Human fatigue’s effect on the risk of maritime groundings – A Bayesian Network modeling approach

• A general method for developing a Bayesian Network (BN) for maritime transport.
• In depth analysis of 93 accident investigation reports.
• Inclusion of human and organizational factors, focus on human fatigue.
• The marginal probability of human fatigue at bridge is calculated to 0.23.
• A fatigued bridge has about 16% higher probability of grounding.

The article introduces a general method for developing a Bayesian Network (BN) for modeling the risk of maritime ship accidents. A BN of human fatigue in the bridge management team and the risk of ship grounding is proposed. The qualitative part of the BN has been structured based on modifying the Human Factor Analysis and Classification System (HFACS). The quantitative part is based upon correlation analysis of fatigue-related factors identified from 93 accident investigation reports. The BN model shows that fatigue has a significant effect on the probability of grounding. A fatigued operator raises the probability of grounding of a large ship in long transit with 23%. Compared to the two watch system (6–6 and 12–12), the 8–4–4–8 watch system seems to generate the least fatigue. However, when manning level, which is influenced by the various watch schemes, is taken into account, the two watch system is preferable, leading to less fatigue and fewer groundings. The strongest fatigue-related factors related to top management are vessel certifications, manning resources, and quality control.