Improved hybrid Z-number Bayesian network approach to predict mooring line failure during cargo operations in ships

• The study combines Bayesian Network (BN) analysis with Z-number theory to predict mooring line failure during cargo operations. This integration addresses uncertainties in human judgment and improves the reliability of risk assessments. • The most significant contributors to mooring line failures are identified as failure to adapt to tidal conditions, adverse sea conditions, improper mooring line arrangement, poor quality of shore fenders and bollards, and improper location of mooring points. • The failure probability of mooring lines during cargo operations was calculated as 0.015. Among the root causes, failure to adapt to tidal conditions is highlighted as the most influential factor. • The findings of the study provide actionable advice on improving crew training, upgrading equipment monitoring systems and enforcing stricter maintenance procedures. Mooring operations are considered one of the most high-risk activities in cargo ship operations due to the complex interplay of human factors, equipment condition and environmental factors. This paper investigates quantitatively the mooring line risk during cargo operations in ships. To achieve this purpose, a robust practical approach integrating improved Z-numbers and a Bayesian network is proposed to perform probabilistic risk analysis. In the approach, whilst improved Z-numbers are employed to model uncertainty more effectively, considering both the probability of failure and the confidence in the data, the Bayesian network is used to analyse causal relationships and update risk assessments dynamically based on real-time operational data and environmental conditions. The proposed approach enhances predictive accuracy, enabling ship crews or technical ship inspectors to make informed decisions on mitigating risks under uncertain and variable conditions. The findings of the paper show that the mooring line failure probability during cargo operations is 0.015, and the root cause, “failure to adapt to tidal conditions”, is the main contributing factor. The proposed risk assessment approach provides valuable contributions for implementing proactive risk mitigation strategies and enhancing operational safety in cargo operations in maritime transportation.