Complex civil structures require the cooperation of many building materials. However, it is difficult to accurately monitor and evaluate the inner damage states of various material systems. Based on a convolutional neural network (CNN) and the acoustic emission (AE) time-frequency diagram, we used the transfer learning method for classifying the AE signals of different materials under external loads. The results show the CNN model can accurately classify cracks that come from different materials based on AE signals. The recognition accuracy can reach 90% just by retraining the full connection layer of the pretrained model, and its accuracy can reach 97% after retraining the top 2 convolutional layers of this model. A realization of cracking source identification mainly depends on the differences in mineral particles in materials. This work highlights the great potential for real-time and quantitative monitoring of the health status of composite civil structures.

中文翻译：

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通过机器学习对不同工程媒体的破解来源进行分类

复杂的土木结构需要多种建筑材料的配合。然而，很难准确地监测和评估各种材料系统的内部损伤状态。基于卷积神经网络（CNN）和声发射（AE）时频图，我们使用迁移学习方法对不同材料在外部负载下的声发射信号进行分类。结果表明，CNN 模型可以根据 AE 信号准确地对来自不同材料的裂纹进行分类。仅对预训练模型的全连接层进行再训练，识别准确率可达90%，再训练该模型的前2个卷积层后，识别准确率可达97%。裂源识别的实现主要取决于材料中矿物颗粒的差异。