A proposal of classification for machine-learning vibration-based damage identification methods

A proposal of classification for machine-learning vibration-based damage identification methods

Francesca Marafini, Michele Betti, Gianni Bartoli, Giacomo Zini, Alberto Barontini, Nuno Mendes

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Abstract. Recent advances in computing power and sensing technology led to a significant evolution of Structural Health Monitoring (SHM) techniques, transforming SHM into a “Big Data” problem. The use of data-driven approaches for damage identification purposes, specifically Machine Learning (ML) methods, has gained popularity. ML can help at various levels of the SHM process: to pre- and post-process input data, extract damage sensitive features, and operate pattern recognition in measured data and output valuable information for damage identification. In this paper, the role of ML in SHM applications is discussed together with a new scheme for classifying ML applications in SHM, especially focusing on vibration-based monitoring, given its consolidated theoretical base. Finally, the implications of the application of these methods to historic structures are discussed, with a brief account of existing case studies. The proposed classification is exemplified using the most recent studies available in the literature on cultural heritage structures.

Machine Learning, Damage Identification, Vibration-Based SHM

Published online 3/17/2022, 6 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Francesca Marafini, Michele Betti, Gianni Bartoli, Giacomo Zini, Alberto Barontini, Nuno Mendes, A proposal of classification for machine-learning vibration-based damage identification methods, Materials Research Proceedings, Vol. 26, pp 593-598, 2023

DOI: https://doi.org/10.21741/9781644902431-96

The article was published as article 96 of the book Theoretical and Applied Mechanics

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

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