Probabilistic assessment of rainfall infiltration-induced instability in deposit slopes

Rainfall infiltration plays a critical role in controlling pore pressure evolution and triggering instability in natural slopes composed of heterogeneous soil–rock deposits. A probabilistic framework combining interval approximation reformulation with adaptive Kriging-assisted Monte Carlo simulation is developed to assess rainfall infiltration-induced instability in deposit slopes. The adaptive Kriging is employed using a U-learning enrichment criterion to surrogate the limit-state response. The approach avoids the dimensional limitations associated with kernel density estimation in high-dimensional probability spaces. The model is applied to the Dahua deposit landslide located along the Lancang River in Yunnan Province, China. Five gradient rainfall scenarios are considered, and the surrogate framework is calibrated against a physical model test. The results show that compared with conventional Monte Carlo approaches, the proposed framework significantly reduces computational cost while preserving accuracy in probabilistic predictions. The interval approximative adaptive Kriging based on Monte Carlo method reproduces the experimental displacement response with a mean relative error of approximately 0.05%, where the simple interval adaptive method based on Monte Carlo method produces an error close to 3%. The computational time is reduced from nearly 18 h to approximately 2 h under single-thread execution on a standard processor. The probabilistic analysis further shows that the maximum failure probability increases monotonically with cumulative infiltration depth as the rainfall return period increases from 25 to 100 years. The study provides insights into uncertainty propagation in rainfall-driven fluid–solid systems and offers a practical tool for probabilistic assessment of infiltration slope instability.