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This paper presents the probabilistic analysis of landslides in spatially variable soil deposits, modeled by a stochastic framework which integrates the random field theory with generalized interpolation material point method (GIMP). Random fields are simulated using Cholesky matrix decomposition (CMD) method and Latin hypercube sampling (LHS) method, which represent material properties discretized into sets of random soil shear strength variables with statistical properties. The approach is applied to landslides in clayey deposits under undrained conditions with random fields of undrained shear strength parameters, in order to quantify the uncertainties of post-failure behavior at different scales of fluctuation (SOF) and coefficients of variation (COV). Results show that the employed approach can reliably simulate the whole landslide process and assess the uncertainties of runout motions. It is demonstrated that the natural heterogeneity of shear strength in landslides notably influences their post-failure behavior. Compared with a homogeneous landslide model which yields conservative results and underestimation of the risks, consideration of heterogeneity shows larger landslide influence zones. With SOF values increasing, the variances of influence zones also increase, and with higher values of COV, the mean values of the influence zone also increase, resulting in higher uncertainties of post-failure behavior.

Coseismic landslides are common secondary earthquake geohazards in meizoseismal areas. Newmark sliding block permanent displacement method has been widely adopted to develop regional coseismic landslide hazard maps. However, uncertainties from the slope parameters (e.g., cohesion, pore water pressure, and block thickness) are not commonly considered in the ground displacement predictions. This study proposes a novel framework that consists of two uncertainty assessment methods of Monte Carlo and logic tree simulations (MCS and LTS) with seven different displacement regression functions to predict the regional coseismic landslides' permanent displacement. Compared with the existing methods, the proposed framework is argument-driven, avoiding huge number of repetitive simulations. The Jiuzhaigou earthquake, in China, is considered as an illustrative example to compare the performance of the framework with considered regression functions. The corresponding results show that using LTS, with a certain regression function, leads to better predictions compared to using MCS. It is demonstrated that the proposed framework can provide a meaningful measure for making informed decisions to diminish the potential risk of earthquake induced landslides, and/or generating emergency strategies to mitigate post-earthquake consequences. It should be noted that the application of the proposed method for deposits where the soil strength parameter values do not fit the normal distribution may be limited as only normal distribution for soil strengths is considered in this study. [ABSTRACT FROM AUTHOR]

Natural soils often exhibit an anisotropic fabric pattern as a result of soil deposition, weathering, or filling. This paper aims to investigate the effect of soil interdependent anisotropy and fabric orientation on runout motions of landslides and evaluate the most critical fabric orientation for the post-failure behavior. The shear strength properties of soil deposit (i.e., cohesion c and friction angle φ ) are modeled as negatively cross correlated bivariate random fields. The results reveal that the spatial variability and the negative cross-correlation of c and φ notably influence the post-failure behavior. In addition, the rotation of soil layer orientation significantly affects the runout motion. Based on the analyses, the deposition orientation of 30 ∘ is identified to produce the highest mean value and standard deviation of the runout distance. The findings from this study highlight the importance of considering the orientation of soil stratification, rather than only the magnitude of shear strength, in assessing the post-failure behavior of a landslide. [ABSTRACT FROM AUTHOR]

In landslide analysis, reliable prediction of landslide's influence zone is significantly difficult due to the inherent heterogeneity of soils and their spatially varying geological properties. In this paper, a probabilistic framework is proposed to evaluate the landslide hazard zoning through the prediction of their influence zone considering the effects of heterogeneous soil properties. The material point method is used to simulate the large deformations during landslide failure. The spatial variation in the shear strength parameters of soils at undrained conditions is modeled by random fields, which are discretized by the Cholesky matrix decomposition method to incorporate the effects of the soil spatial heterogeneity on the postfailure deformations. Furthermore, a practical landslide hazard zoning is conducted to quantitively evaluate the level of disaster for facilities or structures located in the vicinity of the slope by using the exceedance probability of influence distance and runout distance. Five categories based on different thresholds of exceedance probability are used to visualize the area potentially affected by the landslide. To demonstrate the capability of the stochastic assessment framework, a benchmark example of the heterogeneous clayey slope is simulated, and the associated hazard zone is predicted and discussed; it presents a practical procedure for improving the landslide influence zone prediction and provides new insights for hazard zoning. [ABSTRACT FROM AUTHOR]

Accurate assessment of slope failures and their large deformations is critical for effective landslide mitigation. This study introduces the new Cooperative Stochastic Material Point Method (CSMPM), addressing challenges in probabilistic characterization of slope large deformations considering three-dimensional (3D) soil heterogeneities. The method employs an enhanced Karhunen-Loève (KL) expansion to model 3D soil spatial variability efficiently. By using rough and refined grids, derived through the enhanced KL expansion, the study achieves computational efficiency without compromising accuracy. By combining the computational advantages of the rough grid with the precision of the refined grid, the CSMPM enables efficient probabilistic analysis of 3D heterogeneous slopes. The results demonstrate its capability to identify slope large deformation failure modes and quantify the associated failure probability. Notably, the shallow failure mode exhibits fan-shaped horizontal diffusion, introducing uncertainty, while the compound failure mode presents challenges in landslide prevention. The progressive failure mode poses the highest hazard. Horizontal heterogeneities significantly influence both large deformation likelihood and failure modes, emphasizing the importance of 3D soil spatial variability in geotechnical reliability assessments. The CSMPM, with its innovative approach, proves to be a practical tool for enhancing our understanding of geohazards and associated uncertainties, as well as large deformations. It provides valuable insights for improving risk assessment of slope hazards. [ABSTRACT FROM AUTHOR]