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

conference contribution
posted on 2023-05-23, 15:43 authored by Dessi, D, Babak Shabani, Jason Ali-LavroffJason Ali-Lavroff, Damien HollowayDamien Holloway, Penev, S, Thomas, G, Sanchez-Alayo, D
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

Funding

Australian Research Council

Incat Tasmania Pty Ltd

History

Publication title

Proceedings of the 9th International Conference on HYDROELASTICITY IN MARINE TECHNOLOGY Rome, Italy, July 10th -13th, 2022

Editors

Daniele Dessi and Alessandro Iafrati

Pagination

132-132

ISBN

9788876170546

Department/School

School of Engineering

Publisher

Institute of Marine Engineering CNR-INM, National Research Council of Italy

Place of publication

Rome, Italy

Event title

The 9th International Conference on Hydroelasticity in Marine Technology

Event Venue

CNR-INM Institute of Marine Engineering

Repository Status

  • Restricted

Socio-economic Objectives

International passenger water transport (e.g. passenger ships)

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