## 5 Theoretical Case Study, Results, and Discussion
### Result and Discussion
The controlling parameter \(\alpha\) for the ANN model varied from 0.1 to 100 to find the best prediction results, as shown in Figure 11. The predictive performance indices RMSE, R\({}^{2}\), and  
Figure 14: Practical demonstration of TBM rotation. Figure 15: Regression analysis based on the actual and predicted TBM values; (**a1,b1,c1**) represents the penetration rate while (**a2,b2,c2**) represents the advanced rate for SVR, ANN, and SVR-ANN models, respectively. VAF of ANN models showed improvement. The correlation between predicted and actual TBM PR values for the ANN is plotted in Figure 15(b1,b2). In the past, ANN-based studies have shown R\({}^{2}\) values of 0.66 [8], 0.82 [62], 0.83 [63], 0.90 [53], and 0.94 [64]. Furthermore, the ANN algorithm has been used to perform different training functions (lbfgs, sgd, and ADAM) to predict the TBM performance, as shown in Figure 16. Comparing these results with the proposed study shows promising results for predicting TBM performance accurately. The third proposed model of this study is a model that integrates SVR and ANN. The integrated model also predicted accurate results from the dataset used for the previous two models. The correlation between predicted and actual TBM PR values for the SVR-ANN is plotted in Figure 15(c1,c2). These figures show that the ANN-based models provide higher accuracy because of their higher R\({}^{2}\) values. Recently, some optimized and integrated models were presented to predict the PR of TBM in different geological conditions and input parameters. The R\({}^{2}\) values of some of the studies are 0.905 for PSO-ANN, 0.912 for ICA-ANN [8], and 0.961 for PSO-ANN, 0.939 for ICA-ANN [7]. Comparing these results with the proposed model shows the practical applicability of the proposed model under different ground conditions. The performance indices RMSE, R\({}^{2}\), and VAF are evaluated and enlisted in Table 3. As shown in Table 3, compared with the ANN model, the SVM-ANN model provides better understanding. Additionally, a performance comparison of the SVM, ANN, and SVM-ANN models is shown in Figure 17. The VAF values are decimals for a comparative plot with R\({}^{2}\) and RSME. 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%), are the performance predictors of the SVM, ANN, and SVM-ANN models, respectively. Significant improvement can be seen in the performance of these algorithms, respectively. Figure 16: Regression analysis based on the actual and predicted TBM values; (**a1,b1,c1**) represents the penetration rate while (**a2,b2,c2**) represents the advanced rate for training functions lbfgs, sgd, and ADAM, respectively. A graphical user interface (GUI) for visualizing and analyzing the performance of a TBM, as shown in Figure 18, displays real-time data from the TBM. The data from the TBM are continuously monitored and analyzed through the AI, and predictions are carried out simultaneously. This shows the trends of the data generated from the AI through graphic representation in real time. The TBM GUI also provides the opportunity to analyze the data, display charts, and scatter plots for selected algorithms. It provides a user-friendly navigation environment to filter, sort, and select the desired algorithm and alert the system in the case of an undesirable event. TBM DTs provide a virtual replica of a physical TBM. Using real-time data in a machine learning algorithm provides better performance monitoring in a virtual and remote environment. This helps the operator to identify issues, respond faster, and reduce TBM downtime. Thorough monitoring through the machine learning algorithm also helps to identify patterns that may lead to potential failure in advance. This helps with proactive maintenance. Furthermore, real-time data monitoring and TBM data analysis also provides the opportunity to identify optimal operating parameters.