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An Active Kriging-Based Learning Method for Hybrid Reliability Analysis
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DOI：10.1109/TR.2021.3111926.png)
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Abstract
En 中文
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 U-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.
Keywords:
Convergence
Learning systems
Analytical models
Uncertainty
Random variables
Optimization
Correlation
Failure probability
hybrid reliability analysis (HRA)
kriging
Monte Carlo simulation (MCS)
surrogate model
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