# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 5 Bridge Digital Twin Modeling Using Multi-Source Heterogeneous Data
### Establishing Bridge Digital Twin for Smart O&M  
After completing spatial modeling, temporal modeling, and expert knowledge modeling, the next crucial step is integrating these models to finalize the DTM of the bridge. This process consolidates different models, where spatial and temporal models serve as data inputs, and the expert knowledge model functions as an evaluation system that assesses the structural health of the bridge. The 2D image data primarily capture visual information and are often used to monitor surface damage such as corrosion and cracks. The 3D point cloud data provide accurate spatial information, primarily used to monitor overall structural deformations and localized displacements. Sensor data capture the physical state of the bridge, encompassing parameters like stress, strain, and temperature. In this process, the expert knowledge model, functioning as a rule-based system, can identify areas of concentrated stress, allowing for further evaluation of critical regions. By combining data with expert knowledge for comprehensive analysis, integrated monitoring of the bridge's health across spatial and temporal dimensions can be achieved. For example, surface damage detected by 2D images can be precisely located using 3D point cloud data, while sensor data at that location can supplement the physical information. Similarly, the expert knowledge model can further analyze areas of concentrated stress identified in the sensor data, providing more detailed assessments. Furthermore, the temporal model and expert knowledge model can predict the future condition of the bridge, enabling early warnings. By applying LSTM deep learning models to the sensor time series data, trends in stress changes within the bridge can be forecasted [50]. For instance, the predicted development of stress concentrations combined with changes in 2D image and 3D point cloud data can help predict the progression of cracks or deformations. When these predictions are further evaluated using the expert knowledge model, risk areas can be identified earlier, enabling timely maintenance and repairs, thereby minimizing the risk of sudden structural failures. Additionally, the three models can be used for cross-validation to enhance the accuracy and reliability of bridge monitoring. For example, abnormal deformations detected by 3D point cloud data can be validated by checking the corresponding strain data from sensors or referring to historical cases and experiential knowledge in the expert knowledge model. This cross-validation strengthens the robustness of the monitoring system and reduces the likelihood of errors from any single data source. In conclusion, the data and expert knowledge-driven DTM of the bridge provides comprehensive support for SHM. This approach not only detects surface damage but also tracks geometric deformations and physical stress conditions. Furthermore, through time series analysis, it predicts future trends and performs grading assessments using the expert knowledge model. These technologies enable more effective assessments of the bridge's health and improve the accuracy of fault detection, ultimately enhancing the safety and reliability of the bridge. This study evaluated the proposed method through its application to the Hedong Bridge, located in Anhui Province, China. The Hedong Bridge, spanning 612 m, is a prestressed concrete continuous girder bridge, selected for its structural significance and suitability for validating advanced methodologies. The experimental process commenced with the acquisition of 2D image data using UAVs equipped with high-definition cameras, which captured detailed surface imagery along predefined flight paths. Concurrently, UAVs with laser scanners collected 3D point cloud data, providing precise geometric information. In addition, sensor arrays--comprising strain gauges, accelerometers, temperature sensors, and GNSS positioning devices--were strategically deployed at critical locations on the bridge to record time series data of its physical properties.