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| Research of Intelligent Detection Technology for Railway Tunnel Engineering Quality |
| ZHANG Chen1, SHI Feng2, MENG Chao2, LIU Guiwei1, LI Bingxiu1, TIAN Yang1 |
1. China Railway Design Corporation, Tianjin 300251, China;
2. Engineering Quality Supervision Center of the National Railway Administration, Beijing 100038, China |
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Abstract Research purposes: Railway tunnel defects in terms of disease have become an important hidden danger affecting the safety of China's railway operations, in which the quality of tunnel lining is particularly prominent. In response to the low level of intelligence in tunnel lining disease detection, inconsistent manual interpretation standards, and interference from equipment models and technical capabilities of interpretation personnel during the interpretation process of geological radar detection images, research is being conducted on intelligent detection technology suitable for railway tunnel lining quality. Using geological radar detection images of tunnel lining diseases as a sample library, image recognition algorithms are trained to achieve intelligent recognition of various diseases of railway tunnel lining, providing automated and intelligent tools for railway tunnel quality detection.
Research conclusions: (1) The characteristics of geological radar defect images in tunnel lining were studied, and it was found that the determination of tunnel lining quality defects requires a high level of experience from the inspection personnel, and this work has a certain degree of subjectivity. (2) Build a geological radar image sample library for different types of diseases, train intelligent recognition models, and achieve fast and automatic recognition of different diseases. (3) Based on the quality inspection images of tunnel lining in a certain railway project, this technology is used for intelligent detection of lining quality. The recognition results show that this technology can effectively avoid human interference during the detection process, and improve the efficiency of detection and the accuracy of analysis conclusions.
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Received: 26 July 2024
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2024, Vol. 41 
