Selection of Influential Microfabric Properties of Anisotropic Amphibolite Rocks on Its Uniaxial Compressive Strength (UCS): A Comprehensive Statistical Study

Abstract

Occasionally, in complex inherent characteristics of certain rocks, especially anisotropic rocks it may be difficult to measure the uniaxial compressive strength UCS. However, the use of empirical relationships to evaluate the UCS of rock can be more practical and economical. Consequently, this study carried out to predict UCS from microfabrics properties of banded amphibolite rocks using multiple regression analysis. Based on statistical results, rock microfabric parameters, which adequately represent the UCS of a given rock type have been selected. The results show that grain size, shape factor and quartz content have high significant correlation with UCS at 95% confidence level. From multiple regression model, approximately 84% of the variance of the UCS can be estimated by the linear combination of these three parameters. However, according to model performance criteria: correlation coefficient (R = 0.919), variance account for (VAF = 97%) and root mean square error (RMSE = 4.16) the study clearly indicates that the developed model is reliable to predict the UCS. Finally, this approach can be easily extended to the modeling of rock strength in the absence of adequate geological information or abundant data.

Predicting the Engineering Properties of Rocks from Textural Characteristics Using Some Soft Computing Approaches

“…Several researchers have attempted to develop various soft computing models for predicting different parameters from others in material sciences [16][17][18][19][20][21][22][23][24][25] and engineering properties of different rock types from their petrographic characteristics in engineering geology and rock mechanics [26][27][28][29][30][31][32][33][34][35]. In the recent years, some research works have been performed to assess correlations between mineralogical and textural characteristics and mechanical properties of different rocks by using statistical analyses and different soft computing approaches such as genetic programing (GP), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) [36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52]. The main advantages of these approaches are that (i) they have made it possible to solve nonlinear problems, in which mathematical models are not available, and (ii) they have introduced human knowledge such as cognition, recognition, understanding, learning, and others in the fields of computing [53].…”

Relationship between Texture and Uniaxial Compressive Strength of Rocks

“…However, such tests require high-quality core samples which cannot always be obtained, particularly from weak, stratified, highly fractured, and weathered rocks [5]. Thus, many researchers use conventional statistical methods to estimate UCS from simple index parameters such as Schmidt hammer, point load, block punch, and petrographic properties [5][6][7][8][9][10][11][12][13][14]. UCS was shown to be correlated with some mechanical properties such as point load index, Schmidt hammer rebound number, and Los Angeles degradation abrasion loss [15,16].…”