Recursive segmentation for as-is bridge information modelling

Prior studies reported that the time needed to manually convert a point cloud to an as-is geometric model using cutting edge modelling software is ten times greater than the time needed to obtain the point cloud. The laborious nature of manually modelling infrastructure such as bridges is the reason behind the significant cost of modelling which impedes the proliferation of the usage of Bridge Information Models (BrIM) in Bridge Management Systems. Existing commercial solutions can automatically recognize geometric shapes embedded in segmented point cloud data (PCD) and generate the corresponding IFC objects. Researchers have taken further studies and have additionally automated surface reconstruction through generating parametric surface-based primitives in order to automate the segmentation process. However, surface-based segmentation for bridge modelling is an unsolved problem, which is neither straightforward nor consistent, thus hinders the automation of BrIM.This paper presents a top-down PCD detection solution that follows a knowledge-based heuristic approach for BrIM generation that can semi-automatically segment a bridge point cloud recursively. We leverage bridge domain knowledge as strong priors through a histogram-based algorithm to conduct the tasks of segmentation and classification. We implemented this solution and tested on one highway bridge. The experimental results indicated that the detection precision of this solution is 92%.