This paper presents a quantitative approach to Human Reliability Analysis (HRA) during emergency conditions in an offshore environment. Due to the lack of human error data for emergency conditions most of the available HRA methodologies are based on expert judgment techniques. Expert judgment suffers from uncertainty and incompleteness due to partial ignorance, which is not considered in available techniques. Furthermore, traditional approaches suffer from unrealistic assumptions regarding the independence of the human factors and associated actions. The focus of this paper is to address the issue of handling uncertainty associated with expert judgments with evidence theory and to represent the dependency among the human factors and associated actions using a Bayesian Network (BN) approach. The Human Error Probability (HEP) during different phases of an emergency is then assessed using a Bayesian approach integrated with an evidence theory approach. To understand the applicability of the proposed approach, results are compared with an analytical approach: Success Likelihood Index Methodology (SLIM). The comparative study demonstrates that the proposed approach is effective in assessing human error likelihood. In addition to being simple, it possesses additional capability, such as updating as new information becomes available and representing complex interaction. Use of the proposed method would provide an effective mechanism of human reliability assessment in hazardous operations. © 2013 Elsevier Ltd.

Item Type:	Refereed Article
Keywords:	Dependency and Uncertainty in HRA; Human Factor; Human Reliability Analysis (HRA); Offshore Emergency; Risk Assessment; Dependency and Uncertainty in HRA; Expert judgment technique; Human error probability; Human reliability analysis; Errors
Research Division:	Engineering
Research Group:	Maritime engineering
Research Field:	Ocean engineering
Objective Division:	Mineral Resources (Excl. Energy Resources)
Objective Group:	Environmentally sustainable mineral resource activities
Objective Field:	Environmentally sustainable mineral resource activities not elsewhere classified
UTAS Author:	Khan, F (Professor Faisal Khan)
ID Code:	94470
Year Published:	2013
Web of Science® Times Cited:	63
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2014-09-09
Last Modified:	2017-11-03
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