Efficient procedure for failure probability function estimation in augmented space

authored by: Xiukai Yuan, Shaolong Liu, M. A. Valdebenito, Jian Gu, Michael Beer
Abstract: 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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): Xiamen University
Universidad Adolfo Ibanez
University of Liverpool
Tongji University
Type: Article
Journal: Structural Safety
Volume: 92
ISSN: 0167-4730
Publication date: 09.2021
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Civil and Structural Engineering, Building and Construction, Safety, Risk, Reliability and Quality
Electronic version(s): https://doi.org/10.1016/j.strusafe.2021.102104 (Access: Closed)