A Quantitative Approach to Risk-Informed Safety Significance Categorization with an Early Expert Participatory

  • Jun Su Ha
  • Poong Hyun Seong
Conference paper


A risk-informed safety significance categorization (RISSC) is to categorize structures, systems, or components (SSCs) of a nuclear power plant (NPP) into two or more groups, according to their safety significance using both probabilistic and deterministic insights [1]. In the conventional methods for the RISSC, SSCs are quantitatively categorized according to their importance measures for the initial categorization [2]. The final categorizations of SSCs, however, are qualitatively made by expert panel through discussions and adjustments of opinions by using the probabilistic insights compiled in the initial categorization process and combining the probabilistic insights with the deterministic insights. Therefore, owing to the qualitative decision-making process, the conventional methods have the demerits that they are very costly in terms of time and labour; and that it is not easy to reach the final decision, when the opinions of the experts are in conflict.


Analytic Hierarchy Process Nuclear Power Plant Decision Factor Final Categorization Safety Significance 
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  1. 1.
    ASME OMN-3 CODE CASE. Requirements for Safety Significance Categorization of Pump and Valve Components Using Risk Insights for Inservice Testing of LWR Power Plants. 1998Google Scholar
  2. 2.
    Ian B. Wall, John. J. Haugh, David. H. Worlege. Recent Application of PSA for Managing Nuclear Power Plant Safety. Progress in Nuclear Energy, Vol. 39, No. 3-4, pp. 367–425, 2001CrossRefGoogle Scholar
  3. 3.
    W. J. Parkinson. Risk-Based In-Service Testing Program for Comanche Peak Steam Electric Station. EPRI/TR-105870, 1995Google Scholar
  4. 4.
    D. I. Kang. Risk-Informed Importance Analysis of In-Service Testing Components for Ulchin Unit 3. KAERI/TR-1927, 2001Google Scholar
  5. 5.
    Nuclear Energy Institute. Industry Guideline for Monitoring the Effectiveness of Maintenance at Nuclear Power Plant. NUMARC 93-01 (Revision 2), 1996Google Scholar
  6. 6.
    T. L. Saaty. The Analytic Hierarchy Process. McGraw-Hill, 1980Google Scholar
  7. 7.
    Finn V. Jensen. An Introduction to Bayesian Networks. Springer-Verlag New York Inc., 1996Google Scholar

Copyright information

© Springer-Verlag London 2004

Authors and Affiliations

  • Jun Su Ha
    • 1
  • Poong Hyun Seong
    • 1
  1. 1.Department of Nuclear and Quantum EngineeringKAISTDaejeonthe Republic of Korea

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