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Unsupervised and supervised machine learning techniques for slamming classification from SHM data


Dessi, D and Shabani, B and Ali-Lavroff, J and Holloway, DS and Penev, S and Thomas, G and Sanchez-Alayo, D, Unsupervised and supervised machine learning techniques for slamming classification from SHM data, Proceedings of the 9th International Conference on HYDROELASTICITY IN MARINE TECHNOLOGY Rome, Italy, July 10th -13th, 2022, 10-13 July 2022, CNR-INM Institute of Marine Engineering, pp. 132-132. ISBN 9788876170546 (2022) [Refereed Conference Paper]

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In this paper, the problem of identifying slamming impacts for monohulls and catamarans is addressed by using Machine Learning techniques. To highlight differences and similarities, two test cases, separately dealt with in previous work [1]-[2], are here considered: (i) the classification of slamming events based on clustering analysis from data collected on board a fast catamaran during full-scale trials, and (ii) the classification of different types of slams on a fast monohull from data collected from an experimental campaign carried out in the towing tank on an elastic segmented model. The analysis shows the generality and versatility of the ML approach to slamming identification, as well as its robustness in terms of accuracy

Item Details

Item Type:Refereed Conference Paper
Keywords:Machine learning, slamming, high-speed craft
Research Division:Engineering
Research Group:Mechanical engineering
Research Field:Mechanical engineering not elsewhere classified
Objective Division:Transport
Objective Group:Water transport
Objective Field:International passenger water transport (e.g. passenger ships)
UTAS Author:Shabani, B (Dr Babak Shabani)
UTAS Author:Ali-Lavroff, J (Dr Jason Ali-Lavroff)
UTAS Author:Holloway, DS (Associate Professor Damien Holloway)
ID Code:155498
Year Published:2022
Funding Support:Australian Research Council (LP170100555)
Deposited By:Engineering
Deposited On:2023-02-23
Last Modified:2023-03-08

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