## 4 Implementation
### Emergency Response Based on Digital Twin and Knowledge Graph  
The digital twin platform enables intelligent emergency response by integrating real-time monitoring data with knowledge graph-based rule reasoning. As systematically documented in Table 1, the knowledge graph incorporates domain-specific rules that establish quantitative relationships between various monitoring parameters and potential excavation-induced risks. When the platform processes continuous sensor data  
Figure 8: Real-time integration and visualization of monitoring data within the digital twin platform. Figure 7: Screenshot of the developed digital twin platform showing the spatial configuration of the tunnel construction project. through these predefined rules, it automatically identifies and classifies emerging risks with high precision. Figure 9 demonstrates the platform's analytical capabilities through comprehensive visualization outputs. The settlement cloud map provides a spatial representation of the ground deformation patterns, while the accompanying histogram visualizations offer a quantitative analysis of the settlement distribution characteristics. These dual visualization modalities enable engineers to quickly assess both the geographical extent and severity of the excavation impacts. Building on the identified risks in Figure 8, Figure 9 illustrates the structured emergency response mechanism developed within the proposed framework. This response mechanism is composed of two core components: the central emergency organizational structure and the corresponding emergency action plans on either side. The central structure outlines how various stakeholders--including the construction team, monitoring unit, risk assessment team, emergency intervention unit, and external coordination group--should collaborate in the event of an emergency. Each group has clearly defined responsibilities \begin{table}
\begin{tabular}{c l} \hline \hline  & \multicolumn{1}{c}{**SWRL**} \\ \hline
1 & MonitoringPoint(?mp) \({}^{\ast}\) hasSettlementValue(?mp,?s) \({}^{\ast}\) swrlb:greaterThan(?s, 24) \\  & -? triggersAlarm(?mp, \"Excessive Settlement Warning\") \\  & MonitoringPoint(?mp) \({}^{\ast}\) hasUpliftValue(?mp,?u) \({}^{\ast}\) swrlb:greaterThan(?u, 8) \({}^{\ast}\) \\  & triggersAlarm(?mp, \"Excessive Uplift Warning\") \\  & MonitoringPoint(?mp) \({}^{\ast}\) hasSettlementRate(?mp,?sr) \({}^{\ast}\) swrlb:greaterThan(?sr, 2.4) \\  & -? triggersAlarm(?mp, \"Excessive Settlement Rate Warning\") \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of SWRL for early warning based on the knowledge graph. Figure 9: The settlement cloud map based on real-time data. ties, ensuring that all the aspects of risk mitigation and emergency handling are covered efficiently and systematically. The left and right sections of Figure 10 present targeted emergency action plans based on the real-time conditions and knowledge graph-derived risk points. These plans describe how specific elements within the digital twin--such as pipelines, retaining structures, and buildings--should be assessed or acted upon. For instance, the external coordination group is responsible for contacting building owners in surrounding areas; the construction team is tasked with adding monitoring points to nearby pipelines, inspecting the current structural state of floodwalls and bridges, and performing grouting operations in affected surface zones. The status of each task is represented using color codes: red indicates ongoing actions; green represents completed tasks; and yellow denotes actions that are scheduled to begin. The middle part of Figure 10 illustrates the structured emergency response workflow designed for tunnel construction scenarios involving unknown obstacle detection or abnormal monitoring events. The process begins with the immediate suspension of excavation and confirmation of the risk event, followed by hierarchical reporting to responsible parties and the establishment of a dedicated risk assessment team.