Details
| Original language | English |
|---|---|
| Article number | 04024017 |
| Number of pages | 13 |
| Journal | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering |
| Volume | 10 |
| Issue number | 2 |
| Publication status | Published - 28 Feb 2024 |
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.
Keywords
- Bayesian global optimization, Gaussian process, Imprecise probability propagation, Parametric probability box (p-box), Response expectation bounds, Unscented transform
ASJC Scopus subject areas
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Building and Construction
- Engineering(all)
- Safety, Risk, Reliability and Quality
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In: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, Vol. 10, No. 2, 04024017, 28.02.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Estimation of Response Expectation Bounds under Parametric P-Boxes by Combining Bayesian Global Optimization with Unscented Transform
AU - Ding, Chen
AU - Dang, Chao
AU - Broggi, Matteo
AU - Beer, Michael
N1 - Funding Information: 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.
PY - 2024/2/28
Y1 - 2024/2/28
N2 - 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.
AB - 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.
KW - Bayesian global optimization
KW - Gaussian process
KW - Imprecise probability propagation
KW - Parametric probability box (p-box)
KW - Response expectation bounds
KW - Unscented transform
UR - http://www.scopus.com/inward/record.url?scp=85186141404&partnerID=8YFLogxK
U2 - 10.1061/AJRUA6.RUENG-1169
DO - 10.1061/AJRUA6.RUENG-1169
M3 - Article
AN - SCOPUS:85186141404
VL - 10
JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
SN - 2376-7642
IS - 2
M1 - 04024017
ER -