Identification of global main cable line shape parameters of suspension bridges based on local 3D point cloud

作     者：Li, Yurui Dan, Danhui Pan, Ruiyang 

作者机构：College of Civil Engineering Tongji University 1239 Siping Road Shanghai200092 China Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education Shanghai China 

出 版 物：《Low-Carbon Materials and Green Construction》 (Low-Carbon Mater. Green Constr.)

年 卷 期：2025年第3卷第1期

页      面：1-12页

核心收录：

基　　金：This article is jointly funded by the 2024 STCSM Shanghai Natural Science Grants General Project \"Online Intelligent Perception and Warning of Large span Structural Vortex Vibration Based on Structural Health Monitoring\" and the Science and Technology Project \"Research on Intelligent Monitoring System Scheme for Large span Bridges in Mountainous Areas\" of PowerChina Road Bridge Group Co.  Ltd 

主　　题：Channel estimation 

摘      要：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. © The Author(s) 2025.