A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automated extraction of structural elements in steel girder bridges from laser point clouds
Abstract This study proposes a heuristic-based method to automate the semantic segmentation of laser point clouds collected from steel girder bridges and thus to facilitate the applications of laser scanning in bridge inspection and management. In the proposed method, domain knowledge on the geometric and topological constraints of steel girder bridges are utilized to recognize and extract individual instances of the main structural elements, which include the bridge deck, steel girders, cross-frames, piers and abutments. The effectiveness of the proposed method is validated using point clouds acquired by a terrestrial laser scanner and a UAV-based laser scanner. The results demonstrate that the proposed method on average can achieve a 98.3% element-level accuracy and a 96.1% point-level accuracy on terrestrial laser scanning data. For UAV-based laser scanning data, the average accuracy is reduced slightly to 96.0% in element-level and 93.3% in point-level.
Highlights Automated extraction of steel girder bridge structural elements from point clouds Structural elements include steel girders, bridge deck, cross-frames, substructures Object detection algorithms validated against terrestrial and UAV-based laser scanning data
Automated extraction of structural elements in steel girder bridges from laser point clouds
Abstract This study proposes a heuristic-based method to automate the semantic segmentation of laser point clouds collected from steel girder bridges and thus to facilitate the applications of laser scanning in bridge inspection and management. In the proposed method, domain knowledge on the geometric and topological constraints of steel girder bridges are utilized to recognize and extract individual instances of the main structural elements, which include the bridge deck, steel girders, cross-frames, piers and abutments. The effectiveness of the proposed method is validated using point clouds acquired by a terrestrial laser scanner and a UAV-based laser scanner. The results demonstrate that the proposed method on average can achieve a 98.3% element-level accuracy and a 96.1% point-level accuracy on terrestrial laser scanning data. For UAV-based laser scanning data, the average accuracy is reduced slightly to 96.0% in element-level and 93.3% in point-level.
Highlights Automated extraction of steel girder bridge structural elements from point clouds Structural elements include steel girders, bridge deck, cross-frames, substructures Object detection algorithms validated against terrestrial and UAV-based laser scanning data
Automated extraction of structural elements in steel girder bridges from laser point clouds
Yan, Yujie (author) / Hajjar, Jerome F. (author)
2021-01-18
Article (Journal)
Electronic Resource
English
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