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• W4289260156 endingPage "103290" @default.
• W4289260156 startingPage "103290" @default.
• W4289260156 abstract "Ship to ship tanker maneuvering operation, which take place while both ships are underway and in open sea circumstances, present a number of unique challenges in terms of collision that differ from conventional port berthing maneuvers. Given the flammable quality of the commodities transported and the limited access to emergency response vehicles owing to the operation's distance from the land, risk analysis of these berthing maneuvers is critical. Therefore, the fundamental purpose of this research is to conduct a probabilistic risk analysis of collision incidents by integrating fault tree analysis with bayesian network analysis using a fuzzy method. Initially, the fault tree analysis (FTA) was utilized to determine critical events and their logical structure. Expert opinion and fuzzy theory is used to deal effectively with uncertainty in probabilities of basic events. Considering the constraints of FTA in terms of conditional dependencies and static nature, a Bayesian network mapping from FTA is developed to overcome this limitation. BN is utilized to simulate the risk associated with the entire operating process by probability updating and sensitivity analysis. The results indicated that the “mooring line breakdown”, “main engine failure” and “steering system failure” root nodes are the most contributing factors to the STS collision accidents. The findings show that this research technique is realistic and reasonable, and makes an important contribution to the literature by combining FTA-BN as a dynamic risk assessment methodology to the improvement of STS operation." @default.
• W4289260156 created "2022-08-01" @default.
• W4289260156 creator A5027189711 @default.
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• W4289260156 date "2022-09-01" @default.
• W4289260156 modified "2023-10-17" @default.
• W4289260156 title "Risk analysis of collision accidents during underway STS berthing maneuver through integrating fault tree analysis (FTA) into Bayesian network (BN)" @default.
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• W4289260156 doi "https://doi.org/10.1016/j.apor.2022.103290" @default.
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