Traditional post-earthquake structural health monitoring (SHM) methods based on dedicated sensors lack scalability due to installation and maintenance demands, leaving most buildings unmonitored. This study investigates the use of existing in-building surveillance cameras to infer structural demand by tracking earthquake-induced building motion. The proposed methodology repurposes ceiling-mounted surveillance cameras to estimate the inter-story drift (IDR) which is directly correlated with structural damage using FEMA guidelines. Shake-table experiments spanning a wide range of excitation intensities and dominant frequencies demonstrate that off-the-shelf surveillance cameras can estimate displacement with accuracy similar to dedicated vision-based SHM setups. To establish operating limits, we quantify how temporal sampling (frame rate) and spatial sampling (video resolution) affect drift estimation accuracy. We also evaluate peak drift/IDR estimation accuracy and peak timing sensitivity under reduced temporal sampling. The results highlight the potential of widely available camera networks as a low-cost, scalable, and rapidly deployable sensing network for post-earthquake assessment.
Ahmed A. Alzughaibi (Fri,) studied this question.