Structural health monitoring has gained increasing research interest, particularly due to the societal concerns tied to the aging of current civil structures and infrastructures. By managing datasets collected through a network of sensors deployed over monitored structures, (big) data analytics can be executed. Traditional inertial sensors, such as accelerometers or strain gauges, necessitate intricate cable arrangements and lead to high maintenance costs. Lately, there has been a growing interest in non-contact, vision-based approaches to tackle these aforementioned issues. Among these methods, digital image correlation (DIC) can furnish a representation of tracked displacements at various points of a structure, particularly if physically attached targets are employed. In this study, a video capturing the vibrations of a structure was analyzed, with a focus on specific points, such as structural nodes where damage could be initiated or whose responses could be impacted by the mentioned damage. Displacement time histories were acquired, and a blind source identification technique was adopted to delve into the data and assess structural health. The proposed methodology demonstrates its capacity to accurately extract the vibration frequencies and mode shapes of the structure, even when they change in time due to damage.
