Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling
•The LSTM-attention model is more effective than the LSTM model.•LSTM-attention model can predict the attitude and position of shield machine.•Attention mechanism has advantages in predicting multi-input data set.•An effective method is proposed for the operation adjustment of shield machine. Shield...
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| Published in | Underground space (Beijing) Vol. 13; pp. 335 - 350 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.12.2023
KeAi Communications Co., Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2467-9674 2467-9674 |
| DOI | 10.1016/j.undsp.2023.05.006 |
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| Abstract | •The LSTM-attention model is more effective than the LSTM model.•LSTM-attention model can predict the attitude and position of shield machine.•Attention mechanism has advantages in predicting multi-input data set.•An effective method is proposed for the operation adjustment of shield machine.
Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation. This study therefore introduced a framework to predict the attitude and position of shield machine by combining long short-term memory (LSTM) model with attention mechanism. The data obtained from the Wuhan Rail Transit Line 6 project were utilized to verify the feasibility of the proposed method. By adding the attention mechanism into the LSTM model, the proposed model can focus more on parameters with higher weights. Sensitivity analysis based on Pearson correlation coefficient was conducted to improve the prediction efficiency and reduce the irrelevant input parameters. Compared with LSTM model, LSTM-attention model has higher accuracy. The mean value of coefficient of determination (R2) increases from 0.625 to 0.736, and the mean value of root mean square error (RMSE) decreases from 3.31 to 2.24. The proposed LSTM-attention model can provide an effective prediction for attitude and position of shield machine in practical tunneling engineering. |
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| AbstractList | Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation. This study therefore introduced a framework to predict the attitude and position of shield machine by combining long short-term memory (LSTM) model with attention mechanism. The data obtained from the Wuhan Rail Transit Line 6 project were utilized to verify the feasibility of the proposed method. By adding the attention mechanism into the LSTM model, the proposed model can focus more on parameters with higher weights. Sensitivity analysis based on Pearson correlation coefficient was conducted to improve the prediction efficiency and reduce the irrelevant input parameters. Compared with LSTM model, LSTM-attention model has higher accuracy. The mean value of coefficient of determination (R2) increases from 0.625 to 0.736, and the mean value of root mean square error (RMSE) decreases from 3.31 to 2.24. The proposed LSTM-attention model can provide an effective prediction for attitude and position of shield machine in practical tunneling engineering. •The LSTM-attention model is more effective than the LSTM model.•LSTM-attention model can predict the attitude and position of shield machine.•Attention mechanism has advantages in predicting multi-input data set.•An effective method is proposed for the operation adjustment of shield machine. Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation. This study therefore introduced a framework to predict the attitude and position of shield machine by combining long short-term memory (LSTM) model with attention mechanism. The data obtained from the Wuhan Rail Transit Line 6 project were utilized to verify the feasibility of the proposed method. By adding the attention mechanism into the LSTM model, the proposed model can focus more on parameters with higher weights. Sensitivity analysis based on Pearson correlation coefficient was conducted to improve the prediction efficiency and reduce the irrelevant input parameters. Compared with LSTM model, LSTM-attention model has higher accuracy. The mean value of coefficient of determination (R2) increases from 0.625 to 0.736, and the mean value of root mean square error (RMSE) decreases from 3.31 to 2.24. The proposed LSTM-attention model can provide an effective prediction for attitude and position of shield machine in practical tunneling engineering. |
| Author | Luo, Han-Bin Chen, Elton J. Liu, Yong Kang, Qing Li, Zhong-Chao |
| Author_xml | – sequence: 1 givenname: Qing surname: Kang fullname: Kang, Qing organization: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China – sequence: 2 givenname: Elton J. surname: Chen fullname: Chen, Elton J. email: eltonjchen@hust.edu.cn organization: School of Civil & Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan 430074, China – sequence: 3 givenname: Zhong-Chao surname: Li fullname: Li, Zhong-Chao organization: Tunnel Engineering Company, Wuhan Municipal Construction Group Co. Ltd, Wuhan 430023, China – sequence: 4 givenname: Han-Bin surname: Luo fullname: Luo, Han-Bin organization: School of Civil & Hydraulic Engineering, Huazhong University of Science & Technology, Wuhan 430074, China – sequence: 5 givenname: Yong surname: Liu fullname: Liu, Yong organization: State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China |
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| Keywords | Shield machine Attitude and position prediction Attention mechanism LSTM Tunnel excavation |
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| Snippet | •The LSTM-attention model is more effective than the LSTM model.•LSTM-attention model can predict the attitude and position of shield machine.•Attention... Shield machine may deviate from its design axis during excavation due to the uncertainty of geological environment and the complexity of operation. This study... |
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| SubjectTerms | Attention mechanism Attitude and position prediction LSTM Shield machine Tunnel excavation |
| Title | Attention-based LSTM predictive model for the attitude and position of shield machine in tunneling |
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