A demonstration of a digital twin framework for structural health monitoring: application to bridge infrastructures

This study introduces a novel, multi-layered Digital Twin (DT) framework designed to enhance the resilience of ageing bridge infrastructure through real-time structural health monitoring (SHM) and data-driven decision support. The proposed framework integrates physics-based Finite Element Modelling (FEM), drone-based photogrammetry, and wireless sensor networks to construct a dynamic digital representation of the physical asset. By continuously synchronising sensor data with virtual models, the system establishes a foundation for predictive maintenance and lifecycle optimisation. Key innovations include a modular architecture that supports the seamless integration of diverse data sources, a closed-loop feedback mechanism for iterative model updating, and functionality for real-time anomaly detection. The proposed system supports proactive monitoring by enabling dynamic condition tracking, structural behaviour analysis, and long-term trend forecasting. The framework has been demonstrated on an operational railway truss bridge, where live vibration and environmental data were used to calibrate and validate the DT in a real-world setting. The results underscore the system's potential as a robust and scalable monitoring solution for historically significant and ageing transport assets. This work addresses critical limitations of conventional SHM approaches by offering a unified, data-centric strategy for infrastructure management. Beyond operational awareness, the proposed DT platform provides a strategic pathway toward more intelligent, more sustainable infrastructure systems prioritising resilience, informed maintenance planning, and future adaptability.