Prediction Method of TBM Key Tunneling Parameters Based on Real-time Operation Data

Yiheng Wang*, Yaoguang Hu, Jian Shi, Yongchao Zhu, Tao Zhou, Lixiang Zhang

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
编辑Wenjian Cai, Guilin Yang, Jun Qiu, Tingting Gao, Lijun Jiang, Tianjiang Zheng, Xinli Wang
出版商Institute of Electrical and Electronics Engineers Inc.
1827-1832
页数6
ISBN(电子版)9798350312201
DOI
出版状态已出版 - 2023
活动18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023 - Ningbo, 中国
期限: 18 8月 202322 8月 2023

出版系列

姓名Proceedings of the 18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

会议

会议18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023
国家/地区中国
Ningbo
时期18/08/2322/08/23

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