Data processing method for experimental studies of deformation in a rock sample under uniaxial compression

Andrei Golosov; Olga Lubimova; Mikhail Zhevora; Vladislava Markevich; Vladimir Siskov

doi:10.1051/e3sconf/201912901018

All issues

Volume 129 (2019)

E3S Web Conf., 129 (2019) 01018

References

Open Access

Issue		E3S Web Conf. Volume 129, 2019 1^st International Scientific Conference “Problems in Geomechanics of Highly Compressed Rock and Rock Massifs” (GHCRRM 2019)


Article Number		01018
Number of page(s)		12
DOI		https://doi.org/10.1051/e3sconf/201912901018
Published online		08 November 2019

M.A. Guzev, V.N. Odintsev, V.V. Makarov, Principals of geomechanics of highly stressed rock and rock massifs, 506 (2018). [Google Scholar]
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P. Samui Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass, 249 (2013). [Google Scholar]
M. Kumar, P. Samui, A Kumar Naithani, Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Travertine using Machine Learning Techniques, 5, 117 (2013). [Google Scholar]
M. Kumar, G. Bhairevi, P. Samui, Machine Learning Techniques Applied to Uniaxial Compressive Strength of Oporto Granite, 10, 285 (2014). [Google Scholar]
A. Majdi, M. Beiki, Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses, 345 (2010). [Google Scholar]
A. Bahrami, M. Monjezi, K. Goshtasbi, A. Ghazvinian, Prediction of rock fragmentation due to blasting using artificial neural network, 177 (2011) [Google Scholar]
I. Yılmaz, A.G. Yuksek, An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters, 781 (2008). [Google Scholar]
A.M. Golosov, Development of an acoustic-deformation method for determining precursors of fracture of rock samples under uniaxial compression, (Khabarovsk, 2018). -150 pp. [In Russian]. [Google Scholar]

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[1] M.A. Guzev, V.N. Odintsev, V.V. Makarov, Principals of geomechanics of highly stressed rock and rock massifs, 506 (2018). [Google Scholar]

[2] V.V. Makarov, L.S. Ksendzenko, N.A. Opanasyuk, A.M Golosov. Patterns of deformation and fracture highly compressed rocks and massifs, (Vladivostok, 2014) 250 pp. [In Russian]. [Google Scholar]

[3] P. Samui Multivariate Adaptive Regression Spline (Mars) for Prediction of Elastic Modulus of Jointed Rock Mass, 249 (2013). [Google Scholar]

[4] M. Kumar, P. Samui, A Kumar Naithani, Determination of Uniaxial Compressive Strength and Modulus of Elasticity of Travertine using Machine Learning Techniques, 5, 117 (2013). [Google Scholar]

[5] M. Kumar, G. Bhairevi, P. Samui, Machine Learning Techniques Applied to Uniaxial Compressive Strength of Oporto Granite, 10, 285 (2014). [Google Scholar]

[6] A. Majdi, M. Beiki, Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses, 345 (2010). [Google Scholar]

[7] A. Bahrami, M. Monjezi, K. Goshtasbi, A. Ghazvinian, Prediction of rock fragmentation due to blasting using artificial neural network, 177 (2011) [Google Scholar]

[8] I. Yılmaz, A.G. Yuksek, An Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters, 781 (2008). [Google Scholar]

[9] A.M. Golosov, Development of an acoustic-deformation method for determining precursors of fracture of rock samples under uniaxial compression, (Khabarovsk, 2018). -150 pp. [In Russian]. [Google Scholar]