A comparative study of different reliability methods for high dimensional stochastic problems related to earth dam stability analyses

This article presents a probabilistic stability analysis of an existing earth dam including uncertainty quantification of soil properties and a reliability analysis of the dam sliding stability. The analyses are conducted by exploiting the available field measurements, and then by performing the Monte Carlo Simulation (MCS). Random fields and random variables approaches are both used to model the soil variabilities. Two left-and-right-bounded distributions, beta and truncated normal, are considered for the input random variables in the reliability analysis, and the influence of the horizontal autocorrelation distance on the failure probability is investigated. A comparative study of different reliability methods is also carried out by comparing with the results of the MCS. The considered reliability methods are: the Subset Simulation (SS), the Moment Method (MM), the Sparse Polynomial Chaos Expansion in combination with the Global Sensitivity Analysis (SPCE/GSA) and the Sparse Polynomial Chaos Expansion in combination with the Sliced Inverse Regression (SPCE/SIR). The comparative study shows that all these methods can give accurate results in term of the dam failure probability with small errors. It is also found that the most accurate method is the SPCE/GSA and the most efficient method is the SS.