Real-time forecasting of TBM cutterhead torque and thrust force using aware-context recurrent neural networks

Abstract

Tunnel Boring Machines (TBMs) are instrumental in the construction of modern tunnels, known for their operational reliability and efficiency. The real-time prediction of cutterhead loads is essential for effective project scheduling, cost management, and risk reduction. This study develops machine learning models for predicting future cutterhead torque and thrust force, simultaneously. The dataset is derived from the Yingsong water diversion project and consists of 12,962 steady-state boring cycles. Because geological conditions and setting values are available in advance, we build a geological parameter-based model and a setting value-based model for regression analysis of cutterhead torque and thrust force. In one-step forecasts, the operational parameter-based model closely captures the trend of the measured results, employing the recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). We propose an aware-context recurrent neural network (AC-RNN) model that integrates historical operational parameters and the aware context of setting values, leading to a significant improvement in forecasting accuracy. A sensitivity analysis is carried out to quantify the relative importance of input parameters, revealing that the setting value of revolutions per minute is crucial in forecasting. The results of this study offer valuable practical insights into real-time forecasting, thereby informing engineering applications in this field.