Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Non-contact intelligent detection technology for railway arch bridge performance degradation based on UAV Image recognition
Bridges are crucial components of high-speed railway projects, and their structural integrity significantly impacts the operational safety of high-speed railways. This paper introduces a non-contact intelligent detection technology for assessing the deterioration of high-speed railway bridges using unmanned aerial vehicle (UAV) image recognition. The methodology involves collecting image data using a UAV and digital camera and processing them technically to generate consistent point-cloud data. Subsequently, these data are integrated into a unified point-cloud model through point-cloud alignment. Finally, a refined three-dimensional (3D) model of a high-speed railway bridge was developed by fusing heterogeneous data through live 3D reconstruction. The method has the advantages of high detection speed and fewer personnel requirements; this technology can be used for daily monitoring of the technical basis and can arrange a small number of personnel to complete the daily inspection. The empirical results demonstrate that this inspection method is not constrained by skylight points and provides a real-time and highly efficient reflection of the conditions of the bridge. The recognition accuracy and image acquisition range satisfy the inspection requirements for the operation and maintenance of high-speed railway bridges.
Non-contact intelligent detection technology for railway arch bridge performance degradation based on UAV Image recognition
Bridges are crucial components of high-speed railway projects, and their structural integrity significantly impacts the operational safety of high-speed railways. This paper introduces a non-contact intelligent detection technology for assessing the deterioration of high-speed railway bridges using unmanned aerial vehicle (UAV) image recognition. The methodology involves collecting image data using a UAV and digital camera and processing them technically to generate consistent point-cloud data. Subsequently, these data are integrated into a unified point-cloud model through point-cloud alignment. Finally, a refined three-dimensional (3D) model of a high-speed railway bridge was developed by fusing heterogeneous data through live 3D reconstruction. The method has the advantages of high detection speed and fewer personnel requirements; this technology can be used for daily monitoring of the technical basis and can arrange a small number of personnel to complete the daily inspection. The empirical results demonstrate that this inspection method is not constrained by skylight points and provides a real-time and highly efficient reflection of the conditions of the bridge. The recognition accuracy and image acquisition range satisfy the inspection requirements for the operation and maintenance of high-speed railway bridges.
Non-contact intelligent detection technology for railway arch bridge performance degradation based on UAV Image recognition
Shifu Wang (Autor:in) / Shaopeng Yang (Autor:in) / Qi Wang (Autor:in) / Lingfeng Luo (Autor:in) / Feng Wang (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Performance-Based Seismic Design of Long-Span Railway Arch Bridge
| Trans Tech Publications | 2012
Concrete-filled Arch Railway Bridge, China
| British Library Online Contents | 2002
The Rockville stone arch railway bridge
Engineering Index Backfile | 1901
Steel Tied Arch Railway Bridge, Louvain, Belgium
| British Library Online Contents | 2003