摘要
通过建立一个多层神经网络模型ＮＮ（ｎ，ｈ１，ｈ２，１），探讨了描述节理开度与剪切位移之间的非线性关系和尺度效应的新方法，由小尺度试件节理的实测数据建立的非线性模型可以推广地预测出较大一些尺度试件的节理开度值。对３７条现场实测的节理进行了分形特征研究，建立了分形维数与ＪＲＣ关系式。
Aperture and roughness are two of the most important parameters in jointed rockmass mechanics and joint seepage mechanics. In this paper, a new method is proposed to establish nonlinear relationship between normal aperture and shear displacement in joint shear tests, which is described by a BP neural network NN( n,h 1,h 2,1). The model built from measured data of the shorter specimens obtained by cutting the longer specimens in the same length can be generalized to predict normal aperture of joints in the longer specimens. Fractal analysis was conducted for 37 joint profiles measured in site. A regressive formula was built for describing relationship between fractal dimensions and JRC values. The results indicate that these joints have fractal structure and the obtained formula can be used to fractal estimation for JRC values.
出处
《岩土工程学报》
EI
CAS
CSCD
北大核心
1999年第3期268-272,共5页
Chinese Journal of Geotechnical Engineering
基金
国家自然科学基金
关键词
岩石节理
开度
分形维数
剪切位移
非线性
rock joint, aperture, roughness, JRC,fractal dimension, neural network, scale effect, shear displacement
