Determination of deformation modulus in a weak rock mass by using menard pressuremeter
Özet
The deformation modulus of heavily jointed andesitic rock mass was investigated by using 41 Menard pressuremeter test results and geotechnical information obtained from seven geotechnical boreholes. The geotechnical borehole logs and laboratory test results provided by a mining company were used for the assessment of rock mass characterization. The log and the test results were taken into account in the rock mass classification work. The well-known empirical equations were employed to predict the deformation modulus of the rock mass. The predictions and the pressuremeter test results were compared and their performance indicators were presented. Non-linear multiple regression methods were used for predictive modelling of the deformation modulus of the rock mass by considering the available data. Rock Quality Designation (RQD), discontinuity condition rating (Dc) of the Rock Mass Rating (RMR) system and uniaxial compressive strength of the intact rock (sigma(ci)) were taken into consideration as input parameters in four new prediction equations to be used for determination of deformation modulus. One more equation was proposed by the addition of the depth as a predictive parameter. The influence of the depth and vertical to horizontal stress ratio (k) on the deformation modulus was preliminarily examined by using finite element modelling. Initially, the rock mass was assumed to be an isotropic elastic-brittle-plastic medium. Later on, the rock mass was modelled as a discontinuum by imposing a discrete fracture network (DEN). Keeping all of the mechanical properties constant, different depth and k parameters were applied to the pressuremeter models. No influence of the depth and k on the deformation modulus was observed for the isotropic medium while the depth contributed to an increase in the modulus in the anisotropic discontinuum analysis. The numerical modelling findings constituted a basis for the inclusion of the depth parameter into the new predictions.
