Abstract: 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 shortterm 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 MICLSTM 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 backpropagation neural network. The results show that the constructed dynamic prediction model for shield tunnelinginduced surface deformation demonstrates high prediction accuracy.

Key words: metro tunnel, shield, surface deformation, dynamic prediction, maximal information coefficient, long shortterm memory neural network