A classification-based framework for trustworthiness assessment of quantitative risk analysis

In this paper, we develop a classification-based method for the assessment of the trustworthiness of Quantitative Risk Analysis (QRA). The QRA trustworthiness is assumed to be determined by the quality of the QRA process. Six quality criteria, i.e., completeness of documentations, understanding of problem settings, coverage of accident scenarios, appropriateness of analysis methods, quality of input data, accuracy of risk calculation, are identified as the factors most influencing the trustworthiness. The assessment is, then, formulated as a classification problem, solved by a Naive Bayes Classifier (NBC) constructed based on a set of training data, whose trustworthiness is given by experts. NBC learns the expert's assessment from the training data: therefore, once constructed, the NBC can be used to assess the trustworthiness of QRAs other than the training data. Leave-one-out cross validation is applied to validate the accuracy of the developed classifier. A stochastic hypothesis testing-based approach is also developed to check the consistency of the training data. The performance of the developed methods is tested for ten artificially generated scenarios. The results demonstrate that the developed framework is able to accurately mimic a variety of expert behaviors in assessing the trustworthiness of QRA.