An integrated digital twin and sensor-based framework for advanced structural health monitoring of bridges

As global bridge infrastructure faces increasing demands of load, aging, and climate stress, the need for advanced Structural Health Monitoring (SHM) systems becomes critical. This paper presents a comprehensive framework integrating digital twin technology, real-time sensor networks, and intelligent visualization dashboards for continuous assessment and decision-making in bridge monitoring. Leveraging Finite Element Analysis (FEA) tools such as ANSYS and sensor types including strain gauges, accelerometers, and displacement transducers, the system constructs a live digital twin of bridge structures. Dashboards are developed for anomaly detection, visual alerts, and trend forecasting. The study includes simulation-based stress analysis of Bailey bridges, implementation of sensor-based SHM, and structural status visualization. This multi-layered approach allows both engineers and decision-makers to transition from reactive to predictive maintenance strategies. Results demonstrate the potential of the proposed framework to significantly reduce inspection costs and prevent structural failures.