Expert judgments for performance shaping Factors’ multiplier design in human reliability analysis

Peng Liu, Yongping Qiu, Juntao Hu, Jiejuan Tong, Jun Zhao and Zhizhong Li

Reliability Engineering and System Safety, 2020, vol. 194, issue C

Abstract: Human reliability analysis (HRA) still heavily relies on expert judgments to generate reliability data. There exists a widely recognized need to validate and justify the reliability data obtained from expert judgments. For demonstrating such effort, we provide a template of how we base expert elicitations and empirical studies to derive the multipliers of performance shaping factors (PSFs). We applied two expert judgment techniques—absolute probability judgment (APJ) and ratio magnitude estimation (RME)—to update the PSF multiplier design in Standardized Plant Analysis of Risk-Human Reliability Analysis (SPAR-H). Licensed operators (N = 17) from a nuclear power plant were recruited. It is found that APJ and RME have acceptable inter-rater reliability and convergent validity between them. The multipliers estimated by APJ and RME were compared with those from empirical studies in the human performance literature. Certain consistencies between these heterogeneous data sources were found. Combining these heterogeneous data, we suggested the multiplier design of PSFs for SPAR-H. We also bridged the relationship between every PSF and its psychological mechanism to trigger human errors. Our work might suggest the appropriateness of expert elicitations in generating useful data for HRA, and strengthen the empirical and psychological foundations of PSF-based HRA methods.

Keywords: Human reliability analysis; Performance shaping factors; Expert judgment; Absolute probability judgment; Ratio magnitude estimation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:194:y:2020:i:c:s0951832017310293

DOI: 10.1016/j.ress.2018.12.022

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