Correlation Between Uniaxial Compressive Strength and Brazilian Tensile Strength Using Different Rock Types

2016 ◽  
Author(s):  
Pâmmela Caroline Pinazzi da Silva Ribeiro ◽  
Michel Melo Oliveira ◽  
Priscilla P. Nelson
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Brazilian Tensile Strength ◽  
Rock Types

scholarly journals Geotechnical and geomechanical characteristics of the rocks along tunnel of Kulekhani III Hydro-electric Project

2016 ◽  
Vol 50 (1) ◽  
pp. 39-50
Author(s):  
Suman Panthee ◽  
Mahesh Khanal ◽  
T. N. Singh
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Rock Mass ◽  
Young’S Modulus ◽  
Uniaxial Compressive Strength ◽  
Friction Angle ◽  
Geotechnical Properties ◽  
Intact Rock ◽  
Geomechanical Properties ◽  
Rock Types

 Geotechnical and geomechanical properties are important to understand tunnel behaviour and they differ according to rock types. Therefore, tunnel passing through different rock types is selected for the present study. The tunnel alignment of Kulekhani III hydroelectric project crosses five stratigraphic formations which compriseing eight lithological units. The rocks of the area have most dominantly three sets of joints in which the foliation plane is prominent. In geotechnical study of intact rock, seven geotechnical properties - viz unit weight (γ), uniaxial compressive strength (σci), tensile strength (σti), young’s modulus (Ei), poison’s ratio (ν), friction angle (ci) and cohesion ( i) were measured in lab and on the basis of the intact rock properties five geotechnical properties - uniaxial compressive strength (σcm), tensile strength (σtm), young’s modulus (Em), friction angle (cm) and cohesion ( m) of rock mass were determined. RMR, Q and GSI were used for geomechanical classification and the distributions of the geomechanical class values were studied. The relationship between UCS of rock mass and geomechanical classifications RMR and Q were studied for all rock types. Quite similar results were observed with both RMR and Q systems. The trend of correlations of each rock type with both classification systems follows almost analogous order. Power type continuous equation is observed for Q and exponential type relation is obtained for RMR. The general trend of correlation of UCS rock mass with RMR and Q is calculated which has R2 more than 0.9. The observed relations were compared with empirical relations proposed by other researchers and the results of the present study lie between the upper and lower boundaries set by other researchers. Among them, harder rocks have better correlation than softer rocks. It is also observed that higher the poisson’s ratio of the rock shown higher the order of linear correlation of rock mass properties with its geomechanical properties.

scholarly journals PHYSICAL PARAMETERS, TENSILE AND COMPRESSIVE STRENGTH OF DOLOMITE ROCK SAMPLES: INFLUENCE OF GRAIN SIZE

2020 ◽  
Vol 26 (8) ◽  
pp. 789-799
Author(s):  
Ali Lakirouhani ◽  
Farhad Asemi ◽  
Afshin Zohdi ◽  
Jurgis Medzvieckas ◽  
Romualdas Kliukas
Keyword(s):  
Grain Size ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Mineral Composition ◽  
Uniaxial Compressive Strength ◽  
Wave Velocity ◽  
Strong Correlation ◽  
Point Load ◽  
Brazilian Tensile Strength ◽  
Point Load Strength

The purpose of this paper is to investigate the strength, physical and engineering index parameters of selected dolomitic rocks with emphasis on grain size. For this purpose, three groups of dolomite from north western Iran, with the same mineral composition but different grain size, were selected; fine grain, medium grain and coarse grain. Three sets of laboratory experiments are performed on 32 samples: first; petrography tests for determining mineral composition and their percentage, and microstructure of rock containing grain size and grain size distribution, second; experiments to determine the physical properties of the rocks included density, compressional and shear wave velocity, and the third category of experiments included uniaxial compressive strength test, Brazilian tensile strength and point load strength. According to the results; there are significant positive correlation between grain size and uniaxial compressive strength (r = 0.89), point load strength (r = 0.58), Brazilian strength (r = 0.69), and average Young’s modulus (r = 0.64). Also, with increasing grain size, density decreases (r = –0.77). There is strong correlation between compressional wave velocity and shear velocity (r = 0.88). There are also a strong correlation among the uniaxial compressive strength, Brazilian tensile strength and point load strength.

scholarly journals AN INVESTIGATION ON THE EFFECTS OF MICRO-PARAMETERS ON THE STRENGTH PROPERTIES OF ROCK

2021 ◽  
Vol 36 (1) ◽  
pp. 111-119
Author(s):  
Behzad Jafari Mohammadabadi ◽  
Kourosh Shahriar ◽  
Hossein Jalalifar ◽  
Kaveh Ahangari
Keyword(s):  
Mechanical Properties ◽  
Numerical Simulation ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Friction Coefficient ◽  
Critical Stress ◽  
Friction Angle ◽  
Strength Properties ◽  
Brazilian Tensile Strength ◽  
The Relationship

Rocks are formed from particles and the interaction between those particles controls the behaviour of a rock’s mechanical properties. Since it is very important to conduct extensive studies about the relationship between the micro-parameters and macro-parameters of rock, this paper investigates the effects of some micro-parameters on strength properties and the behaviour of cracks in rock. This is carried out by using numerical simulation of an extensive series of Uniaxial Compressive Strength (UCS) and Brazilian Tensile Strength (BTS) tests. The micro-parameters included the particles’ contact modulus, the contact stiff ness ratio, bond cohesion, bond tensile strength, the friction coefficient and the friction angle, and the mechanical properties of chromite rock have been considered as base values of the investigation. Based on the obtained results, it was found that the most important micro-parameters on the behaviour of rock in the compressive state are bond cohesion, bond tensile strength, and the friction coefficient. Also, the bond tensile strength showed the largest effect under tensile conditions. The micro-parameter of bond tensile strength increased the rock tensile strength (up to 5 times), minimized destructive cracks and increased the corresponding strain (almost 2.5 times) during critical stress.

scholarly journals Modeling and Estimation of Production Rate in Ornamental Stones Sawing Based on Brittleness Indexes

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Pan Wang
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Production Rate ◽  
Estimation Model ◽  
Rate Estimation ◽  
Rock Types ◽  
Ornamental Stones ◽  
Method Of Production ◽  
Improved Model ◽  
The Cost

As for estimating the cost and planning the process of the rock sawing plants, it is significant to predict the production rate of ornamental stones sawing. To promote the efficiency in planning these rock sawing projects, scholars have been trying to find a high-accuracy method of production rate estimation. Moreover, targeting at the 28 granite and carbonates stone in the nature, this study examined the connection between two various brittleness indexes in statistics, including the ratio of compressive strength to tensile strength (B1) and places below the line of compressive strength and the line of tensile strength (B2) in rocks and production rate had been studied. Through the results of cross plots analysis, it was indicated that there existed a strong connection between production rate and the brittleness B1 and B2. Finally, in this thesis, through adding B1 factor, it has improved the estimation model for production rate which Mikaeil et al. (2013) have established. What’s more, by virtue of brittleness about B1 and B2, this production rate estimation model has been established successfully for natural stone sawing. Actually, the way of estimating the production rate of 28 rock samples is to utilize the two kinds of models described before. Through the result, it is showed that the production rate estimated by the improved model corresponds to the value of production rate of rock testing. Meanwhile, the precision has been greatly improved with comparison to the model of estimating the production rate designed by Mikaeil et al. (2013). Thus, on the basis of the new model, a dependable prediction for ornamental stones production is put forward in this paper. And it is required to do a further study involving different rock types since limited rock types were used in this study.

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