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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, DIn 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, 2022Editors
Daniele Dessi and Alessandro IafratiPagination
132-132ISBN
9788876170546Department/School
School of EngineeringPublisher
Institute of Marine Engineering CNR-INM, National Research Council of ItalyPlace of publication
Rome, ItalyEvent title
The 9th International Conference on Hydroelasticity in Marine TechnologyEvent Venue
CNR-INM Institute of Marine EngineeringRepository Status
- Restricted