Integration of IoT and Digital Twin for Intelligent Management of Urban Underground Pipe Galleries in Smart Cities

Abstract

The increasing complexity of urban infrastructure demands innovative solutions for effective management. This paper proposes an intelligent management system for urban underground pipe galleries, integrating Internet of Things (IoT) sensors and digital twin technologies to enhance operational efficiency in smart cities. The system enables real-time monitoring, predictive maintenance, and optimization of public services by creating a virtual replica of the underground infrastructure. The methodology involves deploying IoT sensors for continuous data collection and feeding this information into a digital twin model that simulates and predicts potential failures and maintenance needs, as well as measuring flow rate and temperature. This allows for proactive decision-making, minimizing downtime, and reducing maintenance costs. Experimental results demonstrate the effectiveness of the proposed system in optimizing urban infrastructure management. The system achieved a 92% prediction accuracy in identifying potential failures, enabling proactive maintenance, and reducing service disruptions by 40%. Predictive analytics minimized maintenance costs by 35%, while resource optimization improved task prioritization, significantly enhancing operational efficiency. These results highlight the transformative potential of integrating IoT and digital twin technologies for smarter and more sustainable city management. This research underscores the transformative potential of integrating advanced technologies like IoT and digital twin models in managing complex urban systems, with significant implications for smart city development and sustainability.