Accurate prediction of surface deformation caused by
shield tunneling is very important in ensuring successful shield tunneling.
Accordingly, a dynamic prediction model for surface deformation induced by
shield tunneling based on maximal information coefficient (MIC)long
shortterm memory (LSTM) is proposed. First, the main
factors that affect the surface deformation are determined, and the MIC method
is used to determine the degree of correlation between each influencing factor
and surface deformation. Then, an LSTM neural network dynamic prediction model
is established by considering the weighted influencing factors and recent 10
surface deformation data at the center of the shield as input variables. The
next three deformation data are considered as output variables. Finally, to
verify the practicability of the constructed MICLSTM
dynamic prediction model, its prediction results for a shield project in
Kunming Metro Line No. 5 are
compared with those of the LSTM, recurrent neural network, and backpropagation neural network. The results show that the constructed
dynamic prediction model for shield tunnelinginduced
surface deformation demonstrates high prediction accuracy.
LI Zengliang
. Dynamic Prediction of Surface Deformation Induced by
Shield Tunneling Based on Maximal Information
CoefficientLong ShortTerm
Memory[J]. Tunnel Construction, 2021
, 41(2)
: 199
-205
.
DOI: 10.3973/j.issn.2096-4498.2021.02.005
