Digital Twin and IoT Integration for Predictive Maintenance in Smart Civil Infrastructure

Abstract

Emerging technologies like Digital Twins (DT) and the Internet of Things (IoT) have had a significant impact on the rapid development of intelligent civil infrastructure. In order to proactively address structural and operational issues before disasters strike, predictive maintenance has emerged as a crucial component of infrastructure management. In order to support continuous, real-time monitoring, predictive analytics, and automated decision-making in civil infrastructure systems, this paper explores the synergistic combination of Digital Twin (DT) and Internet of Things (IoT). By gathering and synchronizing sensor data with dynamic virtual models, digital twins serve as sophisticated simulators that faithfully replicate the performance and behaviour of assets in the real world.

The Internet of Things (IoT) improves this ability by enabling continuous, high-frequency data collection from integrated sensors, creating a strong cyber-physical feedback loop. A conceptual framework is provided to demonstrate the comprehensive data flow, accompanied with a predictive maintenance flowchart that delineates the operating logic. The study indicates that the convergence of DT and IoT not only increases maintenance accuracy and timeliness but also helps to infrastructure resilience, safety, and long-term sustainability. Real-world applications, present obstacles, and emerging trends are examined to illustrate the practical importance and future direction of this technology paradigm.