深埋隧道围岩变形预测的非线性组合模型

深埋隧道围岩变形受地应力、地下水、开挖方式等多种因素共同影响，表现为位移序列高度的非线性，为此，提出了基于变形信息融合的非线性组合预测模型。该模型以灰色GM（1,1）模型、RBF模型两种单项预测数据为基础，采用神经网络求取组合预测模型中单项模型所占权重，构建非线性组合预测，并将该模型应用于某深埋隧道围岩变形预测，同时将非线性组合预测的结果和简单平均定权组合、最优线性加权组合进行了比较。研究结果表明：所提出的方法较传统的定权方法在预测精度方面有明显的提高，预测结果更为稳健，在深埋隧道围岩变形预测中具有较好的工程和实践价值。

Non-linear Combination Forecast Model to Predict Surrounding Rock Deformation in Deep Buried Tunnels

Affected by stress distribution，groundwater，opening mode，etc.，the surrounding rock deformation of the deep buried tunnels show nonlinearity in displacement series height.Thus，the non-linear combination model based on the deformation information fusion was proposed.In this model，based on the individual forecast data of GM (1，1) and RBF，and with the use of the neural network，the weight of each model among the combined model was optimized to build a nonlinear combined forecast model.Then，the non-linear combination forecast model was applied to predict surrounding rock deformation in deep buried tunnels.Meanwhile，non-linear combination predictions were contrasted with simple average weighting combination and the optimal linear weighted combination.The results showed that compared with the traditional weighting method，the non-linear combination forecast model has a higher and more reliable precision，and owns a more stable prediction result.It is of a certain theoretical and practical significance in surrounding rock deformation prediction for deep buried tunnel.