Extraction and location of subway shield tunnel segment joints from RMLS point clouds
Precisely extracting segment joints in subway shield tunnels is critical for automated safety evaluation such as segment assembly quality inspection, dislocation and deformation detection and lining surface defects diagnosis. However, traditional extraction methods can only extract segment joints of...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528627 |
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| _version_ | 1849224290360098816 |
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| author | Liying Wang Ze You Yong Feng Chunxi Xie Mahamadou Camara |
| author_facet | Liying Wang Ze You Yong Feng Chunxi Xie Mahamadou Camara |
| author_sort | Liying Wang |
| collection | DOAJ |
| description | Precisely extracting segment joints in subway shield tunnels is critical for automated safety evaluation such as segment assembly quality inspection, dislocation and deformation detection and lining surface defects diagnosis. However, traditional extraction methods can only extract segment joints of specific types (circumferential joints or radial joints) or specific assembly patterns (straight joints or staggered joints) and their extraction accuracy is easily affected by nearby tunnel facilities. To meet distinct engineering requirements, a Rail-borne Mobile Laser Scanning (RMLS) point cloud-based method capable of simultaneously identifying all joints types and patterns is presented. The proposed method first employs an elliptical fitting residual statistics algorithm to remove non-lining points, eliminating their adverse effects on joint extraction. Then, cross-sections corresponding to circumferential joints are automatically identified and spatially localized using an adaptive multi-threshold algorithm based on local intensity statistics, dividing the tunnel lining into individual shield rings. Finally, a novel curvature-based ratio metric, derived from the local bulge and liner distribution characteristics, is developed to identify and localize radial joints within each shield ring. Experiment results show that the proposed method achieves average IoU, recall, and accuracy of 92.8%, 95.3%, and 94.3%, respectively, even surpassing the performance of deep learning-based semantic segmentation network. |
| format | Article |
| id | doaj-art-55be29f1ca51445d990738c9debd8235 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-55be29f1ca51445d990738c9debd82352025-08-25T11:28:42ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2528627Extraction and location of subway shield tunnel segment joints from RMLS point cloudsLiying Wang0Ze You1Yong Feng2Chunxi Xie3Mahamadou Camara4School of Geomatics, Liaoning Technical University, Fuxin, People’s Republic of ChinaSchool of Geomatics, Liaoning Technical University, Fuxin, People’s Republic of ChinaDivision of Geoinformation Management, Department of Natural Resources of Liaoning Province, Shenyang, People’s Republic of ChinaInstitute of Surveying, Mapping and Geographic Information, China Railway Design Group Co., Ltd., Tianjin, People’s Republic of ChinaSchool of Geomatics, Liaoning Technical University, Fuxin, People’s Republic of ChinaPrecisely extracting segment joints in subway shield tunnels is critical for automated safety evaluation such as segment assembly quality inspection, dislocation and deformation detection and lining surface defects diagnosis. However, traditional extraction methods can only extract segment joints of specific types (circumferential joints or radial joints) or specific assembly patterns (straight joints or staggered joints) and their extraction accuracy is easily affected by nearby tunnel facilities. To meet distinct engineering requirements, a Rail-borne Mobile Laser Scanning (RMLS) point cloud-based method capable of simultaneously identifying all joints types and patterns is presented. The proposed method first employs an elliptical fitting residual statistics algorithm to remove non-lining points, eliminating their adverse effects on joint extraction. Then, cross-sections corresponding to circumferential joints are automatically identified and spatially localized using an adaptive multi-threshold algorithm based on local intensity statistics, dividing the tunnel lining into individual shield rings. Finally, a novel curvature-based ratio metric, derived from the local bulge and liner distribution characteristics, is developed to identify and localize radial joints within each shield ring. Experiment results show that the proposed method achieves average IoU, recall, and accuracy of 92.8%, 95.3%, and 94.3%, respectively, even surpassing the performance of deep learning-based semantic segmentation network.https://www.tandfonline.com/doi/10.1080/17538947.2025.2528627Mobile laser scanningshield tunnelsegment jointpoint cloudsellipse fittingcurvature-based ratio feature |
| spellingShingle | Liying Wang Ze You Yong Feng Chunxi Xie Mahamadou Camara Extraction and location of subway shield tunnel segment joints from RMLS point clouds International Journal of Digital Earth Mobile laser scanning shield tunnel segment joint point clouds ellipse fitting curvature-based ratio feature |
| title | Extraction and location of subway shield tunnel segment joints from RMLS point clouds |
| title_full | Extraction and location of subway shield tunnel segment joints from RMLS point clouds |
| title_fullStr | Extraction and location of subway shield tunnel segment joints from RMLS point clouds |
| title_full_unstemmed | Extraction and location of subway shield tunnel segment joints from RMLS point clouds |
| title_short | Extraction and location of subway shield tunnel segment joints from RMLS point clouds |
| title_sort | extraction and location of subway shield tunnel segment joints from rmls point clouds |
| topic | Mobile laser scanning shield tunnel segment joint point clouds ellipse fitting curvature-based ratio feature |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528627 |
| work_keys_str_mv | AT liyingwang extractionandlocationofsubwayshieldtunnelsegmentjointsfromrmlspointclouds AT zeyou extractionandlocationofsubwayshieldtunnelsegmentjointsfromrmlspointclouds AT yongfeng extractionandlocationofsubwayshieldtunnelsegmentjointsfromrmlspointclouds AT chunxixie extractionandlocationofsubwayshieldtunnelsegmentjointsfromrmlspointclouds AT mahamadoucamara extractionandlocationofsubwayshieldtunnelsegmentjointsfromrmlspointclouds |