摘要
岩体的复杂性以及围岩分类中存在大量的不确定因素给围岩的正确分类造成了一定的困难。将支持向量机方法用于对围岩的分类,并与ART1神经网络和BP神经网络的预测结果进行对比,表明:将支持向量机用于围岩分类是可行的,并且具有很好的精度。
Surrounding rock classification is an important work for the underground structure and tunnel engineering, it is the necessary gist for design and construction. The method of Support Vector Machines was introduced in this paper, and its contrast with the ARTI neural network and BP neural network was given. The results indicate that surrounding rock classification with the Support Vector Machines model is feasible, and a high precision can be gained.
出处
《地下空间与工程学报》
CSCD
2008年第5期803-805,942,共4页
Chinese Journal of Underground Space and Engineering
关键词
围岩分类
影响因素
支持向量机
surrounding rock classification
effect factors
support vector machines
