Recently, Intelligence-based structural health monitoring (SHM) methods have investigated widely. Most of these methods are for detecting and classifying different structural damages by the means of features extraction from the structural responses signals, for instance different back propagation artificial neural networks SHM based methods. However, automatic features extraction, that eliminates the need for expertise and performing visual inspection to evaluate structures status is still a big challenge. In this study, therefore, a novel convolution neural network-based algorithm along with a hybrid training method has been proposed to detect, quantify and localize structural damage. The proposed method has been evaluated experimentally, many damaged and undamaged structural conditions have been conducted, acquiring samples of time-domain PZT impedance response signals from a beam. As the results show that, the method obtained a significant execution on damage detection, damage size evaluation and damage location recognition with high accuracy and reliability.

最近，基于情报的结构健康监视（SHM）方法已得到广泛研究。这些方法中的大多数都是通过从结构响应信号中提取特征来检测和分类不同的结构破坏的方法，例如基于反向传播人工神经网络SHM的方法。但是，自动特征提取消除了对专业知识的需求，并且无需进行视觉检查即可评估结构状态，这仍然是一个很大的挑战。因此，在这项研究中，提出了一种基于卷积神经网络的新颖算法以及一种混合训练方法来检测，量化和定位结构损伤。通过实验对提出的方法进行了评估，进行了许多受损和未损坏的结构条件，从光束中获取时域PZT阻抗响应信号的样本。结果表明，该方法在损伤检测，损伤尺寸评估和损伤位置识别方面具有很高的执行率，具有较高的准确性和可靠性。