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 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.

References

[1] Sun Y, He X, Li W. Influential parameter study on the main-cable state of self-anchored suspension bridge. Key Engineering Materials, 2014, 619: 99-108

[2] Huang, P., & Li, C. (2023). Review of the main cable shape control of the suspension bridge. Applied Sciences, 13(5). https://doi.org/10.3390/app13053106

[3] Xu X, Zhou S, Dai Q. Measurement of main cable shape for large-span suspension bridge. Construction Technology, 2002, 09: 21-23

[4] Zhao Y. Measurement control in main cable installation construction of suspension bridges: A case study of measurement control in main cable installation of the Wuhan Yingwuzhou Yangtze River Bridge. Small and Medium Enterprise Management and Technology, 2014, 04: 89-92

[5] Technical Specifications for Highway Bridge Construction Monitoring (JTG/T 3650–01–2022). (2023).

[6] Lee G, Kim S, Ahn S, Kim H-K, Yoon H. Vision-based cable displacement measurement using side view video. Sensors, 2022, 22(3): 962

[7] Huang C, Wang Y, Xu S, Shou W, Peng C, Lv D. Vision-based methods for relative sag measurement of suspension bridge cables. Buildings, 2022, 12(5): 667

[8] Lee H-J. Study on the efficient application of vision-based displacement measurements for the cable tension estimation of cable-stayed bridges. Journal of the Korea Academia-Industrial Cooperation Society, 2016, 17(9): 709-717

[9] Xiang X, Pan Z, Tong J. Research on the application of depth cameras in computer vision and graphics. Computer Science and Exploration, 2011, 5(6): 481-492

[10] Huai C, Li B, Yao J, Su P, Wang W. Long cable tension testing technology based on airborne 3D laser scanning. Railway Construction, 2024, 64(1): 58-64

[11] Zhou Y, Huang Z, Wang Y, Xiang Z, Zhou J, Zhang X. Cable force measurement method for cable-stayed bridges based on laser scanning measured cable shape. Journal of China Highway Society, 2021, 34(12): 91-103

[12] Fischler MA, Bolles RC. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 1981, 24(6): 381-395

[13] Dal Poz A, Ywata M. Adaptive random sample consensus approach for segmentation of building roofs in airborne laser scanning point cloud. International Journal of Remote Sensing, 2019, 41: 1-15

[14] Zhang, S., Wang, H., Wang, C., Wang, Y., Wang, S., & Yang, Z. (2024). An improved RANSAC-ICP method for registration of SLAM and UAV-LiDAR point cloud at plot scale. Forests, 15(6). https://doi.org/10.3390/f15060893

[15] Jin, Y.-H., & Lee, W.-H. (2019). Fast cylinder shape matching using random sample consensus in large scale point cloud. Applied Sciences, 9(5). https://doi.org/10.3390/app9050974

[16] Mao, S. (2005). Bayesian statistics. China Statistics Press.

[17] Gelman, A. (2014). Bayesian data analysis (3rd ed.). CRC Press.

[18] Press SJ. Bayesian statistics: Principles, models, and applications, 1989 Wiley

[19] Yuen K-V. Bayesian methods for structural dynamics and civil engineering. John Wiley & Sons Asia, 2010

[20] Liu L, Yuan W. Modern Bayesian analysis and modern statistical inference. Economic Theory and Economic Management, 2004, 6: 6

[21] Liu J, Jin H, Yao Y. Methods for assessing measurement uncertainty and their applications. Metrology and Measurement Technology, 2021, 65(05): 124-131

[22] Livox LiDAR. (n.d.). The specs of Mid-40 and Mid 70. Retrieved October 11, 2024, from https://www.livoxtech.com

Funding

Science and Technology Commission of Shanghai Municipality(24ZR1468300)

PowerChina Road Bridge Group Co., Ltd

RIGHTS & PERMISSIONS

The Author(s)