With the comprehensive effect of structure

stress distribution

groundwater

opening mode

etc.

displacement time series of the deep buried tunnels’ wall-rock display monotone increasing characteristics and nonlinearity. Based on the responding composition model

the displacement of tunnels’ wall-rock is decomposed into trend item and random item. The trend and random items of displacement time series are predicted by Grey-ENN model

which combines the advantages of GM(1

1) and ANN model. GM(1

1) can forecast the trend of increasing sequence

and ANN model can reduce error by self-adapting of network. Thus the correctness of prediction is assured due to avoiding the problem that forecast result is far away from real one by using GM(1

1) only. The Grey-ENN model is applied to predict the short-term convergence displacement of wall-rock in Baozhen Tunnel based on the in-situ displacement data. The reasonable convergence rule of tunnels wall-rock is achieved

which provides eference for choosing opportunity to do the second support.