# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 4 Conclusions  
This study addresses the limitations of traditional stability assessment methods for large underground caverns, which often fail to capture real-time changes in rock behavior, leading to potential safety risks and inefficiencies in rock support systems. It integrates DT with BIM and IFC standards to develop a structured framework to enhance stability monitoring and optimization in underground construction. It incorporates real-time sensor data into virtual models to monitor structural performance continuously and respond proactively to changes in rock mass behavior. This approach enhances both safety and long-term stability during the design, construction, and operational phases. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metric**} & \multicolumn{2}{c}{**Digital Twin-Driven Framework**} & \multirow{2}{*}{**Traditional Method**} \\ \cline{3-4}  & Maximum Convergence (mm) & \(\pm\)10–20\% deviation from predicted values due to real-time updates & \(\pm\)30–50\% deviation due to limited periodic monitoring \\ \cline{2-4}
**1. Deformation monitoring** & Rate of Deformation (mm/day or mm/month) & Continuous monitoring for early detection & Manual readings at fixed intervals; higher risk of late detection \\ \cline{2-4}  & Deformation Reduction (\%) & 30–50\% reduction with optimized rock support & 10–20\% reduction due to static design assumptions \\ \hline
**2. Rock support optimization** & Rock Support Effectiveness (\% reduction in critical failure zones) & 40–60\% due to predictive modeling and feedback loops & 15–30\% due to predefined static designs \\ \cline{2-4}  & Support utilization efficiency (kN/m\({}^{2}\)) & Optimized based on real-time stress analysis & Over-designed due to lack of real-time stress feedback \\ \hline
**3. Real-time monitoring accuracy** & Sensor accuracy (\% deviation from predicted values) & \(\pm\)5–10\% (validated against simulation models) & \(\pm\)25–40\% (manual measurements introduce errors) \\ \cline{2-4}  & Data latency & Real-time data integration & Delayed by hours/days due to manual processing \\ \hline
**4. Stress and stability analysis** & \begin{tabular}{c} State of Safety (FoS) \\ \end{tabular} & \
\begin{tabular}{c} Maintained dynamically \\ (>1.2) with real-time updates \\ \end{tabular} &
\begin{tabular}{c} Calculated in design phase, \\ may not reflect \\ actual conditions \\ \end{tabular} \\ \cline{2-4}
**4. Stress and stability analysis** & \begin{tabular}{c} Stress Redistribution \\ Efficiency (\% change in stress post-excavation) \\ \end{tabular} & \
\begin{tabular}{c} 30–50\% improvement due to \\ adaptive modifications \\ \end{tabular} &
\begin{tabular}{c} 10–20\% improvement with \\ static support design \\ \end{tabular} \\ \cline{2-4}  & Shear strain concentration (\%) & Identified dynamically to \\ \hline
**5. BIM-based construction efficiency** & \begin{tabular}{c} Deviation from Planned \\ Excavation Sequence (\%) \\ \end{tabular} & \
\begin{tabular}{c} \textless{}10\% due to continuous \\ design refinements \\ \end{tabular} &
\begin{tabular}{c} \textgreater{}25\% due to unforeseen \\ site conditions \\ \end{tabular} \\ \cline{2-4}  & Design Modifications (\% changes in geometry or structural design) & Adjusted dynamically with \\ real-time feedback & & Requires significant redesign \\ \cline{2-4}  & Real-time Model Update \\ Frequency (updates per day/week) & Continuous updates & Updated manually (weekly \\ \end{tabular} \\ \cline{2-4}  & Decision Response Time (hours or days) & Immediate corrective actions & Delayed response due to \\ \cline{2-4}  & Decision Response Time (hours or days) & Immediate corrective actions & manual assessments \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics compared with existing traditional method. The practical implementation of this study demonstrates that the integration of DT, BIM, and IFC significantly improves deformation monitoring, optimizes rock support systems, and enhances risk management practices in near-real time.