Efficient Probabilistic Stability Analysis of Geosynthetic Reinforced Slopes Using Collocation-Based Stochastic Response Surface: Fid-Bau Portal

Efficient Probabilistic Stability Analysis of Geosynthetic Reinforced Slopes Using Collocation-Based Stochastic Response Surface

Agarwal, E. / Pain, A.

Abstract

This paper presents an efficient and robust probabilistic approach to analyze the geosynthetic reinforced slopes (GRSs). The deterministic analysis will be performed that employs the rigorous (5N−1) formulation of the horizontal slice method (HSM), which is made efficient using the nonlinear constrained optimization (N = number of slices). The probabilistic analysis will be performed using a surrogated assisted Monte Carlo simulation (MCS). Collocation based stochastic response surface (SRS) will be employed to build the surrogate model using a third-order multidimensional polynomial chaos expansion (PCE). The random variables include the internal friction angle of soil (ϕ), soil unit weight (γ), and tensile strength of the reinforcement (T_u). Dependence between the random variables will be established using the Gaussian copula. A comparative analysis of the results with the First-Order Reliability Method (FORM) will be presented. The performance function will be evaluated 125 times using the SRS method in contrast to the direct MCS where it will be evaluated ≥50,000 times. This will reduce the computation time from approximately 2 days to approximately 20 min. In addition, the influence of correlation between the random variables will be highlighted comprehensively by adopting a wide range of correlation coefficients. This study concludes that the SRS method that incorporates an accurate deterministic model is a highly efficient and powerful approach to analyze GRSs probabilistically.