A data-based comparison of BN-HRA models in assessing human error probability: An offshore evacuation case study

Shokoufeh Abrishami, Nima Khakzad and Seyed Mahmoud Hosseini

Reliability Engineering and System Safety, 2020, vol. 202, issue C

Abstract: Bayesian Network (BN) has been increasingly exploited to improve different aspects of Human Reliability Analysis (HRA), resulting in a new generation of HRA techniques, known as BN-HRA models. However, validating and evaluating the accuracy of BN-HRA models is still a challenging task. In this study, we have assessed and compared the performance of some of well-known BN-HRA techniques using human performance data obtained from an offshore evacuation simulation. Based on the role of data in quantifying the BN-HRA models, three categories of BN-HRA models have been considered: (i) BN-CREAM and BN-SPARH, which are based on predefined rules (rule-based methods), (ii) Bayesian Parameter Learning (BPL), which is entirely based on the available data (data-based method), and (iii) BN-SLIM model which is based on both the available data and the predefined rules (hybrid method). The results of the present study show that the data-based methods, i.e., BN-SLIM and BPL, in general outperform the rule-based methods. Cross-validation analysis further demonstrates the superiority of BN-SLIM over BPL, particularly in case of data scarcity.

Keywords: Human reliability assessment; k-fold cross validation; BN-CREAM; BN-SPARH; BN-SLIM; Bayesian parameter learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:202:y:2020:i:c:s0951832020305445

DOI: 10.1016/j.ress.2020.107043

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