Slope Stability Evaluation Research Articles

Three-dimensional stability of a fill slope reinforced by a frame beam anchor plate

The frame beam anchor plate is a new supporting structure reinforcing fill slopes. At present, only a few studies have evaluated its static stability based on the two-dimensional plane assumption. In this paper, the limit analysis method is combined with the three-dimensional spiral failure mechanism to evaluate the factor of safety (Fs) of a slope supported by a frame beam anchor plate. The pseudo-static approach is used to express the seismic effects. When calculating the internal energy dissipations, both the pull-out failure of anchor plates and tensile failure of tie rods are considered. The accuracy of the approach is verified by comparison with calculation results from the finite element method. Parameter analysis summarizes the influence law of each supporting structure design parameter on the Fs. Therefore, this study develops a theoretical basis for evaluating slope stability reinforced by frame beam anchor plates, which can better guide the design of slopes reinforced by this supporting structure.

Evaluating overhanging slope failure mechanisms and rockfall trajectories through integrated UAV imagery and discrete element modeling

AbstractTo evaluate the threat of rockfall from an overhanging slope above a bridge, this study proposes a comprehensive analysis procedure to assess the failure mechanisms of the slope and the associated rockfall trajectories. The analysis utilizes high-resolution point cloud data obtained through integrated UAV-based imagery technology, enabling a detailed evaluation of joint configurations and slope stability. The geometric reconstruction of unstable rock blocks, combined with stereographic projections, provides an in-depth analysis of potential failure modes. Discrete element modeling (DEM) is employed to simulate the failure processes and predict rockfall trajectories, with particular emphasis on the potential risks to the underlying bridge. The results indicate that rockfall events near the bridge are primarily driven by wedge-shaped failures, a finding confirmed by both the proposed inverted three-dimensional projection method and DEM simulations. The analysis identifies critical rock blocks near the bridge, underscoring the need for targeted mitigation strategies. This case study demonstrates the effectiveness of integrating UAV-derived data with DEM for assessing overhanging slope failures and managing rockfall risks in similar geological settings.

A Novel Strength Reduction Method for a Slope Stability Assessment Based on a Finite Element Method

Ensuring the stability of slopes is critical to the safe operation of geotechnical engineering. Evaluating slope stability to minimize geologic risks induced by destabilization is significant in reducing casualties and property damage. A conventional, single-coefficient strength reduction method is widely applied in slope stability analyses, but this method ignores the attenuation degree of different parameters in the slope destabilization. A new double-strength reduction method considering different contributions of the mechanics’ parameters is proposed in this study for evaluating the stability of nonhomogeneous slope. First, the role of each mechanic’s parameters in the slope destabilization was investigated theoretically and numerically using ABAQUS software 2022. The results indicate that the effect of elasticity (E), Poisson’s ratio (v), and soil gravity (γ) on the evolution of factor of safety (FOS) are insignificant and can be neglected compared with cohesive force (c), and angle of internal friction (φ). Next, an improved method was constructed to correlate the FOS with cohesive force (c) and the angle of internal friction (φ). Then, a numerical method was constructed based on the computation of the mathematical–mechanical relationship between FOS and the mechanical parameters, and the stability of slope is estimation based on the Mohr–Coulomb yield criterion. Finally, the double-strength reduction coefficient method proposed in this study, the limit equilibrium method, and the traditional finite element strength reduction coefficient method were applied to nonhomogeneous slopes and slopes containing a soft underlying layer for comparison, and the difference between them was within the range of ±5%. The results indicate that both the limit equilibrium method and the traditional finite element strength reduction method tend to overestimate the FOS of intricate slopes compared with the evaluated method proposed in this study. This parallel comparison serves to validate the accuracy of the double-strength reduction method proposed in the present study. Further, based on the proposed method, the relationship between slope stability and slope displacement is established, which provides a theoretical basis for the safety assessment of slope engineering.

Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection

The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Engineering geological investigation of Gololcha dam for evaluation of leakage and abutment slope stability, Eastern Ethiopia

The Ethiopian economy is primarily dependent on agriculture, making the construction of water harvesting facilities, such as dams, crucial for improving the productivity of this sector. The ongoing construction of the Gololcha dam on the Kurkura River located in Eastern Ethiopia aims to enhance irrigation schemes in the region. However, the dam site's complex geological and structural conditions pose challenges related to leakage and slope instability. Hence, this study focuses on addressing the abutment slope stability and leakage condition of this dam. This study employed kinematic analysis and the Limit Equilibrium Method (LEM) to assess slope stability. Additionally, engineering geological mapping, discontinuity surveys, seismic refraction tomography (SRT), and in-situ permeability testing were used to evaluate the leakage condition of the dam site. Notably, the permeability and SRT survey results identified potential leakage zones to the depth of 35, 30, and 35 m at the left, right, and central foundations of the dam, respectively. The kinematic method revealed one planar and two wedge modes of failure in the slope section covered by slightly weathered and fractured basalt rock at the right abutment. Further stability analysis of these two modes of failures via LEM analysis indicated slope instability under saturated conditions, emphasizing the role of pore water pressure. Furthermore, LEM modeling was directly utilized using the Slide 6.0 software to analyze the slope stability condition of the left abutment of the dam. This modeling also uncovered instability under saturated conditions. Based on the study findings, this study recommended curtain grouting to address potential leakage, as well as slope flattening and removing unstable rock wedges and loose material to stabilize unstable slope sections.

Analysis of the Stability of a High Fill Slope under Different Gradients and Precipitation Conditions

The stability problem of high fill slopes has always been a research hotspot. Its failure mechanism is complex and prominent, featuring strong concealment, a short occurrence time and great harmfulness. In this paper, the stability of a high fill slope under rainfall conditions will be studied by using indoor tests, numerical simulations, etc. The study is based on a high fill slope in Yichang City. The evolution law of high fill slope stability under the maximum rainfall condition is revealed. The results show the following: The influence of moisture content on stress–strain curves is reflected in both the curve’s shape and the peak value of deviatoric stress. Under the constraint of confining pressure, the curve decreases and the peak value of deviatoric stress decreases with the increase of moisture content at the same confining pressure. The safety factor obtained by a rigid body limit equilibrium analysis and numerical calculation indicates that the safety factor for a 30° slope meets the requirements for slope stability evaluation and remains in a fundamentally stable state. An on-site investigation suggests that surface failure and shallow failure may be primary failure modes for this slope; therefore, it is recommended to implement slope protection measures. This study provides valuable references for similar high fill slopes.

A physics-informed machine learning solution for landslide susceptibility mapping based on three-dimensional slope stability evaluation

A physics-informed machine learning solution for landslide susceptibility mapping based on three-dimensional slope stability evaluation

Slope Stability Analysis of Rockfill Embankments Considering Stress-Dependent Spatial Variability in Friction Angle of Granular Materials

Slope stability is a major safety concern of rockfill embankments. Since rockfills are incohesive materials, only friction angle is considered as a shear strength parameter in the slope stability analysis of rockfill embankments. Recently, it was found that confining pressure can significantly affect the mean value and variance of the friction angle of rockfills. Since the confining pressure spatially varies within a rockfill embankment, the effect of stress-dependent spatial variability in the friction angle of rockfills should be investigated for slope stability evaluation of rockfill embankments. In the framework of the Limit Equilibrium Method (LEM), an approach is proposed for the slope stability analysis of rockfill embankments considering the stress-dependent spatial variability in the friction angle. The safety factors of slope stability are computed with variable values of the friction angle at the bases of slices which are determined by the stress-dependent mean value and variance of the friction angle of rockfills. The slope stability of a homogeneous rockfill embankment is analyzed to illustrate the proposed approach, and a parametric analysis is carried out to explore the effect of variation in the parameters of the variance function of friction angle on slope stability. The illustrative example demonstrates that the stress-dependent spatial variability of friction angle along the slip surface is obvious and is affected by the location of the slip surface and the loading condition. The effects of the stress-dependent spatial variability of the friction angle on the slope stability of high rockfill embankments should be considered.

Submarine slope stability during the exploitation of natural gas hydrate from horizontal wells

Horizontal wells have the advantages of high production, considerable access to reserves, and effective sand production control; thus, they are potential well types for hydrate exploitation. In the process of hydrate mining, the decomposition of hydrate will cause the deterioration of the mechanical properties of the reservoir, which may induce submarine landslides, especially in the case of multiple horizontal wells, and the risk of submarine landslides is further increased. Therefore, it is necessary to study the influence of the horizontal well pattern adopted for hydrate mining on the stability of submarine slopes. First, a triaxial mechanical experiment of gas hydrate-containing sediments was carried out, and the mechanical parameters of the hydrate-containing sediments were obtained. Second, a thermal-fluid-solid multifield coupling model considering the hydrate phase transition during the production process was established. Finally, on the basis of the finite element strength reduction method, a slope stability evaluation model for hydrate extraction from a horizontal well pattern was established, and the influence of hydrate extraction from horizontal wells on the stability of submarine slope was analyzed. The results show that the peak strength, elastic modulus and cohesion of gas hydrate-containing sediments increase with increasing hydrate saturation. In the process of hydrate exploitation via horizontal wells, with the reduction in well spacing and the increase in the number of production wells, the risk of slope instability increases. Wells positioned along the slope strike are more conducive to slope stability than those positioned perpendicular to the slope strike. The slope stability increases gradually as the slope dip decreases. When the number of wells is the same, the slope stability increases gradually with increasing well spacing and production pressure.

Reliability and landslide consequence analysis of long heterogeneous soil infrastructure slopes: A parallel computing investigation

Long infrastructures, like flood defense levees, built upon diverse soils, often face complex landslide failures. Risk assessments for such structures involve evaluating slope stability reliability and potential landslide consequences. Historically, embankment slope reliability and failure assessments focused on shorter sections due to computational limitations. This study, however, delves into a longer 3D soil slope section, 100 times the height, using the random finite element method to analyse the size or length effect. It conducts a parametric study varying the soil strength’s horizontal correlation length to understand slope failure initiation in heterogeneous soils. In contrast to prior work, this paper emphasises several facets: the realised factor of safety (FOS) distribution, quantification of discrete unstable zones and failures, and the size impact on the reliability and landslide consequences of long embankment slopes. Findings indicate the Weibull distribution better fits the realised FOS, aiding in predicting long embankment slope system reliability when combined with the weakest link theory. While multiple failures are possible for very long soil embankment slopes, observations suggest a greater likelihood of a single localised failure despite this potential.

Hybrid random forest models optimized by Sparrow search algorithm (SSA) and Harris hawk optimization algorithm (HHO) for slope stability prediction

The intelligent prediction of slope stability is crucial for slope management and aboveground structure design. Due to the complexity and nonlinearity of slope stability evaluation, intelligent prediction models often perform better. This study developed two optimizers, the Sparrow search algorithm (SSA) and the Harris hawk optimization algorithm (HHO), to optimize the random forest (RF) model for evaluating slope stability. A database of 444 slope cases was established. The data was split into 80% for training and 20% for testing. The optimizers were used to determine the hyperparameters of the models. Four classical classification models were introduced for comparison. Five-fold cross-validation was employed, and model performance was evaluated using accuracy, precision, recall, and F1-score. A comprehensive method was developed for evaluating the performance of models. The SSA-RF model exhibited the best performance with the highest accuracy (0.9101), precision (0.9500), recall (0.8636), F1-score (0.9048), and AUC (0.9407). The Gini coefficient indicated that unit weight was the most influential indicator (41.16). These methods offer a promising approach for predicting slope stability, applicable in slope engineering practice.

Stability evaluation of open-pit mine slope based on Bayesian optimization 1D-CNN

As mechanized open-pit coal mining intensifies, assessing and predicting slope stability has become increasingly important. To address the limitations of traditional mechanical calculations, numerical simulations, and physical experiments, this paper identifies the key factors impacting slope stability in open-pit mines and develops a multi-parameter sample data set. The study employs hyperparameters optimized using a Bayesian algorithm, introduces additional convolutional layers, and combines the Adam optimizer with dropout techniques to enhance the feature extraction and performance of one-dimensional convolutional neural networks (1D-CNN). This leads to a Bayesian-optimized one-dimensional convolutional neural network (B-1D MCNN) model for predicting slope stability.The study evaluates the classification performance and accuracy of various models for slope stability, including BP neural networks, genetic algorithm-optimized convolutional neural networks, 1D-CNN, and B-1D MCNN, using accuracy, precision, and F1-score as metrics. The analysis also examines the influence of factor indicators and training set length on the model's output to assess its generalization capabilities.The research findings suggest that: (1) the B-1D MCNN model for evaluating slope stability demonstrates the capability to accurately depict the nonlinear correlation between influencing factors and slope stability. (2) Compared with other models, the B-1D MCNN model has shown enhancements of 10.96% to 27.85%, 10.26% to 28.55%, and 8.98% to 25.05% in terms of Accuracy, F1-Score, and Precision, respectively. (3) As the length of the training dataset increases, the performance of the model improves accordingly. (4) The B-1D MCNN model shows a generalization power of 87.5%.

Selection of long-term shear strength parameters for strain-softening geosynthetic interfaces (2023 IGS Rowe Lecture)

The behavior of strain-softening geosynthetic interfaces that can lead to progressive failures in lined containment facilities has been a source of confusion in slope stability evaluations for over 30 years. This paper presents 15 mechanisms that can potentially induce displacements along strain-softening interfaces, along with measures that can be considered to reduce strain-softening displacement. New quantifications of shear strength variability that can be caused by manufacturing, installation, and construction practices are introduced. Guidance and recommendations are given that are applicable to numerical continuum as well as limit-equilibrium approaches to assist in selecting appropriate geosynthetic shear strength parameters for containment facilities that have strain-softening interfaces. While most of the paper focuses on deep-seated critical interfaces for high-normal-stress bottom liners, low-normal-stress veneer covers are also addressed.

The Investigation of Stability on Slopes Utilizing Reinforcement Gabion Walls and Concrete Piles for Mitigating Landslide Disasters

Introduction Landslides frequently occur along roads crossing mountainous terrain during the rainy season, posing a significant risk of severe disruption to land transportation routes. Efficient and accurate resolutions are essential in managing landslides to facilitate immediate transportation recovery, such as gabion walls and pile installation. Aim This article aimed to evaluate the effect of installing gabions and piles for safety measures on the stability of slope landslides. The analysis of slope stability was performed utilizing the Plaxis 2D software. For reinforced slopes, the Safety Factor (SF) value utilized as a benchmark for evaluating slope stability was SF ≥ 1.5. Methods An assessment of the stability of the slope was conducted under three conditions: its original state, after reinforcement with gabions, and after the integration of gabions with mini piles. The dimensions of the gabion setting, as determined by the L-W-H notation (length-width- height), were 2 m x 1m x 0.5 m and 1 m x 2 m x 0.5 m. The pile was designed to be 2.5 m long at the gabion's end. The analysis was conducted at 45°, 60°, 70°, and 90° slopes. Results Based on the results of slope stability calculations, an SF = 1.11 was determined under no reinforcement conditions. By applying reinforced gabion walls measuring 2 m in width combined with mini piles at a 45° slope, the best SF was achieved, which was 2.58. Conclusion Given the comparable topographical circumstances, it is expected that the outcomes of this analysis on slope stability will be applicable in mitigating the occurrence of landslides.

Pit Lake Slope Stability under Water Level Variations

This paper presents the results of a geotechnical investigation regarding the slope stability in a pit lake, emphasizing the impact of water level variations. Advanced analysis techniques were utilized for this study. The research was performed by using fully coupled flow-deformation analyses. For the fully coupled approach, Bishop’s effective-stress equation was used, and for the description of soil hydraulic behavior, the Van Genuchten’s model was applied. The analysis of slope stability associated with reservoir water level changes revealed that the slope tended to become unstable as the water level decreased; the stability factor was negatively related to the rate of water level reduction. Concerning the water level fluctuations, the analyses revealed that the soil mass seemed to become less stable as the rate of water level change increased. Under a specific range of rates of water level variation, the safety factor became higher as the number of fluctuations increased. Additionally, the simulation results concerning the water level rising indicate that the pressure due to the external water level acts on the slope surface with a positive impact on the stability factor. The results obtained reflect the effects under a specific site condition, but they can be used as a reference for evaluating slope stability in a pit lake design.

Stability Evaluation of Slope Based on Global Sensitivity Analysis

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability (pf) are different. Therefore, the approaches based on GSA which can integrate the Fs with pf will be a promising approach for slope stability evaluation.

Slope Stability Monitoring of Hydroelectric Dam and Upstream Watershed Areas Utilizing Satellite Interferometric Synthetic Aperture Radar (InSAR)

Dams offer significant benefits such as power generation, flood control, irrigation, and tourism. However, when mismanaged, they pose a high risk of catastrophic failure, potentially releasing large volumes of water rapidly, resulting in severe downstream damage, environmental impacts, injuries, and loss of lives. Effective dam monitoring is essential to evaluate slope stability, detect slope behaviour changes, and deliver early warning for potential hazards. Upon detecting any deformations in the dam or its watershed areas, prompt interventions can be initiated. Interferometric Synthetic Aperture Radar (InSAR) is the measurement technique of signal phase change between two images at the same location and at different times, it will be a proficient method for monitoring ground or slope changes across vast areas without the installation of on-site instrumentation. The dataset used in this work consists of 392 images of Sentinel-1 from 1 September 2018 to 11 August 2023 to conduct long-term InSAR data analysis. Monitoring the dam and its watershed is crucial, given that over 30% of U.S. dam failures result from overtopping. Slope failure events at the upstream area can cause abrupt water level rises, further increasing the risk of overtopping and major instability. This study explores slope deformation around dams and their watersheds using satellite long-term InSAR technology using a study case from one of the dams in Indonesia. The outcomes will provide insights into dam safety and shed light on the strengths and constraints of the InSAR technology.

Analysis of the Consolidation Properties of Soil in the Mucherbari Regulator in Sunamganj

Analysis of the Consolidation Properties of Soil in the Mucherbari Regulator in Sunamganj

Stability evaluation of gentle slopes in spatially variable soils using discretized limit analysis method: a probabilistic study

Stability evaluation of gentle slopes in spatially variable soils using discretized limit analysis method: a probabilistic study

Slope stability prediction based on GSOEM-SV: A mobile application practicably deploy in engineering verification

Slope stability evaluation is a complex and uncertain system problem, and carrying out slope stability prediction is the prerequisite and foundation for slope disaster prevention. In order to achieve fast and accurate prediction of slope stability, this paper considers height, total slope angle, unit weight, cohesion, internal friction angle, and pore water pressure ratio as input features and proposes an intelligent slope stability prediction method based on grid search optimization ensemble learning model by soft voting (GSOEM-SV). First, 390 sets of on-site data were collected to form a dataset, and analyses including correlation coefficients, density estimates, and box lines were carried out. Then, the grid search optimization algorithm is used to optimize the hyperparameters of five algorithms—Gradient Boosting Decision Trees, Light Gradient Boosting Machine, Categorical Boosting, Support Vector Machine, and Random Forest, and integrates them through soft voting. Furthermore, this paper optimizes the hyperparameters of the above five algorithms based on grid search, particle swarm and simulated annealing algorithms, builds 15 improved models and 2 ensemble models and conducts comparison. The results reveal that the GSOEM-SV has the highest slope stability prediction accuracy, up to 91 %, the area under the curve (AUC) is 0.950, and its F1 score 0.917, which are better than the 15 improved and 2 integrated models. In addition, a set of slope stability prediction app based on uni-app is developed in the paper. It provides a technical foundation and an open and shareable information service platform for slope hazard prediction in geotechnical engineering.