Abstract
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.
Highlights
With the rapid development of Chinaâs transportation industry, it is estimated that the total operational mileage of rail transit in China will reach 8565 km by the end of 2020, with a significant portion of the rail being in underground or above ground tunnels
In the field of geotechnical engineering, many disasters are caused by water [2, 3], as water reduces the stability of the tunnel lining structure which reduces the strength of the lining and causes traffic accidents due to slick surfaces and ice on pavements [4]
The recognition model was used to identify cement mortar specimens with different degrees of infiltration, and the results are good, which show that the method is effective
Summary
With the rapid development of Chinaâs transportation industry, it is estimated that the total operational mileage of rail transit in China will reach 8565 km by the end of 2020, with a significant portion of the rail being in underground or above ground tunnels. The main tunnel leakage detection method is manual inspection This is mainly based on the results of visual observations, which are greatly influenced by human factors and have problems of low efficiency and poor accuracy. The feature extraction method uses preengineered features for classifying images, leading to poor generalization capabilities For this problem, a method based on the deep convolutional neural network is proposed to detect and classify defects from CCTV inspections and achieves a better prediction performance. Dung and Anhb [28] proposed a crack detection method based on a deep fully convolutional network (FCN) for semantic segmentation on concrete crack images, and Classification of lining infiltration degree C = {c1, c2, ..., cj} = {[0%, b2), [b2, b3), ... The method of tunnel diseases detection based on deep learning is advantageous because of its automatic construction features, fast recognition speed, and high accuracy. Is established using a deep learning method (Faster RCNN+ResNet101 network model), which shows the feasibility and validity of the recognition method
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