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|Title:||Quantitative risk assessment of seafarers’ nonfatal injuries due to occupational accidents based on Bayesian network modeling||Authors:||Zhang, Guizhen
Thai, Vinh V.
Law, Adrian Wing-Keung
Yuen, Kum Fai
Loh, Hui Shan
|Issue Date:||2019||Source:||Zhang, G., Thai, V. V., Law, A. W.-K., Yuen, K. F., Loh, H. S., & Zhou, Q. (2019). Quantitative risk assessment of seafarers’ nonfatal injuries due to occupational accidents based on Bayesian network modeling. Risk Analysis, 40(1), 8-23. doi:10.1111/risa.13374||Journal:||Risk Analysis||Abstract:||Reducing the incidence of seafarers’ workplace injuries is of great importance to shipping and ship management companies. The objective of this study is to identify the important influencing factors and to build a quantitative model for the injury risk analysis aboard ships, so as to provide a decision support framework for effective injury prevention and management. Most of the previous research on seafarers’ occupational accidents either adopts a qualitative approach or applies simple descriptive statistics for analyses. In this study, the advanced method of a Bayesian network (BN) is used for the predictive modeling of seafarer injuries for its interpretative power as well as predictive capacity. The modeling is data driven and based on an extensive empirical survey to collect data on seafarers’ working practice and their injury records during the latest tour of duty, which could overcome the limitation of historical injury databases that mostly contain only data about the injured group instead of the entire population. Using the survey data, a BN model was developed consisting of nine major variables, including “PPE availability,” “Age,” and “Experience” of the seafarers, which were identified to be the most influential risk factors. The model was validated further with several tests through sensitivity analyses and logical axiom test. Finally, implementation of the result toward decision support for safety management in the global shipping industry was discussed.||URI:||https://hdl.handle.net/10356/136792||ISSN:||0272-4332||DOI:||10.1111/risa.13374||Rights:||© 2019 Society for Risk Analysis. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||IGS Journal Articles|
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