隧道围岩变形预测的对比研究

Comparative Study on Prediction Methods for Tunnel Surrounding Rock Deformation

为了精确预测隧道围岩的收敛变形,文章采用径向基函数神经网络原理和非等时距GM(1,1)灰色系统理论,建立了隧道围岩收敛变形的预测模型,借助MATLAB 2010b平台编写了两种模型的拟合程序,并结合张涿高速公路林里隧道工程围岩收敛的实测数据,对两种模型进行训练,并做了收敛变形预测。通过两种模型预测值与实测值的对比分析,以及模型误差的检验表明,径向基神经网络模型的预测值更为精确,其预测曲线与实测值吻合更好,更能够反映围岩收敛变形的变化规律。

In order to precisely predict the deformation of a tunnel＇s surrounding rocks, the principles of radial basis function neural network and non-equal time-interval GM （1,l） grey system theory were adopted to establish prediction models. The fitting programs for two prediction models were compiled by way of the MATLAB-2010b platform. Based on the measured rock deformation data from the Linli tunnel on the Zhangzhuo Highway, deformation prediction was carried out by model training. According to a comparative analysis of the predicted values and measured values and a model error verification test, it was revealed that the prediction results of a radial basis function neural network （RBFNN） model are more precise and that the predicted deformation curve agrees better with that of measured one, truly reflecting the variation law of surrounding rock deformation.