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Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN

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  • Guo, Yunlong
  • Jin, Yongxing
  • Hu, Shenping
  • Yang, Zaili
  • Xi, Yongtao
  • Han, Bing

Abstract

The risks involved in ship pilotage operations are characterized by random, uncertain and complex features. To reveal the spatiotemporal evolution of ship collision risks in the pilotage operations process, a risk evolution analysis model is developed in this paper by the combination of a Functional Resonance Analysis Method (FRAM) and Dynamic Bayesian Network (DBN). First, based on the analysis results of the functional resonance mechanism of a ship pilotage system, the relevant collision risk influencing factors (RIFs) and their coupling relationships are identified. Second, the DBN is quantified by the employment of various uncertainty treatment methods including the Dempster-Shafer evidence theory for the configuration of the prior probabilities and a Markov model for the dynamic factors’ transition probability calculation. Finally, using the temporal observation data, the temporal risk inference is conducted to reveal the risk evolution law in a ship pilotage operations process. The findings show that the evolution of collision risk in ship pilotage is significantly sensitive to regional locations, resulting in a “U†curve shaped by the action of functional resonance. “Inadequate human look-out†is among the most influential factors, and hence targeted risk control strategies should be formulated to ensure the safety of ship pilotage operations.

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  • Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022004677
    DOI: 10.1016/j.ress.2022.108850
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    2. Liu, Xuan & Meng, Huixing & An, Xu & Xing, Jinduo, 2024. "Integration of functional resonance analysis method and reinforcement learning for updating and optimizing emergency procedures in variable environments," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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