Deep learning for structural health monitoring: An application to heritage structures
This work addresses structural health monitoring for heritage structures, but it is incremental as it applies existing deep learning methods to a new dataset.
The authors tackled the problem of detecting structural anomalies in heritage buildings by applying deep learning to seismic ambient noise data, achieving anomaly detection through a Temporal Fusion Transformer model.
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.