Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model
With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/3743472 |
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| _version_ | 1832565581781401600 |
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| author | Qinglong Zhang Boyu Yang Yanwen Zhu Chen Guo Chong Jiao Anmin Cai |
| author_facet | Qinglong Zhang Boyu Yang Yanwen Zhu Chen Guo Chong Jiao Anmin Cai |
| author_sort | Qinglong Zhang |
| collection | DOAJ |
| description | With tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction. |
| format | Article |
| id | doaj-art-4dce3e9f5fe34dc3b2fae1038422018c |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4dce3e9f5fe34dc3b2fae1038422018c2025-02-03T01:07:12ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/3743472Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT ModelQinglong Zhang0Boyu Yang1Yanwen Zhu2Chen Guo3Chong Jiao4Anmin Cai5Department of Civil EngineeringChina Huaneng Clean Energy Research InstituteDepartment of Civil EngineeringChina Huaneng Clean Energy Research InstituteChina Huaneng Clean Energy Research InstituteChina Huaneng Clean Energy Research InstituteWith tunnel boring machines (TBMs) widely used in tunnel construction, the adaptable adjustment of TBM operating status has become a research focus. Since the prediction of tunnel geological conditions is still challenging before excavating, the prediction of important TBM operating parameters plays an important role in the research on TBM adaptable adjustment. This paper proposes an intelligent prediction method of TBM tunneling parameters based on bidirectional gate recurrent unit incorporating attention mechanism (Bi-GRU-ATT) and selects a complete tunneling cycle to predict the tunneling parameters of the TBM complete tunneling cycle. Relying on the TBM3 bid section of Jilin Water Supply Project, 21 key parameters of the complete tunneling cycle are selected as the input features of the model to realize the prediction of four tunneling parameters in the complete driving cycle section of TBM. Compared with the Bi-GRU, GRU, and Long Short-Term Memory (LSTM) models, it can be seen that the Bi-GRU-ATT model has a goodness of fit for predicting TBM tunneling parameters above 0.92, and the average absolute percentage error is less than 1.8%. The results show that the prediction method of TBM tunneling parameters based on Bi-GRU-ATT model proposed in this paper has stronger learning and prediction capabilities. This prediction method provides a more feasible auxiliary intelligent decision-making method for TBM aided intelligent construction.http://dx.doi.org/10.1155/2022/3743472 |
| spellingShingle | Qinglong Zhang Boyu Yang Yanwen Zhu Chen Guo Chong Jiao Anmin Cai Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model Advances in Civil Engineering |
| title | Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model |
| title_full | Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model |
| title_fullStr | Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model |
| title_full_unstemmed | Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model |
| title_short | Prediction Method of TBM Tunneling Parameters Based on Bi-GRU-ATT Model |
| title_sort | prediction method of tbm tunneling parameters based on bi gru att model |
| url | http://dx.doi.org/10.1155/2022/3743472 |
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