Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network

To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of Bayesian network (BN) and complex (CN) is proposed in present study. The principle event tree (ETA) firstly applied to identify hazardous events involved accident according report, based which CN would then be developed logic relationships among events. improved K-shell decomposition algorithm utilized determine criticality nodes CN, results are used develop BN within framework human factor classification system (HFACS). Then, can simulated probability distribution all BN, obtained basis node criticality. Finally, simulation interpreted from perspectives brief analysis, backward sensitivity analysis. verified existing studies investigation report issued by authority, presented as evidence verify effectiveness methodology evaluate risk ships. this study integrates advantages maritime accidents, seen main innovation work.