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• W3202675557 abstract "In this article, we propose an active kriging-based learning method for hybrid reliability analysis (HRA) with random and interval variables. An improved sampling strategy is proposed to target the sampling areas. Samples with maximum responses greater than 0 and minimum responses less than 0 are selected and regarded as the candidate samples; then, a <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>U</i> -based learning function is developed in which multiple samples of the interval are considered instead of one particular sample. To terminate the proposed method, a hybrid convergence criterion is proposed. Finally, an improved optimization strategy based on the DIRECT algorithm is developed for the Monte Carlo simulation conducted for the HRA. The performance of the proposed method is demonstrated by four numerical cases. The results illustrate that the proposed method is accurate and efficient for HRA." @default.
• W3202675557 created "2021-10-11" @default.
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• W3202675557 date "2022-12-01" @default.
• W3202675557 modified "2023-10-14" @default.
• W3202675557 title "An Active Kriging-Based Learning Method for Hybrid Reliability Analysis" @default.
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• W3202675557 doi "https://doi.org/10.1109/tr.2021.3111926" @default.
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