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Science and Technology of Nuclear Installations
Volume 2014 (2014), Article ID 206258, 12 pages
http://dx.doi.org/10.1155/2014/206258
Research Article

Study on Nuclear Accident Precursors Using AHP and BBN

1Department of Nuclear Engineering, Kyung Hee University, Yongin Si, Gyeonggi Do 446-701, Republic of Korea
2Industrial Services TUV Rheinland Korea Ltd. 197-28 Guro-dong, Guro-gu, Seoul 152-719, Republic of Korea
3Department of Basic Sciences, University of Engineering and Technology, Taxila, Pakistan

Received 5 February 2014; Accepted 19 April 2014; Published 14 May 2014

Academic Editor: Joon-Eon Yang

Copyright © 2014 Sujin Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Most of the nuclear accident reports used to indicate the implicit precursors which are not easily quantified as underlying factors. The current Probabilistic Safety Assessment (PSA) is capable of quantifying the importance of accident causes in limited scope. It was, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study were presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually, it turned out that they represent the lack of knowledge. After four precursors are selected, subprecursors 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 making prioritization for the factors. We tried to prioritize the lessons learned from Fukushima accident to demonstrate the feasibility of the proposed methodology.