Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented TransformSource: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 002::page 04024017-1DOI: 10.1061/AJRUA6.RUENG-1169Publisher: ASCE
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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contributor author | Chen Ding | |
contributor author | Chao Dang | |
contributor author | Matteo Broggi | |
contributor author | Michael Beer | |
date accessioned | 2024-04-27T22:40:19Z | |
date available | 2024-04-27T22:40:19Z | |
date issued | 2024/06/01 | |
identifier other | 10.1061-AJRUA6.RUENG-1169.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4297222 | |
description 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. | |
publisher | ASCE | |
title | Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented Transform | |
type | Journal Article | |
journal volume | 10 | |
journal issue | 2 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering | |
identifier doi | 10.1061/AJRUA6.RUENG-1169 | |
journal fristpage | 04024017-1 | |
journal lastpage | 04024017-13 | |
page | 13 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2024:;Volume ( 010 ):;issue: 002 | |
contenttype | Fulltext |