Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System

A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1) is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1–2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

[1]  Hong-Gyoo Sohn,et al.  Utilization of a Terrestrial Laser Scanner for the Calibration of Mobile Mapping Systems , 2017, Sensors.

[2]  Burcu Akinci,et al.  A semi-automated method for extracting vertical clearance and cross sections in tunnels using mobile LiDAR data , 2016 .

[3]  A. Afana,et al.  A new adaptive method to filter terrestrial laser scanner point clouds using morphological filters and spectral information to conserve surface micro-topography , 2016 .

[4]  Baoqian Wang,et al.  Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds , 2014, Remote. Sens..

[5]  Liming Du,et al.  AUTOMATIC MONITORING OF TUNNEL DEFORMATION BASED ON HIGH DENSITY POINT CLOUDS DATA , 2017 .

[6]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[7]  Soohee Han,et al.  Automated and Efficient Method for Extraction of Tunnel Cross Sections Using Terrestrial Laser Scanned Data , 2013 .

[8]  Horst Bischof,et al.  A Novel Robust Tube Detection Filter for 3D Centerline Extraction , 2005, SCIA.

[9]  Jen-Yu Han,et al.  Monitoring tunnel profile by means of multi-epoch dispersed 3-D LiDAR point clouds , 2013 .

[10]  Mark S. Diederichs,et al.  Geotechnical and operational applications for 3-dimensional laser scanning in drill and blast tunnels , 2010 .

[11]  Fu-Shu Jeng,et al.  Application and validation of profile–image method for measuring deformation of tunnel wall , 2009 .

[12]  Myung Sagong,et al.  Feature extraction of a concrete tunnel liner from 3D laser scanning data , 2009 .

[13]  Robert M. Adams,et al.  A laser profilometer , 1970 .

[14]  LU Xiaoping,et al.  Continuously Extracting Section and Deformation Analysis for Subway Tunnel Based on LiDAR Points , 2015 .

[15]  Mark S. Diederichs,et al.  Development of an elliptical fitting algorithm to improve change detection capabilities with applications for deformation monitoring in circular tunnels and shafts , 2014 .

[16]  Mostafa Arastounia Automated As-Built Model Generation of Subway Tunnels from Mobile LiDAR Data , 2016, Sensors.

[17]  Yuwei Chen,et al.  Multiplatform Mobile Laser Scanning: Usability and Performance , 2012, Sensors.

[18]  Juha Hyyppä,et al.  Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner , 2011, Remote. Sens..

[19]  M. Crosetto,et al.  Deformation measurement using terrestrial laser scanning data and least squares 3D surface matching , 2008 .

[20]  Jin Lei,et al.  Automatic Extraction of Tunnel Lining Cross-Sections from Terrestrial Laser Scanning Point Clouds , 2016, Sensors.