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<p>This study investigates large deformation processes after slope failure using random fields and the Material Point Method (MPM), with a focus on optimizing site investigation through boreholes to reduce uncertainties in predicting soil slope post-failure deformations. A two-dimensional random field was created using covariance matrix decomposition and a two-directional 1-D Markovian covariance function, and a Monte Carlo simulation was conducted to assess the statistical response based on the generated random fields. The strength reduction method (SRM), based on the finite element (FE) technique, was employed to evaluate the FE soil slope model and calculate the Factor of Safety (FoS) using the gradient of maximum slope displacement. The Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier predicted the failure zone identified by the Random Material Point Method (RMPM), while Bayesian updating adjusted the conditional probabilities of decision variables and component reliability. Key findings include capturing the soil slope failure mechanism, evaluating slope failure risk, and determining optimal borehole deployment to mitigate this risk.</p>

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Quantitative Risk Assessment of Static Slope Failure Using Random Fields and the Material Point Method

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Javad Sadoghi Yazdi

摘要

This study investigates large deformation processes after slope failure using random fields and the Material Point Method (MPM), with a focus on optimizing site investigation through boreholes to reduce uncertainties in predicting soil slope post-failure deformations. A two-dimensional random field was created using covariance matrix decomposition and a two-directional 1-D Markovian covariance function, and a Monte Carlo simulation was conducted to assess the statistical response based on the generated random fields. The strength reduction method (SRM), based on the finite element (FE) technique, was employed to evaluate the FE soil slope model and calculate the Factor of Safety (FoS) using the gradient of maximum slope displacement. The Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier predicted the failure zone identified by the Random Material Point Method (RMPM), while Bayesian updating adjusted the conditional probabilities of decision variables and component reliability. Key findings include capturing the soil slope failure mechanism, evaluating slope failure risk, and determining optimal borehole deployment to mitigate this risk.