Abstract
Background: Aging bridges worldwide face increasing risks of structural degradation, necessitating advanced real-time monitoring systems. Digital twins and Internet of Things (IoT) technologies offer promising solutions for continuous structural health monitoring (SHM). However, their integrated application for aging bridges remains underexplored. Methods: This study proposes a novel framework integrating digital twins with IoT-based sensor networks for real-time SHM of aging bridges. The framework includes (i) a semantic digital twin model incorporating bridge geometry, material properties, and historical data; (ii) wireless IoT sensors (accelerometers, strain gauges, temperature sensors) for continuous data acquisition; (iii) cloud-based data fusion and machine learning algorithms for damage detection, localization, and remaining life estimation. A case study on a 50-year-old steel truss bridge is used to validate the framework. Results: The integrated system achieved 95% accuracy in detecting simulated fatigue cracks and 90% accuracy in localizing damage within 0.5 m. Real-time data streaming latency was less than 2 seconds, and the digital twin updated every 10 minutes. Compared to traditional periodic inspection, the proposed approach reduced maintenance costs by 30% and extended bridge service life by an estimated 8 years. Conclusions: The integration of digital twins and IoT enables proactive, data-driven maintenance of aging bridges, improving safety and cost-efficiency. Future work should address scalability, cybersecurity, and standardization.
Keywords
Digital Twin, Internet of Things, Structural Health Monitoring, Aging Bridges, Real-Time Monitoring, Sensor Networks, Machine Learning, Predictive Maintenance