## 3 Digital Twin Framework for TBM Performance Visualization and Monitoring
### Digital Twin-Based Framework for TBM Performance Visualization and Monitoring Method  
Based on the proposed digital twin modules, we designed a digital twin-based framework for TBM performance visualization and monitoring. A digital twin and a machine learning-based six-step process was adopted to visualize and monitor TBM performance, as illustrated in Figure 3.  
Figure 3: Digital Twin-based framework for TBM performance visualization and monitoring method. Step 1: Understanding the geological conditions, rock mass mechanical parameters, TBM design parameters, and TBM operating parameters are key to successfully designing the right type of TBM, accurately executing TBM functions, and meeting the desired cost and scheduling of a tunnel construction project. A detailed feasibility study is used to determine rock mass mechanical parameters and geological settings. A pilot tunnel is constructed in the project area to have a detailed overview of the geological conditions, stresses, and deformation. A TBM is a moving factory that produces a tunnel of a desired geometric design. The tunnel's geometric design and parameters drive the TBM's geometric design. For that purpose, detailed design drawings of the TBM are developed using CAD, providing detailed TBM design parameters for tunnel construction. Furthermore, the operating parameters of the TBM are collected to store in the database for data visualization and provide an input feed for the machine learning algorithm for prediction and analysis. Step 2: Based on the data from the previous step, TBM manufacturing is carried out in a virtual environment. The different TBM parts, such as the cutterhead, gripper, and main drive, are developed and assembled into a single TBM unit. Therefore, the design and assembly of each part of the TBM, especially the cutterhead, are implemented very precisely and accurately. Iterative design checks are conducted to obtain optimal geometric and motion parameters, and the digital mirror of the TBM is developed as per the requirement of the TBM digital twin for performance visualization and monitoring. Any change or error in the TBM design or assembly process incurs additional costs and causes a long delay in the project's schedule. Step 3: After the virtual design, the TBM's geometry, parts, material, parts motion, and assembly process in a virtual environment are completed in step 2. The operating parameters of the TBM's operation during actual construction are selected from step 1 to feed the digital replica of the TBM constructed in the virtual environment. The digital replica is linked with step 1 to stimulate the operational condition in this step. The rotational speed of the cutterhead and other operational parameters of the TBM are controlled in the virtual environment. It is a virtual TBM product replicating and consistent with a TBM in a real-world environment in real time. Step 4: This step mainly deals with data science and machine learning. The desired machine learning algorithms are selected, and their architecture and hyperparameters are optimized for the best possible analysis results. The operating parameters of the TBM and the geological factors of the ground are selected carefully, and the dataset is presented in a format suitable for the desired machine learning algorithm. The cutter rotation speed, torque, and thrust are the key parameters for the qualified TBM excavating the ground of interest. In the proposed framework, steps 3 and 4 execute their tasks simultaneously with steps 2 and 3. Step 5: This step involves training and testing the machine learning algorithm to visualize the prediction performance of the TBM's operating parameters in real time. The model performance predictor results, root mean square error (RMSE), coefficient of determination (R\({}^{2}\)), and variance accounted for (VAF) are estimated. The prediction analysis is evaluated and compared to find the best algorithm.