Decoupled reliability-based optimization using Markov chain Monte Carlo in augmented space
Yuan
X
K
author
Liu
S
L
author
Valdebenito
M
A
author
Faes
M
G
R
author
Jerez
D
J
author
Jensen
H
A
author
Beer
M
author
2021
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.
Reliability-based design optimization
Markov chain simulation
Failure probability function
Bayes' theorem
WOS:000653696200006
exported from refbase (show.php?record=1395), last updated on Wed, 22 Sep 2021 16:58:59 -0300
text
10.1016/j.advengsoft.2021.103020
Yuan_etal2021
Advances in Engineering Software
Adv. Eng. Softw.
2021
continuing
periodical
academic journal
157
103020
0965-9978