Prediction of compressive strength of granite: use of machine learning techniques and intelligent system
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s12145-023-01145-x.pdf
Reference84 articles.
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2. Ali E, Guang W, Ibrahim A (2014) Empirical relations between compressive strength and microfabric properties of amphibolites using multivariate regression, fuzzy inference and neural networks: a comparative study. Eng Geol 183:230–240. https://doi.org/10.1016/j.enggeo.2014.08.026
3. Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32:631–644. https://doi.org/10.1007/s00366-016-0442-5
4. Armaghani DJ, Tonnizam Mohamad E, Momeni E et al (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9:1–16. https://doi.org/10.1007/s12517-015-2057-3
5. Astarita V, Haghshenas SS, Guido G, Vitale A (2023) Developing new hybrid grey wolf optimization-based artificial neural network for predicting road crash severity. Transp Eng 12:100164. https://doi.org/10.1016/j.treng.2023.100164
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