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Xi, YT, Yang, ZL, Fang, QG, Chen, WJ and Wang, J (2017) A new hybrid approach to human error probability quantification-applications in maritime operations. Ocean Engineering, 138. pp. 45-54. ISSN 0029-8018
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A new hybrid approach to human error probability quantification-applications in marine operations.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (419kB) | Preview |
Abstract
Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0905 Civil Engineering, 0911 Maritime Engineering |
| Subjects: | T Technology > TC Hydraulic engineering. Ocean engineering |
| Divisions: | Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20) |
| Publisher: | Elsevier |
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| Date Deposited: | 03 Aug 2017 15:03 |
| Last Modified: | 04 Sep 2021 11:19 |
| DOI or ID number: | 10.1016/j.oceaneng.2017.04.018 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/6876 |
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