Novel learning functions design based on the probability of improvement criterion and normalization techniques

Li, Guofa; Chen, Zequan; Yang, Zhaojun; He, Jialong

doi:10.1016/j.apm.2022.03.029

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…To assess the performance, results from AKMCS+U [27], AKMCS+EFF [27], PRBFM [53], ALK-iRPl2 [54] and ALK-PBA [55] are listed. Additionally, the results from HCC+U and HCC+EFF are also reported to evaluate the effectiveness of the hybrid convergence criterion.…”

Section: Comparisons With Other Advanced Methodsmentioning

confidence: 99%

“…In summary, the delicate balance between accuracy and efficiency highlights the superior performance of the proposed method. Note: a Denotes that the results are taken from [27]; b Denotes that the results are taken from [53]; c Denotes that the results are taken from [54]; d Denotes that the results are taken from [55].…”

Section: Comparisons With Other Advanced Methodsmentioning

confidence: 99%

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Adaptive Kriging-based method with learning function allocation scheme and hybrid convergence criterion for efficient structural reliability analysis

Zhou,

Xu,

Jiang

et al. 2024

Preprint

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Structural reliability analysis poses a considerable challenge in engineering practice, leading to the development of various state-of-the-art methods. Active learning methods, known for their superior performance, have been extensively investigated for assessing failure probabilities in structural reliability analysis. This paper aims to develop an efficient and accurate Kriging-based active learning method for structural reliability analysis, incorporating a novel learning function allocation scheme and a hybrid convergence criterion. Specifically, a novel learning function allocation scheme is introduced to address the challenge of no single learning function demonstrating superior performance across various engineering contexts. Six active learning functions, including EFF, H, REIF, LIF, FNEIF, and KO, are used to form a portfolio of functions in the allocation scheme. The use of the U learning function initiates the active learning process with a truncated candidate sample pool, alleviating computational burden while maintaining accuracy. Additionally, a hybrid convergence criterion, combining the error-based stopping criterion with a stabilization convergence criterion, is proposed to terminate the active learning process at an appropriate stage. The performance of the proposed method is evaluated through four numerical examples and one engineering case, demonstrating its accuracy and efficiency.

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