Estimating failure probabilities using an adaptive variant of stochastic spectral embedding

Authors

P.-R. Wagner, I. Papaioannou, S. Marelli, D. Straub and B. Sudret

Abstract

Reliability analysis aims to assess the probability of structural failure. The main difficulties in computing this quantity lie in its inherently low value, which causes most simulation methods to require a large number of expensive model evaluations. To alleviate the associated computational burden, practitioners today increasingly resort to active learning methods to train a surrogate model that is then used in lieu of the original model for computing the failure probability. In this contribution, we apply an adaptive variant of the recently proposed stochastic spectral embedding (SSE) surrogate modelling technique to solve reliability analysis problems. SSE creates a sequence of polynomial chaos expansions by splitting and refining subdomains of the input space. We propose here modified refinement and splitting criteria that can generate an efficient surrogate model with increased accuracy near the limit state surface. The performance of the algorithm is showcased on two reliability problems from the literature.