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On the Use of Unmanned Aerial Vehicles in Nondestructive Evaluation of Civil Infrastructure

Once America’s proudest achievement, many of the nation’s infrastructure systems today are outdated and overwhelmed, requiring continuous investment for expansion, upgrade, and mainte-nance to meet increasing demands. In this context, a main challenge today is to rebuild, maintain, modernize, and expand infrastructure systems under constrained funding. A large part of this effort is the assessment of the current state of structures to determine the actions that are required for preservation or replacement. Visual inspection is the most reliable and widely used method for monitoring a structure; however, it is time-consuming, expensive, and produces qualita-tive results. In contrast, the research presented in this article provides evidence that rapid and remote sensing systems implemented on an unmanned aerial vehicle (UAV) platform could contribute to the next generation of civil infrastruc-ture assessment. These systems can provide access to difficult-to-reach areas of the structure and can carry multiple types of remote-sensing systems such as cameras. Furthermore, image processing and computer vision algorithms have been developed to identify locations of potential damage, such as cracks, delaminations, or corrosion. Within the scope of developing a general strategy for UAV infrastructure assessment, several activities are presented in this article. Specifically, aerial image data was captured with color and infrared cameras and was post processed to identify surface and subsurface damage on a simulated bridge deck with internal discontinuities. Lab scale tests with static images and images captured during flight are presented to explore automated damage identification. In addition, optical metrology was used to calculate deformation of a lab-scale structure under static loading. The information was then compared to a preexisting finite element model of the structure and validated with traditional displacement sensors.


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