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doi: 10.3390/jmse11010084
Evacuating a large and complex environment, such as a large passenger vessel, either cruise or RoPax, is a safety-critical task that involves thousands of people in motion and a complex decision-making process. Despite the significant enhancement of maritime safety over the years, various hazards still pose threats to passengers and crew. To deal with this reality, the SafePASS project radically redefines the evacuation process by introducing novel technological solutions. In this context, this paper presents, in detail, an enhanced risk model for the ship evacuation process in order to facilitate the understanding of the actual risks of the process in fire and flooding accidents, and to assess various risk control measures and options toward risk mitigation. The risk model covers the entire event sequence in emergency cases on board, until the survival at sea phase, and it is constructed in two levels, following a combination of event tree analysis and Bayesian networks. Results show the risk corresponds to baseline scenarios for each accident case, which are also verified by relevant IMO and EMSA studies, and an example case of risk control option (RCO) is introduced to the model to demonstrate its ability to assess RCO’s efficiency in terms of risk reduction.
Risk Management, risk analysis, Naval architecture. Shipbuilding. Marine engineering, Hydraulic engineering. Ocean engineering, VM1-989, GC1-1581, Oceanography, risk control options, 510, maritime safety, passenger vessels, maritime safety; evacuation; passenger vessels; risk analysis; risk model; Bayesian networks; event trees; risk control options; risk reduction, Bayesian networks, evacuation, risk model, event trees, risk reduction
Risk Management, risk analysis, Naval architecture. Shipbuilding. Marine engineering, Hydraulic engineering. Ocean engineering, VM1-989, GC1-1581, Oceanography, risk control options, 510, maritime safety, passenger vessels, maritime safety; evacuation; passenger vessels; risk analysis; risk model; Bayesian networks; event trees; risk control options; risk reduction, Bayesian networks, evacuation, risk model, event trees, risk reduction
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
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