Estimation of Cerchar abrasivity index of granitic rocks in Turkey by geological properties using regression analysis

The estimation of abrasivity on granitic rocks is very important in mechanical excavators. The mineralogical composition and physico-mechanical properties of the rock affects of abrasivity properties such as the type and size of the mineral grains and strength. Tool wear is an important parameter in...

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Bibliographic Details
Published in:Bulletin of engineering geology and the environment 2016-08, Vol.75 (3), p.1325-1339
Main Authors: Er, Selman, Tuğrul, Atiye
Format: Article
Language:English
Subjects:
Correlation
Correlation coefficient
Earth and Environmental Science
Earth Sciences
Estimating
Excavation
Foundations
Geoecology/Natural Processes
Geoengineering
Geological engineering
Geology
Geotechnical Engineering & Applied Earth Sciences
Grain size
Hydraulics
Investigations
Mechanical properties
Mineralogy
Mining engineering
Nature Conservation
Original Paper
Physical properties
Quartz
Regression
Regression analysis
Rocks
Tunnelling
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Summary:The estimation of abrasivity on granitic rocks is very important in mechanical excavators. The mineralogical composition and physico-mechanical properties of the rock affects of abrasivity properties such as the type and size of the mineral grains and strength. Tool wear is an important parameter in hard rock tunnelling and mining applications that are highly affected by rock abrasivity. The estimation of the Cerchar abrasivity index (CAI) from geological properties is useful for excavation costs. For this purpose, CAI, chemical, petrographical, physical and mechanical tests were carried out on 12 granitic rocks taken from different regions of Turkey. The test results were analysed using the method of least squares regression. Multiple regression models having good correlation coefficients were obtained for the granitic rocks. The CAI tests were correlated with chemical compound and a weak relation was determined. The regression analysis was also repeated for petrographical, physical, and mechanical properties. Statistically meaningful correlations were determined between all geological properties and CAI. Then multiple regression analysis was applied to all geological properties to find the more significant relations. Results show that shore scleroscope hardness, water absorption, and the waveness average prove to be the best for estimating CAI of granitic rocks.
ISSN:1435-9529
1435-9537
DOI:10.1007/s10064-016-0853-y