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

Tao Fu, Tianci Zhang and Xueguan Song
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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

Energies, 2022, vol. 15, issue 8, 1-17

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.

Keywords: tunnel boring machine (TBM); cutterhead torque prediction; operation parameters; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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