基于激光点云技术的地铁盾构隧道错台智能检测方法
谢嘉伟1魏志恒1姚霄汉2陈万里3李春翔3
Intelligent Detection Method for Metro Shield Tunnel Staggering Based on Laser Point Cloud Technology
XIE JiaweiWEI ZhihengYAO XiaohanCHEN WanliLI Chunxiang
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作者信息:1.中国铁道科学研究院集团有限公司城市轨道交通中心， 100081， 北京
2.北京石泰集团有限公司， 100041， 北京
3.北京市轨道交通运营管理有限公司， 100068， 北京
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Affiliation:Urban Rail Transit Center, China Academy of Railway Sciences Group Co., Ltd., 100081, Beijing, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2023.10.021
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中图分类号/CLCN:U456.3+1
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栏目/Col:应用技术
摘要:
目的：为了指导地铁隧道维护，实现盾构隧道错台的智能化检测，需要对基于激光点云技术的地铁盾构隧道错台智能检测方法进行研究。方法：介绍了激光点云技术的检测原理，从隧道断面的提取和拟合去噪技术、灰度图和深度图自动生成技术及错台病害自动分析技术等方面简述了点云数据处理技术。介绍了激光点云技术的检测流程。以武汉某地铁线路为例，基于移动三维激光扫描系统，利用激光点云技术对盾构隧道管片环面错台进行现场检测。利用同一段隧道同一设备不同时段输出的错台数据验证激光点云技术的重复精度，利用同一段隧道不同设备同一时段输出的错台数据验证激光点云技术的相对精度。结果及结论：同一段隧道同一设备不同时段输出的错台量平均偏差为0357 5 mm，同一段隧道不同设备同一时段输出的错台量平均偏差为0360 0 mm，验证了激光点云技术在隧道错台智能检测上的适用性和优越性。
Abstracts:
Objective: To guide metro tunnel maintenance and achieve intelligent detection of shield tunnel staggering, research is conducted on metro shield tunnel staggering intelligent detection method based on laser point cloud technology（abbreviated as LPCT）. Method: The detection principles of LPCT are introduced. Point cloud data processing technology is briefly described from aspects including tunnel profile extraction and fitting noise reduction techniques, grayscale and depth map automatic generation techniques, and automatic analysis of staggering defects. The detection process using LPCT is outlined. Taking a metro line in Wuhan as example, the onsite detection of shield tunnel segment lining surface staggering is conducted using a mobile threedimensional laser scanning system and LPCT. The repetitive accuracy of LPCT is verified using staggering data from the same tunnel and the same equipment but at different time periods, while the relative accuracy of LPCT is verified using staggering data from the same tunnel and different equipment but at the same time period. Result and Conclusion: The average deviation of staggering data output from the same tunnel and the same equipment at different time periods is 0.357 5 mm, while the above average deviation at the same time period is 0.360 0 mm. This validates the applicability and superiority of LPCT in tunnel staggering intelligent detection.
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