Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning
Shield tunnels are increasingly monitored using 3D laser scanning technology to generate high-resolution point cloud data, which serve as a critical foundation for precise deformation analysis. This study introduces an advanced methodology for analyzing tunnel cross-section displacements, leveraging...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/625 |
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| author | Mahamadou Camara Liying Wang Ze You |
| author_facet | Mahamadou Camara Liying Wang Ze You |
| author_sort | Mahamadou Camara |
| collection | DOAJ |
| description | Shield tunnels are increasingly monitored using 3D laser scanning technology to generate high-resolution point cloud data, which serve as a critical foundation for precise deformation analysis. This study introduces an advanced methodology for analyzing tunnel cross-section displacements, leveraging point cloud data captured by the Self-Mobile Intelligent Laser Scanning System (SILSS), a Mobile Laser Scanning (MLS) platform capable of rapid and detailed 3D mapping of shield tunnels. The preprocessing pipeline includes the precise extraction of cross-sectional linings through local point density outlier removal techniques to enhance data accuracy. A custom segmentation algorithm partitions the tunnel cross-section linings into individual shield rings, enabling detailed and time-resolved displacement tracking. Aligned point clouds from different times were processed using the Iterative Closest Point (ICP) algorithm to achieve high-accuracy displacement analysis. Key displacement metrics, including average shield ring point cloud displacement and centerline shift, were computed to quantify displacement. Additionally, ovality analysis was employed to detect shield ring shape changes, providing critical insights into structural deformations. The findings are visualized in 3D, highlighting significant displacement areas in the tunnel cross-section. An analysis of the corresponding data obtained from the Leica Pegasus Two Ultimate scanner system shows that the data collected by SILSS are accurate. This methodology offers a robust tool for continuous tunnel monitoring, supporting the development of safer and more resilient underground infrastructure systems. |
| format | Article |
| id | doaj-art-261e7825d405499a92de600c9fa25133 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-261e7825d405499a92de600c9fa251332025-01-24T13:20:09ZengMDPI AGApplied Sciences2076-34172025-01-0115262510.3390/app15020625Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser ScanningMahamadou Camara0Liying Wang1Ze You2School of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000, ChinaShield tunnels are increasingly monitored using 3D laser scanning technology to generate high-resolution point cloud data, which serve as a critical foundation for precise deformation analysis. This study introduces an advanced methodology for analyzing tunnel cross-section displacements, leveraging point cloud data captured by the Self-Mobile Intelligent Laser Scanning System (SILSS), a Mobile Laser Scanning (MLS) platform capable of rapid and detailed 3D mapping of shield tunnels. The preprocessing pipeline includes the precise extraction of cross-sectional linings through local point density outlier removal techniques to enhance data accuracy. A custom segmentation algorithm partitions the tunnel cross-section linings into individual shield rings, enabling detailed and time-resolved displacement tracking. Aligned point clouds from different times were processed using the Iterative Closest Point (ICP) algorithm to achieve high-accuracy displacement analysis. Key displacement metrics, including average shield ring point cloud displacement and centerline shift, were computed to quantify displacement. Additionally, ovality analysis was employed to detect shield ring shape changes, providing critical insights into structural deformations. The findings are visualized in 3D, highlighting significant displacement areas in the tunnel cross-section. An analysis of the corresponding data obtained from the Leica Pegasus Two Ultimate scanner system shows that the data collected by SILSS are accurate. This methodology offers a robust tool for continuous tunnel monitoring, supporting the development of safer and more resilient underground infrastructure systems.https://www.mdpi.com/2076-3417/15/2/625mobile laser scanningpoint cloud datapoint cloud processingtunnel cross-sectionpoint cloud displacementdeformation |
| spellingShingle | Mahamadou Camara Liying Wang Ze You Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning Applied Sciences mobile laser scanning point cloud data point cloud processing tunnel cross-section point cloud displacement deformation |
| title | Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning |
| title_full | Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning |
| title_fullStr | Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning |
| title_full_unstemmed | Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning |
| title_short | Three-Dimensional Point Cloud Displacement Analysis for Tunnel Deformation Detection Using Mobile Laser Scanning |
| title_sort | three dimensional point cloud displacement analysis for tunnel deformation detection using mobile laser scanning |
| topic | mobile laser scanning point cloud data point cloud processing tunnel cross-section point cloud displacement deformation |
| url | https://www.mdpi.com/2076-3417/15/2/625 |
| work_keys_str_mv | AT mahamadoucamara threedimensionalpointclouddisplacementanalysisfortunneldeformationdetectionusingmobilelaserscanning AT liyingwang threedimensionalpointclouddisplacementanalysisfortunneldeformationdetectionusingmobilelaserscanning AT zeyou threedimensionalpointclouddisplacementanalysisfortunneldeformationdetectionusingmobilelaserscanning |