Empirical models and numerical analysis for assessing strength anisotropy based on block punch index and uniaxial compression tests

The effect of the rock anisotropy on the efficiency of diamond wire cutting machines

“…6 that the curves for UCS have a second order polynomial shape. This is similar to the results reported by Saroglou and Tsiambaos [40] for igneous rocks and Karakul et al [26] for sedimentary and metamorphic rocks. The minimum UCS is obtained for a (roughly) 251 ABP, whereas the maximum UCS (perpendicular to the loading direction) is obtained for the 901 ABP.…”

“…Moreover, it has been demonstrated that stress calculation models that simultaneously consider the nonlinear elasticity and transverse anisotropy of hard rocks were combined successfully by Corthesy et al [25]. Karakul et al [26] investigated the strength anisotropy of intact rock with using UCS and block punch index (BPI) tests on sedimentary and metamorphic rocks selected from Turkey and they developed some empirical relationships to estimate the BPI and UCS in the strongest direction. In addition, the researchers developed transformation equations for an indirect estimation of the UCS from the BPI and investigated the effect of anisotropic conditions with the aid of numerical analyses.…”

Discrete element method modeling of inherently anisotropic rocks under uniaxial compression loading

“…In this study, β is defined as the angle between the normal of weak layers and the maximum principal stress direction. The UCS of 26 anisotropic rocks is collected which belongs to six different rock types: phyllite [24,25], schist [2,[26][27][28], shale [3,4,[27][28][29], slate [5,30], sandstone [31], and limestone [28]. In Table II, the first column lists the UCS when β = 0°, the second column lists the minimum UCS which might be obtained when β is between 30°and 60°, and the third column lists the UCS when β = 90°.…”

Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

“…To date, point load test have yielded the best estimation and prediction of UCS index compared to other tests (Sonmez et al [35]; Diamantis et al [12]; Yilmaz and Yuksek [44]; Basu and Kamran [10]; Heidari et al [17]; Kohno and Maeda [23]; and Wong Li and Diyuan [42]; Khanlari and Abdi-lor [26]). Furthermore, block punch and cylinder punch tests have been used for predicting uniaxial compressive strength of different types of rocks (Van der Schrier [41]; Ulusay and Gokceoglu [39]; Gokceoglu and Aksoy [14]; Ulusay et al [40]; Sonmez et al [34][35]; Sonmez and Tunusluoglu [36]; Aksoy [3]; Aksoy et al [4][5]; Karakul et al [22]; Jafari et al [19]; Mishra and Basu [32]; Khanlari et al [24][25][26]; Abatan et al [1]; Khanlari and Naseri [27]; Heidari et al [18]). More recently, a wide variety of statistical methods have been utilized for developing a proper correlation between UCS index and other engineering properties of rocks, among which different statistics analysis models, multiple regression analysis, ANN model, fuzzy models, and ANFIS models have received a greater attention (Alvarez and Babuska [6]; Sonmez et al [34]; Yilmaz and Yuksek [44]; Kahraman et al [21]; Heidari et al [16][17]; Manouchehrian et al [29]; Mishra and Basu [32]; Torabi-Kaveh et al [38]; Armaghani et al [2]; Jalali [20]).…”