Improved rainfall infiltration model for probabilistic slope stability assessment considering transition layers and spatial variability

Implementing a reliable rainfall infiltration model is crucial for predicting rainfall-induced slope failures, which are pivotal for disaster prevention and control. Although the classic Green-Ampt (GA) model is widely used, it does not account for the influence of multiple soil layers and the presence of transition layers formed during rainfall infiltration. This study introduces an improved and generalized method for calculating infiltration rates across multiple soil layers. The thickness of the transition layer is estimated based on the relationship between infiltration rates at different depths. Building on this, an improved GA model is proposed to analyze rainfall infiltration in slopes with spatially variable saturated hydraulic conductivity. This model is then applied to an infinite slope example to analyze seepage and stability in both homogeneous and heterogeneous soils under rainfall events. The improved GA model is systematically compared with two existing GA models and the numerical solution of the Richards equation in both cases, and the results demonstrate good convergence and high accuracy associated with the improved GA model. In addition, the results identify that the thickness of the transition layer depends on the saturated hydraulic conductivity of the transition layer, as well as the infiltration rate and volumetric water content (VWC) at the top of the transition layer. Furthermore, results show that the improved GA model can capture the influence of the spatial variability of saturated hydraulic conductivity on the distribution of VWC in slopes and, consequently, on slope stability. This improved GA model provides a solid theoretical foundation for analyzing rainfall infiltration in slopes while fully accounting for the spatial variability of soil property. This advancement contributes to more effective strategies for preventing rainfall-induced landslide disasters.