A Practical Framework for Probabilistic Analysis of Embankment Dams

Abstract

Uncertainties, such as soil parameters variability, are often encountered in embankment dams. Probabilistic analyses can rationally account for these uncertainties and further provide complementary information (e.g., failure probability and mean/variance of a model response) than deterministic analyses. This chapter introduces a practical framework, based on surrogate modeling, for efficiently performing probabilistic analyses. An active learning process is used in the surrogate model construction. Two assessment stages are included in this framework by respectively using random variables (RV) and random fields (RF) for the soil variability modeling. In the first stage, a surrogate model is coupled with three probabilistic methods in the RV context for the purpose of providing a variety of useful results with an acceptable computational effort. Then, the soil spatial variability is considered by introducing RFs in the second stage that enables a further verification on the structure reliability. The introduced framework is applied to an embankment dam stability problem. The obtained results are validated by a comparison with direct Monte Carlo Simulations, which also allows to highlight the efficiency of the employed methods.