E3S Web Conf.
Volume 129, 20191st International Scientific Conference “Problems in Geomechanics of Highly Compressed Rock and Rock Massifs” (GHCRRM 2019)
|Number of page(s)||12|
|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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