Computer vision-based substructure isolation method for localized damage identification

In recent years, vision-based structural damage identification techniques have garnered significant attention due to their simplicity and cost-effectiveness. Nevertheless, dynamic response-based vision methods face challenges related to the limitations of the field of view (FOV), i.e., the extent of the FOV is often sacrificed in order to ensure sufficient monitoring accuracy, which reduces the amount of data available for structural health monitoring (SHM). This study presents a solution to this problem by employing the substructure isolation method (SIM), which focuses on local vibrations of the structure and enables local damage identification by isolating the area of interest. Additionally, a method combining kernelized correlation filter (KCF) with sub-pixel template matching is used to extract vibration information recorded by visual sensor. This strategy balances both the efficiency and accuracy of displacement extraction. In numerical simulations, the performance of the SIM based on acceleration response and displacement response is compared, demonstrating the potential compatibility of visual sensors with the SIM. Finally, the effectiveness of the proposed vision-based local damage identification method is validated through vibration testing of a shear frame model.