As a result, many probabilistic methodologies such as regression analysis , stochastic simulation , and graph-based probability models have been adopted to deal with the uncertainties associated with complex engineering application scenarios. These probabilistic methodologies present a more realistic assessment by incorporating random characteristics of various risk factors. Moreover, the obtained results are expressed in the form of probability, which is consistent with the requirements of quantitative risk assessment . Unfortunately, probabilistic methodologies require sufficient and reliable prior knowledge (statistical data, assumptions, analytic models, etc.) to determine the probabilistic distributions of parameters, which may be uneasy to be obtained in practical application, and the results of probabilistic analysis are very sensitive to the probabilistic models of parameters . Due to the lack of relevant experimental demonstration or accident data, the reliability of analysis results cannot be fully guaranteed.