Research on Real-time Prediction of Shield Cutterhead Torque Based on TCN-LSTM
（1. The Third Engineering Co.,Ltd of China Railway Seventh Group, Xi′an 710000; 2. Key Laboratory for Geotechnical Engineering of Ministry of Water Resource, Hohai University, Nanjing 210098; 3. School of Civil Engineering, Sun Yat-sen University,Zhuhai 519082; 4. Key Laboratory for Tunnel Engineering，Guangzhou 510275）
Abstract：
The shield cutterhead torque reflects the mechanical interaction characteristics between the cutterhead and the stratum. Accurately predicting torque changes in real-time can help adjust tunnelling parameters in advance, ensure smooth machine operation, and reduce cutting tool wear. Therefore, this paper proposes a deep learning model based on Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) for real-time prediction of cutterhead torque. The results indicate that the TCN-LSTM model can capture the local features of the input parameters and establish long-term dependencies, achieving the highest prediction accuracy compared to other models. The model performs stably in multi-step predictions, enabling longer lead-time predictions of cutterhead torque. A 4∶1∶1 data set split ratio yields the optimal performance for the prediction model.
FENG Tong1 HU Jinjian2 LI Yan1 ZHANG Jian2 LIANG Yu3,
4 FENG Tugen2
.Research on Real-time Prediction of Shield Cutterhead Torque Based on TCN-LSTM[J] MODERN TUNNELLING TECHNOLOGY, 2024,V61(5): 120-128
2024, Vol. 61 