# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 2 Digital Twin Framework for Real-Time Monitoring and Optimization of Underground Caverns
### Structured Data Representation  
It is essential that data storage in the digital world for civil and underground engineering are based on the Industry Foundation Classes (IFC) standard. This provides the standard universal data exchange format, ensuring interoperability, data consistency, and collaboration across software platforms and project stakeholders for BIM-based applications. Similarly, IFC should be adopted for DT-driven BIM applications for underground caverns. Structured and standardized data representation is crucial to real-time monitoring, stability optimization, and deformation analysis. Some underground space IFC schemas are available and have been proposed by several authors [49]. However, there is a current lack of a dedicated IFC schema that explicitly defines convergence monitoring, where real-time feedback loops between physical and virtual entities rely on the well-structured information model. There is the necessity for the extension and definition of new attribute sets to ensure that both static hardware information and dynamic real-time monitoring data are systematically recorded. For effective convergence monitoring, it is necessary to define three dynamic attribute sets, Pset_SensorCommonFeature, Pset_SensorMonitoringData, and Pset_PerformanceData, which are attached to the IfcSensor and IfcPerformanceHistory entities. These attribute sets ensure that static hardware information, dynamic real-time monitoring data, and performance evaluation are systematically recorded. Some of the common features already defined in existing IFC schemas are adopted as they are. However, some of the new features need to be extended. Table 1 shows the existing and extended standard features of the three Pset for this digital twin. To ensure a well-structured and standardized data exchange framework for digital twin-based convergence monitoring, an IFC schema extension has been proposed, as shown in Table 2. The Unified Modeling Language (UML) class diagram represents the structure and relationships of entities for the proposed digital twin with explicit schema for efficient, reliable, and scalable real-time data transfer between different modules in the virtual and physical world (Figure 3). \begin{table}
\begin{tabular}{c c c c c} \hline
**Property Set** & **Attribute Name** & **Data Type** & **Description** & **Status** \\ \hline \multirow{8}{*}{**SensorType**} & SensorID & String & Unique identifier for sensor. & Existing \\ \cline{2-5}  & SensorType & Enum & Type of sensor (e.g., extensometer or & \\  & & & load cell). & Existing \\ \cline{2-5}  & Manufacturer & String & Manufacturer of sensor device. & Existing \\ \cline{2-5}  & ModelNumber & String & Model number of sensor. & Existing \\ \cline{2-5}  & InstallationDate & Date & Date when sensor was installed. & Existing \\ \cline{2-5}  & MeasurementUnit & String & Unit of measurement (e.g., mm or kN). & Existing \\ \cline{2-5}  & Accuracy & Double & Accuracy of sensor readings. & Existing \\ \cline{2-5}  & LocationReference & String & Position of sensor in BIM model. & Existing \\ \cline{2-5}  & OperatingRange & Double & Range of values that sensor & \\  & & & can measure. & Extended \\ \cline{2-5}  & PowerSupply & String & Type of power supply (e.g., battery & \\  & & & or wired). & Extended \\ \cline{2-5}  & CommunicationProtocol & String & Type of communication protocol (e.g., & \\  & & & LoRa or Zigbee). & Extended \\ \hline \end{tabular}
\end{table} 
Table 1: Definition of sensors and performance attribute sets.