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.

可靠性问题的复杂性在计算上挑战了基于元模型的可靠性分析方法（例如自适应Kriging），因此将这些方法的应用限制在低维或罕见的问题上。在这里，我们通过子集仿真和自适应Kriging（RASA）的深度集成提出了一种可靠性分析方法，用于对高维或罕见事件问题的故障概率进行无偏估计。引入条件故障概率曲线和动态学习函数的概念，将原始问题分解为子可靠性问题，并自适应地确定与子可靠性问题相对应的极限状态函数的中间故障阈值。可靠性分解和目标中间故障阈值的建立受可用计算能力的指导，从而使RASA能够控制与估计每个子集中的中间故障阈值相关的计算成本，从而分析中到高的可靠性维问题或罕见事件。研究了三个数值示例作为基准，以探索该方法的性能。结果表明，该方法具有较高的准确性，并具有适应可用计算资源的能力。使RASA能够控制与估计每个子集中的中间故障阈值相关的计算成本，从而分析中等到高维问题或罕见事件的可靠性。研究了三个数值示例作为基准，以探索该方法的性能。结果表明，该方法具有较高的准确性，并具有适应可用计算资源的能力。使RASA能够控制与估计每个子集中的中间故障阈值相关的计算成本，从而分析中等到高维问题或罕见事件的可靠性。研究了三个数值示例作为基准，以探索该方法的性能。结果表明，该方法具有较高的准确性，并具有适应可用计算资源的能力。