Blind Identification of Very High Spatial-Resolution Vibration Modes of Structures From Video Camera Measurements Only

Abstract eng:
Modal analysis is essential for dynamic modeling and analysis of seismic excited structures. Experimental or operational modal analysis traditionally requires physically-attached wired or wireless sensors for vibration measurement of structures. The sensor instrumentation could result in mass-loading on lightweight structures, and is costly and time-consuming for large civil structures, especially for longterm applications (e.g., structural health monitoring and seismic dynamic analysis) that require significant maintenance and labors for cabling (wired) or energy supply (wireless). In addition, these sensors are discrete point-wise, providing low spatial sensing resolution that is hardly sufficient for larger structures. Non-contact optical methods such as scanning laser vibrometers provide high-resolution sensing capacity without the mass loading effect; however, they operate sequential measurement that requires considerable acquisition time. As an alternative non-contact method, digital video cameras are relatively low-cost, agile, and provide high spatial resolution, simultaneous, measurements. Combined with vision based algorithms (e.g., image correlation or template matching, optical flow, etc.), video camera based measurements have been successfully used for experimental and operational vibration measurement and modal analysis, such as the digital image correlation (DIC) and the point-tracking techniques. However, they typically require speckle pattern or high-contrast markers instrumented on the surface of structures, which raises the instrumentation issue when the measurement area is large or inaccessible. This work explores advanced computer vision and video processing algorithms to develop a novel video measurement and vision based output-only modal analysis method that removes the need of structural surface preparation in existing vision based methods. By manipulating the motion encoded in the video measurements only using multi-scale image decomposition and unsupervised machine learning techniques, the proposed method efficiently and accurately extract modal frequencies, very high spatial (pixel) resolution mode shapes, and damping ratios of the structure. The method is validated by laboratory experiments on bench-scale structures including a building structure and a cantilever beam. Video demos of these experimental results are on http://www.lanl.gov/projects/national-security-educationcenter/engineering/research-projects/blind-modal-id.php.

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