###### Abstract  
The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R\({}^{2}\) (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms. t 20231330  
the main factors for reducing the risk associated with the cost and schedule planning of construction projects [7, 8]. Therefore, estimating TBM performance is critical for effective, economical, safe, and rapid tunnel construction [9, 10]. Statistical models are less reliable in dealing with nonlinear and complex systems; extreme data values and outliners are often produced, making TBM performance prediction a big challenge [11, 12]. However, flexible artificial intelligence (AI) techniques provide an opportunity to deal with highly complex and nonlinear engineering problems [8]. For predicting TBM performance parameters, such as PR, AR, etc., several AI techniques such as fuzzy logic [12, 13], particle swarm optimization (PSO) [7, 14], adaptive neuro-fuzzy inference systems (ANFISs) [12], imperialist competitive algorithms (ICAs) [8], gene expression programming [15], support vector machines (SVMs) [2], and artificial neural networks (ANNs) [7, 8, 16] have been used. Several optimizing algorithms have been integrated with SVMs to predict the TBM AR and energy consumption of the cutterhead drives in TBMs [4, 17]. Similarly, ANNs have also been integrated with other algorithms to enhance the predictive performance of the system [7, 8, 18, 19]. However, extracting and analyzing the required information from prediction analysis often requires extra effort for the stakeholder to visualize and understand the actual position and progress of a construction project. Therefore, a computer-based support system for better visualization of a tunnel construction project is needed to maintain dynamic information, which can automatically update potential information for stakeholders. In the architecture, engineering, and construction (AEC) field, digital twins (DTs) are considered a comprehensive solution for presenting the lifecycle process of physical urban infrastructures in a virtual space, combined with the Internet of Things (IoT), big data, AI, and Semantic Web [20]. DT has been widely used in smart medical systems [21, 22], product assembly shop floors [23, 24, 25], human-machine collaboration [26], studies on speed loss caused by marine fouling [27], additive manufacturing systems [28], engine optimization, maintenance [29, 30], and product lifecycle management [31], blast design optimization [32], and other fields. Digital twins have been implemented for the lifespan prediction of perficated components in noise barrier tunnels [33].