The determination of the strongest attributes of high-performance concrete featuring innovative admixtures via optimal regression-based methodologies
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/s41939-023-00252-y.pdf
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3. Bardhan A et al (2022) A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns. Constr Build Mater 337:127454. https://doi.org/10.1016/j.conbuildmat.2022.127454
4. Behnood A, Golafshani EM (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 202:54–64. https://doi.org/10.1016/j.jclepro.2018.08.065
5. Behnood A, Olek J, Glinicki MA (2015) Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm. Constr Build Mater 94:137–147. https://doi.org/10.1016/j.conbuildmat.2015.06.055
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