Abstract
Digital Image Correlation (DIC) is widely used for remote and non-destructive structural health evaluation of infrastructure. Current DIC applications are limited to relatively small areas of structures and require the use of stationary stereo vision camera systems that are not easy to transfer and deploy in remote areas. The enclosed work describes the development and validation of an Unmanned Aircraft System (UAS, commonly known as drone) with an onboard stereo-vision system capable of acquiring, storing and transmitting images for analysis to obtain full-field, three-dimensional displacement and strain measurements. The UAS equipped with a StereoDIC system has been developed and tested in the lab. The drone system, named DroneDIC, autonomously hovers in front of a prestressed railroad tie under pressure and DIC data are collected. A stationary DIC system is used in parallel to collect data for the railroad tie. We compare the data to validate the readings from the DroneDIC system. We present the analysis of the results obtained by both systems. Our study shows that the results we obtain from the DroneDIC system are similar to the ones gathered from the stationary DIC system. This work serves as a proof of concept for the successful integration of DIC and drone technologies into the DroneDIC system. DroneDIC combines the high accuracy inspection capabilities of traditional stationary DIC systems with the mobility offered by drone platforms. This is a major step towards autonomous DIC inspection in portions of a structure where access is difficult via conventional methods.
Published Version
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