Detection of Structural Components in Point Clouds of Existing RC Bridges. (30th July 2018)

• Record Type: Journal Article
• Authors: Lu, Ruodan; Brilakis, Ioannis; Middleton, Campbell R.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 34:Number 3(2019:Mar.)
• Page Start: 191
• Page End: 212
• Publication Date: 2018-07-30
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12407
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: The cost and effort of modeling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule‐based classification to create labeled cuboids and cylinders from point clouds. Although these methods work well in synthetic data sets or idealized cases, they encounter huge challenges when dealing with real‐world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this article, we propose a novel top‐down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges oversegments into individually labeled point clusters. The results of 10 real‐world bridge point cloud experiments indicate that our method achieves very high detection performance. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labeled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labeled point clusters.