Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea.

The International Regulations for Preventing Collisions at Sea (COLREGs) rely on vague terms, which challenges both human navigators and autonomous systems requiring computable representations. This study quantifies the COLREG concept of safe distance through fuzzy representations derived from expert judgement. In a structured international survey, 194 of 235 experienced navigators supplied data on Closest Point of Approach (CPA) and Bow Crossing Range (BCR) in open sea conditions, from which fuzzy membership functions were derived. Navigators require a larger margin ahead of the bow than at the closest point of approach, with a mean Medium BCR of 1.86 NM against a mean Medium CPA of 1.42 NM. Only experience influenced the elicited values, and the model output proved insensitive to it. A weighted fuzzy rule base was then built from a separate panel of ten experts assessing navigational scenarios on a full mission simulator, and used to construct a fuzzy model of safe distance, validated by leave-one-out cross-validation and against five collisions and one near-miss. The novelty lies in deriving the membership functions from a large expert panel, and in building the rule base from concrete scenarios. The results provide a quantified basis for decision-support and autonomous collision-avoidance systems.

The operational reliability of gears in ship propulsion systems is an important factor affecting safety, efficiency, and cost-effectiveness in ship operation. Gear failures may result in loss of propulsion, increased maintenance costs, and risks to crew safety. This paper presents an integrated methodological framework for assessing gear reliability in ship propulsion systems by integrating qualitative causal analysis, quantitative reliability growth modelling, and system dynamics simulation. The analysis is based on empirical data collected from the AMOS computerised maintenance management system for ship propulsion gear over the course of 20,000 operating hours. The Ishikawa diagram is applied as a qualitative tool to structure potential failure causes related to human, technical, material, procedural, measurement, and environmental factors. Using a system dynamics approach, a qualitative conceptual model of cause-and-effect relationships and a quantitative simulation model were developed, where the mathematical model of Goel–Okumoto reliability growth was applied to quantitatively describe the process of detecting and eliminating failures, with an exponential decrease in failure intensity over time and a high level of agreement with empirical data (R² = 0.9962), corresponding to the part of the bathtub curve related to the running-in of ship systems. The system dynamics simulation implemented in the POWERSIM environment integrates the analytically estimated model parameters and provides a dynamic representation of the relationships between failure intensity, cumulative failures, reliability, and the mean time between failures. The scientific contribution of this work lies in the structured integration of established methods into a single analytical framework, enabling coherent interpretation of empirical reliability data under real operating conditions. The results provide a methodological basis for developing predictive maintenance tools, optimising maintenance strategies, and improving the safety of ship propulsion systems.

Near-miss reporting systems are widely used in maritime safety management, but their effectiveness depends on the informational quality of reported events, not only on reporting frequency. This study models both reporting behaviour and informational quality using two Bayesian Network models, parameterised through expert elicitation and analysed with Monte Carlo simulation and robustness analysis. The first model examines factors influencing the probability that a near-miss event is reported; the second analyses the conditions under which reported events provide reduced informational quality. Reporting quota, clarity of the near-miss definition, and shipboard leadership had the strongest influence on reporting probability. Informational quality was primarily determined by the perceived risk level of the reported event, a relationship tested against 253 operational near-miss reports (p < 0.001). Reporting quotas increased reporting activity while reducing informational quality, creating a trade-off between reporting quantity and quality referred to in this study as reporting inflation. Treating reporting behaviour and informational quality as distinct processes shows that more reports do not necessarily improve safety learning or reflect actual safety performance. Shipping companies should therefore evaluate reporting quality alongside reporting frequency, for example by replacing fixed quotas with periodic review of report content.