Publication | Closed Access
Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles
166
Citations
30
References
2019
Year
EngineeringUnmanned VehicleStructural EngineeringStructural IdentificationModal AnalysisImage AnalysisUnmanned SystemDrone SurveyingSystems EngineeringStructural DynamicVision-based Modal SurveyUnmanned Aerial VehiclesStructural VibrationDynamic Structural DisplacementsMachine VisionSurveyingStructural Health MonitoringComputer VisionComputer Vision TechniquesAerial RoboticsAerospace EngineeringCivil EngineeringStructural AnalysisRemote SensingStructural MechanicsUnmanned Aerial SystemsAir Vehicle System
Computer vision techniques for extracting dynamic structural displacements from videos are gaining acceptance for system identification and structural health monitoring, yet applying video‑based modal analysis to full‑scale civil infrastructure remains limited due to resolution constraints. This study presents a new UAV‑based approach to extract frequencies and mode shapes of full‑scale civil infrastructure from video. The approach overcomes key challenges of vision‑based modal analysis and is evaluated on a laboratory shear‑building model and a full‑scale pedestrian suspension bridge. Results demonstrate the efficacy of the proposed approach.
Computer vision techniques for extracting dynamic structural displacements from videos are gaining increasing acceptance for the purposes of system identification and structural health monitoring. However, the application of video-based techniques for modal analysis of full-scale civil infrastructure has been limited, because obtaining measurements of all points on a large structure with a single video frame with sufficient resolution is seldom feasible. In this study, a new approach is presented to facilitate the extraction of frequencies and mode shapes of full-scale civil infrastructure from video obtained by an unmanned aerial vehicle (UAV). This approach addresses directly a number of difficulties associated with modal analysis of full-scale infrastructure using vision-based methods. The proposed approach is evaluated using a story-story shear-building model excited on a shaking table in a laboratory environment, and on a full-scale pedestrian suspension bridge. The results demonstrate the efficacy of the proposed approach.
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