Rock Mechanics
Amin Jamshidi; Deniz Akbay
Abstract
Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, ...
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Brazilian tensile strength (BTS) is an important parameter in mining activities, particularly in conditions that rocks are under tensile stresses. This test measures the indirect tensile strength of rocks, which is crucial for understanding the mechanical behavior and quality of rocks in the mining context, including slope stability analysis, blast design, rock support systems, excavation and equipment selection, fracture propagation, and hydraulic fracturing and drilling. So far, no classification of tensile strength of rock for mining applications has been presented. In the present study, a new rock classification based on BTS for the various rocks was proposed. To achieve this purpose, by a reviewing previous studies, uniaxial compressive strength (UCS) and BTS of various rock classes, including igneous, sedimentary, and metamorphic were collected. For each rock class, the correlation equations between UCS and BTS were developed using simple regression analysis. Using data analyses, the rocks was categorized into to seven BTS classes. The findings revealed that igneous, sedimentary, and metamorphic rocks have a wide range of BTS values, and subsequent fall into the different BTS classes. The validity of BTS classification was verified using data of BTS and UCS of various rock classes published in the literature, and results showed that BTS can be as a suitable indicator for preliminary assessment of rock quality. This can lead to a better understand from the strength behavior of the rock under tensile stresses in site a mining activity, and therefore, a more accurate design of a mining project.
Rock Mechanics
M. H. Kadkhodaei; E. Ghasemi
Abstract
The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is ...
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The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is trained and tested based on a database collected from the experimental results available in the literature. The proposed GEP model predicts CAI based on two basic geomechanical properties of rocks, i.e. rock abrasivity index (RAI) and Brazilian tensile strength (BTS). Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2) are used to measure the model performance. Furthermore, the developed GEP model is compared with linear and non-linear multiple regression and other existing models in the literature. The results obtained show that GEP is a strong technique for the prediction of CAI.
