Slope Stability Analysis Model Research Articles

Slope Stability Analysis and Sliding Surface Search Model Based on Swedish Slice Method

Slope Stability Analysis and Sliding Surface Search Model Based on Swedish Slice Method

Rainfall-induced Landslide in the Lesser Himalaya: A Case Study of the Pallotari Landslide, Lamjung, Central-West Nepal

The Pallotari area, located in the Marsyangdi Rural Municipality, Lamjung, central-west Nepal, within the Lesser Himalaya, was impacted by a series of rainfall-induced landslides in July 2020. This study focuses on the Pallotari landslide, one of the major landslides in the area. Geological and engineering geological studies were performed to identify the causes of this landslide, and its impacts to the local community. Our results indicate the major causative factors to be intensive rainfall, steep topography with loose colluvial soil, and some anthropogenic activities such as the construction of the rural roads through the crown area. The study area still has hanging colluvial material on the crown as well as on the left flank of the landslide, and the numerical model for slope stability analysis indicates it to be unstable even in the unsaturated condition. The colluvial material in the middle portion of the landslide also has the risk of failure in the saturation condition which can be due to intensive rainfall. However, the rock slope stability analysis using the kinematic analysis does not show any contribution to the orientation of discontinuities on the slope failure. The presence of several transverse and longitudinal cracks in the surrounding of the landslide are signs of future failure.

Novel evaluation method based on critical arch height as instability criterion for sustaining arch locked-segment-type slopes

In sustaining arch locked-segment-type slopes, natural soil arches play a key anti-sliding role in the slope's evolution. In this study, a self-developed model test device was used to simulate the whole process of deformation evolution of sustaining arch locked-segment-type slopes, and the formation of natural sustaining arch and its locking control effect on slope stability were studied. The test results show that the continuous formation and progressive destruction of the sustaining arch were observed. The sustaining arch formed in the second time has the best locking effect, and the anti-sliding force reaches its stress peak point. However, the slope is not in a critically unstable state, instead, the stress is continuously adjusted to form a larger range of soil arch to resist the slope thrust. Consequently, the slope destabilizes until the ultimate shear strength of arch foots is exceeded, at which point the critical arch height of the arch is reached. The critical arch height mechanical model for slope stability analysis was developed based on the soil arching effect and limit equilibrium theory. The applicability of the model was demonstrated by the physical test and Xintan slope data, which can provide some guidance for early warning of landslides.

Application of RSM optimization model for slope stability analysis in geotechnical engineering

Abstract Slope stability is a critical area of research pivotal to ensuring public safety and advancing societal development. A thorough examination of slope stability holds substantial practical value in augmenting geological disaster prevention and shaping engineering economic strategies. This study develops an optimized Response Surface Methodology (RSM) model by incorporating the main effect analysis to enhance the foundational response surface method. Subsequently, to address the requirements of slope stability analysis, a numerical model calculating the stress and reliability of anti-slip piles is formulated. Building on this foundation, the Kriging model is refined using a particle swarm optimization algorithm, which is then integrated with the RSM optimization model to evaluate slope stability in geotechnical engineering applications. The efficacy of the proposed method is underscored by its performance across five-speed boundaries, where it achieves a mean reliability value of 90.40%, with a minimal average error of 0.69%. Moreover, empirical validation of the method through conditional probability calculations yields a value of 19.95%. This figure closely aligns with the results derived from the narrow-boundary method, which estimates slope system failure probabilities between 19.28% and 22.54%. Comparatively, the system failure probability determined by the Probabilistic Network Evaluation Technique (PNET) stands at 20.75%. These results affirm that the RSM-based optimization model described in this study is not only precise but also productive, establishing it as a robust method for conducting slope stability analysis in geotechnical engineering.

Landslide Hazard Assessment Model for Slope Stability Analysis

Soil moisture dynamics is a complex phenomenon that depends on the atmospheric conditions, the geomorphological characteristics of the region under study, and the corresponding land use. It can be formally described by a diffusion model based on Darcy’s law and the law of mass continuity. In this work, the obtained numerical solution of the hydrological model has been exploited to evaluate the soil moisture in a given region and build a risk map for the slope stability of this region. More in detail, the infinite slope model from slope stability analysis has been used for evaluating the safety factor and constructing the corresponding quantitative hazard maps. Some results of the proposed method applied to a real case study are shown and discussed.

Slope stability analysis model for the frost-susceptible soil based on thermal-hydro-mechanical coupling

Slope stability analysis has been performed through various analysis methods ranging from general limit equilibrium methods, such as those proposed by Janbu, Bishop, and Fellenius, to finite element methods. Using these methods, analyzing the slope stability considering the change in soil stiffness due to the freezing or thawing conditions resulting from temperature changes is infeasible. Therefore, considering this shortcoming, a thermal-hydro-mechanical slope stability analysis model (THM-SSA model) is proposed for slope stability analysis. The main feature considered in the THM-SSA model was the slope of the critical state line based on the load angle. Additionally, this model could calculate the local factor of safety (LFS) for the slope based on the isotropic tensile strength of the unfrozen soil and the changes in it after being subjected to repeated freeze–thaw cycles. To verify the proposed THM-SSA model, the stress–strain results were compared with those of the experimental triaxial compression tests that were conducted under different temperatures of the frozen soil with a corresponding number of freeze–thaw cycles. Based on this, a simulation of slope stability analysis was performed as demonstration, and using the verified THM-SSA model, the LFS was calculated according to the freeze–thaw cycles of the slope.

Surrogate Model Development for Slope Stability Analysis Using Machine Learning

In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and the regression problem of predicting the factor of safety (FOS) value. This study explored the implementation of machine learning to analyze slope stability using a comprehensive database of 880 homogenous slopes (266 unstable and 614 stable) based on a simulation model developed as a surrogate model. A classification model was developed to categorize slopes into three classes, including S (stable, FOS > 1.2), M (marginally stable, 1.0 ≤ FOS ≤ 1.2), and U (unstable, FOS < 1.0), and a regression model was used to predict the target FOS value. The results confirmed the efficiency of the developed classification model via testing, achieving an accuracy of 0.9222, with 96.2% accuracy for the U class, 55% for the M class, and 95.2% for the S class. When U and M are in the same class (i.e., the U + M class), the test accuracy is 0.9315, with 93.3% accuracy for the S class and 92.9% accuracy for the U + M class. The low accuracy level for class M led to minor inaccuracies, which can be attributed to a data imbalance. Additionally, the regression model was found to have a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value of 5.03 × 10−4, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.

A pre-trained deep-learning surrogate model for slope stability analysis with spatial variability

This paper presents a pre-trained deep-learning surrogate model for the slope stability problem, which can be used to accelerate the stochastic analysis of slope stability with spatial variability. One major innovation is that the model is trained with a big dataset (>12000 data) covering common soil properties, spatial variabilities, and slope shapes such that the trained model is ready to make predictions without additional training or numerical simulations required. Other two minor contributions are: (1) special treatments for the irregular and varying boundaries of slopes and (2) novel techniques that allow the use of non-uniform mesh in data acquisitions. The proposed model is accurate with a mean-absolute-percentage-error of about 6% for the testing dataset. Seven cases of unseen data are also used to verify the model performance, including cases of different soil parameters, slope angles, and even different slope surfaces (e.g., concave and convex slopes, which are not used in training). The results show that the predict slope factor of safety is high consistent with the values from finite element simulations, and so is the obtained probability density functions. But the surrogate model takes much less computational effort (several minutes compared with hours of computing) – proving the effectiveness of our model for efficient stochastic analyses.

An Analysis of Landslide Risk Using the Change in the Volumetric Water Content Gradient in the Soil Layer Per Unit Time of Effective Cumulative Rainfall

The topographical characteristics of South Korea are coupled with heavy rain and typhoons concentrated from July to September, which take up two-thirds of the country’s annual precipitation, causing substantial damage from slope failures, landslides, debris flows, and other mountain disasters. This study uses the Land-Atmosphere Modeling Package (LAMP) data to study the area, which has been affected by numerous landslides in the past. Specifically, to analyze the likelihood of slope failure, this study simulates the rainfall events in the area at the time of the landslides as well as changes in the volumetric water content in the soil layer and uses the changes in the volumetric water content per unit time, which are affected by the effective cumulative rainfall within the soil layer. To that end, this study assesses the risk of landslides based on how rainwater infiltration in the saturation depth changes the saturation depth ratio and compares the results with the landslide spots in the past. Moreover, this study analyzes the landslide risk by applying the saturation depth ratio (Ht) to an infinite slope stability analysis model. The landslide threshold proposed in this study was obtained by analyzing the rainfall events that caused the landslides in the study area using the LAMP model. It can then be concluded that, when compared with the spatial information on the previous landslides, if the change in the volumetric water content per unit time during the first and second rainfall events is 0.7, a landslide may occur. These results indicated that the landslide risk increases the volumetric water content in the soil layer, along with the congruence between past landslide risk points and spatial landslide risks.

Sensitivity Analysis of a Physically Based Model to Assess Rainfall-Triggered Shallow Landslides

Rainfall-triggered shallow landslides are one of the most frequent geomorphological processes in tropical mountainous terrains, and they deserve special attention due to their potential negative consequences in society. Such processes involve several variables including those regarding precipitation, terrain morphology, and hydraulic and shear strength parameters of saturated and unsaturated soils. A spatially distributed and physically-based model for transient rainfall infiltration and grid-based regional slope stability analysis (TRIGRS), was implemented. It was used to analyze the influence of the mentioned variables on the triggering of shallow landslides by rainfall. A robust sensitivity analysis was carried out to quantify the influence of parameter variation on the change of the factor of safety (Delta f_textrm{s}) for shallow landslides, including a one-parameter-at-a-time analysis of a unique cell representing the simplest model space. Four combinations of saturated/unsaturated models and finite/infinite infiltration models were analyzed. The interaction among the hydraulic parameters was analyzed through small-multiple plots to observe their influence on Delta f_textrm{s}. It was found that Delta f_textrm{s} is most sensitive to variation in the maximum depth at which the landslide can be triggered, the slope angle, the cohesion, and the friction angle. In addition, it was found that the parameters of the soil–water characteristic curve for unsaturated soils have little influence on Delta f_textrm{s}. Finally, the interactions among the remaining parameters determine the impact of each one of these on Delta f_textrm{s}. Thus, the results showed that when the baseline set of parameters is changed, the influence of each parameter is modified.

Development of Slope Stability Analysis Model for Frost-Susceptible Soil Based on Thermal-Hydro-Mechanical Coupling

Development of Slope Stability Analysis Model for Frost-Susceptible Soil Based on Thermal-Hydro-Mechanical Coupling

Stability Analysis of a High-Steep Dump Slope under Different Rainfall Conditions

The existing slope stability research, which is based on the fluid–solid coupling theory, is mainly focused on the slopes of central and eastern China. The impact of rainfall on the stability of the dump slope has often been ignored. It is worthwhile to reveal the mechanism of the fluid–solid coupling mechanics of dump slopes in the arid desertification area of northwest China under the maximum precipitation. The method of combining the seepage mechanics theory with the geomechanics theory was adopted. Darcy’s law and the mass conservation law were introduced to derive and establish the fluid–solid coupling analysis method. Taking the Xinxing Coal Mine in Wuhai City, China, as an example, the finite element software ABAQUS was used to construct the fluid–solid coupling model for slope stability analysis with unsaturated soil. The equivalent rainfall intensity of 68 mm/h for 1 h and 18 mm/h for 24 h was designed in the simulation, respectively. Four different types of initial water content (i.e., 1.72%, 7.34%, 14.69%, and 22.03%) of the dump slopes were defined as the initial conditions. The high-steep slope was compared to the standard slope. Therefore, a set of sixteen rainfall schemes was proposed. The variation regularity of slope stability was thoroughly discussed in regards to four areas: vertical deformation, pore water pressure distribution, equivalent plastic strain, and safety factor. As was expected, the research showed that the slope height and angle have a significant effect on the slope stability. When high-intensity rainfall occurs for a short duration, the slope tends to be more stable as the initial water content increases on the slope. When low-intensity rainfall occurs over a long period, the slope stability reduces if the initial water content is too high or too low in the slope.

Composition analysis and distributed assumption GIS model of normal stress on the slope sliding surface

For the limit equilibrium method based on the assumption model of sliding surface normal stress, the more reasonable the assumed sliding surface normal stress model is, the higher the accuracy of the calculation results will be, which is of great significance to improve the theoretical calculation accuracy. Combined with the powerful spatial data analysis ability of Geographic Information Systems (GIS), the expression of the normal stress distribution function of the slip surface is deduced by establishing a GIS-based three-dimensional slope stability analysis model and limit equilibrium equations. By analyzing the composition of the normal stress distribution on the sliding surface, the composition of the normal stress on the sliding surface is obtained, and then the assumed model of the normal stress distribution on the sliding surface is constructed. Finally, the assumed GIS model of the sliding surface normal stress distribution is verified by calculating the proportion. The model overcomes the problem that the stability factor of the slope will cause large errors in the three-dimensional state, and provides a theoretical calculation basis for the establishment of a three-dimensional slope limit equilibrium method based on the assumption of normal stress on the slip surface in GIS.

Physically based modelling techniques for landslide susceptibility analysis: A comparison

In mountainous areas, landslides are the most common natural catastrophic event, due to these events thousands of people are killed each year, and millions of dollars are lost in property damage. Landslides are mainly induced by earthquakes, rainfall, or manmade activities. Several GIS techniques, such as synthetic aperture radar, ranging data from spaceborne, airborne, and ground-based platforms, optical, and light detection and various physically based models such as SINMAP, TRIGRS, SHALSTAB etc. have been widely used to study slope failures in recent years. Each of these techniques has advantages and limitations for susceptibility analysis of landslides. The current research focuses on landslide susceptibility models that are physically based, their parameterization and working principle. The study infers that TRIGRS is the most commonly used model for slope stability analysis, whereas GEO top model is the only 3D slope stability model which considers the spatial variation of soil parameters hence it can be considered as the most advanced physically based slope stability model.

Structural Analysis of Backfill Highway Subgrade on the Lower Bearing Capacity Foundation Using the Finite Element Method

The present study is to investigate the stability of the backfill subgrade on the lower bearing capacity foundation. A finite element (FE) model was constructed to simulate the high-filled road subjected to the actual self-weight load. The strength reduction method was adopted to establish an analysis model of slope stability. At the same time, the sensitivity analysis of the factors affecting the slope stability was carried out through parametric studies, including the elastic modulus, cohesion, internal friction angle, and slope rate. The results showed that the slope stability analysis model established by the strength reduction method can characterize the stability of the slope by calculating the slope safety coefficient. The mutation point of the relationship curve between the displacement generated in the slope and the reduction coefficient can be used as the criterion. Under the condition of a given strength reduction coefficient, the calculated results obtained through FE modeling can show the development of the equivalent plastic zone in the form of cloud diagrams.

경기도 광주지역의 극한강우 사상을 고려한 산사태위험도 분석

South Korea is featured by the rugged topography that is composed of mountains about 68% of the country and the weather that rainfall is mostly concentrated during the summer. These topographic and meteorological features have caused disasters such as landslide and avalanche from mountains every year. Especially, urban expansion caused urban boundaries to get closer to steep slopes of mountains and residents are seriously exposed to disasters such as landslides so appropriate measures should be required. We, however, are facing the limitations in prevention and mitigation of landslide disasters due to the complexity of the causes of landslides like human factors as well as natural ones. To reduce the limitations, we generated of a hazard map by landslide for Gwangju City, Gyeonggi-do with an assumption of an extreme rainfall situation based on the infinite slope stability analysis model. To derive the landslide hazard map, we collected soil samples from the places where landslides actually occurred and constructed physical data by experiments and derived a landslide hazard map based on 5 rainfall scenarios. The accuracy of the result was evaluated by comparing the reference of the landslide hazard maps published by Korean Ministry of Forest.

A GIS-based 3D slope stability analysis method based on the assumed normal stress on the slip surface

The study proposes a geographic information systems (GIS)-based slope stability analysis method assuming a normal stress distribution acting on the slip surface. Compared with traditional methods, the three-dimensional (3D) safety factor acquired through this method will more closely approximate the actual value. First, a 3D slope stability analysis model is developed using grid column units, and the spatial expression of calculation parameters based on the grid column is given by the spatial analysis capability of GIS. Then, four equilibrium equations are derived under the limit equilibrium condition. The normal stress distribution acting on the slip surface is analyzed to construct a reasonable normal stress distribution approximation function. The 3D safety factor is obtained through the approximation function and the Mohr-Coulomb strength criterion. Moreover, we develop a GIS-based extension module which combines the grid-based data with the 3D slope stability analysis model. The accuracy and feasibility of the module are verified by three typical cases.

A novel artificial intelligence technique for analyzing slope stability using PSO-CA model

This study aims to develop a new artificial intelligence model for analyzing and evaluating slope stability in open-pit mines. Indeed, a novel hybrid intelligent technique based on an optimization of the cubist algorithm by an evolutionary method (i.e., PSO), namely PSO-CA technique, was developed for predicting the factor of safety (FS) in slope stability; 450 simulations from the Geostudio software for the FS of a quarry mine (Vietnam) were used as the datasets for this aim. Five factors include bench height, slope angle, angle of internal friction, cohesion, and unit weight were used as the input variables for estimating FS in this work. To clarify the performance of the proposed PSO-CA technique in slope stability analysis, SVM, CART, and kNN models were also developed and assessed. Three performance indices, such as mean absolute error (MAE), root-mean-squared error (RMSE), and determination coefficient (R2), were computed to evaluate the accuracy of the predictive models. The results clarified that the proposed PSO-CA technique was the most dominant accuracy with an MAE of 0.009, RMSE of 0.025, and R2 of 0.981, in estimating the stability of slope. The remaining models (i.e., SVM, CART, kNN) obtained poorer performance with MAE from 0.014 to 0.038, RMSE 0.030–0.056, and R2 0.917–0.974.

Estimating the Velocity of Landslide Movement Using Visco-Plastic Model in Jeruk Sub-village, Kulon Progo District, Yogyakarta, Indonesia

A ground movement occurred in March and November 2017 on the hills and paddy fields in Jeruk Sub-village, Kulon Progo District, Yogyakarta Special Province. The landslide movement destroyed two houses in the village and the land is still moving especially in the rainy season. The mitigation of landslide hazard requires understanding of landslide triggering factors and its movement mechanism. This paper applies the slope stability analysis and visco-plastic model to predict the movement mechanism and velocity of a translational landslide. The sliding mass is modeled as a low plasticity silt (homogenous soil). The Limit Equilibrium Method is used to estimate the safety factor, whereas the shear strength parameters on the slip surface were determined by using the back analysis approach. The results of the slope stability analysis showed that the shear strength parameters and the fluctuation of groundwater level strongly influence the stability of the landslide. From visco-plastic model simulation, this slope has slow movement velocity with the range of 11.31 to 175.88 mm/day. It is clarified that the velocity of landslide movement is influenced by soil strength parameters, coefficient of dynamic viscosity, and groundwater level fluctuation.

Comparison of single- and dual-permeability models in simulating the unsaturated hydro-mechanical behavior in a rainfall-triggered landslide

Landslide-prone slopes in earthquake-affected areas commonly feature heterogeneity and high permeability due to the presence of cracks and fissures that were caused by ground shaking. Landslide reactivation in heterogeneous slope may be affected by preferential flow that was commonly occurred under heavy rainfall. Current hydro-mechanical models that are based on a single-permeability model consider soil as a homogeneous continuum, which, however, cannot explicitly represent the hydraulic properties of heterogeneous soil. The present study adopted a dual-permeability model, using two Darcy-Richards equations to simulate the infiltration processes in both matrix and preferential flow domains. The hydrological results were integrated with an infinite slope stability approach, attempting to investigate the hydro-mechanical behavior. A coarse-textured unstable slope in an earthquake-affected area was chosen for conducting artificial rainfall experiment, and in the experiment slope, failure was triggered several times under heavy rainfall. The simulated hydro-mechanical results of both single- and dual-permeability model were compared with the measurements, including soil moisture content, pore water pressure, and slope stability conditions. Under high-intensity rainfall, the measured soil moisture and pore water pressure at 1-m depth showed faster hydrological response than its simulations, which can be regarded as a typical evidence of preferential flow. We found the dual-permeability model substantially improved the quantification of hydro-mechanical processes. Such improvement could assist in obtaining more reliable landslide-triggering predication. In the light of the implementation of a dual-permeability model for slope stability analysis, a more flexible and robust early warning system for shallow landslides hazard in coarse-textured slopes could be provided.