Tunnel boring machines (TBM) performance prediction: A case study using big data and deep learning

Abstract

This work explores the potential for predicting TBM performance using deep learning. It focuses on a 17.5-km-long tunnel excavated for the Yingsong Water Diversion Project in Northeastern China with its 728 days’ continuous monitoring of mechanical data. The prediction uses the deep belief network (DBN) proposed by Hinton et al. (2006),on the penetration rate, cutter rotation speed, torque, and thrust force. Field Penetration Index (FPI) is introduced to quantify TBM performance in the field. The DBN algorithm trains on nth number of preceding elements and predicts the performance of the n + 1th element. Prior to the implementation of the DBN, a pilot test was performed to find the optimal values for the network structural parameters (number of input nodes, number of hidden layers, number of nodes in the hidden layers, and learning rate). Predictions on FPIs in all the three rock types were then proceeded with good agreement with the field measured data. The mean relative errors for the predicted measured FPIs are generally less than 0.15 and the correlation coefficients (R) can be higher than 0.78. The predicted and measured FPI values along the length of the tunnel graphically follow the same trends. These results confirm the usefulness of big data and the deep learning in predicting TBM performance.