Geometric models from laser scanning data for superstructure components of steel girder bridges

Abstract

To advance the scan-to-model process for steel girder bridges, this paper presents an automated approach for creating complete geometric models for the steel superstructure elements based on the segmentation results from Yan and Hajjar (2021). The key innovation is two-fold: 1) cross-frames are automatically partitioned into cross-frame members that need to be modeled separately; 2) a 3D occlusion labeling algorithm is developed to identify occluded spaces, which are subsequently used in the modeling process to mitigate the effects of occlusions. The proposed approach is validated using real-world point clouds collected from a highway bridge, and the validation indicates that the errors in computing element widths and thicknesses are less than ±5% and ± 8% respectively, provided a reasonable data completeness. As the level of data completeness decreases, the errors for element widths can still be kept less than ±5%, but the element thicknesses can be overestimated by up to 52%.