A Fuzzy Logic Model for Quantifying the Likelihood of Human Decision-Making in Nuclear Emergency Situations

  • Young A SuhEmail author
  • Jaewhan Kim
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1204)


The purpose of this paper is to develop a fuzzy logic model for quantifying the likelihood of decision-making actions as one of the human reliability analysis (HRA). Especially, under emergency situations in Nuclear Power Plants (NPPs), the reliability of a decision while following a beyond design basis accident (after core damage situation), i.e. Fukushima Accident in 2011, is important part of HRA. In this paper, the fuzzy model is proposed for quantifying the likelihood of human decision-making under Severe Accident Management Guideline (SAMG) implementation corresponding to the nuclear emergency situations. Trial and error techniques were applied to identify suitable input parameters, fuzzification, the inference system, and defuzzification to develop the fuzzy model of SAMG decision actions. The results of fuzzy model show that ambiguous language expression of decision-making by operators can be quantified. In addition, we demonstrates the developed model is feasible to quantify the decision action with comparing the result of expert judgement. This study shows that the fuzzy model can be applied to evaluating human decision likelihood or error in overall HRA as well as SAMG HRA.


Human decision-making Fuzzy logic model Human reliability analysis Quantitative method Severe accident management guidelines 



This research was supported by a Nuclear Research & Development Program of the National Research Foundation of Korea (NRF) grant, funded by the Ministry of Science and ICT (MSIT) (Grant Code: 2017M2A8A4015291).


  1. 1.
    Kim, J., Cho, J.: Technical challenges in modeling human and organizational actions under severe accident conditions for Level 2 PSA. Reliab. Eng. Syst. Saf. 194, 106239 (2018)Google Scholar
  2. 2.
    Pyy, P.: An approach for assessing human decision reliability. Reliab. Eng. Syst. Saf. 68(1), 17–28 (2000)Google Scholar
  3. 3.
    Konstandinidou, M., Nivolianitou, Z., Kiranoudis, C., Markatos, N.: A fuzzy modeling application of CREAM methodology for human reliability analysis. Reliab. Eng. Syst. Saf. 91(6), 706–716 (2006)Google Scholar
  4. 4.
    Baziuk, P.A., Rivera, S.S., Nunez Mc Leod, J.: Fuzzy human reliability analysis: applications and contributions review. Adv. Fuzzy Syst. 2016, 9 (2016)Google Scholar
  5. 5.
    Zio, E., Baraldi, P., Librizzi, M., Podofillini, L., Dang, V.N.: A fuzzy set-based approach for modeling dependence among human errors. Fuzzy Sets Syst. 160(13), 1947–1964 (2009)Google Scholar
  6. 6.
    Ung, S.T., Shen, W.M.: A novel human error probability assessment using fuzzy modeling. Risk Anal.: Int. J. 31(5), 745–757 (2011)Google Scholar
  7. 7.
    Li, P.C., Chen, G.H., Dai, L.C., Li, Z.: Fuzzy logic-based approach for identifying the risk importance of human error. Saf. Sci. 48(7), 902–913 (2010)Google Scholar
  8. 8.
    Sheridan, T.B.: Telerobotics, Automation, and Human Supervisory Control. MIT Press, Cambridge (1992)Google Scholar
  9. 9.
    Richei, A., Hauptmanns, U., Unger, H.: The human error rate assessment and optimizing system HEROS—a new procedure for evaluating and optimizing the man-machine interface in PSA. Reliab. Eng. Syst. Saf. 72(2), 153–164 (2001)Google Scholar
  10. 10.
    Mathworks. Fuzzy Logic Toolbox User’s Guide, MATLAB. Mathworks, inc. (2019)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Risk and Reliability Assessment Research Team, Korea Atomic Energy Research InstituteDaejeonRepublic of Korea

Personalised recommendations