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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.