Abstract
Aerospace, civil, energy, and mechanical engineering structures continue to be used despite reaching their design lifetime. Developing sensing and data analytics to assess the structural condition of the targeted systems is crucial. Traditional contact-based techniques may produce inconsistent results and are labor-intensive to be considered a valid alternative for monitoring large-scale structures such as bridges, large buildings, and wind turbines. Advancements in image-processing algorithms made techniques such as three-dimensional digital image correlation (3D-DIC), infrared thermography (IRT), motion magnification (MM), and structure from motion (SfM) appealing tools for structural health monitoring and non-destructive testing. Besides, as those techniques are implemented within unmanned aerial vehicles (UAVs), the measurement process is expedited while reducing interference with the targeted structure. This paper summarizes the research experience performed at the University of Massachusetts Lowell. The results of these activities show that the combination of autonomous flight with 3D-DIC, IRT, and SfM can provide precious insights into the structural conditions of the inspected systems while reducing downtime and costs. The study includes future research directions to make those approaches suitable for real-world applications.
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Sabato, A., Niezrecki, C., Dabetwar, S., Kulkarni, N.N., Bottalico, F., Nieduzak, T. (2023). Advancements in Structural Health Monitoring Using Combined Computer-Vision and Unmanned Aerial Vehicles Approaches. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2022. Lecture Notes in Civil Engineering, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-031-07258-1_43
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