<<
[an error occurred while processing this directive] | [an error occurred while processing this directive] >>
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.
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 