RESEARCH PAPER
An active learning method for structural reliability combining response surface model with Gaussian process of residual fitting and reliability-based sequential sampling design
1
Dalian Maritime University, China
Submission date: 2024-04-30
Final revision date: 2024-06-28
Acceptance date: 2024-09-16
Online publication date: 2024-09-26
Publication date: 2024-09-26
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(1):193443
HIGHLIGHTS
- A response surface model with the adaptive residual fitting strategy is proposed to estimate the structural reliability.
- The random moving uniform design method is proposed for the surrogate model construction.
- A learning function that considers the feasibility of sample points is introduced.
KEYWORDS
TOPICS
ABSTRACT
It is quite challenging to attain an accurate reliability estimation on complex structures with low computational burden. Therefore, an active learning method combining the response surface model with the Gaussian process (GP) of residual fitting and reliability-based sequential sampling design is proposed for structural reliability analysis. This method first utilizes a random quadrilateral grid to perturb the uniform design sampling and generates a small set of initial DoE to establish a high-precision initial response surface model (RSM) efficiently. Then, a GP model for residual prediction is constructed by using the residuals of the initial RSM, which allows the response surface function to be closer to the limit state function. A reliability-based EI learning function, which inherits the property of the EI function and considers the probability of feasibility of the samples, is developed for the selection of the most feasible points to update the surrogate model. Ultimately, four numerical examples are used to validate the accuracy and efficiency of the proposed method.
ACKNOWLEDGEMENTS
This work is supported by National Natural Science Foundation of China (No. 52371361, 51879026), Dalian Science and Technology Innovation Fund Project (No. 2020JJ25CY016), and the Fundamental Research Funds for the Central Universities of China (No. 3132023516). And this research is also supported by Key Laboratory for Polar Safety Assurance Technology and Equipment of Liaoning Province.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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