Utilizing partial least square and support vector machine for TBM penetration rate prediction in hard rock conditions

Rate of penetration (ROP) of a tunnel boring machine (TBM) in a rock environment is generally a key parameter for the successful accomplishment of a tunneling project. The objectives of this work are to compare the accuracy of prediction models employing partial least squares (PLS) regression and support vector machine (SVM) regression technique for modeling the penetration rate of TBM. To develop the proposed models, the database that is composed of intact rock properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and peak slope index (PSI), and also rock mass properties including distance between planes of weakness (DPW) and the alpha angle (α) are input as dependent variables and the measured ROP is chosen as an independent variable. Two hundred sets of data are collected from Queens Water Tunnel and Karaj-Tehran water transfer tunnel TBM project. The accuracy of the prediction models is measured by the coefficient of determination (R2) and root mean squares error (RMSE) between predicted and observed yield employing 10-fold cross-validation schemes. The R2 and RMSE of prediction are 0.8183 and 0.1807 for SVMR method, and 0.9999 and 0.0011 for PLS method, respectively. Comparison between the values of statistical parameters reveals the superiority of the PLSR model over SVMR one.