Background: Aging civil infrastructure demands advanced monitoring solutions. Traditional structural health monitoring (SHM) systems lack real-time adaptive capabilities, creating critical gaps in safety and maintenance management.
Objective: To develop and evaluate an integrated Digital Twin (DT) framework for real-time SHM that enhances monitoring accuracy, enables predictive maintenance, and improves structural reliability assessments.
Methods: A multi-sensor DT architecture was deployed on a reinforced concrete bridge, integrating accelerometers, strain gauges, and piezoelectric sensors with a physics-based finite element model and machine learning anomaly detection over an 18-month observation period.
Results: The DT framework achieved 97.3% monitoring accuracy, reduced data latency to 38 ms, lowered false alarm rates by 78%, and cut maintenance costs by 34% compared to conventional SHM approaches.
Conclusion: Digital twin integration represents a transformative advancement in structural health monitoring. The framework's real-time data fusion and predictive analytics substantially improve safety outcomes, reduce operational costs, and extend structural service life.