基于遗传-径向基函数神经网络的公路隧道围岩定级方法

围岩分级准确与否直接关系到隧道的施工安全和工程造价。针对现阶段围岩分级方法存在的主要问题，结合宁绩高速公路隧道群施工期围岩定级实践，以国标BQ分级为基准，在大量现场测试和室内试验的基础上，引入径向基函数神经网络，并以分级结果作为遗传-径向基函数神经网络的训练样本，建立了隧道围岩分级的遗传-径向基函数神经网络模型。应用实例表明，该模型分级结果与现场勘测基本一致，为隧道围岩分级提供了一种新方法。

A New Classification Method for Rock Mass of Highway Tunnels Based on Genetic-Radial Basis Function Neural Network

The accuracy of rock mass classification has close relationship with the safety and cost of tunnel construction.Due to the disadvantages of the present rock mass classification methods,a new rock mass classification method based on radial basis function neural network is introduced on basis of the national BQ rock mass classification standard,a large number of site measurements and indoor tests,as well as the rock mass classification practice made in the construction of the tunnel group on Ninguo-Jixi highway.The classification results are served as the training samples for genetic-RBF neural network training.Therefore,the intelligent classification model is established after network training has been finished.The application of this model shows that the result of the classification made on basis of the genetic-RBF neural network agrees with that made on basis of site reconnaissance.The genetic-RBF neural network developed provides a new approach for the classification of surrounding rock mass of tunnels.