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\"This book contains the detailed reflections of its author who has practised and researched in the field for over a half century. It is written in an informal style that makes it an interesting and thought-provoking practitioner guide to landslides and slope problems and their investigation, analysis, and remediation, considering both natural and man-made slopes and earthworks, and without the need for the usual equations and illustrations. Reflections on Slope Stability Engineering is targeted primarily at practitioners working in the investigations of slope instability and the design and construction of treatments of the problem, especially those early in their careers, but the accessible style also suits students who are developing an interest in the subject and even those engineers with only a casual interest in this branch of geotechnics\"-- Provided by publisher.

The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues can affect the accuracy of slope stability prediction. Therefore, a deep learning algorithm called Long short-term memory (LSTM) has been innovatively proposed to predict slope stability. Taking the Ganzhou City in China as the study area, the landslide inventory and their characteristics of geotechnical parameters, slope height and slope angle are analyzed. Based on these characteristics, typical soil slopes are constructed using the Geo-Studio software. Five control factors affecting slope stability, including slope height, slope angle, internal friction angle, cohesion and volumetric weight, are selected to form different slope and construct model input variables. Then, the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors. Each slope stability coefficient and its corresponding control factors is a slope sample. As a result, a total of 2160 training samples and 450 testing samples are constructed. These sample sets are imported into LSTM for modelling and compared with the support vector machine (SVM), random forest (RF) and convolutional neural network (CNN). The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features. Furthermore, LSTM has a better prediction performance for slope stability compared to SVM, RF and CNN models.

This research focuses on developing the optimal machine learning (ML) based predictive model for calculating the factor of safety ( FS MP ) for finite slopes using the Morgenstern-Price method of slices. The ML models utilize geometric and geotechnical parameters, including slope angle, friction angle, cohesion, slope height, unit weight, horizontal seismic acceleration coefficient, and the ratio of horizontal to vertical seismic acceleration coefficients. A comprehensive dataset of 19,128 data points is generated using in-house MATLAB code. These data points are trained with the ML models to learn the underlying correlations for the prediction of FS MP . Various ML predictive models, such as multiple linear regression, support vector regression, Gaussian process regression, random forest, extreme gradient boosting, and artificial neural networks, are considered for constructing the optimal model. The objective is to develop a tailored framework for arriving at the best-performing predictive model for replication of pseudo-static stability analysis of soil slopes in geotechnical engineering. Comparison of different data-driven models are also presented. The study also utilized interpretable machine learning models with Shapley values to mitigate the inherent “black box” nature of ML models. The study also establishes a physically interpretable error validation model to assess model predictions. The findings illustrate the effectiveness and precision of the Gaussian process regression (GPR) model, as evidenced by R 2 error metric values of 99.9% and 99.8% for the training and test sets, respectively. Further, the error metric for the artificial neural network (ANN) achieved values of 99.7% and 99.6% for the training and test sets, respectively. The GPR model offers conservative results over ANN, making it the preferred predictive model for safe FS MP predictions. It serves as an efficient estimation tool for field practitioners, can be integrated into smartphones and above all integrated into the performance function for uncertainty quantification in the otherwise computationally expensive Monte Carlo simulations. Design charts are also generated using the selected optimal model for depicting the generalizability of this model, enabling geotechnical engineers to determine FS MP without complex calculations. This research showcases the potential of ML techniques for complex geotechnical problems, advancing conventional slope stability analysis and opening avenues for their practical and reliable use in geotechnical engineering.

This study presents a seismic stability assessment framework for heterogeneous compound slopes with cracks, for example, highway embankment slopes. Based on limit analysis method, a kinematical approach incorporating a discretization technique is adopted to account for both global and local instabilities of cracked compound slopes under seismic excitations. The pseudo-static approach is applied to describe seismic forces. The soil heterogeneity is considered as linear variation along the depth. An implicit formula for stability factor (or safety factor) is derived through the work rate balance equation. The upper-bound solutions are calculated by combining a bisectional method with an optimization algorithm. The results are thoroughly verified through comparison with existing studies and finite element analysis. The results show that slope geometry plays a dominant role in governing slope stability and failure behavior. In particular, increasing the upper slope inclination significantly reduces the slope stability and induces a transition from local to global failure, especially under a larger depth coefficient. The presence of cracks further weakens slope stability, with their depth and location highly sensitive to geometric configuration. Furthermore, seismic excitation deepens cracks and shifts them upslope, inducing a transition from local to global failure as horizontal acceleration coefficient increases. Soil heterogeneity influences failure mechanisms that stronger upper soil deepens cracks via shear transition and shifts failure surfaces forward, exhibiting a “seesaw effect”. The findings provide the guidance for the design of stable and crack-resistance slope engineering, particularly in topographically complex or geohazard-prone regions.

The point-specific analysis of slope stability of road cut at nine locations was conducted along NH-10, between Rangpo and Ranipool in Sikkim, India. Kinematic analysis of discontinuities shows slope vulnerability to a union of failure types, namely, plane, wedge, and topple failure at different studied sites. Rock mass characterization was carried out on the basis volumetric joint count (J v ), RQD, RMR, and GSI. Appraisal of slope vulnerability to failure was done using traditional slope mass rating (SMR), Continuous SMR, Chinese SMR, and Q-slope stability method. Comparative analysis of different empirical methods proves a decent correspondence and shows that most of the cut slopes are under unstable to a partially stable condition. Stability method, Q-slope was used to analyse the stability conditions for 50%, 30%, 15%, and 1% probability of failure and to further find out stable slope angle for all the considered locations. The coalesced effort of the road cut stability analysis through kinematic and empirical methods will give insight into slope failure vulnerability along this highway by finding the most susceptible failure locations. The study will help to understand the dynamics of slope design for problematic stretches along NH −10 and adopt more effective remedial measures for better stability.

Optimizing blast design and bench geometry is crucial for enhancing the safety, efficiency, and sustainability of open-pit mining operations. This study examines the effects of blast design and bench geometry adjustments on bench slope stability through numerical modelling under static and dynamic loading conditions. Extensive data on rock mass, blast design parameters, and geomechanical properties were analyzed to assess these optimizations. Results indicate that reducing the bench height from 12 to 5 m improves the shear reduction factor (SRF) by 43.78%, while decreasing the bench face angle (BFA) from 90° to 60° enhances the SRF by 17.12%, demonstrating increased stability. Conversely, increasing the overall slope angle from 27.5° to 36.5° improves productivity by 57.14% but reduces the SRF by 17.12%, highlighting the trade-off between stability and extraction efficiency. Optimal conditions balancing stability and productivity were identified with a bench height of 7.5 m, a BFA of 75°, and a bench width of 14 m, yielding an SRF of 1.31 under static conditions and 1.16 under dynamic conditions. Simulations of blast dynamics revealed that the bench blast velocity decreased from 63.18 cm/s at a radial distance of 13 m to 23.99 cm/s at 18.5 m, indicating significant attenuation in particle motion over distance. Blast-induced ground vibrations (BIGV) were also evaluated, with notable peak particle acceleration near the blast zone. The study recommends a powder factor range of 0.31–0.51 kg/m 3 and a peak particle velocity (PPV) threshold of 30–40 cm/s to optimize blast design while ensuring operational safety. These findings provide critical insights for enhancing stability and productivity in large-scale open-pit limestone mining operations.

The majority of India’s coal is mined using opencast methods, which causes more waste dumps to be formed and stability problems. There is a higher chance of dump instability because to the 1148 million cubic meters of overburden that Coal India Limited (CIL) has removed in the past few years. Complex calculations make dump slope stability studies complicated and time-consuming. Analytical and numerical methods are needed to calculate factor of safety (FOS) of dump slope. This research bridges traditional geotechnical methods with emerging computational approaches by integrating advanced ML techniques with rigorous statistical evaluation and a comprehensive dataset to improve dump slope stability prediction accuracy, reliability, and applicability. With so many available options, picking the best ML model can be a challenge. Consequently, for the purpose of this research, the authors selected models using the Lazy predict AutoML algorithm. Using six base models—Gradient Boosting (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGB), Nu-Support Vector Regressor (NuSVR), Extra Tree Regressor (ETR), with Stacking Ensemble, and H2OAutoML—this study proposes an effective method for analysing dump slope stability. In preparation for model calibration and evaluation, databases of 2250 datasets were created. The output is the SLIDE computed factor of safety, and the inputs are six influential parameters such as cohesion (c), angle of internal friction (ϕ), unit weight (γ), overall bench height (H), natural moisture content (m), and overall slope angle (β). The coefficient of determination (R squared or R 2 ), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error(MAE) were used for evaluating the performance of all models. The H2O Auto ML performed best model in comparison to other ensemble models. This research also makes use of the Shapley additive explanations (SHAP) technique to determine which of the six inputs is most crucial. This study shows that sophisticated ML approaches improve dump slope stability prediction in Indian opencast coal mines.

The double wedges sliding along the weak layer of the foundation can be observed on the slope of the waste dump and the sliding body is divided into the active wedge and passive wedge by the weak foundation and the failure surfaces of the waste dump. Because the conventional limit equilibrium slice method cannot reflect the polygonal slip surface of the slope of the waste dump with weak foundation, this study proposed a double wedge calculation method for the slope of the waste dump with weak foundation. The limit equilibrium analysis is performed on double wedges by considering the direction and values of the interaction force between double wedges to obtain the safety factor of the slope of the waste dump. Meanwhile, the quasi-3D double wedges stability analysis method of the waste dump slope with weak foundation is proposed by considering the influence of the geometry and sliding direction of the slope surface on the slope stability. The safety factor of the inverted dump slope is 0.82, the volume of the sliding body is 6.43 million m 3 , and the main sliding direction is 20° south by east. The shear strain rate cloud diagram of the section is ‘y’ type distribution, and the sliding body is divided into two independent blocks. The safety factor of the sliding body section obtained by the double wedge method is between 0.76 and 0.92, and the closer to the boundary of the sliding body, the greater the safety factor of the section. The quasi-three-dimensional safety factor obtained by theoretical analysis is 0.817. The results show that the calculation results of quasi-3D double wedge are basically consistent with the calculation results of strength reduction method, while the proposed method is simpler. It can be used as a quick method to evaluate slope stability in engineering practice.

Over the last two decades, the approach to the investigation of landslides has changed dramatically. The advent of new technologies for engineering geological surveys and slope analyses has led to step-change increases in the quality of data available for landslide studies. However, the use of such technologies in the survey and analysis of slopes is often complex and may not always be either desirable or feasible. In this context, this paper aims to improve the understanding of the use of remote sensing techniques for rock mass characterization and provide guidance and on how and when the data obtained from these techniques can be used as input for stability analyses. Advantages and limitations of available digital photogrammetry and laser scanning techniques will also be discussed in relation to their cost and the quality of data that can be obtained. A critique of recent research data obtained from remote sensing techniques is presented together with a discussion on use of the data for slope stability analysis. This highlights how data use may be optimized to reduce both parameter and model uncertainty in future slope analyses.

In order to solve the disadvantage of considering slopes as a homogeneous layer in intelligent stability assessment, this paper proposes a compatible three-dimensional convolutional neural network (3-D CNN) to improve the prediction performance in the stability of multilayer slopes. In the 3-D CNN, the slope information is encoded in a format similar to RGB images, with three channels corresponding to the mass density, cohesion, and friction angle of the rock and soil materials, and the parameters within each channel are aligned with the geometry of the slopes to reflect the layered rock and soil. The prior knowledge (actual slope cases and landslide inventories) and digital twin technique are carried out to form a database consisting of 4394 slopes for the proposed 3-D CNN model in the case of difficulty in collecting multilayer slope data. The results showed that, the best 3-D CNN framework achieves the R 2  = 0.929 in 879 testing data, which is 6.3% higher than the best 1-D CNN framework. Finally, the stability of 12 real slope cases around the world was predicted by the optimal 3-D CNN, which obtained an R 2 of 0.795 and an RMSE of 0.158 by comparing the predicted and the analyzed safety factors. The results indicate that the proposed 3-D CNN has compatibility through training with the dataset generated by the digital twin and prior knowledge.