التفاصيل البيبلوغرافية
| العنوان: |
Automatic Identification and Segmentation of Long‐Span Rail‐and‐Road Cable‐Stayed Bridges Using UAV LiDAR Point Cloud. |
| المؤلفون: |
Shen, Yueqian1 (AUTHOR), Deng, Zili1 (AUTHOR), Wang, Jinguo1 (AUTHOR), Fu, Shihan1 (AUTHOR), Chen, Dong2 (AUTHOR) chendong@njfu.edu.cn, Sun, Zhen (AUTHOR) |
| المصدر: |
Structural Control & Health Monitoring. 11/13/2024, Vol. 2024, p1-26. 26p. |
| مصطلحات موضوعية: |
*BRIDGE inspection, *AUTOMATIC identification, *POINT cloud, *LIDAR, *TRUSSES |
| مستخلص: |
Bridge information models are essential for bridge inspection, assessment, and management. LiDAR technology, particularly UAV LiDAR, offers a cost‐effective means to capture dense and accurate 3D coordinates of a bridge's surface. However, the structure of large‐scale bridges is complex, and existing commercial software still demands substantial manual effort to segment the components when constructing bridge information models for large‐scale bridges. This study introduces a novel approach to automatically segment the components of a long‐span rail‐and‐road cable‐stayed bridge from the entire point cloud obtained through UAV LiDAR. In this proposed approach, the geometric and topological constraints of various bridge components are thoroughly examined, and a combination of the coarse‐to‐fine concept and top‐down strategy is employed. The key structural elements, including piers, cable towers, wind fairing plate, stay‐cable, main truss, railway surfaces, and deck surfaces, are identified and segmented. The proposed methodology achieves an average accuracy of over 96% at the point level validated using datasets acquired by UAV LiDAR. [ABSTRACT FROM AUTHOR] |
| قاعدة البيانات: |
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