## 3 Methodology  
### Overview  
This study develops a digital twin and knowledge graph-based framework to enhance emergency response strategies for mitigating tunneling-induced disturbances to surrounding buildings and infrastructure. As illustrated in Figure 2, the methodology consists of two key components: scene/data element integration and digital twin and knowledge graph development.  
First, the scene and data elements were systematically collected and integrated, encompassing TBM operational parameters, tunnel structural data, road conditions, surrounding buildings, bridges, and underground utilities. To be specific, the TBM operational parameters included shield tunneling parameters, i.e., thrust force, hydraulic pressure, advance rate, cutterhead torque, rotation speed, stroke length, and tail se  
Figure 2: The overall methodology.  
acquisition frequency for these parameters was set at 1 Hz, which ensured sufficient temporal resolution for capturing dynamic variations during tunneling. All the sensor data underwent standard preprocessing procedures, including noise filtering and outlier removal. The error margins of the key sensors used in the system were in the range of \(\pm 2\%\). These elements were further enriched with detailed tunnel-building-pipeline interaction parameters, historical construction records, and real-time monitoring data (e.g., settlement measurements, ground deformation, and environmental conditions). This comprehensive dataset ensured a holistic representation of the urban subsurface environment and its dynamic interactions during tunnel construction.  
Second, the multi-source dataset was utilized to develop a digital twin model, which served as a real-time visualization and simulation platform. This model enabled continuous tracking of tunnel construction progress, dynamic assessment of its impact on adjacent infrastructure, and predictive analysis of potential risks. Concurrently, a knowledge graph was constructed to establish semantic relationships between key infrastructure components, risk factors, and emergency response mechanisms. The knowledge graph integrates domain-specific knowledge, regulatory standards, and expert insights, providing a structured framework for risk identification and decision-making.  
Finally, the integrated framework was applied to engineering risk warning and emergency response. By leveraging the real-time capabilities of the digital twin and the contextual reasoning of the knowledge graph, the system provided early warnings of potential risks, such as excessive settlement or structural damage, and supported the formulation of optimized emergency response strategies.  
It should be noted that the proposed framework was designed to be modular and flexible, allowing for the integration of additional computational components. For instance, machine learning-based predictive models can be incorporated to complement the knowledge graph reasoning. Time-series forecasting models (e.g., LSTM networks) may be used to predict trends in ground settlement or TBM parameters, while classification or anomaly detection algorithms could assist in identifying early warning signals from real-time monitoring data. These ML modules can enhance the framework's adaptability, support data-driven decision-making, and increase its robustness in handling noisy or incomplete input.