Developing the empirical models for predicting the EPB operating parameters in strong Limestones

The operation parameters of EPB-TBM have always been significant factors in tunnel constructions. So it is crucial to estimate the cutterhead torque and thrust force of the machine. In this study, by employing the multilayer perceptron artificial neural network (ANN-MLP) and multivariate regression (MVR) methods, the empirical models were developed to estimate the EPB operating parameters, including cutterhead torque and thrust force, in the rock section of the Tehran metro line 6, South extension (TML6-SE) project. In this section, the excavation was performed in a strong, blocky to massive rock. The machine was equipped with the disc cutters on the cutterhead as a cutting tool instead of rippers and drag bits. The mechanized excavation in this situation is unusual with using the EPB machines. The input data included the performance parameters such as penetration rate, earth pressure, cutterhead rotation speed, and cutter load. The statistical indices were used to verify the developed models. The results confirmed the accuracy of the models. The MAE loss function determined for torque in both training and testing stages predicted by the ANN was 0.0001 and 0.005, respectively. The MAE loss function determined for thrust force in both training and testing stages predicted by the ANN was 0.00016 and 0.010, respectively. The relationships between parameters in the dataset were investigated to obtain and offer new equations using the multivariable regression statistical method (MVR). The MAE loss function determined for cutterhead torque and thrust force was 0.0018 and 0.0010, respectively.