Rapid and accurate building assessment after an earthquake remains a persistent challenge for engineers in seismic areas. Manual inspections are often slow, hampered by road blockages, damaged utilities, and ongoing aftershock risks. This study presents the design, field deployment, and validation of a scalable IoT-based structural health monitoring (SHM) platform developed for real-time post-earthquake decision support. The system integrates multi-axis MEMS accelerometers and inclinometers, supported by on-site signal processing and a cloud-based analytics backend. A comprehensive damage assessment algorithm evaluates parameters such as frequency changes, inter-storey drift, roof displacement, torsional irregularities, and permanent tilt by combining multiple indicators rather than relying on a single measure. The system was deployed in a 22-storey reinforced concrete office building and continuously recorded several seismic events, including a Mw 6.2 earthquake. The results showed that drift values remained within code-defined limits and no permanent deformation occurred. Event-driven edge processing and optimized data management confirmed the system’s scalability for large building portfolios. The findings indicate that IoT-based SHM platforms can complement conventional inspections by providing rapid, data-driven screening to support resilient urban recovery.
Ergen et al. (Sat,) studied this question.