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Many civil engineering infrastructures including bridges and buildings have been constructed several years ago. They are facing increasing challenges due to climate change and exposed to various external loads such as earthquakes. Extensive research works have been carried out to enable structural and health monitoring (SHM). Modal identification is a crucial part of SHM. Conventionally, it has been accomplished by accelerometers mounted on the structure. Their use may be extremely accurate. However, only a few sensors is usually set up on the structure which may limit modal identification and SHM performance. Vision-based techniques gained increased acceptance as cheaper and easier solution to perform long-range vibration measurements. Video cameras offer the capacity to collect high-spatial resolution data from a distant scene of interest. Various image processing techniques have been developed to extract motion from subtle time changes in the image brightness. Commonly, these motions are then used for modal identification. Instead, this paper explores the use of pixels intensity variations only to perform SHM. Our new approach can be divided in two parallel steps. The first one deals with processing video image flow to effectively selecting the 'active pixels' or pixels belonging to the structure edges. The pixel selection process relies on the power spectral density and an energy criterion. Second, stochastic subspace identification based method that takes into account uncertainty bounds for modal parameters is adopted. So, vision modal parameters from the vision data are recovered. Comparison of identification results with motion signals is finally studied. For that, we have assessed a methodology for extracting motions based on cross-correlation analysis and Taylor-based subpixel refinement. Experiments in a laboratory setting on a cantilever beam are performed to verify the approach. The beam was imaged by a high speed camera and excited on a shaking table.
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