Digital twin for large span cable-stayed bridges, integrating inspection and monitoring data with automatic modeling

To enhance the efficiency and intelligence of bridge operation and maintenance (O&M), this study proposes and validates a digital twin (DT) framework for long-span cable-stayed bridges, in which O&M data are integrated into bridge information modeling (BrIM) to realize comprehensive digital management. The main contributions are as follows: (1) A DT framework is established based on automated geometric modeling and the deep integration of inspection and monitoring data, enabling the synchronization of multi-source physical data with the digital model; (2) An efficient bridge modeling method is developed using Dynamo visual programming to achieve parameterized modeling. Compared with manual modeling, the proposed method reduces modeling time from approximately 240 to 180 min while allowing flexible modification of model parameters; (3) To address the insufficient integration of inspection/monitoring information with BrIM digitalization, a dynamic correlation and visualization method for bridge inspection and monitoring data is introduced, enabling simultaneous fusion of inspection and monitoring data. For inspection data fusion, the processing time is approximately 2 min, with dynamic updating supported; for monitoring data fusion, the processing time is shortened from ≥10 min to approximately 1 min, while editable data are maintained, demonstrating significant improvements in both efficiency and flexibility. Finally, the feasibility and effectiveness of the proposed framework are validated through a case study of the Second Nanjing Yangtze River Bridge. The proposed modeling and data fusion approach optimizes the workflow for complex bridge structures, reduces human intervention, and provides a reference for intelligent O&M of long-span bridges.