The failure probability-based global sensitivity index (FPGSI) analyses how the model inputs affect the failure probability of a model. It is useful for guiding reliability-based design optimization and enhancing reliability by controlling the uncertainty of the important input variables. Based on the law of total variance in successive intervals without overlapping and the dual-stage adaptive kriging (AK) model-based importance sampling (IS) method, an efficient dimensionality-independent method is proposed. First, an interval-conditional failure probability-based formula is established. Secondly, a dual-stage AK model is embedded into the formula to construct the IS probability density function and identify the state (failed or safe) of every IS sample. Thirdly, using different partitions of IS samples, all inputs’ FPGSIs can be simultaneously obtained by taking the corresponding subdomains’ samples into the proposed computational formula. The results of four case studies illustrate the effectiveness of the proposed algorithm, especially for cases with multiple failure regions.

基于故障概率的全局灵敏度指数 (FPGSI) 分析模型输入如何影响模型的故障概率。它有助于通过控制重要输入变量的不确定性来指导基于可靠性的设计优化和提高可靠性。基于不重叠连续区间的总方差规律和基于双阶段自适应克里金（AK）模型的重要性采样（IS）方法，提出了一种高效的维数无关方法。首先，建立了基于区间条件失效概率的公式。其次，将双阶段AK模型嵌入到公式中，构建IS概率密度函数并识别每个IS样本的状态（失败或安全）。第三，利用IS样本的不同分区，通过将相应子域的样本带入建议的计算公式，可以同时获得所有输入的 FPGSI。四个案例研究的结果说明了所提出算法的有效性，特别是对于具有多个故障区域的案例。