A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds
Abstract Three-dimensional (3D) geometric bridge models play an important role in bridge inspection, assessment, and management. Laser scanning nowadays offers a cost-efficient method to capture dense, accurate 3D topographic data of surfaces of the bridge. However, given the typical complexity of bridges, current workflows using commercial software to construct a bridge model still require intensive labour work. This paper presents a new approach to automatically extract the point cloud of surfaces of structural components of box and slab-beam bridges. The proposed method consists of 3 Parts: (1) point-to-surface, (2) superstructure and (3) substructure extraction. The method uses both spatial point clouds and contextual knowledge to extract point cloud subsets corresponding to surfaces of individual bridge components in a consecutive order from superstructure to substructure. For each bridge component, two levels of extraction are (1) coarse extraction to separate candidate points of the component from the full data set and (2) fine filtering to obtain final 3D points of individual surfaces using cell- or voxel-based region growing (CRG or VRG), followed by a connected surface component (CSC) method. An experimental test on one box-girder and one slab-beam bridges shows that the proposed method successfully extracts all surfaces of bridge components with the lowest F1-score of 0.93 based on a point-based evaluation. Moreover, a shape similarity evaluation also shows that discrepancies between extracted surfaces and ground truth ones are no larger than 0.82 for the area overlap ratio and 0.59 degrees for the angular deviation. The proposed method contributes to the automatic generation of 3D geometric bridge models and to give point clouds of individual surface for damage identification.
Highlights Propose a new approach to extract point clouds of individual surfaces of a bridge Propose cell-based region growing to extract point clouds of planar surfaces Develop two levels of surface extraction allowing to handle a massive data set Present a methodology to estimate input parameters for processing point clouds Succeed to extract surfaces of bridge components with the lowest F1-score above 0.93
Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds
Abstract Three-dimensional (3D) geometric bridge models play an important role in bridge inspection, assessment, and management. Laser scanning nowadays offers a cost-efficient method to capture dense, accurate 3D topographic data of surfaces of the bridge. However, given the typical complexity of bridges, current workflows using commercial software to construct a bridge model still require intensive labour work. This paper presents a new approach to automatically extract the point cloud of surfaces of structural components of box and slab-beam bridges. The proposed method consists of 3 Parts: (1) point-to-surface, (2) superstructure and (3) substructure extraction. The method uses both spatial point clouds and contextual knowledge to extract point cloud subsets corresponding to surfaces of individual bridge components in a consecutive order from superstructure to substructure. For each bridge component, two levels of extraction are (1) coarse extraction to separate candidate points of the component from the full data set and (2) fine filtering to obtain final 3D points of individual surfaces using cell- or voxel-based region growing (CRG or VRG), followed by a connected surface component (CSC) method. An experimental test on one box-girder and one slab-beam bridges shows that the proposed method successfully extracts all surfaces of bridge components with the lowest F1-score of 0.93 based on a point-based evaluation. Moreover, a shape similarity evaluation also shows that discrepancies between extracted surfaces and ground truth ones are no larger than 0.82 for the area overlap ratio and 0.59 degrees for the angular deviation. The proposed method contributes to the automatic generation of 3D geometric bridge models and to give point clouds of individual surface for damage identification.
Highlights Propose a new approach to extract point clouds of individual surfaces of a bridge Propose cell-based region growing to extract point clouds of planar surfaces Develop two levels of surface extraction allowing to handle a massive data set Present a methodology to estimate input parameters for processing point clouds Succeed to extract surfaces of bridge components with the lowest F1-score above 0.93
Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds
Truong-Hong, Linh (author) / Lindenbergh, Roderik (author)
2021-12-31
Article (Journal)
Electronic Resource
English
3D , Three dimensional , CAD , Computer aided drawing , mBB , Minimum bounding box , UAV , Unmanned aerial vehicle , TLS , Terrestrial laser scanning , KDE , Kernel density estimation , PDS , Probability density shape , PCA , Principal component analysis , 4NCS , 4-neighbouring cell searching , CRG , Cell-based region growing , CpRG , Cell-patch region growing segmentation , CSC , Connected surface component , VRG , Voxel-based region growing , PM , Proposed method , GT , Ground truth , TP , True positive , FP , False positive , FN , False negative , Comp. , Completeness , Corr. , Correctness , Point cloud , Segmentation , Cell-based segmentation , Bridge deficiencies , Bridge inspection , Bridge modelling , Bridge components , Slab/box bridge
Structural assessment using terrestrial laser scanning point clouds
| Emerald Group Publishing | 2021
Automated registration of dense terrestrial laser-scanning point clouds using curves
| Online Contents | 2014
An automated method to register airborne and terrestrial laser scanning point clouds
| Online Contents | 2015
Stereonet Data from Terrestrial Laser Scanner Point Clouds
| Online Contents | 2009
Scan2BrIM: IFC Model Generation of Concrete Bridges from Point Clouds
| British Library Conference Proceedings | 2019