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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.

The use of vegetation as a Nature-based Solution (NbS) for shallow landslide risk reduction is receiving increased attention in the scientific community. Vegetation can contribute to slope stability through both hydrological and mechanical processes. Slope stability models are valuable tools to quantify the performance of vegetation management as a slope stabilizing measure. The aim of this study is to provide a comprehensive overview of how both the mechanical and hydrological effects of vegetation are parametrized in existing slope stability models. To this end, a systematic review of the peer-reviewed literature published between January 2000 and June 2023 is conducted. The review has shown that existing slope stability models that include effects of vegetation, do so with various degrees of complexity with regard to how accurately they attempt to mimic the physical processes present in nature. There is a need for further validation of existing models, especially extended to areas in the global south and in colder regions. Moreover, studies of time dependency in vegetation reinforcement capabilities are lacking, an aspect which is especially important in the light of climate change. This review provides valuable guidance for researchers and practitioners in their choice of appropriate slope stability models for their studies.

We present the block theory-based secondary toppling stability analysis method (BTSTSA), an advanced and novel method specifically designed to assess secondary toppling failures in slopes. This innovative method comprehensively accounts for various failure mechanisms and computes the factor of safety (F.S) for rock slopes. Grounded in Block theory principles, particularly the key-block method, and supplemented by limit equilibrium techniques, BTSTSA offers a practical and reliable analytical framework. Our investigation focused on five discontinuous rock slopes in the South Pars region, southwest Iran, which are affected by composite toppling failure mechanisms. The stability analysis results were meticulously verified using the Aydan-Kawamoto method, a recognized benchmark in the field. Comparative analysis consistently demonstrated that the BTSTSA approach generates more conservative estimates of the F.S compared to the Aydan-Kawamoto method. This conservatism underscores the robustness and reliability of the BTSTSA framework and highlights its implications for practical engineering applications. The integration of this innovative analytical method with data from these investigations offers crucial insights for geotechnical engineers, equipping them to manage the complexities of secondary toppling failures in discontinuous rock slopes. These findings emphasize the importance of considering conservatism in engineering applications and provide a more accurate and reliable assessment of slope stability, particularly concerning secondary toppling failures, thereby benefiting geotechnical engineering practices.

A rainfall-induced landslide occurred in the north highwall of the Fushun west open pit at 5:00 (UTC + 8) on July 26, 2016, in China. The landslide was about 3.1 × 106 m3 and caused considerable destruction of houses, roads, and railways. Field investigations, laboratory tests, and numerical analyses have been performed to explore the failure characteristics and formation mechanism of the landslide. The landslide was divided into three parts: the crack area, the sliding body area, and the accumulation area. The X-ray diffraction and scanning electron microscope techniques were used to reveal the mineral composition and microstructure of the landslide material. Then, a conceptual model of the landslide mechanism was constructed and the process of the landslide was divided into four stages: unloading and cracking stage; sliding and partial locking stage; shearing out and failure stage; and flowing and accumulating stage. A combined seepage and stability analysis was performed to explore the mechanism of the landslide by numerical simulation. The relationship of volumetric water content and hydraulic conductivity to matric suction was established to describe the hydraulic characteristics of the slope material under infiltration. The results show that the maximum matric suction in the slope decreases continually with the increase of rainfall intensity and duration, and the north highwall will not be stable after 7.2 h for 16 mm/h of rainfall. The original slope was stable and heavy rainfall triggered the landslide, and the obtained critical failure surface matched the field survey closely. The findings improved understanding of the failure mechanism and process of rainfall-induced landslides may be used for evaluating the stability of slopes and early identification for both active and inactive open-pit mines.

Heavy rainfall and engineering slope cutting are two key factors that trigger unstable red clay landslides with red clay soil as the sliding mass in the mountainous and hilly areas of South China. It is important to study the influence of engineering slope cuttings on changes in slope stability under heavy rainfall conditions. First, by summarising the main evolution and failure characteristics of landslides in Ganzhou City, Jiangxi Province, China, a general landslide physical model of red clay landslides with universal significance is constructed. Then, the rainfall characteristics of Ganzhou City are analyzed, and heavy rainfall occurring once in a period of 50 years is applied to the general landslide physical model. Concurrently, the influences of different engineering slope cutting distances and angles on the changes in slope stability are explored. Finally, saturated and unsaturated infiltration theory and nonlinear finite element analysis are used to calculate the stability and pore water pressure changes in the landslides under the above-described conditions of heavy rainfall and engineering slope cutting. The results show that: (1) When there is no rainfall, the stability coefficient of the red clay slope rapidly decreases with increasing distance and/or angle of slope cutting; for a certain slope cutting angle, the stability coefficients of the landslide show a convex upward decrease with increasing slope cutting distance; for a certain slope cutting distance, the stability coefficients show a linear decrease with a gradually increasing slope cutting angle. (2) Under 5 days of heavy rainfall reaching 210 mm, the engineering slope cutting forms have increasing influence on stability reduction in a red clay slope. For a certain slope cutting distance, as the slope cutting angle increases, the slope stability coefficient shows a slow decrease. For a certain slope cutting angle, a greater slope cutting distance means a faster decrease in the slope stability coefficient. (3) The pore water pressure along the potential sliding surface of the red clay slope under heavy rainfall gradually increases, and there is a good inverse correspondence between the changes in the pore water pressure and the stability coefficient.

Slope stability prediction is a crucial aspect of landslide disaster prevention and control. Traditional methods are physics-based or data-driven, which are limited by their respective reliance on geotechnical engineering expertise and scarce real-world slope data. This study developed a slope stability prediction model that integrates physics and data through an innovative transfer learning (PD-TL) framework. Four TrAdaBoost transfer learning models were developed using Support Vector Classification (SVC), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as base learners. At the data level, the finite element analysis cases significantly expanded the sample size to resolve issues with data scarcity and model performance limited by sample size. At the model level, most traditional data-driven methods assume that training and test datasets follow the same distribution; in contrast, the proposed model relaxes this assumption and thus offers a promising solution to the scarcity of training data. At the knowledge level, the model incorporates geotechnical engineering expertise to enhance performance by leveraging field-specific insights and principles. Among the TrAdaBoost (-SVC, LR, DT, RF) models tested, TrAdaBoost-SVC showed superior slope stability prediction performance (ACC = 0.878, Precision = 0.794, Recall = 0.931, F1 = 0.857, AUC = 0.928, TNR = 84%). Its superiority demonstrates the value of integrating physics and data into a double-driven framework, which outperforms traditional data-driven and ensemble learning methods. The results also may represent suggestions for future slope stability analysis and broader engineering applications.

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.

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.