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Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data

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Abstract

Real-time dynamic adjustment of the tunnel bore machine (TBM) advance rate according to the rock-machine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction. This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network (TCN), based on TBM construction big data. The prediction model was built using an experimental database, containing 235 data sets, established from the construction data from the Jilin Water-Diversion Tunnel Project in China. The TBM operating parameters, including total thrust, cutterhead rotation, cutterhead torque and penetration rate, are selected as the input parameters of the model. The TCN model is found outperforming the recurrent neural network (RNN) and long short-term memory (LSTM) model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two. The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment. On the contrary, the influence of the cutterhead rotation and total thrust is moderate. The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.

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Data Availability Statement Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including the model code developed by the authors, and the TBM data collected.

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Acknowledgements

The authors acknowledge the support of intelligent control and support software to safely and efficiently operate TBM tunnels from China Railway Engineering Equipment Group Co., Ltd. and the project team for the National Basic Research Program (973 program). Supports from National Natural Science Foundation of China (Grant No. 11902069), Sichuan University, State Key Lab Hydraul & Mt River Engn (No. SKHL1915), and the Research Project of China Railway First Survey and Design Institute Group Co., Ltd (No. 19-15 and No. 20-17-1) are also acknowledged. The work is partially supported by the 111 Project (B17009) and under the framework of Sino-Franco Joint Research Laboratory on Multiphysics and Multiscale Rock Mechanics.

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Institute of Deep Engineering and Intelligent Technology, Northeastern University, Shenyang, 110819, China

    Zaobao Liu, Yongchen Wang, Long Li & Junze Wang

  2. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China

    Zaobao Liu

  3. Software College, Northeastern University, Shenyang, 110819, China

    Xingli Fang

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  1. Zaobao Liu
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Correspondence to Zaobao Liu.

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Liu, Z., Wang, Y., Li, L. et al. Realtime prediction of hard rock TBM advance rate using temporal convolutional network (TCN) with tunnel construction big data. Front. Struct. Civ. Eng. 16, 401–413 (2022). https://doi.org/10.1007/s11709-022-0823-3

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