A Hybrid Probabilistic Risk Analytical Approach to Ship Pilotage Risk Resonance with FRAM

Collision risk in ship pilotage process has complex characteristics that are dynamic, uncertain, and emergent. To reveal collision risk resonance during ship pilotage process, a hybrid probabilistic risk analysis approach is proposed, which integrates the Functional Resonance Analysis Method (FRAM), Dempster-Shafer (D-S) evidence theory, and Monte Carlo (MC) simulation. First, FRAM is used to qualitatively describe the coupling relationship and operation mechanism among the functions of the pilotage operation system. Then, the D-S evidence theory is used to determine the probability distribution of the function output in the specified pilotage scenario after quantitatively expressing the function variability, coupling effect, and the influence of operation conditions through rating scales. Finally, MC simulation is used to calculate the aggregated coupling variability between functions, and the critical couplings and risk resonance paths under different scenarios are identified by setting the threshold and confidence level. The results show that ship collision risk transmission is caused by function resonance in the pilotage system, and the function resonance paths vary with pilotage scenarios. The critical coupling 'F2-F7(I)' emerges as a consistent factor in both scenarios, emphasizing the significance of maintaining a proper lookout. The hybrid probabilistic risk analytical approach to ship pilotage risk resonance with FRAM can be a useful method for analysing the causative mechanism of ship operational risk.

Automated summary

This study proposes a hybrid probabilistic risk analysis approach to reveal collision risk resonance during ship pilotage process. By integrating the Functional Resonance Analysis Method (FRAM), Dempster-Shafer (D-S) evidence theory, and Monte Carlo (MC) simulation, the coupling relationship and operation mechanism among the functions of the pilotage operation system are qualitatively described, the probability distribution of the function output is determined, and the critical couplings and risk resonance paths are identified by setting the threshold and confidence level.