Machine learning-aided reliability analysis of rainfall-induced landslide of root-reinforced slopes

Abstract

Estimating the failure probability of rainfall-induced landslides is often challenging as the triggering mechanism is influenced by a number of parameters whose uncertainty is difficult to quantify and, in practice, is neglected. The reinforcing effect of vegetation on natural slopes adds to the complexity of the stability analysis. In this study, we present the application of a coupled hydro-mechanical model for the effect of plant roots on soil shear strength. First, a deterministic approach is adopted. Then, a reliability analysis of a root-reinforced slope subjected to rainfall is performed by considering the inherent variability of the soil and root properties. The probability of failure is estimated with machine learning surrogate models, which approximate the nonlinear relationship between constitutive parameters and slope displacements at different time steps. The machine learning algorithms are trained on a small dataset. The extreme gradient boosting is the best-performing algorithm with R2 ≥ 0.975 and is then employed to estimate the probability of failure on a larger dataset of one million datapoints with higher accuracy.