Browse > Article
http://dx.doi.org/10.5516/NET.2008.40.5.327

RISK-INFORMED REGULATION: HANDLING UNCERTAINTY FOR A RATIONAL MANAGEMENT OF SAFETY  

Zio, Enrico (Dept. of Energy, Polytechnic of Milan Via Ponzio)
Publication Information
Nuclear Engineering and Technology / v.40, no.5, 2008 , pp. 327-348 More about this Journal
Abstract
A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.
Keywords
Risk-Informed; Nuclear Power Plant; Uncertainty; Epistemic; Aleatory; Probability; Dempster-Shafer Theory; Possibility; Monte Carlo; Event Tree;
Citations & Related Records

Times Cited By Web Of Science : 0  (Related Records In Web of Science)
Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 M. Modarres, Advanced Nuclear Power Plant Regulation Using Risk-Informed and Performance-Based Methods, Reliability Engineering and System Safety, 2008, doi:10.1016/j.ress.2008.02.019
2 F.R. Farmer, The Growth of Reactor Safety Criteria in the United Kingdom, Anglo-Spanish Power Symposium, Madrid, 1964
3 Garrick, B.J. and Gekler, W.C., Reliability Analysis of Nuclear Power Plant Protective Systems, US Atomic Energy Commission, HN-190, 1967
4 U.S. Nuclear Regulatory Commission Regulatory Guide 1.174, An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on Plant-Specific Changes to the Current Licensing Basis, USNRC, Regulatory Guide 1.1.74, Revision 1, November 2002
5 U.S. Nuclear Regulatory Commission Regulatory Guide 1.176, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Graded Quality Assurance, USNRC, August 1998
6 A.C. Kadak and T. Matsuo, The Nuclear Industry's Transition to Risk-Informed Regulation and Operation in the United States, Reliability Engineering and System Safety, Vol. 92, 2007, pp. 609-618   DOI   ScienceOn
7 Da Ruan, J. Kacprzyk and M. Fedrizzi, Soft Computing for Risk Evaluation and Management, Physica-Verlag, 2001
8 Soft Methods in Safety and Reliability, Special Sessions IIII, Proceedings of ESREL 2007, Stavanger, Norway, 25-27 June 2007, Volume 1
9 R.E. Moore, Methods and Applications of Interval Analysis, Philapdelphia, PA: SIAM, 1979
10 C. Baudrit, D. Dubois and D. Guyonnet, Joint Propagation of Probabilistic and Possibilistic Information in Risk Assessment, IEEE Transactions on Fuzzy Systems, Vol. 14, 2006, pp. 593-608   DOI   ScienceOn
11 D. Dubois, Possibility Theory and Statistical Reasoning, Computational Statistics and Data Analysis, Vol. 51, 2006, pp. 47-69   DOI   ScienceOn
12 R. Yager, On the Dempster-Shafer Framework and New Combination Rules, Information Sciences, Vol. 16, pp. 37-41
13 K. Sentz and S. Ferson, Combination of Evidence in Dempster-Shafer Theory, SAND 2002-0835, Sandia National Laboratories, USA
14 Nuclear Power Plant 2 Operating Living PRA Report (Draft). Nuclear Energy Research Center, Tao Yuan, Taiwan, 1995
15 S. Ferson and V. Kreinovich, Representation, Propagation and Aggregation of Uncertainty, SAND Report
16 P. Baraldi and E. Zio, A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis, 2008
17 M. H. Kalos, P. A. Whitlock, Monte Carlo methods. Volume I: Basics, Wiley, 1986
18 IAEA, Risk informed regulation of nuclear facilities: overview of the current status, IAEA-TECDOC-1436, 2005
19 K.-Y Cai., System Failure Engineering and Fuzzy Methodology. An Introductory Overview, Fuzzy Sets and Systems 83, 1996, pp. 113-133   DOI   ScienceOn
20 D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty, New York, Plenum Press, 1988
21 C. Baudrit and D. Dubois, Practical Representations of Incomplete Probabilistic Knowledge, Computational Statistics and Data Analysis, Vol. 51, 2006, pp. 86-108   DOI   ScienceOn
22 U.S. Nuclear Regulatory Commission Regulatory Guide 1.175, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Inservice Testing, USNRC, August 1998
23 G.J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Upper Saddle River, NJ, Prentice-Hall, 1995
24 G. de Cooman, Possibility Theory Part I : Measure- and Integral-Theoretic Groundwork; Part II: Conditional Possibility; Part III: Possibilistic Independence, Int. J. Gen. Syst., 1997, Vol. 25 (4), pp. 291-371   DOI   ScienceOn
25 G.E. Apostolakis, The Concept of Probability in Safety Assessments of Technological Systems, Science, 1990, pp. 1359-1364
26 U.S. Nuclear Regulatory Commission Regulatory Guide 1.177, An Approach for Plant-Specific, Risk-Informed Decisionmaking: Technical Specifications, USNRC, August 1998
27 J.C. Helton, Alternative Representations of Epistemic Uncertainty, Special Issue of Reliability Engineering and System Safety, Vol. 85, 2004
28 A.P. Dempster, Upper and Lower Probabilities Induced by a Multivalued Mapping, Ann. Mat. Stat., Vol. 38, 1967, pp. 325-339   DOI
29 L.A. Zadeh, Fuzzy Sets, Information and Control, Vol. 8, 1965, pp. 338-353   DOI
30 WASH-1400, Reactor Safety Study, US Nuclear Regulatory Commission, 1975
31 D. Huang, T. Chen, M. J. Wang, A fuzzy set approach for event tree analysis, Fuzzy Sets ans Systems 118, pp 153-165, 2001   DOI   ScienceOn
32 G. Shafer, A Mathematical Theory of Evidence, University Press, Princeton, 1976