A significant part of the energy consumed during the tunnelling process of Earth Pressure Balanced (EPB) Tunnel Boring Machines (TBMs) is related to the main drive, consisting of a set of motors driving the rotation of the cutting wheel. An energy efficient EPB design requires the optimization of the main drive to avoid over- or under powering of the machine. Key aspect is therefore a precise and reliable estimation of the expected cutting wheel torque. In this paper state-of-the-art torque estimation models are compared to supervised machine learning (ML) approaches, including classification and regression trees (CART), support vector machines (SVM), Gaussian process regression (GPR) and decision tree ensembles (DTE). Feature selection algorithms are compared to models using manually selected input features. ML models are evaluated using accuracy metrics, residual analyses, and model validation. Torque prediction for a real-world validation project shows that utilization rates can be increased distinctively due to the application of ML techniques.
