## 3 Methodology
### Digital Twin Establishment  
The establishment of the digital twin was based on the framework proposed by [PERSON] et al., which defines a digital twin as \(M_{DT}=(PE,VE,Ss,DD,CN)\). Here, PE represents the physical entities; VE denotes the virtual entities; Ss refers to the services; DD represents the twin data; and CN describes the connections between the components. As illustrated in Figure 3, the digital twin framework was constructed with a comprehensive set of physical entities, including the TBM, geotechnical soil and rock mass, tunnel structure, underground pipelines, Hongkou Port Bridge, flood protection walls, surrounding buildings, roadways, and traffic systems. Each of these physical entities had a corresponding virtual entity in the digital twin, creating a seamless mapping between the physical and virtual worlds. The CN between the physical and virtual entities enables real-time data exchange and control. For instance, CN_TBM facilitates TBM data acquisition and operational control; CN_Geotechnical handles borehole data updates, settlement monitoring, and grouting control; while CN_Traffic manages traffic flow modeling and optimization. These connections ensure that the digital twin remains synchronized with the physical environment, providing a dynamic and accurate representation of the tunneling process. Building upon the individual digital twin models (or single-entity digital twins), a system-level digital twin model was established to capture complex interactions and systemic behaviors. This included the MVE_Settlement virtual entity for soil displacement induced by TBM excavation, the MVE_Effect virtual entity for TBM construction-environmental impact-risk assessment, the MVE_Environment virtual entity for environmental impact under soil displacement fields, the MVE_Improve virtual entity for grouting reinforcement and environmental resilience enhancement, the MVE_Traffic virtual entity for \"road-traffic\" traffic management, and the AVE_Scene virtual entity for TBM construction-environmental impact-emergency response. These system-level virtual entities enable a holistic understanding of the interactions between tunneling activities and the surrounding environment. The analysis results from the virtual world were fed back to the physical world through four key services: the Ss_Cal service for environmental impact calculation, the Ss_Risk service for environmental risk assessment and prediction, the Ss_Construction service for TBM construction optimization, and the Ss_Response service for emergency response decision-making. Together, these services ensured that the digital twin not only monitored and simulated the tunneling process but also provided actionable insights for risk mitigation and emergency response, enhancing the overall safety and efficiency of the project. Figure 4 presents the development of a single digital twin model in this study, which takes the geotechnical soil and rock mass as an example. The development of the digital twin model for geotechnical soil and rock mass involved the systematic integration of real-world data collection, virtual modeling, and predictive risk assessment, as illustrated in Figure 4. In the physical world, geotechnical conditions are represented by soil layers, groundwater, and boreholes (PE_Soil). Through a combination of manual and automated data acquisition methods, a comprehensive dataset (DD_Soil) was collected, encompassing soil layer distribution, physical and chemical properties, borehole acquisition data, geological information obtained from shield tunneling at the tunnel face, and settlement monitoring data. These multi-source datasets formed the foundation for creating corresponding virtual entities in the digital twin. Within the virtual world, a geometric model was constructed as a 3D geological representation of the subsurface conditions, while a physical model was established to capture the constitutive behavior of soil and rock masses.