论文标题
深度学习结构性健康监测：遗产结构的应用
Deep learning for structural health monitoring: An application to heritage structures
论文作者
论文摘要
多亏了数值方法，计算机功率和监视技术方面的最新进步，地震环境噪声提供了有关旧建筑物结构行为的宝贵信息。人类和环境来源产生的振动的测量及其用于建筑物的动态识别和结构健康监测的使用，引发了新兴的，跨学科的领域，吸引了地震学家，工程师，数学家和计算机科学家。在这项工作中，我们采用了最新的深度学习技术来预测时间序列，以检查和检测在卢卡（Lucca）San Frediano Bell Tower进行的长期监测活动中记录的大型数据集中的异常情况。我们将问题作为无监督的异常检测任务，并训练时间融合变压器以学习结构的正常动力学。然后，我们通过查看预测频率和观察到的频率之间的差异来检测异常。
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.
