关键词: 地铁隧道, 组合预测模型, 深度学习, 盾构竖向姿态, 长短期记忆神经网络, 支持向量回归

Abstract: To accurately predict the vertical posture of shields, a deeplearning combination prediction model based on long shortterm memory (LSTM) neural network and support vector regression (SVR) is proposed. The LSTM and SVR verticalposture prediction models are developed by performing the corresponding data preprocessing operations on the collected verticalposture data. Further, the prediction results of the two models are weighted by the optimal combination of weights to obtain a LSTMSVR combined prediction model. Finally, to verify the reliability of the developed LSTMSVR combined deeplearning prediction model, the prediction results are compared with those of the LSTM, SVR, and BP models based on the Kunming metro project. The results show that the LSTMSVR combined deeplearning prediction model has high prediction accuracy.

Key words: metro tunnel, combined prediction model, deep learning, shield vertical posture, long shortterm memory (LSTM) neural network, support vector regression (SVR)