Digital twin-based structural health monitoring and measurements of dynamic characteristics in balanced cantilever bridge

Abstract

This study developed a digital twin (DT) and structural health monitoring (SHM) system for a balanced cantilever bridge, utilizing advanced measurement techniques to enhance accuracy. Vibration and dynamic strain measurements were obtained using accelerometers and piezo-resistive strain gauges, capturing low-magnitude dynamic strains during operational vibrations. 3D-LiDAR scanning and Ultrasonic Pulse Velocity (UPV) tests captured the bridge's as-is geometry and modulus of elasticity. The resulting detailed 3D point cloud model revealed the structure's true state and highlighted discrepancies between the as-designed and as-built conditions. Dynamic properties, including modal frequencies and shapes, were extracted from the strain and acceleration measurements, providing critical insights into the bridge's structural behavior. The neutral axis depth, indicating stress distribution and potential damage, was accurately determined. Good agreement between vibration measurement data and the as-is model results validated the reliability of the digital twin model. Dynamic strain patterns and neutral axis parameters showed strong correlation with model predictions, serving as sensitive indicators of local damage. The baseline digital twin model and measurement results establish a foundation for future bridge inspections and investigations. This study demonstrates the effectiveness of combining digital twin technology with field measurements for real-time monitoring and predictive maintenance, ensuring the sustainability and safety of the bridge infrastructure, thereby enhancing its overall resilience to operational and environmental stressors.