Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin
ABSTRACTIt is very important to construct digital twin scenes, which can accurately describe the dynamically changing geographical environment and improve the level of refined management in bridge construction. This article proposes a feature constrained automatic diagnostic analysis method for geom...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2312219 |
| _version_ | 1797321594753777664 |
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| author | Jun Zhu Niya Luo Zhihao Guo Jianbo Lai Li Zuo Chuanjun Zhang Yukun Guo Ya Hu |
| author_facet | Jun Zhu Niya Luo Zhihao Guo Jianbo Lai Li Zuo Chuanjun Zhang Yukun Guo Ya Hu |
| author_sort | Jun Zhu |
| collection | DOAJ |
| description | ABSTRACTIt is very important to construct digital twin scenes, which can accurately describe the dynamically changing geographical environment and improve the level of refined management in bridge construction. This article proposes a feature constrained automatic diagnostic analysis method for geometric deformation of bridge digital twins. The geometric deformation feature library of bridge twins was first created to accurately describe structural relationships and behavior characteristics. Secondly, line surface feature constraints were used to extract geometric deformation information from bridge digital twins. Then, a geometric deformation diagnosis algorithm was designed based on an improved Hausdorff method. Finally, a case study was conducted to implement experimental analysis. The experimental results show that the method proposed in this paper can automatically extract the geometric morphology and rapidly calculate line and surface deformations for point cloud bridge digital twins. It achieves an efficiency improvement above 90% and with millimeter-level accuracy, which effectively enhances the diagnostic analysis capabilities for geographical digital twin models. |
| first_indexed | 2024-03-08T05:00:50Z |
| format | Article |
| id | doaj.art-d091bcf5d91e4d409816aacbb89ec3a7 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-08T05:00:50Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-d091bcf5d91e4d409816aacbb89ec3a72024-02-07T12:40:25ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552024-12-0117110.1080/17538947.2024.2312219Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twinJun Zhu0Niya Luo1Zhihao Guo2Jianbo Lai3Li Zuo4Chuanjun Zhang5Yukun Guo6Ya Hu7Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, People’s Republic of ChinaABSTRACTIt is very important to construct digital twin scenes, which can accurately describe the dynamically changing geographical environment and improve the level of refined management in bridge construction. This article proposes a feature constrained automatic diagnostic analysis method for geometric deformation of bridge digital twins. The geometric deformation feature library of bridge twins was first created to accurately describe structural relationships and behavior characteristics. Secondly, line surface feature constraints were used to extract geometric deformation information from bridge digital twins. Then, a geometric deformation diagnosis algorithm was designed based on an improved Hausdorff method. Finally, a case study was conducted to implement experimental analysis. The experimental results show that the method proposed in this paper can automatically extract the geometric morphology and rapidly calculate line and surface deformations for point cloud bridge digital twins. It achieves an efficiency improvement above 90% and with millimeter-level accuracy, which effectively enhances the diagnostic analysis capabilities for geographical digital twin models.https://www.tandfonline.com/doi/10.1080/17538947.2024.2312219Feature-constrainedbridge digital twinsgeometric deformationautomatic diagnostic analysisimproved Hausdorff algorithm |
| spellingShingle | Jun Zhu Niya Luo Zhihao Guo Jianbo Lai Li Zuo Chuanjun Zhang Yukun Guo Ya Hu Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin International Journal of Digital Earth Feature-constrained bridge digital twins geometric deformation automatic diagnostic analysis improved Hausdorff algorithm |
| title | Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| title_full | Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| title_fullStr | Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| title_full_unstemmed | Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| title_short | Feature-constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| title_sort | feature constrained automatic geometric deformation analysis method of bridge models toward digital twin |
| topic | Feature-constrained bridge digital twins geometric deformation automatic diagnostic analysis improved Hausdorff algorithm |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2024.2312219 |
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