## 6 Conclusions  
Prediction of TBM performance is the most important factor for reliable cost estimation, project planning, and feasibility of mechanized excavation methods in tunneling projects. TBM tunneling is highly recommended for higher performance and better safety conditions than other tunneling methods. However, the heterogeneous and diverse data make it difficult to visualize the tunneling operation in its raw form. Visualizing TBM performance is critical for safe, effective, rapid, and risk-free tunnel construction according to the planned schedule. Therefore, the digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring is proposed for a better and easier way to visualize TBM performance. The proposed framework predicted the performance of the TBM operation through machine learning models and simultaneously visualized the results through digital twin modeling. For the theoretical demonstration of the framework, the data were collected from the open gripper hard rock TBM used at the Neelum Jhelum Hydroelectric Power (NJHEP) project tunnel. TBM digital twin modeling was performed based on the design drawing, design dimensions of electric and mechanical devices, CAD, and sensor-based data of the physical TBM. One thousand two hundred eighty-five (1285) data points of nine geotechnical and machine parameters, including boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolutions, Q-value (theo), and Q-value (rBM), were considered for the development of these predictive models. SVR and ANN (BFGS quasi-Newton backpropagation algorithm)-based machine learning algorithms were used for TBM performance prediction. R\({}^{2}\) (0.9607, 0.9690, and 0.9694), RMSE (0.0436, 0.0343, and 0.0339), and VAF (96.07%, 96.90%, and 96.94%), were the SVM, ANN, and SVM-ANN model performance predictors, respectively, used for PR prediction. We conclude that the performance predictors (R\({}^{2}\), RSME, and VAF) showed higher accuracy in predicting TBM performance. Furthermore, the predicted results of machine learning and operating parameters of the TBM were then linked with the TBM digital twin. Through AI scripting, the TBM RPM values were provided for the virtual TBM model, and TBM cutterhead rotation at  
Figure 18: TBM performance visualizer. different RPMs were recorded and visualized in a gaming platform. Digital twin modeling provides a visual demonstration of TBM performance to ease understanding for stakeholders. Based on the results, we conclude that the proposed digital twin-driven TBM framework has excellent potential to enhance the visual capabilities of construction performance, construction management, and decision-making. In terms of the limitations of the proposed framework, the digital twin framework lacks actual demonstration in a real environment with limited TBM access, considering the cost of the TBM. Secondly, for the theoretical demonstration of the framework, only rotational data were linked with the virtual TBM model. Future work may involve the thorough implementation of TBM DTs with ground interaction. Furthermore, the proposed framework considers data structure, and that interoperation of TBMs have a huge impact on the tunneling process, which will be covered in future studies. Conceptualization K.L. and A.S.; Methodology, K.L. and A.S.; Software, K.L. ; Validation, K.L. ; Formal Analysis, A.S.; Investigation, K.L. and A.S.; Resources, A.S. and J.S. ; Data Curation, K.L. and A.S.; Writing--Original Draft Preparation, K.L. ; Writing--Review and Editing, A.S. and J.S. ; Visualization, K.L. and A.S.; Supervision, A.S. and J.S. ; Project Administration, J.S. ; Funding Acquisition, J.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Land, Infrastructure and Transport (National Research for Smart Construction Technology) (MSIT), Grant 20 SMIP-A158708-01, National Research Foundation of Korea (NRF) grant funded by the Korean government (No.