TBM Performance Prediction in Rock Tunneling Using Various Artificial Intelligence Algorithms

With widespread increasing applications of mechanized tunneling in almost all ground conditions, prediction of tunnel boring machine (TBM) performance is required for time planning, cost control and choice of excavation method in order to make tunneling economical. Penetration rate is a principal measure of full-face TBM performance and is used to evaluate the feasibility of the machine and predict advance rate of excavation. In this study, a database of actual machine performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 with 14.3 km available data has been compiled. To clarify the effective parameters on penetration rate, first principal component analysis (PCA) was performed. Furthermore, well-known Artificial Intelligence (AI) based methods, including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed. As statistical indices, root mean square error (RMSE), correlation coefficient (R), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed AI models for TBM performance. According to the obtained results, it was observed that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS and ANN models. A high conformity was observed between predicted and measured TBM performance for the SVR model.