse of the structure when subjected to shearing (molding), flowing as a viscous liquid. From the numerical modeling, we concluded that r.avaflow performs better than Flo-2D. The mudflow was satisfactorily simulated using a water content in the mixture ranging from 30 % to 40 %, which would have required a source of about 3 million m3 of water. Coupling the simulations and the soil tests that we performed, we estimated that in the area scoured by the mudflow, there were probably around 2 800 000 m3 of water within the soil. Therefore, the conditions of the valley were crucial to enhancing the impacts of the landslide. This result is relevant because it highlights the importance of evaluating the complete chain of events to map hazards. We suggest that in future hazard mapping, geotechnical studies in combination with hydrodynamic simulation should be included, in particular when human lives are at risk.

Probabilistic stability and sensitivity analysis of rock slope subjected to seismic loading

Being located on the Pacific Ring of Fire, Indonesia has to cope with the constant risk of earthquakes which often trigger landslides in mountainous area, causing significant and even catastrophic damage. This paper presents probabilistic stability and sensitivity analysis of rock slope subjected to seismic loading based on Generalized Hoek-Brown failure criterion. Two approaches namely Limit Equilibrium Method (LEM) and Finite Element Method (FEM) were utilized using computer programs of Rocscience Slide and Phase2, respectively. The Generalized Hoek-Brown parameters such as, σci, GSI and mi were distributed normally with their coefficients of variation (COV) of 0.25, 0.125 and 0.1, respectively. The different seismic loadings expressed as Kh of 0.1, 0.3, and 0.5 were then subjected to the rock slopes and the probability of failure (Pf) was evaluated for each slope. Eventually, the results of Pf obtaining from LEM and FEM were compared and the most decisive parameter in slope stability were evaluated in sensitivity analysis. Based on the presented results of the analysis, the probability of failure (Pf) of the rock slopes which are subjected to the seismic loading generated by both FEM and LEM will decrease as the mean of GSI, σci, and mi increase. Moreover, the increment of the D value and seismic loading will induce the probability of failure (Pf) of the rock slopes which are generated by both FEM and LEM increase.