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ESTIMATION OF COMPRESSIVE STRENGTH LIMIT AND MODULUS OF DEFORMATION OF LAYERED ARTIFICIAL AND NATURAL GEOMATERIALS USING REGRESSION RELATIONSHIPS
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P. Tsoi;O. Usol'tseva;V. Semenov
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1314-2704
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English
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18
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1.3
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Rocks can be structurally represented as layered bodies, whereby the layers can be located at different orientation angles with respect to the original ground. Such angles are usually called the bedding angles. To avoid mistakes in the calculations of the stress-strain state of rock massifs, it is very important to take into account the dependencies of mechanical properties of layered rocks on the bedding angle. Also, rocks are at a certain depth under hydrostatic compression conditions. For an adequate simulation of the mechanical behavior of rocks, a triaxial compression test is used. Depending on the depth at which the core was selected, a confining pressure is applied to the wall of the cylindrical rock sample. Previously, the authors worked on this topic. This research is a continuation of the last one. On the basis of experimental data on the study of the mechanical properties of artificial geomaterials, meta-siltstone and schist, multiple nonlinear regression relationships were constructed. As the regression factors for assessing the strength and modulus of deformation of the examined geomaterials, the bedding angles and the confining pressure values were used.
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conference
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18th International Multidisciplinary Scientific GeoConference SGEM 2018
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18th International Multidisciplinary Scientific GeoConference SGEM 2018, 02-08 July, 2018
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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415-420
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02-08 July, 2018
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website
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cdrom
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282
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layered geomaterials; compressive strength limit; modulus of deformation; laboratory test; regression relationships
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