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Title: Monitoring the earthquake response of full‐scale structures using UAV vision‐based techniques
Vision-based sensing, when utilized in conjunction with camera-equipped unmanned aerial vehicles (UAVs), has recently emerged as an effective sensing technique in a variety of civil engineering applications (e.g., construction monitoring, conditional assessment, and post-disaster reconnaissance). However, the use of these non-intrusive sensing techniques for extracting the dynamic response of structures has been restricted due to the perspective and scale distortions or image misalignments caused by the movement of the UAV and its on-board camera during flight operations. To overcome these limitations, a vision-based analysis methodology is proposed in the present study for extracting the dynamic response of structures using unmanned aerial vehicle (UAV) aerial videos. Importantly, geo-referenced targets were strategically placed on the structures and the background (stationary) region to enhance the robustness and accuracy related to image feature detection. Image processing and photogrammetric techniques are adopted in the analysis procedures first to recover the camera motion using the world-to-image correspondences of the background (stationary) targets and subsequently to extract the dynamic structural response by reprojecting the image feature of the (moving) targets attached to the structures to the world coordinates. The displacement tracking results are validated using the responses of two full-scale test structures measured by analog displacement sensors more » during a sequence of shake table tests. The high level of precision (less than 3 mm root-mean-square errors) of the vision-based structural displacement results demonstrates the effectiveness of the proposed UAV displacement tracking methodology. Additionally, the limitations and potential solutions associated with the proposed methodology for monitoring the dynamic responses of real structures are discussed. « less
Authors:
; ; ; ;
Award ID(s):
1663569
Publication Date:
NSF-PAR ID:
10341550
Journal Name:
Structural control health monitoring
Volume:
29
Issue:
1
ISSN:
1545-2255
Sponsoring Org:
National Science Foundation
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