Bayesian updating with two-step parallel Bayesian optimization and quadrature

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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a Bayesian updating approach called parallel Bayesian optimization and quadrature (PBOQ). It applies Gaussian process priors and explores a constant c in BUS through parallel infill sampling strategy. The proposed approach effectively reduces computational burden of model updating by leveraging prior knowledge and parallel computing. Numerical examples are used to demonstrate its potential benefits and advocate a coherent Bayesian fashion for BUS analysis.