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MODERN TUNNELLING TECHNOLOGY 2023, Vol. 60 Issue (5) :158-166    DOI:
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Monitoring Method for Multi-phase Crown Deformation of Highway Tunnels Based on Fixed-station Laser Scanning Data
(1. Shandong University of Science and Technology, Qingdao 266590; 2.Qingdao Digital World Information Technology Co., Ltd.,Qingdao 266000)
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Abstract The laser point cloud data are characterized by high precision, high density and good adaptability. In this study, the laser point cloud technology is applied to the deformation monitoring for highway tunnels, and the study is conducted on the methods of continuous extraction, filtering and multi-phase deformation monitoring for highway tunnel cross sections based on fixed station laser scanning data. In order to ensure that observed cross sections with multi-phase deformation are in the same position, the scanner with fixed position is installed in the tunnel, and the 3D central axis and the registration algorithm are used to ensure the identity of multi-phase cross sections. A twostep iterative elliptic curve fitting method is proposed. In the first iteration, the point cloud of the sub-crown section of the tunnel crown is filtered based on the elliptical shape constraint, and in the second iteration, the filtered crown section is fitted with the elliptic curve using the iterative least square fitting algorithm. The multi-phase deformation of the section is monitored based on the fitting elliptic curve and the polar coordinate method. Compared with the traditional RANSAC method, the two two-step iterative elliptic curve fitting method shows better filtering effect and higher accuracy in extracting the tunnel elliptic curve. In this study, the tunnel model is built for deformation simulation and monitoring test, and the extracted multi-phase data are analyzed. The test results show that the proposed method is feasible in multi-phase deformation monitoring for highway tunnels, and the mean square error is less than 2 mm, which high accuracy.
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LUO Chao1 LI Zeyu1 LIU Rufei1 LI Yanyan1 LI Ming2
KeywordsHighway tunnels   Laser point cloud   Deformation monitoring   Section extraction     
Abstract: The laser point cloud data are characterized by high precision, high density and good adaptability. In this study, the laser point cloud technology is applied to the deformation monitoring for highway tunnels, and the study is conducted on the methods of continuous extraction, filtering and multi-phase deformation monitoring for highway tunnel cross sections based on fixed station laser scanning data. In order to ensure that observed cross sections with multi-phase deformation are in the same position, the scanner with fixed position is installed in the tunnel, and the 3D central axis and the registration algorithm are used to ensure the identity of multi-phase cross sections. A twostep iterative elliptic curve fitting method is proposed. In the first iteration, the point cloud of the sub-crown section of the tunnel crown is filtered based on the elliptical shape constraint, and in the second iteration, the filtered crown section is fitted with the elliptic curve using the iterative least square fitting algorithm. The multi-phase deformation of the section is monitored based on the fitting elliptic curve and the polar coordinate method. Compared with the traditional RANSAC method, the two two-step iterative elliptic curve fitting method shows better filtering effect and higher accuracy in extracting the tunnel elliptic curve. In this study, the tunnel model is built for deformation simulation and monitoring test, and the extracted multi-phase data are analyzed. The test results show that the proposed method is feasible in multi-phase deformation monitoring for highway tunnels, and the mean square error is less than 2 mm, which high accuracy.
KeywordsHighway tunnels,   Laser point cloud,   Deformation monitoring,   Section extraction     
Cite this article:   
LUO Chao1 LI Zeyu1 LIU Rufei1 LI Yanyan1 LI Ming2 .Monitoring Method for Multi-phase Crown Deformation of Highway Tunnels Based on Fixed-station Laser Scanning Data[J]  MODERN TUNNELLING TECHNOLOGY, 2023,V60(5): 158-166
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