中南大学学报（自然科学版）2024，Vol.55Issue(4)：1477-1491,15.DOI:10.11817/j.issn.1672-7207.2024.04.019
基于深度学习的超大直径盾构姿态预测研究
Research on attitude prediction of super large diameter shield based on deep learning
摘要
Abstract
The traditional attitude correction measure of the shield machine is mostly a passive control measure taken after the actual axis of the shield machine has deviated from the design axis,which has a certain lag.The lagged attitude correction of the shield will bring serious harm to the construction process and the tunnel itself after completion.To accurately predict the attitude deviation of the shield and provide decision support for correction in advance,a CNN-EMD-LSTM based deep learning model was proposed in this paper based on the Jiangyin—Jingjiang Yangtze River Tunnel super large diameter shield construction project,which can not only capture the dimensional and time-varying features of the time series,but also improve the prediction accuracy by decomposing and reconstructing the attitude data of the shield.The importance of each part of the CNN-EMD-LSTM model was discussed through ablation experiment.Furthermore,the prediction effect of CNN-EMD-LSTM model under different window lengths and different sliding steps was compared.The results show that the CNN-EMD-LSTM model has a good effect on predicting the attitude of the super large diameter shield.The attitude deviation of the shield can be corrected by adjusting the pressure in different advancing regions.The importance of each part of the CNN-EMD-LSTM model is EMD,CNN and LSTM in descending order.Too large or too small window length will increase the prediction error of the model,and the smaller the sliding step length,the better the prediction effect of the model.关键词
超大直径盾构/姿态预测/姿态纠偏/消融实验/CNN-EMD-LSTMKey words
super large diameter shield/attitude prediction/attitude correction/ablation experiment/CNN-EMD-LSTM分类
交通工程引用本文复制引用
丰土根,胡锦健,张箭..基于深度学习的超大直径盾构姿态预测研究[J].中南大学学报（自然科学版）,2024,55(4):1477-1491,15.基金项目
国家自然科学基金资助项目(52178386,52378336) （52178386,52378336）
中央高校基本科研业务费专项资金资助项目(B220202016)(Projects(52178386,52378336)supported by the National Natural Science Foundation of China （B220202016）
Project(B220202016)supported by the Fundamental Research Funds for the Central Universities) （B220202016）
