Details

Autor(en) / Beteiligte

• Truong-Hong, Linh
• Lindenbergh, Roderik

Titel

Automatically extracting surfaces of reinforced concrete bridges from terrestrial laser scanning point clouds

Ist Teil von

• Automation in construction, 2022-03, Vol.135, p.104127, Article 104127

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• 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. •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