Assessment of geotechnical parameters of fine-grained and coarse-grained soil using actual EPB-TBM operating data

Abstract The need to predict geotechnical parameters in soft ground is highly essential in evaluating the performance parameters of earth pressure balance machine (EPB-TBM) and ensuring the safety and efficiency of the tunnel boring machine during tunnel construction. In this study, several EPB operating parameters, including cutterhead torque (CT), thrust force (TF), chamber pressure (S), and the cutterhead speed revolution (RPM) were taken into account to estimate the geotechnical parameters such as friction angle (φ) and shear strength (τ) for ET1 to ET5 engineering geology units (fine-grained and coarse-grained soil) in the tunnels route which are indicator units for the whole path. Statistical methods and computing machinery techniques called support vector regression (SVR) and feed-forward neural network (FFNN), were trained with EPB operating parameters and geotechnical specification obtained from Tehran metro line 6-southern extension sector (TML-SE6) and the East-west lot of line 7, Tehran metro project (TML-EW7). A comprehensive dataset consists of borehole logging results along the tunnel path was collected and 85% of the data for training were randomly selected, while the remaining were considered for model testing. For the purpose of assessing the performance of the applied method and for evaluating the accuracy and precision, several loss functions were brought on stream. The results of the proposed models indicate an acceptable and reliable accuracy of the approaches. In fact, the results show that the estimated values are in good agreement with the measured data.