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Machine learning-based enhanced Monte Carlo simulation for low failure probability structural reliability analysis
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DOI：10.1016/j.istruc.2025.108530.png)
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摘要
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Low failure probability problems with high computational costs are difficult to solve. Regarding this, a structural reliability analysis method (called AK-EMCS-SVR) combining active Kriging model, support vector regression and enhanced Monte Carlo simulation is proposed. To achieve global modeling, the uniform sampling strategy and expected feasibility function are also adopted. Besides, this paper proposes an adaptive training interval combining with the support vector regression algorithm to achieve more accurate and robust prediction. Five numerical cases and a finite element engineering case are used to illustrate the effectiveness of the proposed method. The results comparison showed that the AK-EMCS-SVR has an advantage in the number of calls to the limit state function and to the surrogate model. The method shows higher accuracy and robustness solving low failure probability problems with high computational cost.
Keyword:
Structural reliability analysis
Enhanced Monte Carlo simulation
Support vector regression
Kriging model
期刊
IF:
4.3
论文数:
1.2W
被引数:
2.7W
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引用论文
Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis混合增强型蒙特卡洛模拟与先进的机器学习方法相结合，可实现准确高效的结构可靠性分析
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