Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented Transform
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10, Issue 2
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.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
Chen Ding acknowledges the support of the European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie project GREYDIENT-Grant Agreement No. 955393. Chao Dang thanks the support from the China Scholarship Council (CSC). Michael Beer appreciates the support of National Natural Science Foundation of China under Grant No. 72271025.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 10 • Issue 2 • June 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 27, 2023
Accepted: Nov 7, 2023
Published online: Feb 28, 2024
Published in print: Jun 1, 2024
Discussion open until: Jul 28, 2024
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