Probabilistic fracture mechanics (PFM) simulates the behavior of cracked structures and propagates uncertainties from input parameters to a failure probability or its uncertain estimate. In nuclear technology, this approach supports the assessment of the rupture probability of highly reliable pipes, which is an important parameter for the safety analysis of a nuclear power plant. For the appropriate probabilistic modelling of a structure with consideration of uncertainties, but also for the analysis of PFM application cases, the question arises, which input parameter of a probabilistic model has a higher impact on the estimate of computed failure probability, and which has a minor impact. This question is associated with the sensitivity measures or importance factors of the input parameters and their ranking concerning their influence.

In this paper, six different approaches for the quantification of the sensitivity of parameters PFM evaluations are investigated: the amplification ratio, the direction cosine, the degree of separation, the analysis of the most probable failure point, the separation of uncertainty method, and the simple sample-based sensitivity study. Each method is described, visualized, applied to a common test case, and compared. The application case and the comparison are part of the Coordinated Research Project (CRP), “Methodology for Assessing Pipe Failure Rates in Advanced Water-Cooled Reactors (AWCRs)” by the International Atomic Energy Agency (IAEA), which is dedicated to the development of failure rates of piping in AWCRs. The participants used different PFM computer codes to analyze the test case and individual sensitivity methods to rank the input parameters, which motivated the comprehensive survey.

The predicted parameter ranking of the approaches is consistent between the methods and between different PFM codes, but the approaches differ in the scope and the required effort. A conclusion is drawn and recommendations for the six different approaches are given.

概率断裂力学（PFM）模拟裂纹结构的行为，并将不确定性从输入参数传播到失效概率或其不确定性估计。在核技术中，这种方法支持对高度可靠的管道破裂概率的评估，这是核电厂安全分析的重要参数。对于考虑不确定性的结构的适当概率建模，以及对于PFM应用案例的分析，都出现了一个问题，即概率模型的哪个输入参数对计算出的失效概率的估计有较高的影响，并且哪个具有轻微的影响。该问题与输入参数的敏感度度量或重要性因子及其与它们的影响有关的等级有关。

本文研究了六种不同的量化参数PFM评估灵敏度的方法：放大率，方向余弦，分离程度，最可能的故障点分析，不确定性分离方法以及简单的基于样本的敏感性研究。每种方法都进行了描述，可视化，应用于通用测试用例并进行了比较。该应用案例和比较是国际原子能机构（IAEA）协调研究项目（CRP）“评估先进水冷反应堆（AWCR）中管道失效率的方法”的一部分，该研究致力于AWCR中管道的故障率。

方法之间的预测参数排名在方法之间以及不同的PFM代码之间是一致的，但是方法的范围和所需的工作有所不同。得出结论，并提出了针对六种不同方法的建议。