1. An efficient method combining polynomial-chaos kriging and adaptive radial-based importance sampling for reliability analysis.
- Author
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Pan, Qiu-Jing, Zhang, Rui-Feng, Ye, Xin-Yu, and Li, Zheng-Wei
- Subjects
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KRIGING , *ACTIVE learning , *ALGORITHMS , *POLYNOMIAL chaos , *SPHERES - Abstract
This paper develops an efficient algorithm that combines polynomial-chaos kriging (PCK) and adaptive radial-based importance sampling (ARBIS) for reliability analysis. The key idea of ARBIS is to adaptively determine a sphere with the center at the origin and radius equal to the smallest distance of the failure domain to the origin, also known as the optimal β -sphere, and only those samples outside the optimal β -sphere have a possibility of failure and thus need to evaluate the limit-state function to judge their states (safe or failure). In the proposed algorithm, both the PCK model and β -sphere are updated adaptively. In each iteration of determining the optimal β -sphere, the PCK model is updated sequentially based on an active learning function, which is used to select the most informative sample from the samples between the last and current β -spheres. Once the stopping criterion is met, the learning process of PCK in this iteration terminates, and the obtained PCK model is then used to determine the next β -sphere. The updating iteration of the β -sphere proceeds until the optimal sphere is found. Five representative examples are revisited, in which the results demonstrate the high accuracy and efficiency of the proposed PCK-ARBIS algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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