Assessment of brittleness using rock strength and density with punch penetration test

Yağız, Saffet

doi:10.1016/j.tust.2008.04.002

Cited by 239 publications

(67 citation statements)

References 15 publications

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“…Using the linear and nonlinear regression techniques including simple or multiple analysis for predicting the unknown from known variables are commonly encountered in the literature (O'Rourke 1989;Cargill and Shakoor 1990;Kahraman 2001;Sharma and Singh 2007;Yagiz 2008Yagiz , 2009a. In this study, to develop the sets of empirical equations between V p and other rock properties including uniaxial compressive strength, modulus of elasticity, Schmidt hardness, slake durability index, effective porosity, water absorption by weight, and both saturated and dry density of rocks, linear and nonlinear simple regression analysis were performed with 95% confidence limits.…”

Section: Statistical Analysis and Discussionmentioning

confidence: 99%

P-wave velocity test for assessment of geotechnical properties of some rock materials

Yağız

2011

Bull Mater Sci

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164

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P-wave velocity test, a non-destructive and easy method to apply in both field and laboratory conditions, has increasingly been conducted to determine the geotechnical properties of rock materials. The aim of this study is to predict the rock properties including the uniaxial compressive strength, Schmidt hardness, modulus of elasticity, water absorption and effective porosity, slake durability index, saturated and dry density of rock using P-wave velocity (V p ). For this purpose geotechnical properties of nine different rock types were determined in the laboratory and their mineralogical composition examined using thin section analysis. Utilizing the generated data, sets of empirical equations were developed between V p and relevant quantified rock parameters. The validity of the obtained empirical equations was confirmed using statistical analysis. It is evident that rock texture and mineralogical compositions affect the geotechnical properties of rock materials. Therefore, the best relationship obtained between both E and UCS with V p in the correlation coefficient of 0⋅92 and 0⋅95 in that order. It is concluded that V p could be practically used for estimating the measured rock properties except dry and saturated density of rocks (r = 0⋅58 and 0⋅46 respectively).

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Section: Statistical Analysis and Discussionmentioning

confidence: 99%

P-wave velocity test for assessment of geotechnical properties of some rock materials

Yağız

2011

Bull Mater Sci

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164

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“…If the spacing of holes is suitable, it will create a crack between two neighboring holes, and the rock will fracture along the high-stress concentration path between the holes. However, if the medium material is brittle, e.g., granite and quartzite, neither yield nor plastic behavior in the material will be observed before the fracture [7][8][9][10][11][12][13][14]. The stress concentration analysis between two neighboring circular holes under the internal pressure of NEEM was previously carried out by Arshadnejad et al [15].…”

Section: Introductionmentioning

confidence: 99%

A model to determine hole spacing in the rock fracture process by non-explosive expansion material

Arshadnejad

Goshtasbi

Aghazadeh

2011

Int J Miner Metall Mater

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The application of the non-explosive expansion material (NEEM) is widely used as the controlled fracture method in quarry mining, especially in hard rocks. The pressure of NEEM is an important parameter in causing rock fracture. An empirical model based on hole spacing was developed to determine the pressure of NEEM in the rock fracture process. Primarily, the empirical model was developed by the mathematical method, utilizing dimensional analysis. Then, the Phase 2 code, which is based on the finite element method, was utilized to predict crack growth in rocks. The results of numerical analysis show slight deviations from the empirical model. Hence, the polynomial regression analysis was used to modify the model. Finally, the modified model shows a good agreement with the results gained from numerical modeling.

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“…Some simple or multiple regression equations were introduced to estimate the machine performance using intact rock properties, including uniaxial compressive and Brazilian tensile strength, Schmidt hardness, Taber abrasion, Point load index, brittleness, Drilling rate index etc. (Tarkoy 1975;McFeat-Smith 1977;Roxborough and Phillips 1975;Pang et al 1989;Sanio 1985;Yagiz 2008Yagiz , 2009. A relationship between rock structure rating (RSR) and ROP was developed by Cassinelli et al (1982).…”

Section: Background On Tunnel Boring Machine and Rock Mass Interactionmentioning

confidence: 99%

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

2014

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List of symbolsAlpha (a) The angle between tunnel axis and the planes of weakness AR Advanced rate BI Brittleness index BTS Brazilian tensile strength BWI Bit wear index CLI Cutter life index CAI Cherchar abrasivity index CCS Constant cross section CSM Colorado School of Mines Model DPW Distance between planes of weakness DRI Drilling rate index FPI Field penetration index GRRD Geology and rock related downtime MCSM Modified Colorado School of Mines Model NTNU Norwegian University of Sciences and University ORD Other related downtime Q Rock mass classification system Q TBM Modified Q system RME Rock mass excavation index RMi Rock mass index RMR Rock mass rating ROP Rate of penetration RQD Rock quality designation RSR Rock structure rating U (%) Utilization UCS Uniaxial compressive strength 1 Introduction

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Assessment of brittleness using rock strength and density with punch penetration test

Cited by 239 publications

References 15 publications

P-wave velocity test for assessment of geotechnical properties of some rock materials

P-wave velocity test for assessment of geotechnical properties of some rock materials

A model to determine hole spacing in the rock fracture process by non-explosive expansion material

Application of RMR for Estimating Rock-Mass–Related TBM Utilization and Performance Parameters: A Case Study

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