## 4 Implementation  
### Development of the Digital Twin Platform  
To effectively integrate real-time monitoring, knowledge-driven reasoning, and interactive visualization, a digital twin platform is developed as the central hub for data fusion, simulation, and decision support in urban tunnel construction. The platform integrates multi-source data from geotechnical monitoring, TBM operations, structural sensors, and environmental conditions, providing a high-fidelity virtual representation of the tunneling environment. By continuously updating with real-time field data, the digital twin serves as a dynamic simulation environment, allowing engineers to track tunnel progress, assess structural responses, and detect potential risks as they emerge.  
The integration between the digital twin platform and the knowledge graph is supported by a real-time data fusion and semantic mapping pipeline developed on top of the iS3 platform. Monitoring devices (e.g., ground settlement sensors) transmit high-frequency data using the MQTT protocol (Figure 6a), ensuring lightweight and low-latency communication. These messages are published to specific topic channels via the platform's message broker module. Upon reception, the system unpacks the raw messages and performs semantic standardization, in which each data field is mapped to a unified schema with structured labels and ontology-aligned attributes based on a predefined data model. This ensures cross-platform interpretability and consistency.  
As shown in Figure 6, the standardized data are then archived into a structured database, forming the basis for dynamic knowledge graph reasoning. This mechanism enables the digital twin to maintain synchronization with physical site conditions, allowing for real-time updates and near-instantaneous triggering of risk alerts. The end-to-end process--from data acquisition to semantic integration and storage--ensures that monitoring data are transmitted, processed, and analyzed with both high frequency and reliability, supporting continuous alignment between the real and virtual environments.  
Figure 7 illustrates the interface of the developed digital twin platform, which visualizes the tunnel project in its real-world spatial context. The platform integrates three-dimensional BIM models of the surrounding buildings, geographic map, and critical infrastructure within the project area, including detailed modeling of rivers, roads, and  
Figure 6: Data integration and fusion process. (**a**) MQTT-based data publishing from field sensors, and semantic mapping and standardization into unified schema. (**b**) Dynamic data access results.  
other key urban elements. Key infrastructure elements are annotated for intuitive navigation, and users can access real-time data through the interface for location-specific risk monitoring and decision support.  
The developed digital twin platform demonstrated robust capabilities in real-time data integration and visualization, as evidenced in Figure 8. The left panel enumerates key monitoring points established for the project, including the DB744 and DB747 monitoring points, while the central section displays temporal monitoring data trends for a representative point (DB747). Notably, the lower-left visualization presents the extracted monitoring data from engineering reports, demonstrating the platform's ability to automatically integrate and display real-time field measurements. This functionality enables the continuous monitoring of critical parameters including ground settlement, structural deformation, and environmental impacts.