Rare event estimation using stochastic spectral embedding. (May 2022)

• Record Type: Journal Article
• Authors: Wagner, P.-R.; Marelli, S.; Papaioannou, I.; Straub, D.; Sudret, B.
• Is Part Of: Structural safety. Volume 96(2022)
• Publication Date: 2022-05
• Subjects: Reliability analysis -- Uncertainty quantification -- Surrogate modelling -- Stochastic spectral embedding -- Active learning -- Rare event estimation -- Sparse polynomial chaos expansions; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2021.102179
• Languages: English
• ISSNs: 0167-4730

Abstract

Abstract: Estimating the probability of rare failure events is an essential step in the reliability assessment of engineering systems. Computing this failure probability for complex non-linear systems is challenging, and has recently spurred the development of active-learning reliability methods. These methods approximate the limit-state function (LSF) using surrogate models trained with a sequentially enriched set of model evaluations. A recently proposed method called stochastic spectral embedding (SSE) aims to improve the local approximation accuracy of global, spectral surrogate modelling techniques by sequentially embedding local residual expansions in subdomains of the input space. In this work we apply SSE to the LSF, giving rise to a stochastic spectral embedding-based reliability (SSER) method. The resulting partition of the input space decomposes the failure probability into a set of easy-to-compute conditional failure probabilities. We propose a set of modifications that tailor the algorithm to efficiently solve rare event estimation problems. These modifications include specialized refinement domain selection, partitioning and enrichment strategies. We showcase the algorithm performance on four benchmark problems of various dimensionality and complexity in the LSF. Highlights: We propose an active-learning reliability method using stochastic spectral embedding. We introduce improved refinement selection, partitioning, and enrichment strategies. The method expresses the failure probability as a sum of conditional probabilities. We test our method on four limit-state functions with different complexities.