Sample regeneration algorithm for structural failure probability function estimation

Yuan, Xiukai, Wang, Shanglong, Valdebenito, Marcos A, Faes, Matthias GR and Beer, Michael ORCID: 0000-0002-0611-0345
(2023) Sample regeneration algorithm for structural failure probability function estimation. PROBABILISTIC ENGINEERING MECHANICS, 71. p. 103387.

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An efficient strategy to approximate the failure probability function in structural reliability problems is proposed. The failure probability function (FPF) is defined as the failure probability of the structure expressed as a function of the design parameters, which in this study are considered to be distribution parameters of random variables representing uncertain model quantities. The task of determining the FPF is commonly numerically demanding since repeated reliability analyses are required. The proposed strategy is based on the concept of augmented reliability analysis, which only requires a single run of a simulation-based reliability method. This paper introduces a new sample regeneration algorithm that allows to generate the required failure samples of design parameters without any additional evaluation of the structural response. In this way, efficiency is further improved while ensuring high accuracy in the estimation of the FPF. To illustrate the efficiency and effectiveness of the method, case studies involving a turbine disk and an aircraft inner flap are included in this study.

Item Type: Article
Uncontrolled Keywords: Bayesian theory, Failure probability function, Maximum Entropy method, Regeneration algorithm, Reliability
Divisions: Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 09 Jan 2023 09:39
Last Modified: 19 Nov 2023 02:30
DOI: 10.1016/j.probengmech.2022.103387
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