Analysis of the Performance of Cutting Tools of Tunnel Boring Machine (TBM) in Silty-Sand Soils Using Artificial Neural Network (ANN) – Case Study: Tabriz Metro Line 2 Project

Abstract

The choice of cutting tools for soil excavation has a significant impact on the performance of tunnel boring machines. In this study, the data of two excavated sections of the Tabriz metro line 2 project have been used, and the crossed soils were sandy silts. Two sections have investigated the performance of two kinds of cutting tools (disk cutters and rippers). The cutting tools used in the first section of the drilling route are disc cutters, while in the second section, rippers have been used. To predict and evaluate the performance of the two types of cutting tools in silty-clay soils, artificial neuron networks were used, and the Levenberg-Marquardt algorithm was chosen for training the ANNs. Three important TBM parameters (torque, thrust force, and speed) were considered as input parameters, and the TBM penetration rate is chosen as the output variable in the developed artificial neuron network models. The obtained results showed that the best-predicted value obtained from the ANN was obtained with one hidden layer that contains four neurons. Finally, considering the performance parameter observed in silty soils, the ripper’s performance is better compared to the disc cutter one.