Seo Hyungjoon, Zhao Yang, Chen Cheng
Department of Civil Engineering and Industrial Design, University of Liverpool, Liverpool L69 3BX, UK.
Department of Civil Engineering, Xi'an Jiaotong Liverpool University, Suzhou 215000, China.
Sensors (Basel). 2021 Nov 5;21(21):7370. doi: 10.3390/s21217370.
Point clouds were obtained after laser scanning of the concrete panel, SMW, and sheet pile which is most widely used in retaining structures. The surface condition of the point cloud affects the displacement calculation, and hence both local roughness and global curvature of each point cloud were analyzed using the different sizes of the kernel. The curvature of the three retaining structures was also analyzed by the azimuth angle. In this paper, artificial displacements are generated for the point clouds of 100%, 80%, 60%, 40%, and 20% of the retaining structures, and displacement and analysis errors were calculated using the C2C, C2M, and M3C2 methods. C2C method is affected by the resolution of the point cloud, and the C2M method underestimates the displacement by the location of the points in the curvature of the retaining structures. M3C2 method had the lowest error, and the optimized M3C2 parameters for analyzing the displacement were presented.
通过对用于挡土结构的混凝土面板、SMW工法桩和板桩进行激光扫描获取点云。点云的表面状况会影响位移计算,因此使用不同大小的内核分析了每个点云的局部粗糙度和全局曲率。还通过方位角分析了三种挡土结构的曲率。本文针对挡土结构的100%、80%、60%、40%和20%的点云生成人工位移,并使用C2C、C2M和M3C2方法计算位移和分析误差。C2C方法受点云分辨率的影响,C2M方法会因挡土结构曲率中的点位置而低估位移。M3C2方法误差最低,并给出了用于分析位移的优化M3C2参数。