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ISSN:2394-3661 | Crossref DOI | SJIF: 5.138 | PIF: 3.854

International Journal of Engineering and Applied Sciences

(An ISO 9001:2008 Certified Online and Print Journal)

Analysis and Study on the Handling Leakage Accident of Bulk Chemical Carriers Based on Bayesian Network

( Volume 7 Issue 4,April 2020 ) OPEN ACCESS

Wang Hong, Li Huabin


Ship Accident; Bayesian Network; Accident Type; chemical; cause analysis


On the basis of studying and studying a large number of literatures, this paper explores the types of navigation accidents of bulk carriers, focusing on self-sinking, grounding and collision accidents. This paper probes into the causes of bulk carrier navigation accidents from four aspects: human factors, ship factors, environmental factors and organizational factors. Through the investigation report of 58 accidents of cosmetics bulk carriers collected from the official websites of the Maritime Safety Administration, the network node was identified, the chain of accidents of bulk carriers was found and the Bayesian network of accidents of bulk carriers was constructed. Then the Bayesian network model was run, and 18 samples were selected to validate the model and confirm the feasibility of the model. Secondly, the types of dry bulk ships navigation accidents are analyzed to find out the types of frequent accidents. The most probable causal chain of collision, self-sinking and grounding of dry bulk carrier is obtained by analyzing the causal chain of these three accidents. Finally, the sensitivity of the Bayesian network is analyzed to determine the most likely cause of a ship accident. According to the research results, the countermeasures to prevent accidents are put forward and summarized. The research in this paper has certain guiding significance for relevant management departments to prevent the accidents of dry bulk carriers, and also provides a good guidance for shipping companies on the operation of dry bulk carriers, it provides a basis for the design, maintenance and operation of ships, and puts forward a good prediction and preventive measures.



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