Multi-step prediction model enhanced by adaptive denoising and encoder-decoder for shield machine cutterhead torque in complex conditions
•A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working condi...
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| Published in: | Tunnelling and underground space technology Vol. 158; p. 106398 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.04.2025
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| Subjects: | |
| ISSN: | 0886-7798 |
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| Abstract | •A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working conditions.
Cutterhead torque reflects the obstruction extent of geological environment on the shield machine, and its prediction can assist operators to adjust control parameters to improve construction efficiency and avoid machine jamming. However, tunneling in complex geological or working conditions often results in high cutterhead torque fluctuations and noise, which seriously affects the accuracy of torque prediction. This study proposes a multi-step prediction model for cutterhead torque enhanced by adaptive denoising and encoder-decoder. In this model, a novel adaptive denoising method for cutterhead torque is employed to improve prediction accuracy under complex conditions. Moreover, by introducing encoder-decoder method, the processing capability for multi-time dimensional data and multi-step prediction performance of LSTM neural networks are further improved. The effectiveness of proposed model is verified through an application to the Heyan Road River Crossing project. The results of this study can assist operators in achieving precise adjustment of control parameters under complex conditions. |
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| AbstractList | •A multi-step prediction model for cutterhead torque is proposed.•Adaptive denoising and encoder-decoder are used to improve the model performance.•The model was validated using site data from the Heyan Road tunnel project.•This model has promising performance in complex geological and working conditions.
Cutterhead torque reflects the obstruction extent of geological environment on the shield machine, and its prediction can assist operators to adjust control parameters to improve construction efficiency and avoid machine jamming. However, tunneling in complex geological or working conditions often results in high cutterhead torque fluctuations and noise, which seriously affects the accuracy of torque prediction. This study proposes a multi-step prediction model for cutterhead torque enhanced by adaptive denoising and encoder-decoder. In this model, a novel adaptive denoising method for cutterhead torque is employed to improve prediction accuracy under complex conditions. Moreover, by introducing encoder-decoder method, the processing capability for multi-time dimensional data and multi-step prediction performance of LSTM neural networks are further improved. The effectiveness of proposed model is verified through an application to the Heyan Road River Crossing project. The results of this study can assist operators in achieving precise adjustment of control parameters under complex conditions. |
| ArticleNumber | 106398 |
| Author | Wang, Yuan Zhou, Kun Xu, Deming Huang, Jingqi Xu, Shujun |
| Author_xml | – sequence: 1 givenname: Deming surname: Xu fullname: Xu, Deming organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 2 givenname: Yuan surname: Wang fullname: Wang, Yuan organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 3 givenname: Jingqi surname: Huang fullname: Huang, Jingqi email: huangjingqi11@163.com organization: Beijing Key Laboratory of Urban Underground Space Engineering, School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China – sequence: 4 givenname: Shujun surname: Xu fullname: Xu, Shujun organization: China Railway 14th Bureau Group Co, Ltd, Nanjing, Jiangsu 210098, China – sequence: 5 givenname: Kun surname: Zhou fullname: Zhou, Kun organization: China Railway 14th Bureau Group Co, Ltd, Nanjing, Jiangsu 210098, China |
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| Keywords | Shield machine Long short-term memory Cutterhead torque Encoder-decoder model Multi-step prediction |
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