Research on the analysis of bridge quality monitoring data based on laser point cloud technology

Bridge construction is an important link in the construction of transportation infrastructure, which plays a key role in ensuring the smoothness and safety of road traffic.This paper systematically organizes the process of laser point cloud technology in bridge quality monitoring, and proposes an improved adaptive hyperparametric RANSAC point cloud segmentation algorithm to realize the bridge quality monitoring.Firstly, the basic process of RANSAC algorithm is sorted out, and the mean downsampling operation is adopted to replace the center of gravity downsampling method, which improves the point average degree of downsampling.Next, the FPS algorithm is combined with the method of selecting seed points to expand the range of selected values of seed points under the premise of meeting the relevant requirements.After splitting multiple fitting surfaces, the split fitting surfaces are combined to optimize the unfitted points and improve the fitting rate of the algorithm.The detection accuracy of the bearing flatness of bridge number 3 under the method of this paper is improved by 78.26%, and the maximum deviation of the detected bridge constitutive point offset is only 0.623m, which is within the acceptable range of bridge error monitoring.The feasibility of laser point cloud technology for bridge quality monitoring is verified.