A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine
A tunnel boring machine (TBM) is an important large-scale engineering machine, which is widely applied in tunnel construction. Precise cutterhead torque prediction plays an essential role in the cost estimation of energy consumption and safety operation in the tunneling process, since it directly in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/8/2907 |
| _version_ | 1797434791152320512 |
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| author | Tao Fu Tianci Zhang Xueguan Song |
| author_facet | Tao Fu Tianci Zhang Xueguan Song |
| author_sort | Tao Fu |
| collection | DOAJ |
| description | A tunnel boring machine (TBM) is an important large-scale engineering machine, which is widely applied in tunnel construction. Precise cutterhead torque prediction plays an essential role in the cost estimation of energy consumption and safety operation in the tunneling process, since it directly influences the adaptable adjustment of excavation parameters. Complicated and variable geological conditions, leading to operational and status parameters of the TBM, usually exhibit some spatio-temporally varying characteristic, which poses a serious challenge to conventional data-based methods for dynamic cutterhead torque prediction. In this study, a novel hybrid transfer learning framework, namely TRLS-SVR, is proposed to transfer knowledge from a historical dataset that may contain multiple working patterns and alleviate fresh data noise interference when addressing dynamic cutterhead torque prediction issues. Compared with conventional data-driven algorithms, TRLS-SVR considers long-ago historical data, and can effectively extract and leverage the public latent knowledge that is implied in historical datasets for current prediction. A collection of in situ TBM operation data from a tunnel project located in China is utilized to evaluate the performance of the proposed framework. |
| first_indexed | 2024-03-09T10:37:45Z |
| format | Article |
| id | doaj.art-692631c51e044429a776c10ba9215b66 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T10:37:45Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-692631c51e044429a776c10ba9215b662023-12-01T20:49:38ZengMDPI AGEnergies1996-10732022-04-01158290710.3390/en15082907A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring MachineTao Fu0Tianci Zhang1Xueguan Song2School of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, ChinaSchool of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, ChinaA tunnel boring machine (TBM) is an important large-scale engineering machine, which is widely applied in tunnel construction. Precise cutterhead torque prediction plays an essential role in the cost estimation of energy consumption and safety operation in the tunneling process, since it directly influences the adaptable adjustment of excavation parameters. Complicated and variable geological conditions, leading to operational and status parameters of the TBM, usually exhibit some spatio-temporally varying characteristic, which poses a serious challenge to conventional data-based methods for dynamic cutterhead torque prediction. In this study, a novel hybrid transfer learning framework, namely TRLS-SVR, is proposed to transfer knowledge from a historical dataset that may contain multiple working patterns and alleviate fresh data noise interference when addressing dynamic cutterhead torque prediction issues. Compared with conventional data-driven algorithms, TRLS-SVR considers long-ago historical data, and can effectively extract and leverage the public latent knowledge that is implied in historical datasets for current prediction. A collection of in situ TBM operation data from a tunnel project located in China is utilized to evaluate the performance of the proposed framework.https://www.mdpi.com/1996-1073/15/8/2907tunnel boring machine (TBM)cutterhead torque predictionoperation parameterstransfer learning |
| spellingShingle | Tao Fu Tianci Zhang Xueguan Song A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine Energies tunnel boring machine (TBM) cutterhead torque prediction operation parameters transfer learning |
| title | A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine |
| title_full | A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine |
| title_fullStr | A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine |
| title_full_unstemmed | A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine |
| title_short | A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine |
| title_sort | novel hybrid transfer learning framework for dynamic cutterhead torque prediction of the tunnel boring machine |
| topic | tunnel boring machine (TBM) cutterhead torque prediction operation parameters transfer learning |
| url | https://www.mdpi.com/1996-1073/15/8/2907 |
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