关键词: 地铁隧道, 循环神经网络, 监测数据, 预测, 深度学习

Abstract: The existing data processing methods cannot effectively analyze and predict the discrete and volatile measurement data. Accordingly, a model is established for metro tunnel construction monitoring data based on the deep learning recurrent neural network algorithm (RNN). Moreover, a stacked long shortterm memory model is constructed to analyze and predict the deformation of crown subsidence of a metro tunnel. The results of the models are compared with those of grey prediction, BP neural network, nonlinear autoregression neural network, and support vector regression. The result shows the following: (1) The RNN is sensitive to time series, and it is superior to other methods in terms of prediction of monitoring data. The RNN data prediction method recommended realizes the accurate prediction of all kinds of monitoring data. (2) The RNN is characterized by fast convergence, good stability, and high prediction accuracy.

Key words: metro tunnel, recurrent neural network, monitoring data, prediction, deep learning