Dynamic Accident Network Model for Predicting Marine Accidents in Narrow Waterways Under Variable Conditions: A Case Study of the Istanbul Strait DOI Creative Commons
Serdar Yıldız, Özkan Uğurlu, Xinjian Wang

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(12), P. 2305 - 2305

Published: Dec. 14, 2024

Accident analysis models are crucial tools for understanding and preventing accidents in the maritime industry. Despite advances ship technology regulatory frameworks, human factors remain a leading cause of marine accidents. The complexity behavior, influenced by social, technical, psychological aspects, makes accident challenging. Various methods used to analyze accidents, but no single approach is universally chosen use as most effective. Traditional often emphasize errors, technical failures, mechanical breakdowns. However, hybrid models, which combine different approaches, increasingly recognized providing more accurate predictions addressing multiple causal factors. In this study, dynamic model based on Human Factors Analysis Classification System (HFACS) Bayesian Networks proposed predict estimate risks narrow waterways. utilizes past data expert judgment assess potential ships encounter when navigating these confined areas. Uniquely, enables prediction probabilities under varying operational conditions, offering practical applications such real-time risk estimation vessels before entering Istanbul Strait. By insights, supports traffic operators implementing preventive measures enter high-risk zones. results study can serve decision-support system not only VTS operators, shipmasters, company representatives also national international stakeholders industry, aiding both probability development measures.

Language: Английский

Risk Assessment of Polar Drillship Operations Based on Bayesian Networks DOI Creative Commons
Qi Wang, Zixin Wang, Hongen Li

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(10), P. 1873 - 1873

Published: Oct. 18, 2024

In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting risk assessment, potential hazards can be predicted and identified, thereby significantly reducing frequency of accidents promoting sustained stability economic activities. This paper investigates Bayesian-network-based assessment model for operations. Grey relational analysis was employed identify main factors. The is trained using 525 valid incident sample data combined with expert knowledge. accuracy rate above 88%. Additionally, corresponding decision-making recommendations are provided through sensitivity analysis. three most sensitive elements fire nodes human error, other causes, equipment damage, coefficients 0.046, 0.042, 0.022, respectively. terms deck/handrail collision nodes, highly related lifting (totally more than 0.1). For events that have already transpired, probabilities 0.73 0.74, both which 0.5, validating forward backward reasoning. Risk assessments based on Bayesian networks offer pertinent preventive measures.

Language: Английский

Citations

0
Dynamic Accident Network Model for Predicting Marine Accidents in Narrow Waterways Under Variable Conditions: A Case Study of the Istanbul Strait DOI Creative Commons
Serdar Yıldız, Özkan Uğurlu, Xinjian Wang

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(12), P. 2305 - 2305

Published: Dec. 14, 2024

Accident analysis models are crucial tools for understanding and preventing accidents in the maritime industry. Despite advances ship technology regulatory frameworks, human factors remain a leading cause of marine accidents. The complexity behavior, influenced by social, technical, psychological aspects, makes accident challenging. Various methods used to analyze accidents, but no single approach is universally chosen use as most effective. Traditional often emphasize errors, technical failures, mechanical breakdowns. However, hybrid models, which combine different approaches, increasingly recognized providing more accurate predictions addressing multiple causal factors. In this study, dynamic model based on Human Factors Analysis Classification System (HFACS) Bayesian Networks proposed predict estimate risks narrow waterways. utilizes past data expert judgment assess potential ships encounter when navigating these confined areas. Uniquely, enables prediction probabilities under varying operational conditions, offering practical applications such real-time risk estimation vessels before entering Istanbul Strait. By insights, supports traffic operators implementing preventive measures enter high-risk zones. results study can serve decision-support system not only VTS operators, shipmasters, company representatives also national international stakeholders industry, aiding both probability development measures.

Language: Английский

Citations

0