The impedance-based structural health monitoring technique uses measured signatures changes to identify incipient damages in structures. The purpose is to perform a correlation of these changes with the physical phenomena. However, since electromechanical coupling exists, some environmental influences such as temperature changes may lead to false decision regarding the condition of the structure. As a result, innovative machine learning tools have been extensively investigated to avoid errors in structural prognosis and, in this sense, recent applications of convolutional neural networks (CNN) have emerged within the scope of SHM research, focusing mainly on vibration analysis. However, studies that aim to combine neural architectures with intelligent materials for structural monitoring purposes have been poorly evaluated. Consequently, its integration with the electromechanical impedance method is still considered as being a new application of CNN. Thus, in order to contribute ...

中文翻译：

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卷积神经网络和基于阻抗的SHM在损伤检测中的应用

基于阻抗的结构健康监测技术使用已测量的特征变化来识别结构中的初期损坏。目的是使这些变化与物理现象相关。但是，由于存在机电耦合，因此某些环境影响（例如温度变化）可能导致对结构条件的错误判断。结果，为避免结构预测中的错误，已经广泛研究了创新的机器学习工具，从这个意义上说，卷积神经网络（CNN）的最新应用已经出现在SHM研究的范围内，主要集中在振动分析上。但是，旨在将神经体系结构与智能材料相结合以进行结构监视的研究并未得到充分评估。所以，它与机电阻抗方法的集成仍然被认为是CNN的新应用。因此，为了贡献...