岩石节理力学参数的非线性估计

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，该关系式可用于ＪＲＣ值的近似分形预测

Nonlinear estimation of rock joint mechanical parameters

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.