# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 3 Practical Implementation and Case Study
### Digital Twin Application for Monitoring and Stability Assessment
#### 3.2.3 Virtual Design Optimization (Stress and Convergence Analysis)  
This is the core of the proposed digital twin, which provides 3D numerical modeling in underground cavern design. It provides a comprehensive understanding of stress behavior, failure mechanisms, and reinforcement strategies. Several existing numerical modeling software packages can be integrated into the proposed framework through IFC-based interoperability [52]. For 2D FEM, tools such as RS2, Plaxis 2D, and FLAC2D can be utilized, while for 3D geomechanical and stress analysis, RS3, Plaxis 3D, and FLAC3D offer robust capabilities [53; 54]. These tools support the simulation of rock deformation, support performance, and dynamic ground response, aligning well with the digital twin's objective of real-time structural behavior representation. The findings reinforce the importance of integrating numerical simulations, field monitoring, and adaptive support measures to achieve optimal stability in underground caverns. For the optimization of the case study powerhouse complex, a numerical analysis of the underground powerhouse cavern was conducted by using advanced computational tools, finite element modeling (FEM), and finite difference methods (FDMs) to assess stress distribution, deformation patterns, structural stability, and reinforcement requirements. The geological information was integrated from borehole logs and site investigations, providing a detailed understanding of rock discontinuities, weak zones, and stress distribution from Step 2. The analysis incorporated key parameters, including rock mass properties, initial stress conditions, staged excavation sequences, and groundwater influence, to simulate real-world conditions accurately. By systematically examining stress concentration zones, this study identified potential failure mechanisms and provided insights into optimizing excavation strategies and support systems. The proposed digital twin used a two-phase approach to numerical modeling and stability assessment, by transitioning from 2D finite element analysis (FEA) to 3D numerical modeling as excavation progressed, incorporating feedback from actual construction information. In the case study, 2D FEA was initially employed during the pre-excavation phase to facilitate preliminary support design, leveraging its efficiency for planar stress analysis in uniform tunnel sections. As construction advanced and more detailed geometrical and geotechnical data became available, the model was upgraded to a 3D finite difference model (FDM). This enabled the digital twin to capture complex interactions, such as cavern intersections, non-uniform geometries, and the effects of sequential excavation, thereby improving the reliability of stability assessments and support optimization in real time. In the first phase (pre-excavation), initial numerical modeling was carried out by using 2D finite element analysis (FEA). The objective was to determine the support requirements necessary to maintain cavern stability throughout its operational life. In this stage, the 2D analysis primarily considered overall stress distribution and wedge stability, employing a simplified representation of the excavation geometry. The results guided preliminary support recommendations, including the use of rock bolts, shotcrete linings, and initial anchor placements. Figure 7 shows one of the 2D FEA analyses for a Geological Strength Index (GSI) of 32, the strength factor analysis for K = 1.75 in plane and K = 2.25 out of plane indicates that the Factor of Safety (FoS) is 1.2 or above in the pillar without rock support. The yielded elements are marked with X for shear failure and O for tensile failure, highlighting areas of structural weakness (Figure 7a). With maximum rock support, the Factor of Safety remains at 1.2, but the failure zones are better controlled, ensuring improved overall stability (Figure 7b).