【作者】 郝哲； 王来贵； 王振伟； 于永江；

【Author】 HAO Zhe, WANG Lai-gui, WANG Zhen-wei, YU Yong-jiang (Department of Mechanics and Engineering Science, Liaoning Technical University, Fuxin 123000, China)

【机构】 辽宁工程技术大学力学与工程科学系；

【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。更多还原

【Abstract】 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.更多还原