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Rainfall infiltration analysis has a great significance to the mitigation and risk assessment of rainfall-induced landslides. The original Green–Ampt (GA) model ignored the fact that a transitional layer exists in infiltration regions of soils under the rainfall permeation; moreover, it cannot effectively analyze the rainfall-infiltrated heterogeneous slope considering the spatial variability of saturated hydraulic conductivity (ks). In this study, an improved GA model is proposed for the rainfall-infiltration analysis of heterogeneous slopes. Four common slope cases are investigated to validate the effectiveness of the proposed model. An infinite slope model is taken as an illustrative example to investigate the distributions of volumetric water content and slope stability under the rainfall infiltration. The results show that the distributions of volumetric water content and factor of safety (Fs) obtained from the proposed model are in very good agreement with the numerical results of the Richards' equation. In contrast, the modified GA model obtains biased distributions of volumetric water content and smaller Fs for the same cases. The results show that the proposed GA model can accurately identify the location of critical slip surface of the slope, and as such it provides an efficient method for risk analysis and control of slopes susceptible to landslide. [ABSTRACT FROM AUTHOR]

Bayesian estimation of spatially varying soil parameters is a challenging task in geotechnical engineering because a large number of random variables need to be learned. To address this challenge, three Bayesian methods are revisited, including Differential Evolution Adaptive Metropolis with sampling from past states [DREAM(zs)] method, Bayesian Updating with Structural reliability methods using Subset Simulation (BUS + SS), and modified BUS with Subset Simulation (mBUS + SS). The differences between the performances (i.e. convergences, computational accuracies, and efficiencies) of these three methods are not well understood. This study systematically investigates the differences of these three methods in the generation of random samples, convergence criterion, model evidence, and estimation of posterior probability of failure in slope reliability updating. Two slope examples are used for the comparative study. It is found that the BUS + SS method performs well not only in the low-dimensional Bayesian inverse problems but also in the high-dimensional Bayesian inverse problems of spatially varying soil parameters. The DREAM(zs) method is preferentially recommended to deal with the low-dimensional Bayesian inverse problems whereas the mBUS + SS method can well tackle the high-dimensional problems. [ABSTRACT FROM AUTHOR]

Deterministic single factor of safety method cannot explicitly account for the influences of various sources of uncertainties (e.g., spatial variability of geomaterials, measurement and transformation uncertainties) in stability design of slopes. Many probabilistic methods have been applied to the reliability‐based design (RBD) of slopes, but they typically require performing numerous deterministic slope stability analyses. In this paper, an efficient reliability‐based design method for spatially varying slopes based on field data is proposed. Here, the RBD of a slope angle is concerned. Reliability‐based design is implemented using an inverse First Order Reliability Method (inverse FORM ‐ IFORM). A sandy slope and a cohesive slope are investigated as examples, respectively, to illustrate the proposed method. The results indicate that the proposed method can quickly obtain rational design schemes of slope angles accounting for the spatial variability of soil properties, measurement and transformation uncertainties based on the field data. It can act as a practical and effective tool for the RBD of slopes in two‐dimensional spatially variable soils. Additionally, it is found that the random field mesh size affects the RBD results significantly, while the probability distribution and horizontal autocorrelation of soil parameters have slight influences on the RBD results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A novel methodology that involves the coupling of Convolutional Neural Networks (CNNs) and a data augmentation technique is proposed for slope reliability calculations. The methodology starts from generating a small set of random field samples, which are then calculated using the shear strength reduction method in the finite-difference scheme to obtain the associated factors of safety. Based on the theoretical relationship between the factor of safety and soil property values derived from the underlying mechanism of the shear strength reduction method, an innovative data augmentation technique is developed to enhance both the quantity and comprehensiveness of the dataset. A CNN model is then trained using the augmented dataset to learn the relationship between the factors of safety and random fields and predict the probability of slope failure. The effectiveness of the methodology is illustrated and validated using a c - φ soil slope and a multi-layered S u slope. The results show that CNNs are effective in interpreting high-dimensional random fields. In addition, through using the data augmentation technique, not only has the predictive capability of the CNN model been effectively boosted, particularly for cases involving low probability levels of failure, but the computational efficiency of the slope reliability analysis has also been improved. [ABSTRACT FROM AUTHOR]

Quantitative risk assessment of slope failure is an important prerequisite for formulating rational strategies for landslide risk mitigation and developing a landslide risk-based warning system. Efficient risk assessment of slope failure in two-dimensional (2-D) spatially variable soils is a challenging problem. This paper proposes an efficient approach for quantitative risk assessment of slope failure considering the 2-D spatial variation of soil properties in the limit equilibrium analysis framework. To facilitate the risk assessment of slope failure, an empirical method is developed for the identification of slope key failure modes. With the proposed approach, the key failure modes of the spatially variable slope can be well identified and their contributions to the risk of slope failure can be effectively quantified. Finally, a two-layered soil slope is studied to illustrate the proposed approach. It is found that the prominent advantages of the proposed approach include being computationally efficient and accounting for the effect of the correlations among the failure modes. [ABSTRACT FROM AUTHOR]

How to efficiently assess the system reliability of rock slopes is still challenging. This is because when the probability of failure is low, a large number of deterministic slope stability analyses are required. Based on Subset simulation, this paper proposes an efficient approach for the system reliability analysis of rock slopes. The correlations among multiple potential failure modes are properly accounted for with the aid of the “max” and “min” functions. A benchmark rock slope and a real engineered rock slope with multiple correlated failure modes are used to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Monte Carlo simulation (MCS) provides a conceptually simple and robust method to evaluate the system reliability of slope stability, particularly in spatially variable soils. However, it suffers from a lack of efficiency at small probability levels, which are of great interest in geotechnical design practice. To address this problem, this paper develops a MCS-based approach for efficient evaluation of the system failure probability of slope stability in spatially variable soils. The proposed approach allows explicit modeling of the inherent spatial variability of soil properties in a system reliability analysis of slope stability. It facilitates the slope system reliability analysis using representative slip surfaces (i.e., dominating slope failure modes) and multiple stochastic response surfaces. Based on the stochastic response surfaces, the values of are efficiently calculated using MCS with negligible computational effort. For illustration, the proposed MCS-based system reliability analysis is applied to two slope examples. Results show that the proposed approach estimates properly considering the spatial variability of soils and improves the computational efficiency significantly at small probability levels. With the aid of the improved computational efficiency offered by the approach, a series of sensitivity studies are carried out to explore the effects of spatial variability in both the horizontal and vertical directions and the cross-correlation between uncertain soil parameters. It is found that both the spatial variability and cross-correlation affect significantly. The proposed approach allows more insights into such effects from a system analysis point of view. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to investigate the effect of rock bolt corrosion on time-dependent system reliability of anchored rock slopes. First, a corrosion degradation model for reinforcing steel bars in concrete is selected to model the uniform corrosion of rock bolts. Second, two typical failure modes of rock bolts due to corrosion and the resultant slope failure modes are identified. Subsequently, a Monte Carlo simulation-based reliability approach is proposed to perform system reliability analysis of anchored rock slopes. Finally, an example of an anchored rock slope is worked out to investigate the effect of rock bolt corrosion on the time-dependent reliability of the anchored rock slope. The results indicate that the probability of slope failure upon yield failure of rock bolts at the free length only increases slightly with time. On the contrary, the probability of slope failure upon bond failure at the bolt–grout interface increases dramatically with time. The system probability of failure of the anchored rock slope decreases with increasing thickness of bolt cover and increases with increasing water–cement ratio of the grout. During the design and construction of pre-stressed rock bolts, a certain thickness of bolt cover should be guaranteed and the water–cement ratio of the grout should be strictly controlled to enhance the long-term stabilization effect of rock bolts. [Copyright &y& Elsevier]

Spatial variability of soil properties was rarely taken into account directly in traditional slope stability analyses, rather some "average" or suitably "pessimistic" properties are assumed to act across the whole region of interest. In the last two decades, a large portion of published research papers on slope stability have tried to explicitly model the spatial variability of soil properties. In the first decade, research mainly focused on showing the importance of modeling the spatial variability directly in probabilistic slope stability analysis. In the last decade, a rapid development was observed including quantitative risk assessment of slope failure, improving computational efficiency, and directly using site investigation and field monitoring data. This review tries to summarize these advances in the hope that future research directions can be identified. [ABSTRACT FROM AUTHOR]

The probability distributions of soil parameters can be updated with limited site-specific information via probabilistic back analyses. The updated probability distributions can be further used for a more realistic slope reliability assessment. However, few attempts have been made to conduct probabilistic back analyses accounting for the inherent spatial variability of soil properties. The main challenge is the so-called curse of dimensionality encountered when thousands of random variables are used to model spatial variability. The BUS approach (Bayesian Updating with Structural reliability methods) can tackle the high-dimensional back analysis problem by transforming it into an equivalent structural reliability problem, but it requires an evaluation of the likelihood multiplier that is tedious and time-consuming. This paper proposes a modified BUS approach for the probabilistic back analysis of soil parameters and reliability updating of slopes in spatially variable soils. With this approach, the curse of dimensionality and evaluation of the likelihood multiplier can be effectively avoided, and the computational accuracy is significantly improved. Two slope examples are investigated to illustrate the effectiveness of the proposed approach. The soil parameters and their probability distributions for a slope section can be well determined through a probabilistic back analysis, which facilitates an accurate identification of the causes of slope failures and a deep understanding of the actual performance of in-service slopes. • A modified BUS approach is proposed for the probabilistic back analysis of spatially varying soil parameters. • A stopping criterion for the subset simulation in the modified BUS approach is developed. • Two slope examples are investigated to illustrate the effectiveness of the proposed approach. • The high-dimensional probabilistic back analysis problems can be well tackled by the proposed approach. • The proposed approach provides a practical tool for the probabilistic back analysis of spatially varying soil parameters. [ABSTRACT FROM AUTHOR]