Risk-Informed Decision Making in Nuclear Power Plants

  • A. K. Verma
  • Ajit Srividya
  • Vinod Gopika
  • Karanki Durga Rao
Part of the Springer Series in Reliability Engineering book series (RELIABILITY)


Probabilistic Safety Assessment (PSA), also called Probabilistic Risk Assessment (PRA), is currently being widely applied to many fields, viz., nuclear facilities, chemical and process plants, aerospace, and even to financial management. PSA has been accepted all over the world as an important tool to assess the safety of a facility and to aid in ranking safety issues by order of importance. PSA essentially aims at identifying the events and their combination(s) that can lead to severe accidents, assessing the probability of occurrence of each combination, and evaluating the consequences. The main benefit of PSA is to provide insights into design, performance, and environmental impacts, including the identification of dominant risk contributors and the comparison of options for reducing risk. PSA provides the quantitative estimate of risk which is useful for comparison of alternatives in different design and engineering areas. Furthermore, PSA is a conceptual and mathematical tool for deriving numerical estimate of risk and quantifying the uncertainties in these estimates.


Failure Probability Limit State Function Thomas Model Core Damage Pipe Segment 
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  1. 1.
    Pranab K. Samanta (1992) Optimisation of technical specifications applications in USA, Lecture 54.4.4. IAEA course: use of PSA in the operation of NPPsGoogle Scholar
  2. 2.
    Martorell S, Carlos S, Sanchez A, Serradell V (2001) Constrained optimization of test intervals using steady-state genetic algorithms: application to safety systems. Reliab Eng Syst Saf 72:59–74CrossRefGoogle Scholar
  3. 3.
    Gopika V, Kushwaha HS, Verma AK, Srividya A (2004) Optimization of ISI interval using genetic algorithms for risk informed in-service inspection. Reliab Eng Syst Saf 86:307–316CrossRefGoogle Scholar
  4. 4.
    Vaurio JK (1995) Optimization of test and maintenance intervals based on risk and cost. Reliab Eng Syst Saf 49:23–36CrossRefGoogle Scholar
  5. 5.
    Munoz A, Martorell S, Serradell V (1997) Genetic algorithms in optimizing surveillance and maintenance of components. Reliab Eng Syst Saf 57:107–120CrossRefGoogle Scholar
  6. 6.
    Vaurio JK (1999) Availability and cost functions for periodically inspected preventively maintained units. Reliab Eng Syst Saf 63:133–140CrossRefGoogle Scholar
  7. 7.
    Goldberg DE (1989) Genetic algorithm in search, optimization and machine learning reading. Addison-Wesley, MAGoogle Scholar
  8. 8.
    Durga Rao K et al (2007) Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach. Reliab Eng Syst Saf 92(7):895–901CrossRefGoogle Scholar
  9. 9.
    IAEA TECDOC-737, Advances in reliability analysis and probabilistic safety assessment for nuclear power reactors, March 1994Google Scholar
  10. 10.
    ASME Code CASE N-560, Alternative examination requirements for Class1, Category B-J Piping weldsGoogle Scholar
  11. 11.
    ASME Code CASE N-578, Risk informed methods for in-service inspection of pipe weldsGoogle Scholar
  12. 12.
    Balkey et al. Developments on US NRC Approved WOG/ASME research risk informed in-service inspection methodology. A ReportGoogle Scholar
  13. 13.
    Balkey ART et al (1998) ASME risk-based in-service inspection and testing: an outlook for the future. Risk Anal 18:407–421CrossRefGoogle Scholar
  14. 14.
    EPRI, USNRC (1999). Risk informed in-service inspection evaluation procedure, TR-112657, July 1999Google Scholar
  15. 15.
    COMED (2000). Risk informed in-service inspection evaluation. Final report, Engineering and Research Inc., July 2000Google Scholar
  16. 16.
    NUREG-1661, Technical elements of risk informed in-service inspection for pipingGoogle Scholar
  17. 17.
    Regulatory Guide 1.178 (1998) An approach for plant specific risk informed decision making: in-service inspection, USNRC, August 1998Google Scholar
  18. 18.
    Rouhan A (2002) Reliable NDT data for risk based inspection for offshore structures. Proceedings of the 3rd European–American workshop on reliability of NDE and demining, Berlin, 2002Google Scholar
  19. 19.
    RIBA PROJECT (2001) Risk informed approach for in-service inspection of nuclear power plant components, EUR 20164 EN, Project Summary, December 2001Google Scholar
  20. 20.
    Fleming KN, Gosselin S, Mitman J (1999) Application of markov models and service data to evaluate the influence of inspection on pipe rupture frequencies. Proceedings of the ASME pressure vessels and piping conference, Boston August 1–5Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • A. K. Verma
    • 1
  • Ajit Srividya
    • 1
  • Vinod Gopika
    • 2
  • Karanki Durga Rao
    • 3
  1. 1.Indian Institute of Technology BombayMumbaiIndia
  2. 2.Bhabha Atomic Research CentreMumbaiIndia
  3. 3.Paul Scherrer InstitutVilligen PSISwitzerland

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