• Geomechanics and Engineering
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• v.17 no.6
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• pp.527-534
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• 2019

There have been developed a number of methods to assess the abrasivity of rock materials with the increased use of mechanized rock excavation. These methods range from determination of abrasive and hard mineral content using petrographic thin section analysis to weight loss or development of wear flat on a specified cutting tool. The Cerchar abrasivity index (CAI) test has been widely accepted for the assessment of rock abrasiveness. This test has been considered to provide a reliable indication of rock abrasiveness for isotropic rocks. However, a great amount of rocks in nature are anisotropic. Hence, viability assessment of Cerchar abrasivity test for the anisotropic rocks is investigated in this research. The relationship between CAI value and quartz content for the isotropic rocks is well known in literature. However, a correlation between EQ, F-Schimazek value, Rock Abrasivity Index (RAI) and CAI of anisotropic rocks such as phyllite was done first time in literature with this research. The results obtained with this research show F-Schimazek values and RAI values should be considered when determination of the abrasivity of anisotropic rocks instead of just using Cerchar scratch test.

• Geomechanics and Engineering
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• v.29 no.3
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• pp.219-228
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• 2022

The most widely used parameter to represent rock abrasiveness is the Cerchar abrasivity index (CAI). The CAI value can be applied to predict wear in TBM cutters. It has been extensively demonstrated that the CAI is affected significantly by cementation degree, strength, and amount of abrasive minerals, i.e., the quartz content or equivalent quartz content in rocks. The relationship between the properties of rocks and the CAI is investigated in this study. A database comprising 223 observations that includes rock types, uniaxial compressive strengths, Brazilian tensile strengths, equivalent quartz contents, quartz contents, brittleness indices, and CAIs is constructed. A linear model is developed by selecting independent variables while considering multicollinearity after performing multiple regression analyses. Machine learning-based regression methods including support vector regression, regression tree regression, k-nearest neighbors regression, random forest regression, and artificial neural network regression are used in addition to multiple linear regression. The results of the random forest regression model show that it yields the best prediction performance.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.