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.

中文翻译：

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深度学习和结构健康监测：用于砖石塔异常检测的时间融合变压器

在结构健康监测 (SHM) 框架中，检测古老建筑振动特征的异常至关重要。 SHM 技术可以利用现场测量和环境源的信息来识别受监控结构的动态属性（例如频率），搜索随时间推移可能出现的偏差或异常行为。时间融合变压器（TFT）网络是一种深度学习算法，专为多水平时间序列预测而设计，并在电力、交通、零售和波动性问题上进行了初步测试。本文将其应用于SHM。更准确地说，采用 TFT 方法来研究位于卢卡（意大利）的吉尼吉塔的行为，并对其进行长期动态监测活动。 TFT 网络根据富含其他环境参数的塔实验频率进行训练。然后使用变压器来预测振动特征（固有频率、速度时间序列的均方根值），并通过检查实际频率与预测频率的偏差来检测可能的异常或意外事件。 TFT 技术用于检测 2022 年 2 月 6 日发生的维亚雷焦地震的影响，以及三个模拟损坏场景引起的结构损坏。