Prediction of TBM penetration rate from brittleness indexes using multiple regression analysis
نویسندگان
چکیده
منابع مشابه
Rock Brittleness Prediction Using Geomechanical Properties of Hamekasi Limestone: Regression and Artificial Neural Networks Analysis
The cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. Therefore, this paper attempts to investigate the Hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. The freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. The geo mechanical properties and brittl...
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The aim of this work is to use Data Mining tools to develop models for the prediction of hard rock tunnel boring machine (TBM) penetration rate (ROP). A database published by Yagiz (2008) was used to develop these models. The parameters of the database were the uniaxial compressive strength (UCS), an index used to quantify the brittleness and toughness and denominated peak slope index (PSI), th...
متن کاملrock brittleness prediction using geomechanical properties of hamekasi limestone: regression and artificial neural networks analysis
the cold climate is a favorable parameter for the development of tension cracks and decrease of rock brittleness. therefore, this paper attempts to investigate the hamekasi porous limestone in order to predict the brittleness indices during freeze-thaw cycles. the freeze–thaw test was executed for one cycle including 16 h of freezing, and 8 h of thawing. the geo mechanical properties and brittl...
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key factor in the successful application of a tunnel boring machine (TBM) in tunneling is the ability to develop accurate penetration rate estimates for determining project schedule and costs. Thus establishing a relationship between rock properties and TBM penetration rate can be very helpful in estimation of this vital parameter. However, this parameter cannot be simply predicted since there ...
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ژورنال
عنوان ژورنال: Modeling Earth Systems and Environment
سال: 2018
ISSN: 2363-6203,2363-6211
DOI: 10.1007/s40808-018-0432-2