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A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis


Musharraf, M and Bradbury-Squires, D and Khan, FI and Veitch, B and Mackinnon, S and Imtiaz, S, A virtual experimental technique for data collection for a Bayesian network approach to human reliability analysis, Reliability Engineering and System Safety, 132 pp. 1-8. ISSN 0951-8320 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Elsevier Ltd.

DOI: doi:10.1016/j.ress.2014.06.016


Bayesian network (BN) is a powerful tool for human reliability analysis (HRA) as it can characterize the dependency among different human performance shaping factors (PSFs) and associated actions. It can also quantify the importance of different PSFs that may cause a human error. Data required to fully quantify BN for HRA in offshore emergency situations are not readily available. For many situations, there is little or no appropriate data. This presents significant challenges to assign the prior and conditional probabilities that are required by the BN approach. To handle the data scarcity problem, this paper presents a data collection methodology using a virtual environment for a simplified BN model of offshore emergency evacuation. A two-level, three-factor experiment is used to collect human performance data under different mustering conditions. Collected data are integrated in the BN model and results are compared with a previous study. The work demonstrates that the BN model can assess the human failure likelihood effectively. Besides, the BN model provides the opportunities to incorporate new evidence and handle complex interactions among PSFs and associated actions.

Item Details

Item Type:Refereed Article
Keywords:Bayesian network, human factor, human reliability analysis, data acquisition, human engineering, reliability analysis, virtual reality, Bayesian networks, complex networks, data acquisition, factor analysis, human engineering, reliability
Research Division:Engineering
Research Group:Engineering practice and education
Research Field:Risk engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Khan, FI (Professor Faisal Khan)
ID Code:120716
Year Published:2014
Web of Science® Times Cited:48
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-30
Last Modified:2017-11-01

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