Carrara F., Falchi F., Girardi M., Messina N., Padovani C., Pellegrini D.
Deep learning Heritage structures Anomaly detection
Thanks to recent advancements in numerical methods, computer power, and monitoring technology, seismic ambient noise provides precious information about the structural behavior of old buildings. The measurement of the vibrations produced by anthropic and environmental sources and their use for dynamic identification and structural health monitoring of buildings initiated an emerging, cross-disciplinary field engaging seismologists, engineers, mathematicians, and computer scientists. 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.
Source: AIMETA 2022 - XXV National Congress of the Italian Association of Theoretical and Applied Mechanics, pp. 581–586, Palermo, Italy, 4-8/09/2022
@inproceedings{oai:it.cnr:prodotti:471833, title = {Deep learning for structural health monitoring: an application to heritage structures}, author = {Carrara F. and Falchi F. and Girardi M. and Messina N. and Padovani C. and Pellegrini D.}, doi = {10.21741/9781644902431-94}, booktitle = {AIMETA 2022 - XXV National Congress of the Italian Association of Theoretical and Applied Mechanics, pp. 581–586, Palermo, Italy, 4-8/09/2022}, year = {2022} }
Carrara, Fabio
0000-0001-5014-5089
Falchi, Fabrizio
0000-0001-6258-5313
Girardi, Maria
0000-0002-7358-5607
Messina, Nicola
0000-0003-3011-2487
Padovani, Cristina
0000-0002-2467-569X
Pellegrini, Daniele
0000-0002-3416-771X
Artificial Intelligence for Media and Humanities (2021-ongoing)
Mechanics of Materials and Structures (2002-ongoing)