Abstract In this paper, based on the Kriging, an efficient minimum limit-state-surface search strategy is proposed for hybrid reliability analysis with both random and multi-super-ellipsoidal variables. The super-ellipsoidal model can represent the commonly used convex model (like ellipsoidal and interval models) in uncertainty analysis, which is a wise choice to represent the uncertainty for the available experimental data. For furtherly improving the approximation accuracy near the minimum limit state surface, a minimum limit-state-surface search strategy based on the active learning Kriging is proposed, where the separate sampling method for different uncertain variables is applied during the sequential sampling process. Combined with the constructed Kriging metamodel, the Monte Carlo Sampling is performed for the hybrid reliability problem with random and multi-super-ellipsoidal variables to evaluate the maximum failure probability. Finally, the effectiveness and precision of the proposed method are validated by four practical applications.

中文翻译：

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基于新的主动学习克里金模型的随机和多超椭球变量混合可靠性分析

摘要 本文基于克里金法，针对随机变量和多超椭球变量的混合可靠性分析，提出了一种有效的最小极限状态曲面搜索策略。超椭球模型可以表示不确定性分析中常用的凸模型（如椭球模型和区间模型），是表示可用实验数据不确定性的明智选择。为进一步提高最小极限状态面附近的逼近精度，提出了一种基于主动学习克里金法的最小极限状态面搜索策略，在顺序采样过程中采用不同不确定变量的分离采样方法。结合构建的克里金元模型，Monte Carlo Sampling 是针对具有随机和多超椭球变量的混合可靠性问题执行的，以评估最大故障概率。最后，通过四个实际应用验证了所提出方法的有效性和精度。