Deep learning for structural health monitoring: An application to heritage structures

Deep learning for structural health monitoring: An application to heritage structures

Fabio Carrara, Fabrizio Falchi, Maria Girardi, Nicola Messina, Cristina Padovani, Daniele Pellegrini

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Abstract. In this work, we employ recent deep learning techniques for time-series forecasting to inspect and detect anomalies in the large dataset recorded during a long-term monitoring campaign conducted on the San Frediano bell tower in Lucca. We frame the problem as an unsupervised anomaly detection task and train a Temporal Fusion Transformer to learn the normal dynamics of the structure. We then detect the anomalies by looking at the differences between the predicted and observed frequencies.

Keywords
Heritage Structures, Anomaly Detection, Deep Learning

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: Fabio Carrara, Fabrizio Falchi, Maria Girardi, Nicola Messina, Cristina Padovani, Daniele Pellegrini, Deep learning for structural health monitoring: An application to heritage structures, Materials Research Proceedings, Vol. 26, pp 581-586, 2023

DOI: https://doi.org/10.21741/9781644902431-94

The article was published as article 94 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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