Machine Learning Technique for the Prediction of Blended Concrete Compressive Strength
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-024-0854-5.pdf
Reference103 articles.
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3. Ahmad A, Farooq F, Niewiadomski P, Ostrowski K, Akbar A, Aslam F, Alyousef R (2021b) Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials 14:794, DOI: https://doi.org/10.3390/ma14040794
4. Albattat RA, Jamshidzadeh Z, Alasadi AK (2020) Assessment of compressive strength and durability of silica fume-based concrete in acidic environment. Innovative Infrastructure Solutions 5:1–7, DOI: https://doi.org/10.1007/s41062-020-0269-1
5. Algaifi HA, Alqarni AS, Alyousef R, Bakar SA, Ibrahim MW, Shahidan S, Ibrahim M, Salami BA (2021) Mathematical prediction of the compressive strength of bacterial concrete using gene expression programming. Ain Shams Engineering Journal 12:3629–3639, DOI: https://doi.org/10.1016/j.asej.2021.04.008
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