Abstract This paper introduces a novel application of remote sensing systems and digital twins to investigate the collapse potential of existing buildings. As the first multi-disciplinary team to approach the monitoring of existing structures from this perspective, we focused on the building’s performance after a sudden loss of its critical components. A field experiment was conducted for a six-story reinforced concrete (RC) building, distantly monitoring the structure’s performance using various camera placements (in-situ and remote cameras, cell phones, and drones) and LiDAR. To address damage from the loss of columns and floors, deep learning methods are employed for both damage detection and vibration measurement using the captured camera data. We created digital twins of the building before and after a column was removed, allowing us to accurately measure residual deflections of damaged beams and slabs. During the test, vibration signals were recorded by wireless accelerometers to verify the building’s dynamic response to impacts from the removal equipment, while remote cameras were placed to automatically survey these same vibrations. Non-linear structural analyses and acceleration measurements validated our observations from the remote sensing systems. This research offers practical solutions for remotely monitoring full-scale RC structures and evaluating their real performance using these advanced technologies, bridging a significant remote-monitoring gap for inaccessible buildings. These new insights will provide substantial benefits for engineers and researchers and meet their practical needs.
Bai et al. (Wed,) studied this question.