A physical information-assisted method for efficient system reliability analysis of soil slope

Monte Carlo simulation (MCS) is widely applied to slope system reliability analysis, but it is often criticized for the drawback of the heavy computational burden involved in deterministic evaluations of slope systems. To address this issue, this paper proposes a novel method for efficient system reliability analysis of soil slope based on MCS, which incorporates the physical information of a soil slope to enhance computational efficiency. The method employs strength reduction sampling to identify a limited number of critical slope samples in a limit state, and considers the intrinsic relationship between soil shear strength parameters and the slope stability to determine the safety or failure of a slope sample based on the obtained critical samples without extra calculations of safety factor. As byproducts, the failure slope samples obtained are further utilized to identify the most probable points (MPPs) of failure, which are essential for determining the significant failure modes and understanding the slope failure mechanisms. A soil slope example is provided to validate the method.