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A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine

Author

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  • Tao Fu

    (School of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China)

  • Tianci Zhang

    (School of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China)

  • Xueguan Song

    (School of Mechanical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian 116024, China)

Abstract

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.

Suggested Citation

  • Tao Fu & Tianci Zhang & Xueguan Song, 2022. "A Novel Hybrid Transfer Learning Framework for Dynamic Cutterhead Torque Prediction of the Tunnel Boring Machine," Energies, MDPI, vol. 15(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2907-:d:794678
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    References listed on IDEAS

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    1. Hu, Qinghua & Zhang, Rujia & Zhou, Yucan, 2016. "Transfer learning for short-term wind speed prediction with deep neural networks," Renewable Energy, Elsevier, vol. 85(C), pages 83-95.
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    Cited by:

    1. Amichai Mitelman & Alon Urlainis, 2023. "Investigation of Transfer Learning for Tunnel Support Design," Mathematics, MDPI, vol. 11(7), pages 1-15, March.

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