Digital Twinning Framework for Advanced Remote Dam Monitoring using Internet-of-Things

This study presents a digital twin (DT) framework that leverages Internet-of-Things (IoT) technology, providing real-time SHM and remote visualization of public infrastructure. The proposed system delivers an economical and scalable DT solution that integrates drone-enabled modeling, LoRaWAN-based microstrain sensing, and a cloud-based visualization platform. A high-resolution 3D building information model (BIM) of a floodway dam facility is created using photogrammetry and LiDAR, with sensor nodes connected to the ThingsBoard dashboard interface. The system architecture supports data-driven decision-making through automated data collection, visualization, and threshold-based alerts for microstrain levels. The performance and scalability of the proposed system are validated through lab-scale and full-scale deployments. The successful demonstration of system performance under field conditions, drone-based modeling, and real-time data stream visualization is shown using a full-scale floodway dam facility. The developed DT framework holds potential for SHM application across diverse infrastructure assets, and can be extended towards automated predictive maintenance.