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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.

Particle swarm optimization (PSO) is an evolutionary computation approach to solve nonlinear global optimization problems. The PSO idea was made based on simulation of a simplified social system, the graceful but unpredictable choreography of birds flock. This system is initialized with a population of random solutions that are updated during iterations. Over the last few years, PSO has been extensively applied in various geotechnical engineering aspects such as slope stability analysis, pile and foundation engineering, rock and soil mechanics, and tunneling and underground space design. A review on the literature shows that PSO has utilized more widely in geotechnical engineering compared with other civil engineering disciplines. This is due to comprehensive uncertainty and complexity of problems in geotechnical engineering which can be solved by using the PSO abilities in solving the complex and multi-dimensional problems. This paper provides a comprehensive review on the applicability, advantages and limitation of PSO in different disciplines of geotechnical engineering to provide an insight to an alternative and superior optimization method compared with the conventional optimization techniques for geotechnical engineers.

Rainfall leads to the deterioration of slope stability conditions, while intense rainfall has been commonly associated with landslides on natural or engineered slopes. Deep excavations, typically related to geo-resources exploitation, e.g., in the case of surface mining, are often affected by rainfall events that jeopardize their stability. In this work, rainfall infiltration is directly incorporated in the slope stability analysis; this investigation is currently missing from the literature as mainly empirical methods are used regarding deep excavations. The very deep slopes from lignite mines are employed as typical examples, often reaching 200 m and presenting smooth inclinations and fine-grained soils. A general numerical framework was used; the safety factor’s deterministic analysis was supplemented by a Monte Carlo investigation to determine the probability of failure. The importance of the involved parameters—slope geometry, rainfall intensity, and soil properties—was studied through a parametric analysis. Initially, a typical slip surface is presented, relatively deep and reaching from toe to crest. The critical mechanism was the development—after the rainfall—of a smaller and more local than the initial (before rainfall) slip surface. Although the final surface is smaller than the initial one, it can be more than 50 m high denoting a significant hazard. The most influential parameters are rainfall intensity, soil permeability, and slope height. This study can serve as a basis for similar preliminary analysis in practice. Stability and reliability analysis reveals the need to supplement conventional safety factors with the probability of failure for a broader and improved overview.

Climate change is known to cause alterations in weather patterns and disturb the natural equilibrium. Changes in climatic conditions lead to increased environmental stress on embankments, which can result in slope failures. Due to wetting–drying cycles, expansive clayey soil often swells and shrinks, and matric suction is a major factor that controls the behavior. Increased temperature accelerates soil evaporation and drying, which can cause desiccation cracks, while precipitation can rapidly reduce soil shear strength. Desiccated slopes on embankments built with such soils can cause surficial slope failures after intense precipitation. This study used slope stability analysis to quantify how climate-change-induced extreme weather affects embankments. Historic extreme climatic events were used as a baseline to estimate future extremes. CMIP6 provided historical and future climatic data for the study area. An embankment was numerically modeled to evaluate the effect on slope stability due to the precipitation change induced by climate change. Coupled hydro-mechanical finite element analyses used a two-dimensional transient unsaturated seepage model and a limit equilibrium slope stability model. The study found that extreme climatic interactions like precipitation and temperature due to climate change may reduce embankment slope safety. The reduction in the stability of the embankment due to increased precipitation resulting from different greenhouse gas emission scenarios was investigated. The use of unsaturated soil strength and variation of permeability with suction, along with the phase transition of these earthen embankments from near-dry to near-saturated, shows how unsaturated soil mechanics and the hydro-mechanical model can identify climate change issues on critical geotechnical infrastructure.

This study aims to enhance the domain of slope stability assessments by integrating unsaturated soil mechanics into machine learning (ML) methodologies. It addresses both regression and classification problems to predict the factor of safety (FOS) without the need to determine the critical slip surface iteratively. Orthogonal Latin hypercube sampling is employed to generate a comprehensive dataset of 16,219 data points. This synthetic data is obtained using an advanced analytical model in MATLAB, based on the grid and radius method coupled with the Morgenstern Price method of slope analysis to identify FOS corresponding to the critical slip surface. Six ML models—multiple linear regression , support vector regression, XGBoost, random forest (RF), Gaussian process regression (GPR), and artificial neural network (ANN)—are evaluated and compared to identify the best model for FOS prediction. Among these, the GPR model exhibits superior performance, with R 2 values of 99.8%, 99.7%, and 99.4% for training, validation, and testing datasets, respectively. The mean absolute error metrics are 1.0%, 1.2%, and 1.6%, and the mean squared error metrics are 0.3%, 0.5%, and 1.2%, respectively, demonstrating the model’s robustness on unseen geotechnical slope data. For slope failure classification, five ML models—logistic regression (LR), support vector machine (SVM), RF, XGBoost, and ANN—are utilized. The ANN model exhibited the highest performance metrics with a perfect recall (100%), as well as the highest F1-score (99.9%), specificity (98.1%), precision (99.8%), and accuracy (99.8%), making it the most effective model for this task. The results indicate that while all models perform well, the ANN and SVM models exhibit the highest overall performance metrics. The ANN model’s superior performance in the rank analysis demonstrates its robustness and reliability for slope failure detection. A modified representation of the confusion matrix is also presented in the current work for a reader-friendly visualization of the results of each model based on the precision, recall, F1-score, specificity, and accuracy metrics. The study also emphasizes the importance of interpretability by using Shapley Additive Explanations to ensure transparency and practical applicability. This approach advances data-centric geotechnics and provides reliable predictive capabilities suited to real-world geological conditions with fluctuating moisture and complex soil–water interactions.

The common slope stability analysis is incapable of accurately forecasting shallow slides where suction pressures play a critical role. This realization is used for elaborate stability analyses which include soil suction to better predict rainfall-induced slides at railway embankment at Malda where three known cases of slope failures and train derailments occurred after heavy rainfall. The relationship between the soil–water content and the matric suction is established for the embankment soil. It is then used in the coupled analyses of seepage and slope stability to estimate performances of the embankment at different intensity and duration of rainfall. The numerical simulations are performed with the FE code Geo-Studio. The numerical results show significant reduction in the factor of safety of the railway embankment with the increase in the intensity and duration of rainfall. The effectiveness of the proposed mitigation measures including placement of 2 m-wide free draining rockfill across the slopes and drilling 5-m-long sheet pile wall at the toe of the embankment is studied numerically. The study confirms that the proposed mitigation measures effectively increase the factor of safety of the embankment and stabilizing it even in case of a heavy rainfall of 25 mm/h over 12 h.

The influence of vegetation on mechanical and hydrological soil behavior represents a significant factor to be considered in shallow landslides modelling. Among the multiple effects exerted by vegetation, root reinforcement is widely recognized as one of the most relevant for slope stability. Lately, the literature has been greatly enriched by novel research on this phenomenon. To investigate which aspects have been most treated, which results have been obtained and which aspects require further attention, we reviewed papers published during the period of 2015–2020 dealing with root reinforcement. This paper—after introducing main effects of vegetation on slope stability, recalling studies of reference—provides a synthesis of the main contributions to the subtopics: (i) approaches for estimating root reinforcement distribution at a regional scale; (ii) new slope stability models, including root reinforcement and (iii) the influence of particular plant species, forest management, forest structure, wildfires and soil moisture gradient on root reinforcement. Including root reinforcement in slope stability analysis has resulted a topic receiving growing attention, particularly in Europe; in addition, research interests are also emerging in Asia. Despite recent advances, including root reinforcement into regional models still represents a research challenge, because of its high spatial and temporal variability: only a few applications are reported about areas of hundreds of square kilometers. The most promising and necessary future research directions include the study of soil moisture gradient and wildfire controls on the root strength, as these aspects have not been fully integrated into slope stability modelling.

Long description: Barodesy is a constitutive model for granular materials such as sand and clay. It is based on the asymptotic behaviour of granular media at a constant deformation rate. In this work the existing sand version of Barodesy is improved. For this purpose, the underlying scalar equations are simplified using different concepts from soil mechanics. The improved version is also compared with laboratory tests and different elastoplastic and hypoplastic constitutive relations. Also the stability of slopes and advanced stress paths such as the rotation of the princple stresses are investigated with these models.

Soil nailing is widely used to improve slope stability, yet the mechanical response of soil-nailed slopes subjected to surcharge loading remains insufficiently understood, particularly at the mesoscopic level. In this study, the discrete element method (DEM) was employed to investigate the progressive failure process, macroscopic stability, and micromechanical behaviors of a soil-nailed slope subjected to point loading and distributed linear loading with varying magnitudes and loading widths. The numerical results show that surcharge loading significantly reduces slope stability and increases deformation. Under point loading, the safety factor decreases continuously with increasing load magnitude and the deformation remains highly localized. Under linear loading, the safety factor decreases sharply as the loading width increases from 0 to approximately 1.5–2.0 m, and then tends to stabilize, whereas displacement continues to increase monotonically. Micromechanical analyses indicate that surcharge loading promotes rapid accumulation of elastic strain energy, intensifies particle rotation, and increases stress heterogeneity within the slope. Load distribution also strongly affects the force transfer mechanism in the reinforcement system: narrow loads are mainly resisted by upper nails, while wide distributed loads mobilize deeper nails and shift the stabilizing effect to lower reinforcement layers. The results suggest that surcharge loads near the slope crest should be carefully controlled, and that sufficient setback distance is important for limiting stress transmission into the active failure zone. These findings provide insight into the failure mechanism of soil-nailed slopes under surcharge loading and offer practical guidance for reinforcement design.

Vegetation is widely recognized as a key factor controlling the occurrence of shallow landslides in vegetation-covered areas. In such areas, the root system plays a critical role both in enhancing root-soil mechanical properties and in changing soil hydrological properties. However, owing to its complexity and nonuniformity, the root system is always neglected or simplified in existing infiltration process models, making it difficult for such models to reflect the influence of root systems on shallow landslides. Considering the shallow landslide cluster that happened in Mengdong (Yunnan Province, Southwest China) in 2018, this study quantitatively investigated the root distribution and obtained the prevailing physical and hydraulic properties through density tests, shear strength tests, and saturated seepage tests. Field investigation indicated that the root system distribution obeys an exponentially decayed polynomial model. In the entire profile, the maximum root area density was 0.145 mm2 cm−2 at depth of 20–40 cm, which comprised 483 roots, and 80% of the roots were distributed above the slip surface. Laboratory test results indicated that root-soil above the slip surface had lower density (minimum density: 1.04 g cm−3) and higher porosity (maximum porosity: 61.23%) than soil below, which induced permeability 10–17 times higher above the slip surface. A potential relationship was found between slip surface location and root system distribution. Differences in root distribution and resultant changes in the hydrological properties of soil might reduce slope stability during extreme rainfall, which could induce shallow landslides. This research could be used as reference for slope stability and hydraulic process analysis in forested areas.