Machine Learning Techniques to Predict Rock Strength Parameters-Reference-Cited by-同舟云学术

Machine Learning Techniques to Predict Rock Strength Parameters

Published:2022-01-06 Issue:3 Volume:55 Page:1721-1741
ISSN:0723-2632
Container-title:Rock Mechanics and Rock Engineering
language:en
Short-container-title:Rock Mech Rock Eng

Author:

Mahmoodzadeh Arsalan,Mohammadi Mokhtar,Ghafoor Salim Sirwan,Farid Hama Ali Hunar,Hashim Ibrahim Hawkar,Nariman Abdulhamid Sazan,Nejati Hamid Reza,Rashidi Shima

Publisher

Springer Science and Business Media LLC

Subject

Geology,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00603-021-02747-x.pdf

Reference43 articles.

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2. Awad M, Khanna R (2015) Support vector regression. Efficient learning machines. Apress, Berkeley. https://doi.org/10.1007/978-1-4302-5990-9_4

3. Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169. https://doi.org/10.1016/j.ijrmms.2013.08.004

4. Beyhan S (2008) The determination of G.L.I and E.L.I marl rock material properties depending on triaxial compressive strength. PhD thesis, Osman Gazi University, 224

5. Cai M (2010) Practical estimates of tensile strength and hoek-brown strength parameter m i of brittle rocks. Rock Mech Rock Eng 43:167–184. https://doi.org/10.1007/s00603-009-0053-1

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