Utilising of linear and non-linear prediction tools for evaluation of penetration rate of Tunnel Boring Machine in hard rock condition

Predicting Tunnel Boring Machine (TBM) penetration rate is a crucial issue for the successful fulfilment of a mechanical tunnel project. Penetration rate depends on many factors such as intact rock properties, rock mass conditions and machine specifications. In this paper, linear and non-linear multiple regression as well as Artificial Neural Network (ANN) techniques were applied to predict the penetration rate of TBM. In developing of the proposed models, five parameters, which include Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), peak slope index (punch penetration), spacing of discontinuities (of weakness planes) and orientation of discontinuities with respect to the tunnel axis (β angle), were incorporated. For this study, 46 datasets were collected. Performance of these models was assessed through the R2, RMSE and MAPE. As a result, these indices revealed that the prediction performance of the ANN model is higher than that of the non-linear and linear multiple regression models.