Lifecycle Risk Assessment for Steel Cargo Vessel Sinkings: An Interpretive Structural Modeling and Fuzzy Bayesian Network Approach

Abstract

Steel cargo vessel sinking accidents (SCVSA) threaten maritime safety and disrupt global steel supply chains. This study integrates interpretive structural modeling (ISM) and fuzzy Bayesian networks (FBN) to evaluate SCVSA risks across the incident lifecycle. ISM identifies hierarchical relationships among multifaceted risk factors. FBN assesses lifecycle risks using fuzzy scoring, modular nodes, and a hierarchical structure, with muti-source data drawn from accident reports, expert opinions, and research studies. Experts estimate probabilities based on observations and causal scenarios involving steel cargo vessels at Shanghai Port. The ISM–FBN framework visualizes hierarchical risk factors and incorporates uncertainty in the data and causal relationships through fuzzy scoring, structural updates, and probability learning. This approach provides a robust and adaptable tool for assessing SCVSA risks, advancing maritime risk assessment methodologies. Key findings identify advanced vessel age, severe weather and sea conditions, and inadequate regulatory oversight as primary root causes. Poor cargo loading and stowage practices are direct contributors. Intermediate risk factors from deeper to surface layers flow from shipping companies to crew and further to vessel and environmental conditions. Multi-stage risk factors include inadequate emergency responses and improper cargo securing. To mitigate these risks, actionable insights are provided, including fleet modernization, enhanced regulatory compliance, crew training, and improved emergency preparedness.

Keywords

• steel cargo vessel sinking
• lifecycle risk assessment
• maritime transport risks
• fuzzy Bayesian network
• interpretive structure modeling
• cascading risk effects