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Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling

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  • Guizhen Zhang
  • Vinh V. Thai
  • Adrian Wing‐Keung Law
  • Kum Fai Yuen
  • Hui Shan Loh
  • Qingji Zhou

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.

Suggested Citation

  • Guizhen Zhang & Vinh V. Thai & Adrian Wing‐Keung Law & Kum Fai Yuen & Hui Shan Loh & Qingji Zhou, 2020. "Quantitative Risk Assessment of Seafarers’ Nonfatal Injuries Due to Occupational Accidents Based on Bayesian Network Modeling," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 8-23, January.
    • Handle: RePEc:wly:riskan:v:40:y:2020:i:1:p:8-23
    DOI: 10.1111/risa.13374
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    2. Podofillini, Luca & Reer, Bernhard & Dang, Vinh N., 2023. "A traceable process to develop Bayesian networks from scarce data and expert judgment: A human reliability analysis application," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    3. Abroon Qazi & Mecit Can Emre Simsekler & Steven Formaneck, 2023. "Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation," Annals of Operations Research, Springer, vol. 322(1), pages 241-272, March.
    4. Nguyen, Son & Shu-Ling Chen, Peggy & Du, Yuquan, 2022. "Risk assessment of maritime container shipping blockchain-integrated systems: An analysis of multi-event scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    5. Xinchen Zhang & Yeqing Sun, 2021. "The Predictive Role of ADRA2A rs1800544 and HTR3B rs3758987 Polymorphisms in Motion Sickness Susceptibility," IJERPH, MDPI, vol. 18(24), pages 1-15, December.

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