Bulletin of Engineering Geology and the Environment, ( ISI ), Volume (75), No (2), Year (2016-5) , Pages (841-851)

Title : ( Utilizing water, mineralogy and sedimentary properties to predict LCPC abrasivity coefficient )

Authors: hashem hashem nejad , Mohammad Ghafoori , Sadegh Tarigh Azali ,

Citation: BibTeX | EndNote

Drilling, blasting and mechanical methods using road headers or tunnel boring machines (TBMs) are among the methods used for underground excavation of rock and soil. The interaction between the tools used and the ground leads to fragmentation of rocks and soil grains as well as tool wear. Wear is defined as the loss of tool material as a result of the interaction between rocks (or soil) and the drilling tools. The LCPC abrasivity test is a quick and easy procedure used widely to assess the abrasivity of soil and rock for predicting the rate of wear of cutting and drilling tools. The LCPC test device is designed to measure the abrasivity of particles as small as fine gravel. Various parameters can affect the LCPC abrasivity coefficient (LAC). In this paper, equations relating the index properties and the LAC were applied to 27 different samples. The derivation of models predicting the engineering geological properties of rocks and soils is useful because providing specimens of rocks at depth is difficult and expensive in the preliminary design of underground projects. Regression analysis was applied in developing some models for the LAC based on indirect methods including the equivalent quartz content (EQC), grain shape, grain size, grain angularity and water saturation applied to data from rock and soil samples in Iran. The results showed that EQC is the most important parameter affecting the LAC, with the other parameters having lower levels of importance.

Keywords

, LCPC abrasivity coefficient , Tunnel boring machine , Abrasivity , Wear , Equivalent quartz
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@article{paperid:1050483,
author = {Hashem Nejad, Hashem and Ghafoori, Mohammad and Tarigh Azali, Sadegh},
title = {Utilizing water, mineralogy and sedimentary properties to predict LCPC abrasivity coefficient},
journal = {Bulletin of Engineering Geology and the Environment},
year = {2016},
volume = {75},
number = {2},
month = {May},
issn = {1435-9529},
pages = {841--851},
numpages = {10},
keywords = {LCPC abrasivity coefficient ; Tunnel boring machine ; Abrasivity ; Wear ; Equivalent quartz content},
}

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%0 Journal Article
%T Utilizing water, mineralogy and sedimentary properties to predict LCPC abrasivity coefficient
%A Hashem Nejad, Hashem
%A Ghafoori, Mohammad
%A Tarigh Azali, Sadegh
%J Bulletin of Engineering Geology and the Environment
%@ 1435-9529
%D 2016

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