Deep Learning and Structural Health Monitoring: A TFT-Based Approach for Anomaly Detection in Masonry Towers

52 Pages Posted: 9 Jan 2024

See all articles by Fabrizio Falchi

Fabrizio Falchi

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI)

Maria Girardi

Institute of Information Science and Technologies "A. Faedo", National Research Council of Italy

Gianmarco Gurioli

University of Florence

Nicola Messina

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI)

Cristina Padovani

Institute of Information Science and Technologies "A. Faedo", National Research Council of Italy

Daniele Pellegrini

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI)

Abstract

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 forecastingand 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 anomaliesor 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.

Keywords: structural health monitoring, Masonry towers, Deep learning, damage detection, Long-term dynamic monitoring

Suggested Citation

Falchi, Fabrizio and Girardi, Maria and Gurioli, Gianmarco and Messina, Nicola and Padovani, Cristina and Pellegrini, Daniele, Deep Learning and Structural Health Monitoring: A TFT-Based Approach for Anomaly Detection in Masonry Towers. Available at SSRN: https://ssrn.com/abstract=4679906 or http://dx.doi.org/10.2139/ssrn.4679906

Fabrizio Falchi

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI) ( email )

Maria Girardi (Contact Author)

Institute of Information Science and Technologies "A. Faedo", National Research Council of Italy ( email )

Via Moruzzi 1
Pisa
Italy

Gianmarco Gurioli

University of Florence ( email )

Nicola Messina

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI) ( email )

Cristina Padovani

Institute of Information Science and Technologies "A. Faedo", National Research Council of Italy ( email )

Daniele Pellegrini

National Research Council (CNR) - "Alessandro Faedo" Institute of Information Science and Technology (ISTI) ( email )

Via Giuseppe Moruzzi, 1
Pisa
Italy

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