It is difficult to form a method for recognizing the degree of infiltration of a tunnel lining. To solve this problem, we propose a recognition method by using a deep convolutional neural network. We carry out laboratory tests, prepare cement mortar specimens with different saturation levels, simulate different degrees of infiltration of tunnel concrete linings, and establish an infrared thermal image data set with different degrees of infiltration. Then, based on a deep learning method, the data set is trained using the Faster R-CNN+ResNet101 network, and a recognition model is established. The experiments show that the recognition model established by the deep learning method can be used to select cement mortar specimens with different degrees of infiltration by using an accurately minimized rectangular outer frame. This model shows that the classification recognition model for tunnel concrete lining infiltration established by the indoor experimental method has high recognition accuracy.

中文翻译：

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混凝土衬砌入渗程度的室内试验模拟与识别

很难形成一种识别隧道衬砌下渗程度的方法。为了解决这个问题，我们提出了一种使用深度卷积神经网络的识别方法。我们进行室内试验，制备不同饱和度的水泥砂浆试件，模拟隧道混凝土衬砌不同渗透程度，建立不同渗透程度的红外热像数据集。然后，基于深度学习方法，使用Faster R-CNN+ResNet101网络训练数据集，建立识别模型。实验表明，利用深度学习方法建立的识别模型，可以利用精确最小化的矩形外框选择不同渗透程度的水泥砂浆试件。