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The rapidly advancing automation of the maritime industry – for instance, through onboard Decision Support Systems (DSS) – can facilitate the introduction of advanced solutions supporting the process of collision avoidance at sea. Nevertheless, relevant solutions that aim to correctly predict a ship's behavior in irregular waves are only available to a limited extent by omitting the impact of wave stochastics on resulting evasive maneuvers. This is mainly due to the complexity of the phenomena, the existing couplings therein, and the time inefficacy in resolving the problem through real-time simulations. Therefore, this paper attempts to fill this knowledge gap by presenting a probabilistic, data-driven meta-model trained using an extensive set of 6DOF numerical simulations of vessel motions in irregular waves. For this purpose, machine learning adopting causal probabilistic modeling with Bayesian Belief Network (BBN) was employed. The latter offers two-way reasoning in the presence of uncertainty and provides insight into the meta-model's outcome. This, in turn, helps estimate a set of safety-critical parameters for a large passenger ship performing an evasive maneuver. This set comprises a huge quantity of ship turning circle parameters as well as the hull's rotational motions and resulting lateral accelerations, all simulated multiple times to consider the stochastic realization of the waves. The proposed meta-model can be used to assist watchkeeping officers’ decisions or raise their awareness concerning the possible consequences of evasive maneuvers performed. The achieved accuracy of the meta-model's prediction lies within a range from 81% to 98%, which makes it suitable for this purpose.
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Autorzy (3)
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Mateusz Gil
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Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.ress.2024.110765
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Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
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RELIABILITY ENGINEERING & SYSTEM SAFETY
nr 256,
ISSN: 0951-8320 - Język:
- angielski
- Rok wydania:
- 2025
- Opis bibliograficzny:
- Gil M., Montewka J., Krata P.: Predicting a passenger ship's response during evasive maneuvers using Bayesian Learning// RELIABILITY ENGINEERING & SYSTEM SAFETY -Vol. 256, (2025), s.110765-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1016/j.ress.2024.110765
- Źródła finansowania:
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- IDUB
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 5 razy