Deep learning and structural health monitoring: Temporal Fusion Transformers for anomaly detection in masonry towers

• Published in: Mechanical systems and signal processing 2024-06, Vol.215, p.111382, Article 111382
• Main Authors: Falchi, Fabrizio, Girardi, Maria, Gurioli, Gianmarco, Messina, Nicola, Padovani, Cristina, Pellegrini, Daniele
• Format: Article
• Language: English
• Subjects: Damage detection; Deep learning; Long-term dynamic monitoring; Masonry towers; Structural health monitoring
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2024.111382

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. The Temporal Fusion Transformer (TFT) network is a deep learning algorithm designed for multi-horizon time series forecasting and initially tested on electricity, traffic, retail, and volatility problems. In this paper, it is applied to SHM. More precisely, 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.