Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space

verfasst von

Xiukai Yuan, Shaolong Liu, Marcos A. Valdebenito, Matthias G.R. Faes, Danko J. Jerez, Hector A. Jensen, Michael Beer

Abstract

An efficient framework is proposed for reliability-based design optimization (RBDO) of structural systems. The RBDO problem is expressed in terms of the minimization of the failure probability with respect to design variables which correspond to distribution parameters of random variables, e.g. mean or standard deviation. Generally, this problem is quite demanding from a computational viewpoint, as repeated reliability analyses are involved. Hence, in this contribution, an efficient framework for solving a class of RBDO problems without even a single reliability analysis is proposed. It makes full use of an established functional relationship between the probability of failure and the distribution design parameters, which is termed as the failure probability function (FPF). By introducing an instrumental variability associated with the distribution design parameters, the target FPF is found to be proportional to a posterior distribution of the design parameters conditional on the occurrence of failure in an augmented space. This posterior distribution is derived and expressed as an integral, which can be estimated through simulation. An advanced Markov chain algorithm is adopted to efficiently generate samples that follow the aforementioned posterior distribution. Also, an algorithm that re-uses information is proposed in combination with sequential approximate optimization to improve the efficiency. Numeric examples illustrate the performance of the proposed framework.

Organisationseinheit(en)

Institut für Risiko und Zuverlässigkeit

Externe Organisation(en)

Xiamen University
Universidad Adolfo Ibanez
KU Leuven
Universidad Tecnica Federico Santa Maria
The University of Liverpool
International Joint Research Center for Engineering Reliability and Stochastic Mechanics
Tongji University

Typ

Artikel

Journal

Advances in Engineering Software

Band

157-158

ISSN

0965-9978

Publikationsdatum

07.2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Ingenieurwesen (insg.)

Elektronische Version(en)

https://doi.org/10.1016/j.advengsoft.2021.103020 (Zugang: Geschlossen)