Bayesian updating with two-step parallel Bayesian optimization and quadrature

authored by

Masaru Kitahara, Chao Dang, Michael Beer

Abstract

This work proposes a Bayesian updating approach, called parallel Bayesian optimization and quadrature (PBOQ). It is rooted in Bayesian updating with structural reliability methods (BUS) and offers a coherent Bayesian approach for the BUS analysis by assuming Gaussian process priors. The first step of the method, i.e., parallel Bayesian optimization, effectively explores a constant c in BUS by a novel parallel infill sampling strategy. The second step (parallel Bayesian quadrature) then infers the posterior distribution by another parallel infill sampling strategy using subset simulation. The proposed approach enables to make the fullest use of prior knowledge and parallel computing, resulting in a substantial reduction of the computational burden of model updating. Four numerical examples with varying complexity are investigated for demonstrating the proposed method against several existing methods. The results show the potential benefits by advocating a coherent Bayesian fashion to the BUS analysis.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

University of Liverpool
Tongji University

Type

Article

Journal

Computer Methods in Applied Mechanics and Engineering

Volume

403

No. of pages

21

ISSN

0045-7825

Publication date

01.01.2023

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Mechanics, Mechanics of Materials, Mechanical Engineering, Physics and Astronomy(all), Computer Science Applications

Electronic version(s)

https://doi.org/10.1016/j.cma.2022.115735 (Access: Closed)