A Digital Twin Framework for Real-Time Monitoring and Full-Field Analysis of Bridges

Digital twin technology has been recognized as a transformative approach for civil structural health monitoring (SHM), but real-time structural analysis and result visualization remain challenging because a large-scale civil structure contains thousands of components, making real-time finite element analysis very time-consuming. This study proposes an integrated digital twin framework to address the limitations of traditional finite element methods for real-time analysis through a synergistic combination of SHM and advanced machine learning techniques. A footbridge with an SHM system is used as the testbed. A novel transformer-based surrogate model, named SAE-Transformer, is developed for real-time calculation of the three-dimensional full-field temperature distribution and thermal stress of the entire bridge. One year of bridge data was adopted for training the SAE-Transformer. Equipped with a sequence-aware embedding module and a SwiGLU activation layer, the SAE-Transformer model reduces the temperature prediction error by 39% and the stress estimation error by 45% compared to the state-of-the-art deep learning models. Moreover, the modal superposition technique is adopted to recover the full-field vibration of the bridge using monitoring data from sparse sensors, enabling real-time visualization at 50 frames per second. The implementation demonstrates that the proposed machine learning-empowered technique realizes real-time full-field analysis of large-scale civil structures for the first time.