An extended method for evaluating assumptions deviations in quantitative risk assessment and its application to external flooding risk assessment of a nuclear power plant

Tasneem Bani-Mustafa, Roger Flage, Dominique Vasseur, Zhiguo Zeng and Enrico Zio

Reliability Engineering and System Safety, 2020, vol. 200, issue C

Abstract: In quantitative risk assessment, assumptions are typically made, based on best judgement, conservative, or (sometimes) optimistic judgments. Best judgment and optimistic assumptions may result in failing to meet the quantitative safety objectives, whereas conservative assumptions may increase the margins which the objectives are met with but result in cost-ineffective design or operation. In the present paper, we develop an extended framework for the analysis of the criticality of assumptions in risk assessment by evaluating the risk that deviations from the assumptions lead to a reduction of the safety margins. The framework aims to support risk-informed decision making by identifying important assumptions and integrating the assessment of their criticality into the quantitative risk assessment (QRA). The framework is, finally applied within the quantitative risk assessment of a Nuclear Power Plant (NPP) exposed to external flooding. Compared to previous works on the subject, we consider also conservative assumptions and introduce decision flow diagrams to support the classification of the criticality of the assumptions. The framework provides a more comprehensive and transparent evaluation of the assumptions deviation risk through the decision flow diagrams that facilitate the standardization of the evaluation of the assumption deviation effects on the risk assessment.

Keywords: Quantitative risk assessment; Conservative assumption; Assumption deviation; Strength of knowledge; Decision flow diagram; Nuclear power plants; External flooding (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:200:y:2020:i:c:s0951832018312079

DOI: 10.1016/j.ress.2020.106947

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