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Use of evidential reasoning for eliciting bayesian subjective probabilities in human reliability analysis: A maritime case

Yang, Z, Abujaafar, KM, Qu, Z, Wang, J, Nazir, S and Wan, C (2019) Use of evidential reasoning for eliciting bayesian subjective probabilities in human reliability analysis: A maritime case. Ocean Engineering, 186. ISSN 0029-8018

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Abstract

Modelling the interdependencies among the factors influencing human error (e.g. the common performance conditions (CPCs) in Cognitive Reliability Error Analysis Method (CREAM)) stimulates the use of Bayesian Networks (BNs) in Human Reliability Analysis (HRA). However, subjective probability elicitation for a BN is often a daunting and complex task. To create conditional probability values for each given variable in a BN requires a high degree of knowledge and engineering effort, often from a group of domain experts. This paper presents a novel hybrid approach for incorporating the evidential reasoning (ER) approach with BNs to facilitate HRA under incomplete data. The kernel of this approach is to develop the best and the worst possible conditional subjective probabilities of the nodes representing the factors influencing HRA when using BNs in human error probability (HEP). The proposed hybrid approach is demonstrated by using CREAM to estimate HEP in the maritime area. The findings from the hybrid ER-BN model can effectively facilitate HEP analysis in specific and decision-making under uncertainty in general.

Item Type: Article
Uncontrolled Keywords: 0905 Civil Engineering, 0911 Maritime Engineering, 0405 Oceanography
Subjects: H Social Sciences > HF Commerce > HF5001 Business
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Publisher: Elsevier
Related URLs:
Date Deposited: 17 Feb 2020 11:23
Last Modified: 24 Jun 2020 00:50
DOI or Identification number: 10.1016/j.oceaneng.2019.05.077
URI: https://researchonline.ljmu.ac.uk/id/eprint/12258

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