Statistical Estimate of Uniaxial Compressive Strength of Rock Based on Shore Hardness

Závacký, Martin; Štefaňák, Jan; Horák, Vladislav; Miča, Lumír

doi:10.1016/j.proeng.2017.05.178

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…The sample surfaces were polished using a disc polishing machine to avoid causing damage to the rocks during the polishing process. The heating temperatures and duration were chosen with reference to previous studies 10) to ensure that samples do not experience cracking due to uneven thermal expansion caused by temperature gradients during HQ experiments. As a result, heating temperatures of 400°C for limestone, 350°C for sandstone, 400°C for granite, and 400°C for basalt were chosen for this study.…”

Section: Data Collection Procedures 21 Accelerated Degradation Testmentioning

confidence: 99%

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

CAO,

JIANG,

TEZUKA

et al. 2024

J. Soc. Mat. Sci., Japan

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In the context of engineering field operations, extracting the physical properties from rocks poses a challenge, necessitating fast and non-destructive remote sensing methods. The objective of this research is to characterize the rock hardness variations induced by the Heating and Quenching (HQ) cyclic experiment, utilizing photosensitivity differences generated during these cycles through multispectral imaging. Laboratory experiments were conducted to capture hardness changes and near-infrared spectral data of rocks subjected to HQ experiments, comparing degraded rocks to their undegraded counterparts. Furthermore, Maximum Information Coefficient (MIC) method is used in this study to explore the integration of hardness changes resulting from artificial degradation with spectral feature information, information and rock physical parameters. Elucidating the coordinated variations between spectral information and rock physical parameters. The paper discusses the potential of using a hyperspectral camera as a substitute for geological sketches and provides insights into its application.

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Section: Data Collection Procedures 21 Accelerated Degradation Testmentioning

confidence: 99%

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

CAO,

JIANG,

TEZUKA

et al. 2024

J. Soc. Mat. Sci., Japan

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“…Parameter σ c was modelled by Gauss pdf with the COV 0.134 ascertained in the study of Závacký et al (2017). Th e authors of study stochastically processed values measured both directly and indirectly of the rock samples taken from the analyzed rock massif.…”

Section: Initial Random Variables Of Rockmentioning

confidence: 99%

“…The COV 0.134, matching matches the range of the values reported in above listed studies, was employed in the presented calculations. The statistical characteristics of unit weight γ of rock were expressed by the Gauss pdf (see Table 1) on the basis on the statistical evaluation of laboratory test results, presented in Závacký et al (2017).…”

Section: Initial Random Variables Of Rockmentioning

confidence: 99%

Global Sensitivity Analysis for Transformation of Hoek-Brown Failure Criterion for Rock Mass

Štefaňák

Kala

Miča

et al. 2018

Journal of Civil Engineering and Management

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A variety of engineering activities require reliable evaluation of rock strength. For instance, the stability of rock slopes depends on structural geology of rock massif in which the slope is excavated. Hoek-Brown (HB) failure criterion applied in rock design practice introduces factors based on the properties of jointed rock. The non-linear finite element safety calculation is conveniently used for calculation safety the factor of slope stability. The Mohr-Coulomb (MC) failure (strength) criterion for soil is widely applied in geotechnical design. Therefore, the appropriate transformation from HB to the equivalent MC, employing angle of shearing resistance φ and cohesion c, is necessary. This article studies the effect of jointed rock massif properties on the transformed MC parameters by using Sobol’s global sensitivity analysis (SSA) and HB transformation equations. Statistical parameters needed for the evaluation of sensitivity analysis are processed using classical statistical methods upon the emulation of Latin Hypercube Sampling simulation methods. Developed and adapted by authors techniques are illustrated by processing real rock investigation data from survey of the trachyte massif located in the Czech Republic. The first and higher order effects of random inputs are identified using SSA. It is illustrated that the effects of inputs on the MC parameters varies significantly depending on the discontinuity distribution and height of the slope.

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Surrogate Models in Rock and Soil Mechanics: Integrating Numerical Modeling and Machine Learning

Furtney

Thielsen

et al. 2022

Rock Mech Rock Eng

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Statistical Estimate of Uniaxial Compressive Strength of Rock Based on Shore Hardness

Cited by 6 publications

References 7 publications

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

Utilizing Near-Infrared Spectral Imaging for Rock Hardness Assessment

Global Sensitivity Analysis for Transformation of Hoek-Brown Failure Criterion for Rock Mass

Surrogate Models in Rock and Soil Mechanics: Integrating Numerical Modeling and Machine Learning

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