Marine safety and data analytics : vessel crash stop maneuvering performance prediction

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Oneto, Luca and Coraddu, Andrea and Sanetti, Paolo and Karpenko, Olena and Cipollini, Francesca and Cleophas, Toine and Anguita, Davide; (2017) Marine safety and data analytics : vessel crash stop maneuvering performance prediction. In: Artificial Neural Networks and Machine Learning – ICANN 2017. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer-Verlag, ITA, pp. 385-393. ISBN 9783319686110 (https://doi.org/10.1007/978-3-319-68612-7_44)

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Abstract

Crash stop maneuvering performance is one of the key indicators of the vessel safety properties for a shipbuilding company. Many different factors affect these performances, from the vessel design to the environmental conditions, hence it is not trivial to assess them accurately during the preliminary design stages. Several first principal equation methods are available to estimate the crash stop maneuvering performance, but unfortunately, these methods usually are either too costly or not accurate enough. To overcome these limitations, the authors propose a new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance. Results on real-world data provided by the DAMEN Shipyards show the effectiveness of the proposal.

ORCID iDs

Oneto, Luca, Coraddu, Andrea ORCID logoORCID: https://orcid.org/0000-0001-8891-4963, Sanetti, Paolo, Karpenko, Olena, Cipollini, Francesca, Cleophas, Toine and Anguita, Davide;
CORE (COnnecting REpositories)