Journal article  Open Access

Deep learning and structural health monitoring: a TFT-based approach for anomaly detection in masonry towers

Falchi F., Girardi M., Gurioli G., Messina N., Padovani C., Pellegrini D.

Structural health monitoring  Masonry towers  Deep learning  Damage detection  Long-term dynamic monitoring 

Detecting anomalies in the vibrational features of age-old buildings is crucial within the Structural Health Monitoring (SHM) framework. The SHM techniques can leverage information from onsite measurements and environmental sources to identify the dynamic properties (such as the frequencies) of the monitored structure, searching for possible deviations or unusual behavior over time. In this paper, the Temporal Fusion Transformer (TFT) network, a deep learning algorithm initially designed for multi-horizon time series forecasting and tested on electricity, traffic, retail, and volatility problems, is applied to SHM. The TFT approach is adopted to investigate the behavior of the Guinigi Tower located in Lucca (Italy) and subjected to a long-term dynamic monitoring campaign. The TFT network is trained on the tower's experimental frequencies enriched with other environmental parameters. The transformer is then employed to predict the vibrational features (natural frequencies, root mean squares values of the velocity time series) and detect possible anomalies or unexpected events by inspecting how much the actual frequencies deviate from the predicted ones. The TFT technique is used to detect the effects of the Viareggio earthquake that occurred on 6 February 2022, and the structural damage induced by three simulated damage scenarios.

Source: Social Science Research Network (2024). doi:10.2139/ssrn.4679906

Publisher: Social Science Electronic Pub., [Rochester NY], Stati Uniti d'America


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BibTeX entry
	title = {Deep learning and structural health monitoring: a TFT-based approach for anomaly detection in masonry towers},
	author = {Falchi F. and Girardi M. and Gurioli G. and Messina N. and Padovani C. and Pellegrini D.},
	publisher = {Social Science Electronic Pub., [Rochester NY], Stati Uniti d'America},
	doi = {10.2139/ssrn.4679906},
	journal = {Social Science Research Network},
	year = {2024}