Microfabrics-Based Approach to Predict Uniaxial Compressive Strength of Selected Amphibolites Schists Using Fuzzy Inference and Linear Multiple Regression Techniques

Ali, Esamaldeen; Wu, Guang; Ibrahim, Abdelazim Makki

doi:10.2113/gseegeosci.21.3.235

Cited by 3 publications

(3 citation statements)

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“…The uniaxial compressive strength (UCS) is one of the most important indices in rock mechanical studies, and it is commonly used for a variety of engineering applications such as rock mass classification and rock failure criteria. However, such tests require high-quality core samples which cannot always be obtained, particularly from weak, stratified, highly fractured, and weathered rocks [5]. Thus, many researchers use conventional statistical methods to estimate UCS from simple index parameters such as Schmidt hammer, point load, block punch, and petrographic properties [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…However, such tests require high-quality core samples which cannot always be obtained, particularly from weak, stratified, highly fractured, and weathered rocks [5]. Thus, many researchers use conventional statistical methods to estimate UCS from simple index parameters such as Schmidt hammer, point load, block punch, and petrographic properties [5][6][7][8][9][10][11][12][13][14]. UCS was shown to be correlated with some mechanical properties such as point load index, Schmidt hammer rebound number, and Los Angeles degradation abrasion loss [15,16].…”

Section: Introductionmentioning

confidence: 99%

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Relationship between Texture and Uniaxial Compressive Strength of Rocks

et al. 2017

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Uniaxial compressive strength (UCS) is one of the most important parameters of rocks that is routinely used in rock engineering designs. This parameter is influenced greatly by textural properties of rocks; hence it is possible to estimate it from quantified texture coefficient (TC). In this paper, fourteen different types of rocks were experimentally studied to evaluate the effect of texture coefficient on UCS. Thin sections were first prepared, and then some digital photographs were taken from each section and were digitized in computer. Then, the texture coefficient for all samples were calculated. Subsequently, UCS of the samples were measured in laboratory. Finally, relationships between TC and UCS of rock samples were evaluated and related mathematical equations were presented. Results showed that the UCS has a power relationship with TC which can be utilized for future estimation purposes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Relationship between Texture and Uniaxial Compressive Strength of Rocks

et al. 2017

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“…The most commonly measured or estimated mechanical property of rocks is the uniaxial compressive strength (UCS, MPa), as it is a consistent relative measure indicating the resistance of rock materials under axial loading conditions. The UCS varies remarkably with changing parameters such as lithological variances, rock mineralogy, texture, and degree of weathering [3][4][5][6][7][8][9][10][11][12][13]. nevertheless, the deformation properties of rocks (i.e., tangential Young Modulus (E ti , GPa) and tangential Poisson's ratio (v ti )) are other crucial rock properties in performing numerical modelling of rock masses [14][15][16].…”

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confidence: 99%

Archives of Mining Sciences

Köken

2021

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The deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. in this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (E ti , GPa) and tangential Poisson's Ratio (v ti ) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), nW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, d 50 , and sorting coefficient, S c ) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the E ti and v ti . The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. it was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.

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