Akademik Veri Yönetim Sistemi
MEASUREMENT, cilt.91, ss.114-123, 2016 (SCI-Expanded)
During the last decades, the rapid developments in mining operations and tunnel construction have lead to a rapid increase in the number of excavation machines. In order to achieve the expected benefits of mechanical excavation machines, these machines should be selected in accordance with the characteristics of rocks. Tool abrasion is an important factor in hard rock tunnelling, mining, and it is highly affected by rock abrasivity. There are several methods to identify the rock abrasivity. One of the commonly used abrasion test in rock is the Cerchar abrasivity index (CAI). Before selection and implementation of excavating machines, physical and mechanical properties of the rocks should be determined. It's known that, physical-mechanical properties of granitic rocks are generally better than those of many rock types although they cause some difficulties (tool wear, lost time, etc.) in excavation and increase the cutter costs. The purpose of the present study is to determine empirical relationships between CAI and physical-mechanical properties of different granitic rocks using regression method. In this study, some laboratory experiments were conducted on samples collected from granite quarries in different parts of Turkey, particularly from the Marmara Region. Firstly, petrographical, mineralogical and physical-mechanical characteristics of the collected granitic rocks were determined. Then, empirical relationships between these properties and CAI were determined using method of regression analysis. According to the results obtained, a strong correlation is found between CAI value, quartz content and quartz size of the granitic rocks. In addition, the uniaxial compressive strength and indirect tensile strength of the studied granitic rocks increase as CAI increases. Surface roughness, waviness and peak number of granitic rocks lead to an increase in CAI. On the contrary, Bohme abrasion resistance increases while CAI decreases. (C) 2016 Elsevier Ltd. All rights reserved.