Time-dependent slope stability during intense rainfall with stratified soil water content

Abstract

The Green-Ampt (GA) model is one of the most widely used analytical methods of slope stability under rainfall. However, it may overestimate the soil water content above the wetting front. In this study, a novel approach to evaluate the time-dependent slope stability during intense rainfall based on a modified GA model is presented, and is known as the stratified Green-Ampt (SGA) model. By considering the stratified soil water content above the wetting front, the soil above the wetting front can be divided into saturated and transitional layers, and the SGA model is used to analyze the infiltration process of intense rainfall into slopes. Thereafter, safety factors (Fs) of infinite and finite slopes are derived using the SGA model. In the analysis of an infinite slope, the conventional limit equilibrium method is adopted to calculate the safety factor; as for a finite slope, the residual thrust method is introduced to obtain the safety factor with sliding mass divided into multiple soil slices. The performance of the SGA model is illustrated in two cases: an infinite slope and the Majiagou landslide as a finite slope. The results indicate that compared to the GA model, the calculated wetting front based upon the SGA model moves faster, and the wetting front depth shows a positive correlation with the slope surface angle and rainfall intensity. The evolution of the safety factor above the sliding surface can be divided into three phases, while the evolution of the safety factor above the wetting front can be divided into two phases. The critical time of the slope reaching a less stable state (safety factor is 1.05) or unstable state (safety factor is 1.00) decreases exponentially with an increase in rainfall intensity. In addition, the rainfall has a significant influence on the design of stabilizing piles for the Majiagou landslide. The presented SGA model appears to be accurate to investigate slope stability during intense rainfall events.