Dynamic prediction of penetration rate based on TBM operational data

Abstract

In this paper, an artificial neural network-based dynamic prediction model of penetration rate (PR) is proposed. Four tunnel boring machine (TBM) operational parameters, including cutterhead rotational speed (RPM), cutterhead torque (T), total thrust (F), and advance rate (AR) are introduced as input of the network. Their data in the trial excavation phase, are employed to predict the PR in the stable excavation phase. Therefore, the training data will have enough offsets from the test data, and the TBM operators will have sufficient time to either fine-tune the operational parameters or shut off the machine if undesired prediction results come out. To examine the performance of the established model, it is applied to a 20 km water conveyance tunnel. The results show that the network converges fast and steadily with acceptable performance on the test set. The major contribution of this paper is verifying the possibility to estimate the PR based on the historical data of the TBM.