Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate

Determination of the uniaxial compressive strength (UCS) and modulus of elasticity of block-in-matrix rocks (bimrocks) is often impossible in the laboratory since the preparation of the standard core samples from bimrocks is extraordinarily difficult. For this reason, some predictive models were developed to estimate the UCS and modulus of elasticity based on the volumetric portion of blocks in Ankara Agglomerate, which is composed of black and pink andesite blocks in a tuff matrix. The ratio of E_imin of blocks (5.99 GPa) to E_imax of the tuff matrix (2.83 GPa) is 2.2 for Ankara Agglomerate. In addition to this contrast, the minimum ratio of UCS values of andesite blocks (34.99 MPa) to matrix tuff (14.4 MPa) is 2.4. In the first stage of the study, fuzzy logic was used as a tool for the prediction of the UCS of Ankara Agglomerate based on its block and matrix constituents. UCS values for 164 agglomerate cores were evaluated in the prediction model based on fuzzy logic. A triangular chart expressed by “if-then” rules considers different constituent composition of the agglomerate. Considering the membership functions depending on the portion of constituents, a Mamdani fuzzy algorithm was constructed and a fuzzy triangular chart was obtained for the estimation of the UCS of the agglomerate. The ‘variance accounts for’ (VAF) and the root mean square error (RMSE) indices were calculated as 56.9% and 7.3, respectively, to characterize the prediction performance of the triangular chart. In the second stage of the study, the goal was to construct a prediction model for the estimation of the modulus of the elasticity. Regression analyses were performed using 103 UCSs and the unit weight data obtained from core samples prepared from tuff matrix, black and pink andesite blocks and agglomerate. An equation having a correlation coefficient of 0.951 was obtained from the regression analyses. The VAF and RMSE indices for the multiple regression equation were obtained as 88.8% and 0.84, respectively. Both correlation coefficient and the performance indices indicated that the prediction capacity of the equation is high.