Digital Twin Integration, Maintenance Decisions, and Smart Bridge Operations

Abstract

The digital twin paradigm represents a significant shift in civil infrastructure management, offering a dynamic cyber-physical connection to bridge structural health monitoring. Although the technical concepts of digital twins have been widely discussed, empirical evidence of their direct integration into operational maintenance decision-making remains limited. This paper examines this integration through a detailed case analysis of the Hanjiang River Smart Bridge, a major cable-stayed asset featuring a multi-layered digital twin system. We outline the architectural framework of the digital twin, focusing on how real-time sensor streams are integrated with a dynamically calibrated finite element model to provide predictive analytics. The operational evidence shows that integrating the digital twin into the decision-making workflow reduced the time to detect structural anomalies from over seventy-two hours to just over two hours, while decreasing the false alarm rate to approximately three percent. By substituting scheduled preventive repairs with demand-driven predictive interventions, the operating agency lowered scheduled maintenance costs by nearly thirty percent. These results highlight the potential of digital twins to transform infrastructure maintenance from reactive and schedule-based approaches to proactive, reliability-centered paradigms.