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 shortterm 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 autoregression 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.
HE Rongguo, ZHANG Xiaoyong, WANG Xu, ZHAO Ziyue, AN Shunbiao
. Analysis and Prediction Method for Metro Tunnel Monitoring
Data Based on Deep Learning[J]. Tunnel Construction, 2021
, 41(S2)
: 261
-267
.
DOI: 10.3973/j.issn.2096-4498.2021.S2.033
