Prediction of TBM penetration rate using the imperialist competitive algorithm (ICA) and quantum fuzzy logic

Quantum computer brings many advantages to the world of computations. Also, the tunnel boring machine (TBM) has been industrialized over the prior decades to make the tunneling process safer and more affordable. Several factors such as economic considerations, rock properties, and schedule deadlines play an important role in the use of TBMs mining projects. Hence, future projects are interested in enhanced means for predicting the TBM performance. The TBM penetration is usually an essential factor for the prosperous implementation of a plan for tunneling in a rock situation. In this research, the statistical analyses of rock features and the measured penetration rate of TBMs are presented using the imperialist competitive algorithm (ICA) and quantum fuzzy logic. Also, a database has been used that includes the parameters of rock properties of the Queens Water Tunnel. The proposed hybrid method is applied to provide a new predictive model for improving TBM performance. Results demonstrated that this method has a high capability to predict the performance of TBM with R-2=0.93 and RMSE=0.09. This shows that the application of the proposed approach is preferable compared to the prior models in terms of penetration rate.