Abstract This paper proposes a sampling-based system reliability-based design optimization (SRBDO) method with local approximation of constraints. To enhance the optimization efficiency of SRBDO problems with time-consuming constraints, Kriging metamodels are employed to replace the true constraint functions. A new composite active learning strategy based on the possibility of correctly predicting the state of the cut-set system is developed to locally approximate constraints. Furthermore, to ensure the accuracy of the system reliability analysis at the final SRBDO solution, the Kriging update in the developed strategy is terminated by quantifying the influence of the Kriging uncertainty on the prediction of the system failure probability and the confidence that the solution satisfies the prescribed system failure probability. This approach can avoid the unnecessary burden of Kriging construction during system reliability analysis at intermediate solutions far from the final solution. Based on the updated Kriging metamodel, the system failure probability of constraints is estimated by Monte Carlo simulation, and its partial derivative is calculated by stochastic sensitivity analysis. The performance of the proposed method is tested and verified by using four examples. Compared with the existing methods, the proposed method has high computational accuracy and efficiency for solving SRBDO problems.

中文翻译：

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使用复合主动学习克里金法进行基于采样的系统可靠性设计优化

摘要 本文提出了一种基于抽样的基于系统可靠性的设计优化（SRBDO）方法，该方法具有约束的局部近似。为了提高具有耗时约束的 SRBDO 问题的优化效率，克里金元模型被用来代替真正的约束函数。基于正确预测割集系统状态的可能性，开发了一种新的复合主动学习策略来局部逼近约束。此外，为了确保最终 SRBDO 解决方案中系统可靠性分析的准确性，通过量化克里金不确定性对系统故障概率预测的影响和解决方案满足该解决方案的置信度，终止开发策略中的克里金更新。规定的系统故障概率。这种方法可以避免在远离最终解决方案的中间解决方案的系统可靠性分析过程中不必要的克里金构造负担。基于更新后的克里金元模型，通过蒙特卡罗模拟估计系统约束失效概率，并通过随机灵敏度分析计算其偏导数。通过四个例子对所提出方法的性能进行了测试和验证。与现有方法相比，该方法在求解SRBDO问题时具有较高的计算精度和效率。其偏导数通过随机敏感性分析计算。通过四个例子对所提出方法的性能进行了测试和验证。与现有方法相比，该方法在求解SRBDO问题时具有较高的计算精度和效率。其偏导数通过随机敏感性分析计算。通过四个例子对所提出方法的性能进行了测试和验证。与现有方法相比，该方法在求解SRBDO问题时具有较高的计算精度和效率。