Predicting the Uniaxial Compressive and Tensile Strengths of Gypsum Rock by Point Load Testing

Rock Fracture and Rock Strength

“…For example, the uniaxial compressive strength and point load index of gypsum rock had a good linear relation [21,22]. Similarly, tensile strength and point load index had a roughly linear relation [22]. Moreover, in point load testing the rock samples were usually separated into two pieces from one crack or one surface (sometimes maybe more), similar to the failure pattern in tensile strength testing.…”

“…For example, the uniaxial compressive strength and point load index of gypsum rock had a good linear relation [21,22]. Similarly, tensile strength and point load index had a roughly linear relation [22].…”

Selection of site-specific regression model for characterization of uniaxial compressive strength of rock

“…Many authors have correlated UCS with Is 50 ð Þ in the past, this has brought about many regression equations. For example, Broch and Franklin [6], Chau and Wong [15], Yasar et al [17], Heidari et al [18] and Tugrul and Zarif [19] among others have come up with different regression models between UCS and Is 50 ð Þ . With equations and conversion factors in literature varying a lot, it is clear that there is no unique regression equation which can be used even for the same type of rock.…”

“…This leads to the need to have a rational method of selecting the most appropriate model for a specific site/deposit. Approaches in the past have always relied on both Is 50 ð Þ and UCS test data to generate regressions [6][7][8][9][10][11][12][13][14][15][16][17][18][19] and to evaluate different regressions for a specific site/deposit. However, if the concerned rock is weak or thinly bedded or densely fractured, most times there are difficulties in preparing the rock samples for laboratory compression test.…”

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]).…”