Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
LiDAR-RGB Data Fusion for Four-Dimensional UAV-Based Monitoring of Reinforced Concrete Bridge Construction: Case Study of the Fern Hollow Bridge Reconstruction
https://orcid.org/0000-0002-8938-6506
https://orcid.org/0000-0003-4289-5909
The complexity of large-scale bridge construction necessitates continuous monitoring to perform quality assurance and control, both of which have a great influence on structural performance and durability. Conventional approaches to monitoring bridge construction heavily rely on human inspection and expert judgment performed on-site by trained technicians. However, these often lack the required spatial and/or temporal resolution in the obtainable data, and are inefficient and potentially dangerous for the operators. In this context, the development of automated systems and advanced sensing solutions brings the opportunity to enhance accuracy and efficiency, minimize human error, and mitigate safety risks. Laser scanning technology, known for its precision in target representations, combined with uncrewed aerial vehicles (UAVs) serving as multifunctional sensor platforms, are a promising means for safe and efficient monitoring of bridge construction. This manuscript presents an experimental case study focused on the application of UAV-based LiDAR-RGB data fusion for bridge reconstructions, to assess their effectiveness in monitoring bridge construction. Four-dimensional models of the bridge were constructed based on data collected via LiDAR and photogrammetry. The models generated with the two approaches then were compared formally with the target design values to ascertain their accuracy. The results demonstrate that construction monitoring strategies that only rely on photogrammetric information can provide results with insufficient accuracy, particularly in terms of the integrity of the three-dimensional (3D) reconstructions and the accuracy of the geometry and slope of the bridge superstructure. In contrast, LiDAR-RGB data fusion can provide precise and comprehensive spatial information on bridge construction, such as global and local geometry of the bridge and information on the positioning of different structural elements in the deck, which can significantly benefit construction management and construction quality assessment and control.
LiDAR-RGB Data Fusion for Four-Dimensional UAV-Based Monitoring of Reinforced Concrete Bridge Construction: Case Study of the Fern Hollow Bridge Reconstruction
https://orcid.org/0000-0002-8938-6506
https://orcid.org/0000-0003-4289-5909
The complexity of large-scale bridge construction necessitates continuous monitoring to perform quality assurance and control, both of which have a great influence on structural performance and durability. Conventional approaches to monitoring bridge construction heavily rely on human inspection and expert judgment performed on-site by trained technicians. However, these often lack the required spatial and/or temporal resolution in the obtainable data, and are inefficient and potentially dangerous for the operators. In this context, the development of automated systems and advanced sensing solutions brings the opportunity to enhance accuracy and efficiency, minimize human error, and mitigate safety risks. Laser scanning technology, known for its precision in target representations, combined with uncrewed aerial vehicles (UAVs) serving as multifunctional sensor platforms, are a promising means for safe and efficient monitoring of bridge construction. This manuscript presents an experimental case study focused on the application of UAV-based LiDAR-RGB data fusion for bridge reconstructions, to assess their effectiveness in monitoring bridge construction. Four-dimensional models of the bridge were constructed based on data collected via LiDAR and photogrammetry. The models generated with the two approaches then were compared formally with the target design values to ascertain their accuracy. The results demonstrate that construction monitoring strategies that only rely on photogrammetric information can provide results with insufficient accuracy, particularly in terms of the integrity of the three-dimensional (3D) reconstructions and the accuracy of the geometry and slope of the bridge superstructure. In contrast, LiDAR-RGB data fusion can provide precise and comprehensive spatial information on bridge construction, such as global and local geometry of the bridge and information on the positioning of different structural elements in the deck, which can significantly benefit construction management and construction quality assessment and control.
LiDAR-RGB Data Fusion for Four-Dimensional UAV-Based Monitoring of Reinforced Concrete Bridge Construction: Case Study of the Fern Hollow Bridge Reconstruction
https://orcid.org/0000-0002-8938-6506
https://orcid.org/0000-0003-4289-5909
The complexity of large-scale bridge construction necessitates continuous monitoring to perform quality assurance and control, both of which have a great influence on structural performance and durability. Conventional approaches to monitoring bridge construction heavily rely on human inspection and expert judgment performed on-site by trained technicians. However, these often lack the required spatial and/or temporal resolution in the obtainable data, and are inefficient and potentially dangerous for the operators. In this context, the development of automated systems and advanced sensing solutions brings the opportunity to enhance accuracy and efficiency, minimize human error, and mitigate safety risks. Laser scanning technology, known for its precision in target representations, combined with uncrewed aerial vehicles (UAVs) serving as multifunctional sensor platforms, are a promising means for safe and efficient monitoring of bridge construction. This manuscript presents an experimental case study focused on the application of UAV-based LiDAR-RGB data fusion for bridge reconstructions, to assess their effectiveness in monitoring bridge construction. Four-dimensional models of the bridge were constructed based on data collected via LiDAR and photogrammetry. The models generated with the two approaches then were compared formally with the target design values to ascertain their accuracy. The results demonstrate that construction monitoring strategies that only rely on photogrammetric information can provide results with insufficient accuracy, particularly in terms of the integrity of the three-dimensional (3D) reconstructions and the accuracy of the geometry and slope of the bridge superstructure. In contrast, LiDAR-RGB data fusion can provide precise and comprehensive spatial information on bridge construction, such as global and local geometry of the bridge and information on the positioning of different structural elements in the deck, which can significantly benefit construction management and construction quality assessment and control.
LiDAR-RGB Data Fusion for Four-Dimensional UAV-Based Monitoring of Reinforced Concrete Bridge Construction: Case Study of the Fern Hollow Bridge Reconstruction
J. Constr. Eng. Manage.
Zhu, Yingbo (Autor:in) / Brigham, John C. (Autor:in) / Fascetti, Alessandro (Autor:in)
01.01.2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
The fern hollow highway arch bridge
Engineering Index Backfile | 1902
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IBC 23-55: Fern Hollow Bridge Emergency Replacement Design-Build
| TIBKAT | 2023
DESIGN, CONSTRUCTION, AND MONITORING OF FIBER REINFORCED POLYMER REINFORCED CONCRETE BRIDGE DECK
| British Library Conference Proceedings | 1999