基于注意力加强Bi-LSTM模型的TBM掘进参数预测研究  

Study on Prediction of TBM Tunnelling Parameters Based on Attentionenhanced Bi-LSTM Model

作　　者：张庆龙 朱燕文 马睿[2] 严冬 杨传根 崔同欢 李庆斌[2] ZHANG Qinglong;ZHU Yanwen;MA Rui;YAN Dong;YANG Chuangen;CUI Tonghuan;LI Qingbin(School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083;State Key Laboratory of Water and Sediment Science and Water Conservancy and Hydropower Engineering,Tsinghua University,Beijing 100084;Huaneng Tibet Hydropower Safety Engineering Technology Research Center,Chengdu 610041)

机构地区：[1]北京科技大学土木与资源工程学院,北京100083 [2]清华大学水沙科学与水利水电工程国家重点实验室,北京100084 [3]华能西藏水电安全工程技术研究中心,成都610041

出　　处：《现代隧道技术》2022年第4期69-80,共12页Modern Tunnelling Technology

基　　金：中央高校基本科研业务费专项资金资助项目(FRF-TP-20-043A1);水沙科学与水利水电工程国家重点实验室开放研究基金资助课题(sklhse-2021-C-04);华能集团总部科技项目(HNKJ19-H15)。

摘　　要：使用Bi-LSTM模型预测TBM掘进参数时在模型训练用时与收敛速度方面存在不足。对传统注意力机制使用方式进行改进,提出了一种以并行融合方式形成的注意力加强的双向长短时记忆网络(Bi-LSTM+EMB_ATT)智能预测模型,并选取完整掘进循环数据预测TBM稳定段的掘进参数。采集吉林引松工程TBM3标段的运行数据并划分训练集和测试集,采用二值状态判别函数等方法预处理数据,利用皮尔逊分析结果选取完整掘进循环段21维掘进参数作为预测模型的输入,并对比分析并行连接的Bi-LSTM+EMB_ATT模型与串行连接的BiLSTM+ATT模型。结果表明,Bi-LSTM+EMB_ATT模型对TBM掘进参数的预测拟合优度均达0.91以上,平均绝对误差均小于2.7%,比Bi-LSTM+ATT模型的预测精度更高。Prediction of TBM tunnelling parameters with Bi-LSTM model is subject to shortcomings in model training time and convergence speed. So, an intelligent prediction model of attention-enhanced bidirectional long-/shortterm memory network(Bi-LSTM+EMB_ATT) formed by parallel fusion was proposed by improving the use of traditional attention mechanisms, and the complete tunnelling cycle data was selected to predict the tunnelling parameters in the stable section of TBM. The operation data of TBM3 Section of Jilin Songhua River Diversion Project were collected and divided into a training set and a test set and preprocessed by the binary state discriminant function and other methods, and then the Pearson method was adopted to analyze the results. The 21-dimensional tunnelling parameters of the complete tunnelling cycle section was used as the input of the prediction model to compare and analyze the parallel-connected Bi-LSTM+EMB_ATT model and serial-connected Bi-LSTM+ATT model. The results show that the goodness of fit of Bi-LSTM+EMB_ATT model for predicting TBM tunnelling parameters is above0.91 with average absolute error less than 2.7%, which is higher than that of Bi-LSTM+ATT model.

关 键 词：TBM 注意力机制加强 Bi-LSTM模型 完整掘进周期 掘进参数预测 

分 类 号：U455.43[建筑科学—桥梁与隧道工程]