Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented Transform

authored by

Chen Ding, Chao Dang, Matteo Broggi, Michael Beer

Abstract

In engineering analysis, propagating parametric probability boxes (p-boxes) remains a challenge because a computationally expensive nested solution scheme is involved. To tackle this challenge, this paper proposes a novel optimization-integration method to propagate parametric probability boxes (p-boxes), mainly focusing on estimating the lower and upper bounds of structural response expectation for linear and moderately nonlinear problems. A model-based optimization scheme, named Bayesian global optimization, is first introduced to explore the space of distribution parameters. Subsequently, an efficient numerical integration method, named unscented transform, is employed to estimate the response expectation with a given set of distribution parameters. Compared with existing optimization-integration methods, the proposed method has three advantages. First, the response expectation bounds are successively estimated, allowing for the reuse of samples generated from the lower-bound estimation in the upper-bound estimation. Second, the approximation error introduced by the numerical integration method is considered. Third, computational efficiency in both the optimization and integration processes is improved. Four applications are investigated to validate the effectiveness of the proposed method, showing its ability to balance computational efficiency and accuracy when evaluating response expectation bounds.

Organisation(s)

Institute for Risk and Reliability

External Organisation(s)

University of Liverpool
Tongji University

Type

Article

Journal

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

Volume

10

No. of pages

13

ISSN

2376-7642

Publication date

28.02.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Civil and Structural Engineering, Building and Construction, Safety, Risk, Reliability and Quality

Electronic version(s)

https://doi.org/10.1061/AJRUA6.RUENG-1169 (Access: Closed)