@inproceedings{56700ec309d04a179e1b179d26542e51,
title = "Prediction Method of TBM Key Tunneling Parameters Based on Real-time Operation Data",
abstract = "The optimal configuration of key tunnelling parameters of Tunnel Boring Machine (TBM) is the key to the safety and efficiency of tunnel construction. This paper proposes a method for predicting key tunnel parameters based on real-time TBM operation data to ensure real-time prediction and accuracy. The TBM working phases are first divided, then extract the important parameters affecting the prediction, and finally the key parameters are predicted using the Gated Recurrent Unit (GRU)neural network algorithm. The method was validated using TBM operation data from the Jilin Yin Song Project. The results shown that the method in this paper is highly accurate in predicting the three key parameters of total thrust, propulsion speed and cutter torque during the stable operation phase of the TBM. and a comparative study with three other algorithms proved that the chosen algorithm works best.",
keywords = "TBM parameter prediction, machine learning, time series forecasting",
author = "Yiheng Wang and Yaoguang Hu and Jian Shi and Yongchao Zhu and Tao Zhou and Lixiang Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 ; Conference date: 18-08-2023 Through 22-08-2023",
year = "2023",
doi = "10.1109/ICIEA58696.2023.10241471",
language = "English",
series = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1827--1832",
editor = "Wenjian Cai and Guilin Yang and Jun Qiu and Tingting Gao and Lijun Jiang and Tianjiang Zheng and Xinli Wang",
booktitle = "Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023",
address = "United States",
}