Estimating shield tunnel boring machine penetration rate in mixed face conditions: feature selection and multicollinearity effects on machine and deep learning models

This research compares the support vector machine (SVM), gene expression programming (GEP), feedforward neural network (FFNN), gated recurrent unit (GRU), long short-term memory (LSTM), support vector regressor (SVR), and bidirectional long short-term memory (BiLSTM) models in predicting penetration (PR) rate of earth pressure balance shield tunnel boring machine (E TBM ). A dataset has been compiled using the cutterhead rotation speed (CRS), mean thrust (F/A), mean cutterhead torque (T/D 3 ), upper earth pressure (UEP), lower earth pressure (LEP), and torque penetration index (TPI) features of 1,197 E TBM events. The presence of multicollinearity was analyzed using the variance inflation factor (VIF) method. It was observed that CRS, F/A, T/D 3 , UEP, LEP, and TPI have weak, moderate, considerable, moderate, problematic, and considerable multicollinearity, respectively. The performance (R) comparison revealed that the BiLSTM models predicted PR (=1.0000 in testing and validation) with higher performance than SVM, SVR, GEP, FFNN, GRU, and LSTM models. In addition, the score analysis (=285), error characteristics curve (=7.03E-07), generalizability (m and n < 0.00), Wilcoxon test (confidence = 95.02%), uncertainty analysis (first rank), Anderson-Darling test (accept the normality hypothesis), and objective function criterion (=0.0003) presented that the BiLSTM model is an optimal performance computational model in predicting PR of E TBM . It was also noted that the CRS, F/A, T/D 3 , UEP, LEP, and TPI features are more reliable for accurately predicting PR.