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

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Advances in Safety Management and Human Performance (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1204))

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Abstract

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.

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Acknowledgments

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).

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Correspondence to Young A Suh .

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Suh, Y.A., Kim, J. (2020). A Fuzzy Logic Model for Quantifying the Likelihood of Human Decision-Making in Nuclear Emergency Situations. In: Arezes, P., Boring, R. (eds) Advances in Safety Management and Human Performance. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1204. Springer, Cham. https://doi.org/10.1007/978-3-030-50946-0_42

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  • DOI: https://doi.org/10.1007/978-3-030-50946-0_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50945-3

  • Online ISBN: 978-3-030-50946-0

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