Browse > Article

Radiological Risk Assessment for the Public Under the Loss of Medium and Large Sources Using Bayesian Methodology  

Kim, Joo-Yeon (Dept. of Nuclear Engineering, Hanyang University)
Jang, Han-Ki (Dept. of Nuclear Engineering, Hanyang University)
Lee, Jai-Ki (Dept. of Nuclear Engineering, Hanyang University)
Publication Information
Journal of Radiation Protection and Research / v.30, no.2, 2005 , pp. 91-97 More about this Journal
Abstract
Bayesian methodology is appropriated for use in PRA because subjective knowledges as well as objective data are applied to assessment. In this study, radiological risk based on Bayesian methodology is assessed for the loss of source in field radiography. The exposure scenario for the lost source presented in U.S. NRC is reconstructed by considering the domestic situation and Bayes theorem is applied to updating of failure probabilities of safety functions. In case of updating of failure probabilities, it shows that 5 % Bayes credible intervals using Jeffreys prior distribution are lower than ones using vague prior distribution. It is noted that Jeffreys prior distribution is appropriated in risk assessment for systems having very low failure probabilities. And, it shows that the mean of the expected annual dose for the public based on Bayesian methodology is higher than the dose based on classical methodology because the means of the updated probabilities are higher than classical probabilities. The database for radiological risk assessment are sparse in domestic. It summarizes that Bayesian methodology can be applied as an useful alternative lot risk assessment and the study on risk assessment will be contributed to risk-informed regulation in the field of radiation safety.
Keywords
Bayesian methodology; PRA; exposure scenario; prior distribution; risk-informed regulation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 U.S. Nuclear Regulatory Commission, Risk Analysis and Evaluation of Regulatory Options for Nuclear Byproduct Material Systems, NUREG/CR-6642(2000)
2 D.J. Strom, R.L. Hill, J.S. Dukelow and G.R. Ciotte, Technical Letter Report: Task 7, Final Review of the 1987 Report by Oak Ridge Associated Universities, 'Improper Transfer/Disposal Scenarios for Genarally Licensed Devices', PNL-11905(1994)
3 J.O. Lubenau and J.G. Yusko, 'Radioactive Materials in Recycled Metals-An Update,' Health Physics, 74, 293-299(1998)   DOI   PUBMED   ScienceOn
4 윤길현, 재활용고철에 대한 방사선안전관리 지침개발에 관한 연구, 한국원자력안전기술원 보고서 KINS/AR-749(2000)
5 Nathan O. Siu and Dana L. Kelly, 'Bayesian Parameter Estimation in Probabilistic Risk Assessment,' Reliability Engineering and System Safety, 62, 89-116(1998)   DOI   ScienceOn
6 S. James Press, Bayesian Statistics-' Principles, Models, and Applications, pp. 23-41, John Wiley & Sons Ltd., Chichester, New York(1989)
7 한국원자력안전기술원, 사이버방사선안전정보센터 (http://rinet.kins.re.kr/)
8 U.S. Nuclear Regulatory Commission, Handbook of Parameter Estimation for Probabilistic Risk Assessment, NUREG/CR-6823, SAND2003-3348P(2003)
9 Tat-Chi Chow, Robert M. Oliver, G. Anthony Vignaux, 'A Bayesian Escalation Model to Predict Nuclear Accidents and Risk,' Operations Research, 38(2), 265-277(1990)   DOI   ScienceOn
10 이영의, '베이즈적 추리와 가설 확증,' 한국인지과학회 논문지, 8(2), 49-57(1997)
11 George Apostolakis, 'The Concept of Probability in Safety Assessments of Technical Systems,' Science, 250(4986), 1359-1364(1990).   DOI   PUBMED
12 George E.P. Box and George C. Tiao, Bayesian Inference in Statistical Analysis, pp. 25-46, John Wiley and Sons, Inc., New York(1992)
13 R.M. Oliver and H.J. Yang, 'Bayesian Updating of Event Tree Parameters to Predict High Risk Incidents,' in: Influence Diagrams, Belief Nets and Decision Analysis, R.M. Oliver and J.Q. Smith, eds., pp. 277-296, John Wiley & Sons Ltd., Chichester, New York(1990)