A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Vision-based modal analysis of built environment structures with multiple drones
https://orcid.org/0000-0002-9957-4535
https://orcid.org/0000-0002-7624-5210
https://orcid.org/0000-0002-0109-6484
https://orcid.org/0000-0002-9353-5439
https://orcid.org/0000-0003-3320-9030
Abstract Unmanned Aerial Vehicles are employed for vision-based modal analysis of civil infrastructure, as they overcome range limitations of fixed cameras and measure the oscillations of a structure up close. Nevertheless, their potential is not fully exploited: they are often piloted manually and one at a time, though one drone is unable to capture high resolution displacement of a whole structure. An approach is explored here, employing multiple drones simultaneously to estimate natural frequencies and modal shapes of a structure, by synchronizing their measurement. The ability of the method to detect modal parameter variations is assessed, such that it can identify anomalies in the structure. Procedures are applied to a test structure, yielding maximum natural frequency estimation errors of 0.2% with respect to accelerometers. The results suggest the accuracy of the approach is high enough to warrant further development and support autonomous, multi-drone applications to the inspection of the built environment.
Highlights Multiple drone-mounted cameras can capture the oscillation of a structure at once. Cross-correlation synchronizes footage from several airborne sources. High accuracy in displacement tracking and mode estimation does not require markers.
Vision-based modal analysis of built environment structures with multiple drones
https://orcid.org/0000-0002-9957-4535
https://orcid.org/0000-0002-7624-5210
https://orcid.org/0000-0002-0109-6484
https://orcid.org/0000-0002-9353-5439
https://orcid.org/0000-0003-3320-9030
Abstract Unmanned Aerial Vehicles are employed for vision-based modal analysis of civil infrastructure, as they overcome range limitations of fixed cameras and measure the oscillations of a structure up close. Nevertheless, their potential is not fully exploited: they are often piloted manually and one at a time, though one drone is unable to capture high resolution displacement of a whole structure. An approach is explored here, employing multiple drones simultaneously to estimate natural frequencies and modal shapes of a structure, by synchronizing their measurement. The ability of the method to detect modal parameter variations is assessed, such that it can identify anomalies in the structure. Procedures are applied to a test structure, yielding maximum natural frequency estimation errors of 0.2% with respect to accelerometers. The results suggest the accuracy of the approach is high enough to warrant further development and support autonomous, multi-drone applications to the inspection of the built environment.
Highlights Multiple drone-mounted cameras can capture the oscillation of a structure at once. Cross-correlation synchronizes footage from several airborne sources. High accuracy in displacement tracking and mode estimation does not require markers.
Vision-based modal analysis of built environment structures with multiple drones
https://orcid.org/0000-0002-9957-4535
https://orcid.org/0000-0002-7624-5210
https://orcid.org/0000-0002-0109-6484
https://orcid.org/0000-0002-9353-5439
https://orcid.org/0000-0003-3320-9030
Abstract Unmanned Aerial Vehicles are employed for vision-based modal analysis of civil infrastructure, as they overcome range limitations of fixed cameras and measure the oscillations of a structure up close. Nevertheless, their potential is not fully exploited: they are often piloted manually and one at a time, though one drone is unable to capture high resolution displacement of a whole structure. An approach is explored here, employing multiple drones simultaneously to estimate natural frequencies and modal shapes of a structure, by synchronizing their measurement. The ability of the method to detect modal parameter variations is assessed, such that it can identify anomalies in the structure. Procedures are applied to a test structure, yielding maximum natural frequency estimation errors of 0.2% with respect to accelerometers. The results suggest the accuracy of the approach is high enough to warrant further development and support autonomous, multi-drone applications to the inspection of the built environment.
Highlights Multiple drone-mounted cameras can capture the oscillation of a structure at once. Cross-correlation synchronizes footage from several airborne sources. High accuracy in displacement tracking and mode estimation does not require markers.
Vision-based modal analysis of built environment structures with multiple drones
Bolognini, Michele (author) / Izzo, Giovanni (author) / Marchisotti, Daniele (author) / Fagiano, Lorenzo (author) / Limongelli, Maria Pina (author) / Zappa, Emanuele (author)
2022-08-24
Article (Journal)
Electronic Resource
English
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