System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling

This paper presents a system probabilistic stability evaluation method for slopes based on Gaussian process regression (GPR) and Latin hypercube sampling. The analysis is composed of three parts. Firstly, Latin hypercube sampling is adopted to generate samples for constructing the response surface. Then, based on the samples, Gaussian process regression, which is a popular machine learning technique for nonlinear system modeling, is used for establishing the response surface to approximate the limit state function. Finally, Monte Carlo simulation is performed via the GPR response surface to estimate the system failure probability of slopes. Five case examples were examined to verify the effectiveness of the proposed methodology. Computer simulation results show that the proposed system reliability analysis method can accurately give the system failure probability with a relatively small number of deterministic slope stability analyses.