TY - JOUR

T1 - A framework for global reliability sensitivity analysis in the presence of multi-uncertainty

AU - Ehre, Max

AU - Papaioannou, Iason

AU - Straub, Daniel

N1 - Publisher Copyright:
© 2019 Elsevier Ltd

PY - 2020/3

Y1 - 2020/3

N2 - In reliability analysis with numerical models, one is often interested in the sensitivity of the probability of failure estimate to changes in the model input. In the context of multi-uncertainty, one whishes to separate the effect of different types of uncertainties. A common distinction is between aleatory (irreducible) and epistemic (reducible) uncertainty, but more generally one can consider any classification of the uncertain model inputs in two subgroups, type A and type B. We propose a new sensitivity measure for the probability of failure conditional on type B inputs. On this basis, we outline a framework for multi-uncertainty-driven reliability sensitivity analysis. A bi-level surrogate modelling strategy is designed to efficiently compute the new conditional reliability sensitivity measures. In the first level, a surrogate is constructed for the model response to circumvent possibly expensive evaluations of the numerical model. By solving a sequence of reliability problems conditional on samples of type B random variables, we construct a level 2-surrogate for the logarithm of the conditional probability of failure, using polynomial bases which allow to directly evaluate the variance-based sensitivities. The new sensitivity measure and its computation are demonstrated through two engineering examples.

AB - In reliability analysis with numerical models, one is often interested in the sensitivity of the probability of failure estimate to changes in the model input. In the context of multi-uncertainty, one whishes to separate the effect of different types of uncertainties. A common distinction is between aleatory (irreducible) and epistemic (reducible) uncertainty, but more generally one can consider any classification of the uncertain model inputs in two subgroups, type A and type B. We propose a new sensitivity measure for the probability of failure conditional on type B inputs. On this basis, we outline a framework for multi-uncertainty-driven reliability sensitivity analysis. A bi-level surrogate modelling strategy is designed to efficiently compute the new conditional reliability sensitivity measures. In the first level, a surrogate is constructed for the model response to circumvent possibly expensive evaluations of the numerical model. By solving a sequence of reliability problems conditional on samples of type B random variables, we construct a level 2-surrogate for the logarithm of the conditional probability of failure, using polynomial bases which allow to directly evaluate the variance-based sensitivities. The new sensitivity measure and its computation are demonstrated through two engineering examples.

KW - Decision support

KW - Multi-Uncertainty

KW - Rare event simulation

KW - Reliability-Oriented sensitivity analysis

KW - Surrogate modelling

UR - http://www.scopus.com/inward/record.url?scp=85074889868&partnerID=8YFLogxK

U2 - 10.1016/j.ress.2019.106726

DO - 10.1016/j.ress.2019.106726

M3 - Article

AN - SCOPUS:85074889868

SN - 0951-8320

VL - 195

JO - Reliability Engineering and System Safety

JF - Reliability Engineering and System Safety

M1 - 106726

ER -