Human Factor Risk Modeling for Shipyard Operation by Mapping Fuzzy Fault Tree into Bayesian Network

Yang Liu, Xiaoxue Ma, Weiliang Qiao, Huiwen Luo and Peilong He
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Yang Liu: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Xiaoxue Ma: Public Administration and Humanities College, Dalian Maritime University, Dalian 116026, China
Weiliang Qiao: Marine Engineering College, Dalian Maritime University, Dalian 116026, China
Huiwen Luo: College of Arts and Sciences, New York University Shanghai, Shanghai 200120, China
Peilong He: Public Administration and Humanities College, Dalian Maritime University, Dalian 116026, China

IJERPH, 2021, vol. 19, issue 1, 1-31

Abstract: The operational activities conducted in a shipyard are exposed to high risk associated with human factors. To investigate human factors involved in shipyard operational accidents, a double-nested model was proposed in the present study. The modified human factor analysis classification system (HFACS) was applied to identify the human factors involved in the accidents, the results of which were then converted into diverse components of a fault tree and, as a result, a single-level nested model was established. For the development of a double-nested model, the structured fault tree was mapped into a Bayesian network (BN), which can be simulated with the obtained prior probabilities of parent nodes and the conditional probability table by fuzzy theory and expert elicitation. Finally, the developed BN model is simulated for various scenarios to analyze the identified human factors by means of structural analysis, path dependencies and sensitivity analysis. The general interpretation of these analysis verify the effectiveness of the proposed methodology to evaluate the human factor risks involved in operational accidents in a shipyard.

Keywords: human factors; shipyard operation; Bayesian network; fuzzy fault tree; risk assessment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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