Simulation and Recognition of Concrete Lining Infiltration Degree via an Indoor Experiment. (11th November 2020)

• Record Type: Journal Article
• Authors: Wang, Dongsheng; Feng, Jun; Zhao, Xinpeng; Bai, Yeping; Wang, Yujie; Liu, Xuezeng
• Other Names: Xiong Feng Academic Editor.
• Is Part Of: Geofluids. Volume 2020(2020)
• Publication Date: 2020-11-11
• Subjects: Hydrogeology -- Periodicals; Sedimentary basins -- Periodicals; Fluids -- Migration -- Periodicals; Groundwater flow -- Periodicals; Geothermal resources -- Periodicals; Fluid dynamics -- Periodicals; Earth -- Crust -- Periodicals; 551.49
• DOI: 10.1155/2020/8873315
• Languages: English
• ISSNs: 1468-8115

Abstract

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.