Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud
Abstract Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges. 3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target, offering high accuracy and efficienc...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Low-Carbon Materials and Green Construction |
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| Online Access: | https://doi.org/10.1007/s44242-024-00062-6 |
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| author | Yurui Li Danhui Dan Ruiyang Pan |
| author_facet | Yurui Li Danhui Dan Ruiyang Pan |
| author_sort | Yurui Li |
| collection | DOAJ |
| description | Abstract Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges. 3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target, offering high accuracy and efficiency. As a result, it is expected to be used in applications requiring rapid, large-scale measurements, such as main cable line shape measurement for suspension bridges. However, due to the large span and tall main towers of suspension bridges, the LiDAR field of view often encounters obstructions, making it difficult to obtain high-quality point clouds for the entire bridge. The collected point clouds are typically unevenly distributed and of poor quality. Therefore, LiDAR is used to monitor the local cable line shape. This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change. Additionally, the Random Sample Consensus (RANSAC) algorithm, the ordinary least squares, and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable. Given the strong design prior information available during suspension bridge construction, Bayesian theory is applied to predict and adjust the global line shape of the main cable. The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data, significantly enhancing data collection efficiency. The method proposed in this paper offers advantages such as highly automated, low risk, low cost, and sustainability, making it suitable for green monitoring throughout the entire main cable construction process. |
| format | Article |
| id | doaj-art-2fc77bd3a02b4d79b4892035a8fa3f27 |
| institution | Kabale University |
| issn | 2731-6319 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Low-Carbon Materials and Green Construction |
| spelling | doaj-art-2fc77bd3a02b4d79b4892035a8fa3f272025-01-19T12:35:51ZengSpringerLow-Carbon Materials and Green Construction2731-63192025-01-013111210.1007/s44242-024-00062-6Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloudYurui Li0Danhui Dan1Ruiyang Pan2College of Civil Engineering, Tongji UniversityCollege of Civil Engineering, Tongji UniversityCollege of Civil Engineering, Tongji UniversityAbstract Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges. 3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target, offering high accuracy and efficiency. As a result, it is expected to be used in applications requiring rapid, large-scale measurements, such as main cable line shape measurement for suspension bridges. However, due to the large span and tall main towers of suspension bridges, the LiDAR field of view often encounters obstructions, making it difficult to obtain high-quality point clouds for the entire bridge. The collected point clouds are typically unevenly distributed and of poor quality. Therefore, LiDAR is used to monitor the local cable line shape. This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change. Additionally, the Random Sample Consensus (RANSAC) algorithm, the ordinary least squares, and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable. Given the strong design prior information available during suspension bridge construction, Bayesian theory is applied to predict and adjust the global line shape of the main cable. The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data, significantly enhancing data collection efficiency. The method proposed in this paper offers advantages such as highly automated, low risk, low cost, and sustainability, making it suitable for green monitoring throughout the entire main cable construction process.https://doi.org/10.1007/s44242-024-00062-6Suspension BridgeMain CableLine Shape Parameter IdentificationLiDAR Point CloudBayesian Theory |
| spellingShingle | Yurui Li Danhui Dan Ruiyang Pan Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud Low-Carbon Materials and Green Construction Suspension Bridge Main Cable Line Shape Parameter Identification LiDAR Point Cloud Bayesian Theory |
| title | Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud |
| title_full | Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud |
| title_fullStr | Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud |
| title_full_unstemmed | Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud |
| title_short | Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud |
| title_sort | identification of global main cable line shape parameters of suspension bridges based on local 3d point cloud |
| topic | Suspension Bridge Main Cable Line Shape Parameter Identification LiDAR Point Cloud Bayesian Theory |
| url | https://doi.org/10.1007/s44242-024-00062-6 |
| work_keys_str_mv | AT yuruili identificationofglobalmaincablelineshapeparametersofsuspensionbridgesbasedonlocal3dpointcloud AT danhuidan identificationofglobalmaincablelineshapeparametersofsuspensionbridgesbasedonlocal3dpointcloud AT ruiyangpan identificationofglobalmaincablelineshapeparametersofsuspensionbridgesbasedonlocal3dpointcloud |