Author(s): Vaibhav Srivastava and DI Narkhede

Abstract: 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.

DOI: 10.22271/27078388.2025.v6.i1a.43

Pages: 91-100 | Views: 138 | Downloads: 91

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