WANG Shi-lei, GAO Yan, QI Fa-lin, KE Zai-tian, LI Hong-yan, LEI Yang, PENG Zhan. Review on inspection technology of railway operation tunnels[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 41-57. doi: 10.19818/j.cnki.1671-1637.2020.05.003
Citation: WANG Shi-lei, GAO Yan, QI Fa-lin, KE Zai-tian, LI Hong-yan, LEI Yang, PENG Zhan. Review on inspection technology of railway operation tunnels[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 41-57. doi: 10.19818/j.cnki.1671-1637.2020.05.003

Review on inspection technology of railway operation tunnels

doi: 10.19818/j.cnki.1671-1637.2020.05.003
Funds:

National Natural Science Foundation of China U1434211

Science and Technology Research and Development Project of China Railway Corporation P2018G002

Consulting Research Project of Chinese Academy of Engineering 2019-ZD-19

More Information
  • Author Bio:

    WANG Shi-lei(1985-), male, senior engineer, PhD, thilei@qq.com

  • Received Date: 2020-03-17
  • Publish Date: 2020-10-25
  • To understand the research and application status of inspection technology for railway operation tunnels, the characteristics and inspection methods of tunnel damage were summarized. The current status of inspection technology in domestic and foreign was analyzed from five aspects: exterior state, internal state, geometric shape, high-precision ground mobile inspection robot, and data informatization. The inspection technology framework and development direction were discussed. Analysis result shows that the exterior state inspection mainly includes the camera shooting and laser scanning technology. The camera shooting system is suitable for the vehicle platform with an inspection speed of 80 km·h-1. The laser scanning system has a compact structure and inspection speed of approximately 5 km·h-1. The image processing and computer vision are technologies for the exterior damage recognition. Regarding the image processing technology, a potential development direction is to expand and design the damage characteristics, improve the recognition efficiency, and reduce the interference of nondamage factors. The key to the promotion of computer vision is to build an industry-level disease sample database. The ground-penetrating radar is the key technology for the internal state detection, in which the speed of ground-coupled radar is approximately 10 km·h-1, while that of the air-coupled radar is 80 km·h-1. The air-coupled radar detection system focuses on optimizing the antenna structure, signal enhancement, and suppression of interference from the mechanical system vibration and electrification facilities. Detection technologies such as the ground-penetrating radar, infrared thermal imaging, ultrasonic tomography, and laser defect detection are complementaries in the detection range, accuracy, and efficiency, which can constitute acomprehensive application strategy of multi-technology. The geometric shape detection mainly includes the laser scanning, laser photography, and inertial measurement technology. The measurement accuracy of laser scanning is high. Its speed is approximately 10 km·h-1, while the laser photography speed is up to 60 km·h-1. The system calibration and vibration compensation are crucial to improve the measurement accuracy of laser photography. In addition, based on an inertial measurement technology, the deformation detection of inverted arch uplift can be carried out. The development and promotion of a high-precision ground mobile inspection robot and the informatization of detection data are guarantee measures that adapt to the scale of tunnels and match the state accurate management.Resultssuggest that the inspection technology framework should be composed of three parts: the vehicle mounted rapid comprehensive detection, in-situ and ground mobile accurate detection, and data information platform.The development direction should focus on the rapid inspection with an air-coupled radar, fast and accurate measurement of compound deformation, high-precision ground movement inspection, intelligent damage identification, and multi-source data fusion analysis.

     

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