摘要
根据公路隧道岩体分级标准的要求，全面考虑各种分级指标，建立了公路隧道岩体分级的神经网络方法。对各类定性和定量指标数据进行了向量规范化处理，选取适宜的分级指标，建立起神经网络学习样本，并进行样本学习训练，其结果可作为各种公路隧道岩体分级的评判依据。据此，对韩家岭隧道围岩进行了分级和评价，证实所建立分级方法的可行性、准确性和优越性。
According to classification criterion of highway tunnel, the classification indexes are comprehensively considered, and neural networks method of rock mass classification of highway tunnel is established. Proper classification indexes are chosen and standardization of indexes data are conducted by vector method, and training samples of neural networks are built. Training results of neural networks can provide the judgement basis for rock mass classification of highway tunnel. Hereby, surrounding rock mass in Hanjialing tunnel is classified. The feasibility, veracity, and advantage of founded neural networks method are verified.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2005年第A01期5248-5255,共8页
Chinese Journal of Rock Mechanics and Engineering
基金
交通部重点科技攻关项目（2002-3533207）
关键词
隧道工程
公路隧道
岩体分级
神经网络
tunneling engineering
highway tunnel
rock mass classification
neural networks
