Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion
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RESEARCH PAPER
Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion Jiaxiang Yi 1 & Qi Zhou 2 & Yuansheng Cheng 1,3 & Jun Liu 1,3 Received: 24 December 2019 / Revised: 27 April 2020 / Accepted: 30 April 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The Kriging-based reliability analysis is extensively adopted in engineering structural reliability analysis for its capacity to achieve accurate failure probability estimation with high efficiency. Generally, the Kriging-based reliability analysis is an active-learning process that mainly includes three aspects: (1) the determination of the design space; (2) the rule of choosing new samples, i.e., the learning function; and (3) the stopping criterion of the active-learning process. In this work, a new learning function and an error-based stopping criterion are proposed to enhance the efficiency of the active-learning Kriging-based reliability analysis. First, the reliability-based lower confidence bounding (RLCB) function is proposed to select the update points, which can balance the exploration and exploitation through the probability density-based weight. Second, an improved stopping criterion based on the relative error estimation of the failure probability is developed to avoid the pre-mature and latemature of the active-learning Kriging-based reliability analysis method. Specifically, the samples that have large probabilities to change their safety statuses are identified. The estimated relative error caused by these samples is derived as the stopping criterion. To verify the performance of the proposed RLCB function and the error-based stopping criterion, four examples with different complexities are tested. Results show that the RLCB function is competitive compared with state-of-the-art learning functions, especially for highly non-linear problems. Meanwhile, the new stopping criterion reduces the computational resource of the active-learning process compared with the state-of-the-art stopping criteria. Keywords Reliability analysis . Kriging model . Learning function . Reliability-based lower confidence bounding . Error-based stopping criterion
Abbreviations PDF Probability density function CDF Cumulative density function
LSF LS KRA
Limit state function Limit state Kriging-based reliability analysis
Highlights • A new learning function called reliability-based lower confidence bounding is proposed. • A new error-based stopping criterion using bootstrap confidence estimation is developed. • The active-learning process of the ALKRA is more efficient combining the proposed learning function and stopping criterion. • The accuracy and effectiveness of the proposed approach are tested through four cases Responsible Editor: Palaniappan Ramu * Jun Liu [email protected] 1
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, People’s Republic of China
2
School of Aerospace Engineering, Huazhong University o
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