[1]吴昌睿,黄宏伟.地铁隧道渗漏水的激光扫描检测方法及应用[J].自然灾害学报,2018,(04):059-66.[doi:10.13577/j.jnd.2018.0408]
 WU Changrui,HUANG Hongwei.Laser scanning inspection method and application for metro tunnel leakage[J].,2018,(04):059-66.[doi:10.13577/j.jnd.2018.0408]
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地铁隧道渗漏水的激光扫描检测方法及应用
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《自然灾害学报》[ISSN:/CN:23-1324/X]

卷:
期数:
2018年04期
页码:
059-66
栏目:
出版日期:
2018-09-28

文章信息/Info

Title:
Laser scanning inspection method and application for metro tunnel leakage
作者:
吴昌睿1 黄宏伟12
1. 同济大学 地下建筑与工程系, 上海 200092;
2. 同济大学 岩土及地下工程教育部重点实验室, 上海 200092
Author(s):
WU Changrui1 HUANG Hongwei12
1. Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China;
2. Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai 200092, China
关键词:
地铁隧道渗漏水病害激光扫描数据处理图像识别
Keywords:
metro tunnelleakagelaser scanningdata processingimage recognition
分类号:
U45;X9
DOI:
10.13577/j.jnd.2018.0408
摘要:
地铁隧道渗漏水病害已成为威胁地铁运营安全性与耐久性的重要风险源,急需快速准确的检测识别技术。激光扫描技术是近年来国内外兴起的一种新型无损检测技术,该技术获得的点云含有几何空间信息和反射强度信息,为实现隧道病害数字化管理开辟了新的视野。依托于激光扫描技术,本文提出了一种地铁隧道渗漏水检测方法。该方法通过空间变换生成二维点云,利用强度修正和栅格化方法获取隧道内表面图像,采用图像处理算法实现隧道渗漏水病害的自动识别和特征统计。研究表明,本文方法可快速检测渗漏水病害的位置、面积,并且检测准确率达到92%,可为后续隧道病害数字化管理所借鉴。
Abstract:
The leakages of metro tunnel are becoming a great threat to the operational safety and durability of metros. The leakages need to be inspected fastly and accurately. Laser scanning technology is a new type of non-destructive inspection technology emerging at home and abroad in recent years. The point cloud obtained by this technology contains geometric and color information, which opens up a new vision for the digital management of tunnel defects. In this paper, based on laser scanning technology, an inspection method of metro tunnel leakage is proposed. The method generates two-dimensional point cloud through spatial transformation, uses intensity correction and rasterization to obtain the inner surface image of tunnel, and furthermore adopts image processing algorithm to realize the inspection and statistics of tunnel leakages. The result shows that the method can recognize the location and area of leakage defects quickly with 92% accuracy. The study can be used as a reference for the digital management of tunnel defects.

参考文献/References:

[1] 王如路. 上海轨道交通隧道结构安全性分析[J]. 地下工程与隧道, 2011(4):37-43. WANG Rulu. Analysis on structural safety of Shanghai rail transit tunnel[J]. Underground Engineering and Tunnels, 2011(4):37-43. (in Chinese)
[2] 黄宏伟, 李庆桐. 基于深度学习的盾构隧道渗漏水病害图像识别[J]. 岩石力学与工程学报, 2017, 36(12):2861-2871. HUANG Hongwei, LI Qingtong. Water leakage image recognition of shield tunnel by deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(12):2861-2871. (in Chinese)
[3] Huang H W, Sun Y, Xue Y D, et al. Inspection equipment study for subway tunnel defects by grey-scale image processing[J]. Advanced Engineering Informatics, 2017(32):188-201.
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[5] Puente I, Akinci B, González-Jorge H, et al. A semi-automated method for extracting vertical clearance and cross sections in tunnels using mobile LiDAR data[J]. Tunnelling and Underground Space Technology Incorporating Trenchless Technology Research, 2016(59):48-54.
[6] Tan K, Cheng X J. Intensity data correction based on incidence angle and distance for terrestrial laser scanner[J]. Journal of Applied Remote Sensing, 2015, 9(1):094094.
[7] Qiu W G, Cheng Y J. High-resolution DEM generation of railway tunnel surface using terrestrial laser scanning data for clearance inspection[J]. Journal of Computing in Civil Engineering, 2016, 31(1):04016045.
[8] 符祥, 郭宝龙. 图像插值技术综述[J]. 计算机工程与设计, 2009, 30(1):141-144+193. FU Xiang, GUO Baolong. Overview of image interpolation technology[J]. Computer Engineering and Design, 2009, 30(1):141-144+193. (in Chinese)
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备注/Memo

备注/Memo:
收稿日期:2018-05-01;改回日期:2018-05-20。
基金项目:国家自然科学基金项目(51778474)
作者简介:吴昌睿(1994-),男,硕士研究生,主要从事隧道结构健康检测的研究工作.E-mail:wuchangrui@tongji.edu.cn
通讯作者:黄宏伟(1966-),男,教授,博士,主要从事隧道结构健康监测与检测、地下工程风险评估、预警与控制方面的研究工作.E-mail:huanghw@tongji.edu.cn
更新日期/Last Update: 1900-01-01