Development of neuro-fuzzy models for predicting shear behavior of rock joints

The purpose of this article is to present predictive models of dilation and shear stress of rock discontinuities by applying the neuro-fuzzy technique, which uses a) the high capacity of artificial neural networks (ANN) to understand and to model complex multivariate phenomena, and b) the concepts of fuzzy sets theory to consider the variability of the input parameters in the proposed models' responses. To develop the proposed models, experimental results were obtained from large-scale direct shear tests performed on different types of rock discontinuities and boundary conditions. The input variables of the proposed neuro-fuzzy models are the normal boundary stiffness, the ratio of fill thickness to asperity height, the initial normal stress, the joint roughness coefficient, the uniaxial compressive strength of the intact rock, the basic friction angle of the intact rock, the friction angle of the infill, and the shear displacement. The proposed models for dilation and shear stress provided results that fitted satisfactorily the experimental data, and the analyses of their performances indicated that they can represent the influence of the input variables on the shear behavior parameters of the rock discontinuities. The results from the neuro-fuzzy systems developed are also closer to the experimental data than those estimated by using traditional analytical methodologies existing in Rock Mechanics. This occurs because once considering the uncertainty of the input data, a more representative shear behavior prediction can be made by the neuro-fuzzy models.