DIAG Department, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
Sensors (Basel). 2023 Jul 4;23(13):6152. doi: 10.3390/s23136152.
For the purpose of maintaining and prolonging the service life of civil constructions, structural damage must be closely monitored. Monitoring the incidence, formation, and spread of damage is crucial to ensure a structure's ongoing performance. This research proposes a unique approach for multiclass damage detection using acceleration responses based on synchrosqueezing transform (SST) together with deep learning algorithms. In particular, our pipeline is able to classify correctly the time series representing the responses of accelerometers placed on a bridge, which are classified with respect to different types of damage scenarios applied to the bridge. Using benchmark data from the Z24 bridge for multiclass classification for different damage situations, the suggested method is validated. This dataset includes labeled accelerometer measurements from a real-world bridge that has been gradually damaged by various conditions. The findings demonstrate that the suggested approach is successful in exploiting pre-trained 2D convolutional neural networks, obtaining a high classification accuracy that can be further boosted by the application of simple voting methods.
为了维护和延长民用建筑的使用寿命，必须密切监测结构的损坏情况。监测损坏的发生、形成和传播对于确保结构的持续性能至关重要。本研究提出了一种使用基于同步挤压变换（SST）和深度学习算法的加速度响应进行多类损伤检测的独特方法。特别是，我们的管道能够正确分类代表放置在桥梁上的加速度计响应的时间序列，这些序列是根据应用于桥梁的不同类型的损伤情况进行分类的。使用 Z24 桥的基准数据对不同损伤情况下的多类分类进行验证。该数据集包括来自真实桥梁的已被各种条件逐渐损坏的标记加速度计测量值。研究结果表明，该方法成功地利用了预训练的 2D 卷积神经网络，获得了较高的分类准确性，通过应用简单的投票方法可以进一步提高。
