• 터널과지하공간
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• 제22권4호
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• pp.284-295
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• 2012

본 연구에서는 암석 절삭 장비의 마모에 직접적인 영향을 주는 인자인 암석의 마모도(abrasiveness) 측정에 관한 연구를 수행하였다. 몇 가지 방법 중 세르샤 마모 시험(Cerchar abrasiveness test)을 통하여 암석의 마모도에 영향을 미치는 인자를 확인하고 효율적인 시험을 수행하기 위한 조건들을 연구하였다. 국내 19종 암석에 대한 시험 결과를 통하여, 세르샤 마모 지수(CAI, Cerchar Abrasiveness Index)에 영향을 미치는 암석의 역학적 물성(단축압축강도, 간접인장강도, 탄성계수, 포아송비, 공극률, 쇼어경도)과의 상관관계를 찾아보았고 X선 회절 분석을 통하여 암석의 구성 광물 중 마모도에 가장 큰 영향을 미치는 석영 함량, 등가 석영 함량과의 관계도 확인하였다. 그 결과로 암석의 입자 결합 특성보다 광물의 특성이 CAI에 영향을 더 미치는 것으로 관찰되었고, 단축압축강도와 등가 석영함량의 함수로 CAI를 예측하는 모델을 제시하였으며 핀의 경도가 커질수록 CAI값이 선형적으로 작아짐을 확인하였다. 수치해석적 연구를 통해 세르샤 마모 시험을 모사한 결과 초기 긁힘 거리에서 대부분의 마모가 발생함을 확인하였고 하중이 증가할수록 CAI값이 증가함을 확인하였다.

• Geomechanics and Engineering
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• 제17권6호
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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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• 제29권3호
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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.