Details
| Original language | English |
|---|---|
| Article number | 109939 |
| Number of pages | 12 |
| Journal | Reliability Engineering and System Safety |
| Volume | 244 |
| Early online date | 12 Jan 2024 |
| Publication status | Published - Apr 2024 |
Abstract
For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples.
Keywords
- Acquisition function, Bayesian optimization, Bayesian reliability analysis, Gaussian process simulation, Reliability-based design optimization
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Reliability Engineering and System Safety, Vol. 244, 109939, 04.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A sequential sampling-based Bayesian numerical method for reliability-based design optimization
AU - Hong, Fangqi
AU - Wei, Pengfei
AU - Fu, Jiangfeng
AU - Beer, Michael
N1 - Funding Information: This work is supported by the National Natural Science Foundation of China under grant number 72171194 , and the Sino-German Mobility Programme under grant number M-0175 (2021–2024) .
PY - 2024/4
Y1 - 2024/4
N2 - For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples.
AB - For efficiently solving the Reliability-Based Design Optimization (RBDO) problem with multi-modal, highly nonlinear and expensive-to-evaluate limit state functions (LSFs), a sequential sampling-based Bayesian active learning method is developed in this work. The penalty function method is embedded to transform the constrained optimization problem into a non-constrained one to reduce the model complexity. The proposed method for solving RBDO problems starts by training a Gaussian process (GP) model, in the augmented space of random and design variables. It is then based on an efficient sampling scheme for simulating the GP model, the adaptive Bayesian optimization (BO) and Bayesian reliability analysis (BRA) procedures are combined in a collaborative way for sequentially producing the joint training points. BO driven by expected improvement (EI) function is used for inferring the global optimum in the design space with global convergence, and the BRA equipped with U function is implemented for inferring the failure probabilities at the identified design points with the desired accuracy. The superiority of the proposed method is demonstrated with two numerical and two real-world engineering examples.
KW - Acquisition function
KW - Bayesian optimization
KW - Bayesian reliability analysis
KW - Gaussian process simulation
KW - Reliability-based design optimization
UR - http://www.scopus.com/inward/record.url?scp=85182718015&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2024.109939
DO - 10.1016/j.ress.2024.109939
M3 - Article
AN - SCOPUS:85182718015
VL - 244
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
SN - 0951-8320
M1 - 109939
ER -