关键词: 深埋隧道, 围岩变形预测, 灰色-进化神经网络模型

Abstract: 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.

Key words: deep buried tunnel, prediction of wall-rock deformation, Grey-ENN model