Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis
Metamodel-based subset simulation adaptable to target computational capacities: the case for high-dimensional and rare event reliability analysis
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DOI：
10.1007/s00158-021-02864-9
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发表时间：
2021-04
影响因子：
3.9
通讯作者：
Zeyu Wang;A. Shafieezadeh
中科院分区：
文献类型：
--
作者：
Zeyu Wang;A. Shafieezadeh
基于元模型的可靠性分析方法，如自适应Kriging，在计算上受到可靠性问题复杂性的挑战，从而限制了这些方法在低维或不罕见的问题中的应用。在这里，我们提出了一种可靠性分析方法，通过子集模拟和自适应克里格（RASA）的深度集成来无偏估计高维或罕见事件问题的失效概率。引入条件失效概率曲线和动态学习函数的概念，将原问题分解为亚可靠度问题，并自适应识别亚可靠度问题对应的极限状态函数的中间失效阈值。在可用计算能力的指导下进行可靠性分解和目标中间失效阈值的建立，从而使RASA能够控制每个子集中与估计中间失效阈值相关的计算成本，从而分析中高维问题或罕见事件的可靠性。以三个数值算例为基准，探讨了该方法的性能。结果表明，该方法具有较高的精度，并能适应现有的计算资源。
Metamodel-based approaches to reliability analysis, e.g., adaptive Kriging, are computationally challenged by the complexity of reliability problems, thus limiting the application of these methods to problems that are low-dimensional or not rare. Here, we propose a reliability analysis approach via a deep integration of subset simulation and adaptive kriging (RASA) for an unbiased estimation of failure probabilities of high-dimensional or rare event problems. Concepts of conditional failure probability curves and dynamic learning function are introduced to decompose the original problem to subreliability problems and adaptively identify intermediate failure thresholds of limit state functions corresponding to the subreliability problems. The reliability decomposition and the establishment of target intermediate failure thresholds are guided by the available computational capacity, thus, enabling RASA to control the computational cost associated with the estimation of the intermediate failure thresholds in each subset and consequently to analyze the reliability of medium to high-dimensional problems or rare events. Three numerical examples are investigated as benchmark to explore the performance of the proposed method. Results indicate that the proposed method has high accuracy and has the ability to adjust to available computational resources.