Digital Twin–Based Structural Health Monitoring and Remaining Useful Life Prediction for Aging Urban Bridges

Abstract

Urban bridge infrastructure is currently facing an unprecedented crisis of accelerated structural degradation due to intensified heavy vehicle traffic and long-term environmental weathering. Traditional manual inspection regimes are increasingly inadequate, as they are inherently subjective and fail to provide the continuous, real-time data required for modern safety assessments. This research addresses this critical gap by developing an integrated predictive bridge maintenance system using Digital Twin technology and real-time IoT sensor networks. The methodology employs a Design and Development Research (DDR) framework, where high-frequency telemetry from strain gauges and accelerometers is synchronized with a parametric Level of Development (LOD) 400 3D model. The novelty of this research lies in the direct embedding of Structural Health Index (HI) and Remaining Useful Life (RUL) mathematical formulations into an interactive 3D virtual environment, effectively bridging the gap between raw data and tactical decision-making. Experimental results demonstrate high algorithmic precision, achieving a Root Mean Squared Error (RMSE) of less than 0.032 across various traffic scenarios, with the system effectively filtering thermal noise to maintain a stable HI. Furthermore, the cyber-physical framework achieves a total long-term maintenance cost efficiency of 77.97% compared to conventional periodic inspections. These findings demonstrate that the proposed digital twin approach provides a robust, automated solution that significantly enhances structural safety, reduces economic losses from road closures, and facilitates a transition from reactive to preventive asset management in aging metropolitan transportation networks.