Utilizing several multivariable mathematical and M5P-tree models to predict uniaxial compressive strength of rocks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Mechanics of Materials,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s41939-023-00297-z.pdf
Reference71 articles.
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3. Abdi Y, Yusefi-Yegane B, Jamshidi A (2021) Estimation of mechanical properties of sandstones from petrographic characteristics using artificial neural networks (ANNs). Bull Geol Soc Malays 71:13
4. Aghda SF, Kianpour M, Mohammadi M (2018) Estimation of uniaxial compressive strength and modulus of deformability of the Asmari limestone, using neuro-fuzzy system. Iran J Sci Technol Trans A Sci 42:2005–2020
5. Ahmed HU, Mohammed AS, Faraj RH, Qaidi SM, Mohammed AA (2022) Compressive strength of geopolymer concrete modified with nano-silica: experimental and modeling investigations. Case Stud Construct Mater 16:e01036
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