| Abstrakt: |
In this study, the static E50 and υ parameters of rock materials were investigated using P-S wave velocities and Shore hardness (SH), using non-destructive measurement methods. In this study, the multiple linear regression (MLR), multiple non-linear regression (MNLR), and artificial neural network (ANN) models were used to estimate and determine the static E50 and υ parameters. When comparing the models defined by MLR, MNLR, and ANN to the R2 values, it was found that the ANN models, which estimate the E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, ρd, and SH), achieved higher accuracy than the MLR and MNLR models. The originality of this study is rooted in the fact that ores such as galena, chromite, and barite were studied for the first time from a rock mechanics perspective, providing an innovative viewpoint. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the importance of the study findings. [ABSTRACT FROM AUTHOR] |
