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

Luca Oneto, Andrea Coraddu, Paolo Sanetti, Olena Karpenko, Francesca Cipollini, Toine Cleophas, Davide Anguita

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2017 - 26th International Conference on Artificial Neural Networks, Proceedings
EditorsAlessandra Lintas, Alessandro E. Villa, Stefano Rovetta, Paul F. Verschure
PublisherSpringer Verlag
Pages385-393
ISBN (Print)9783319686110
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event26th International Conference on Artificial Neural Networks, ICANN 2017 - Alghero, Italy
Duration: 11 Sep 201714 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10614 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Artificial Neural Networks, ICANN 2017
CountryItaly
CityAlghero
Period11/09/1714/09/17

Keywords

  • Crash stop
  • Data-driven methods
  • Marine safety
  • Performance assessment
  • Performance estimation
  • Random forests
  • Vessel maneuvering

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