The failure probability-based global sensitivity is proposed to evaluate the influence of input variables on the failure probability. But for the problem that the distribution parameters of variables are uncertain due to the lack of data or acknowledgement, if the original failure probability-based global sensitivity is employed to evaluate the influences of different uncertainty sources directly, the computational cost will be prohibitive. To address this issue, this work proposes the novel predictive failure probability (PFP) based on global sensitivity. By separating the overall uncertainty of variables into inherent uncertainty and distribution parameter uncertainty, the PFP can be evaluated by a single loop with equivalent transformation. Then, the PFP based global sensitivities with respect to (w.r.t) the overall uncertainty, inherent uncertainty and distribution parameter uncertainty are proposed, respectively, and their relationships are discussed, which can be used to measure the influences of different uncertainty sources. To compute those global sensitivities efficiently, the Monte Carlo method and Kriging-based method are employed for comparison. Several examples including two numerical examples and three engineering practices are investigated to validate the reasonability and efficiency of the proposed method.

提出了基于失效概率的全局灵敏度来评估输入变量对失效概率的影响。但对于由于缺乏数据或确认而导致变量分布参数不确定的问题，如果采用原始的基于失效概率的全局灵敏度来直接评估不同不确定源的影响，计算成本将令人望而却步。为了解决这个问题，这项工作提出了基于全局敏感性的新型预测故障概率（PFP）。通过将变量的整体不确定性分为固有不确定性和分布参数不确定性，可以通过具有等效变换的单个循环来评估 PFP。然后，基于 PFP 的全球敏感性（wrt）整体不确定性，分别提出了固有不确定性和分布参数不确定性，并讨论了它们之间的关系，可用于衡量不同不确定性来源的影响。为了有效地计算这些全局灵敏度，采用蒙特卡罗方法和基于克里金法的方法进行比较。研究了包括两个数值例子和三个工程实践在内的几个例子，以验证所提出方法的合理性和有效性。