Prioritization of Underlying Precursors in Nuclear Accidents

The facts that the implicit precursors which are not easily quantified are underlying factors are already known. The current Probabilistic Safety Assessment (PSA) is limited in its ability to quantify the importance of accident causes. It is, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study is presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually it turned out they represent the lack of knowledge. After four precursors are selected, sub-precursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables to make prioritization for the factors. Authors tried to prioritize the lessons-learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.