TY - JOUR

T1 - Efficient procedure for failure probability function estimation in augmented space

AU - Yuan, Xiukai

AU - Liu, Shaolong

AU - Valdebenito, M. A.

AU - Gu, Jian

AU - Beer, Michael

N1 - Publisher Copyright:
© 2021 Elsevier Ltd

PY - 2021/9

Y1 - 2021/9

N2 - An efficient procedure is proposed to estimate the failure probability function (FPF) with respect to design variables, which correspond to distribution parameters of basic structural random variables. The proposed procedure is based on the concept of an augmented reliability problem, which assumes the design variables as uncertain by assigning a prior distribution, transforming the FPF into an expression that includes the posterior distribution of those design variables. The novel contribution of this work consists of expressing this target posterior distribution as an integral, allowing it to be estimated by means of sampling, and no distribution fitting is needed, leading to an efficient estimation of FPF. The proposed procedure is implemented within three different simulation strategies: Monte Carlo simulation, importance sampling and subset simulation; for each of these cases, expressions for the coefficient of variation of the FPF estimate are derived. Numerical examples illustrate performance of the proposed approaches.

AB - An efficient procedure is proposed to estimate the failure probability function (FPF) with respect to design variables, which correspond to distribution parameters of basic structural random variables. The proposed procedure is based on the concept of an augmented reliability problem, which assumes the design variables as uncertain by assigning a prior distribution, transforming the FPF into an expression that includes the posterior distribution of those design variables. The novel contribution of this work consists of expressing this target posterior distribution as an integral, allowing it to be estimated by means of sampling, and no distribution fitting is needed, leading to an efficient estimation of FPF. The proposed procedure is implemented within three different simulation strategies: Monte Carlo simulation, importance sampling and subset simulation; for each of these cases, expressions for the coefficient of variation of the FPF estimate are derived. Numerical examples illustrate performance of the proposed approaches.

KW - Bayesian theory

KW - Failure probability function

KW - Reliability analysis

KW - Reliability-based optimization

UR - http://www.scopus.com/inward/record.url?scp=85104959492&partnerID=8YFLogxK

U2 - 10.1016/j.strusafe.2021.102104

DO - 10.1016/j.strusafe.2021.102104

M3 - Article

AN - SCOPUS:85104959492

SN - 0167-4730

VL - 92

JO - Structural Safety

JF - Structural Safety

M1 - 102104

ER -