Abstract
AbstractThe 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.
Highlights
The global infrastructure market is poised for an explosive adoption of bridge information modeling (BrIM), which provides a shared knowledge resource for information exchange to support a reliable basis for decision making during a bridge’s life cycle (Fanning et al, 2014)
The hypothesis of this work is that the top-down bridge-component detection approach is efficient and reliable and there is no significant difference in detection performance for different reinforced concrete (RC) bridges
4.1 Assumptions According to national standards (Highways England, 2017), the proposed method is feasible in the context of RC bridge modeling under the following conditions, which are confirmed in our experiments
Summary
The global infrastructure market is poised for an explosive adoption of bridge information modeling (BrIM), which provides a shared knowledge resource for information exchange to support a reliable basis for decision making during a bridge’s life cycle (Fanning et al, 2014). The generation of as-is BrIM models for existing bridges is very limited, despite the widespread adoption of laser-scanning for faster and better data collection (Park et al, 2007; Park et al, 2015). This is because the automatic generation of as-is models from point cloud data (PCD) remains an unsolved problem. Current methods of point cloud clustering generally follow a “bottomup” approach, which goes from points to surfaces or patches followed by semantic labeling to derive objects. The higher level features are typically the surface normal (Sampath, 2010), meshes (Marton et al, 2009), patches (Zhang et al, 2015), and nonuniform B-Spline surfaces (Dimitrov et al, 2016)
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