Document Type : Research Paper
Authors
1
Department of Geology, Faculty of Sciences, Bu-Ali Sina University, Hamedan, Iran
2
Bu-Ali Sina University
3
Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran, Iran
4
Département de géologie et de génie géologique, Faculté des sciences et de génie, Université Laval, Québec, Canada
5
Department of Geology, Faculty of Sciences, Ferdowsi University, Mashhad, Iran
Abstract
Cerchar Abrasivity Index (CAI) test is commonly used to assess the abrasiveness of rocks due to its efficiency and simplicity. This research focuses on estimating CAI values based on the petrographical, textural, and mechanical characteristics of igneous rock. The study examines the potential correlation between CAI values, petrographical, and textural characteristics using a dataset comprising 15 samples from 5 different types of igneous rocks. The researchers employed a range of statistical analyses, including Pearson's correlations, Simple and Multiple linear and non-linear regression, and artificial neural network (ANN) analyses. These methods were used to examine the relationship between CAI values and various parameters. CAI has a direct correlation with Texture Coefficient (TC), Heterogeneity (H), Saturation Index (SI), Uniaxial Compressive Strength (UCS), Abrasivity Index (ABI), and Rock Abrasivity Index (RAI), with the exception of Feldspathic Index (FI) and Porosity (P). Results showed that by increasing CAI values, the TC, H, RAI, ABI, and SI increased, and FI decreased. By increasing TC and H, the percentage of quartz increases, and alkali feldspar decreases. The study suggests SI, FI, TC, and H are appropriate in assessing the abrasiveness of igneous rocks. Validation of the results displayed that new models can be used for predicting CAI with acceptable accuracy.
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