Use of multivariate regression for assessing rock mass permeability in Khersan 2 dam site using discontinuity system parameters

Abstract

Permeability assessment is one of the important properties in geomechanical investigation of rock mass determined from water pressure test (WPT) and Lugeon number. Results of these tests are used to determine the amount of groutability in tunnel, dam site and other water related constructions. Due to complex discontinuity patterns, it is almost impossible to determine the permeability of rock mass without a proper testing method. The purpose of this study is to develop various multivariate regression models to estimate the rock mass permeability in Khersan 2 dam site. To do this, a dataset including 28 cases with Lugeon test results and corresponding RQD (Rock Quality Designation), spacing of discontinuities and SCR (Surface Condition Rating) properties are employed. Three different models were developed to estimate the rock mass permeability. The inputs of the first model are RQD and SCR (Model 1), the inputs of the second model are discontinuity spacing and SCR (Model 2) and those of the third model are discontinuity spacing and RQD (Model 3). Simple regression analyses indicate that there is no statistically meaningful relationship between the Lugeon values and SCR. There is a statistically meaningful relationship, however, between the Lugeon values with discontinuity spacing and RQD. Non-linear multivariate regression analyses was conducted for two independent variables and one dependent variable because of nonlinear relationships between input and output. Finally, the validation of the results using statistical indicators shows that the accuracy of the proposed multivariate nonlinear regression relationship between Lugeon with RQD and the discontinuity spacing is higher than other relationships and it is more consistent with real data. The results show that with increasing the number of regression variables, the accuracy of the predicted Lugeon values increases and the experimental relationships are obtained with a higher correlation coefficient.