In recent years, three-dimensional ( 3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the ...
详细信息
ISBN:
(纸本)9781510600454
In recent years, three-dimensional ( 3D) terrestrial laser scanning technologies with higher precision and higher capability are developing rapidly. The growing maturity of laser scanning has gradually approached the required precision as those have been provided by traditional structural monitoring technologies. Together with widely available fast computation for massive point cloud data processing, 3D laser scanning can serve as an efficient structural monitoring alternative for civil engineering communities. currently most research efforts have focused on integrating/ calculating the measured multi- station point cloud data, as well as modeling/ establishing the 3D meshes of the scanned objects. Very little attention has been spent on extracting the information related to health conditions and mechanical states of structures. In this study, an automated numerical approach that integrates various existing algorithms for geometric identification and damage detection of structural elements were established. Specifically, adaptive meshes were employed for classifying the point cloud data of the structural elements, and detecting the associated damages from the calculated eigenvalues in each area of the structural element. Furthermore, kd- tree was used to enhance the searching efficiency of plane fitting which were later used for identifying the boundaries of structural elements. The results of geometric identification were compared with m3c2 algorithm provided by cloudcompare, as well as validated by LVDT measurements of full- scale reinforced concrete beams tested in laboratory. It shows that 3D laser scanning, through the established processing approaches of the point cloud data, can offer a rapid, nondestructive, remote, and accurate solution for geometric identification and damage detection of structural elements.
Three-dimensional (3D) point clouds are widely used for geomorphicchange detection. However, the lack of efficient change-detection algorithms for complex terrain limits the use of 3D point clouds in area-wide morpho...
详细信息
Three-dimensional (3D) point clouds are widely used for geomorphicchange detection. However, the lack of efficient change-detection algorithms for complex terrain limits the use of 3D point clouds in area-wide morphological change studies. In this study, a complex terrain development process was simulated on a natural slope in the hilly and gully Loess Plateau in china using 28 runoff scouring experiments conducted in two plots. Highly precise point clouds were obtained using terrestrial laser scanning (TLS) before and after each experiment. A slice contraction change detection (SccD) algorithm was developed based on slicing, Laplacian-based contraction, and differential principles for detecting geomorphic and volumetricchanges on complex terrain, and the level of detection (LoD) was derived with respect to that of the multiscale model to model the cloud comparison (m3c2) algorithm. The accuracy of SccD was compared with that of the 3D-m3c2 algorithm (i.e., a 3D volumetricchange calculation algorithm based on m3c2) and the digital elevation model (DEm) of difference (DoD) algorithm based on the measured sediment yield from the plots. The comparison was performed also under different point cloud densities and morphologies. Results showed the following: (1) The precisions of SccD and 3D-m3c2 were comparable and considerably higher than that of DoD. The mean relative errors of SccD, 3D-m3c2, and DoD for the two plots were 13.32% and 10.37%, 10.07% and 10.84%, and 35.30% and 27.23%, respectively. The relative error fluctuations of the three algorithms for the individual experiments followed the sequence of DoD (standard deviation (std.): 10.18) > 3D-m3c2 (std.: 8.29) > SccD (std.: 5.79). (2) The sensitivity to point cloud density changes followed the sequence of 3D-m3c2 > SccD > DoD as the point cloud density varied between 10,000 and 1000 points m- 2. The mean relative errors of 3D-m3c2, SccD, and DoD for the two plots were 10.07-18.59% and 10.84-13.62%, 13.32-16.83
暂无评论