Marine safety and data analytics

Vessel crash stop maneuvering performance prediction

Conference paper (2017)

Authors

Luca Oneto University of Genova
A. Coraddu Newcastle University
Paolo Sanetti University of Genova
O. Karpenko DAMEN Shipyards Gorinchem
Francesca Cipollini University of Genova
Toine Cleophas DAMEN Shipyards Gorinchem
Davide Anguita University of Genova
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Published Date

2017

Language

English

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External organisation

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.