RMQGS-APS-Kriging-based Active Learning Structural Reliability Analysis Method

doi:10.3901/JME.2022.16.420

Abstract

Abstract: In order to take into account the accuracy and efficiency of structural reliability analysis of mechanical products with black box problems, a novel Kriging method named RMQGS-APS-Kriging for active learning structural reliability analysis based on random moving quadrilateral grid sampling (RMQGS) and alternate point strategy (APS) is proposed. The RMQGS method is used to select the initial sampling points and estimate the real performance function. Combined with the differential evolution (DE) method, the Kriging-based surrogate model with high accuracy is obtained. Through Euclidean distance, the sampling limited area is constructed to determine the sample selection range of alternate points. According to the number of iterations, the active learning U function and the improved EI (IEI) function are employed to alternately select the best sampling points, which are added to the sample database of each iteration to update the optimized Kriging surrogate model. The subset simulation (SS) method is then used to calculate the reliability of the performance function fitted by the optimized Kriging surrogate model in the iterative process, and the final structural reliability is determined by convergence criteria. The examples analysis show that compared with traditional surrogate model-based reliability calculation methods, the proposed method has stronger local and global performance function fitting ability, and can estimate the exact failure probability with less times of performance function calls and reliability calculation time.

Key words: structural reliability, Kriging surrogate model, active learning, subset simulation

CLC Number:

U270

ZHI Pengpeng, WANG Zhonglai, LI Yonghua, TIAN Zongrui. RMQGS-APS-Kriging-based Active Learning Structural Reliability Analysis Method[J]. Journal of Mechanical Engineering, 2022, 58(16): 420-429.

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