Developing machine learning models to predict the fly ash concrete compressive strength
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-024-01125-6.pdf
Reference61 articles.
1. Ahmad, W., Ahmad, A., Ostrowski, K. A., Aslam, F., Joyklad, P., & Zajdel, P. (2021). Application of advanced machine learning approaches to predict the compressive strength of concrete containing supplementary cementitious materials. Materials, 14(19), 5762. https://doi.org/10.3390/ma14195762
2. Alaka, H. A., & Oyedele, L. O. (2016). High volume fly ash concrete: The practical impact of using superabundant dose of high range water reducer. Journal of Building Engineering, 8, 81–90. https://doi.org/10.1016/j.jobe.2016.09.008
3. Albostami, A. S., Al-Hamd, R. K. S., Alzabeebee, S., Minto, A., & Keawsawasvong, S. (2023). Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate. Asian Journal of Civil Engineering, 25(1), 183–196. https://doi.org/10.1007/s42107-023-00767-2
4. Al-Gburi, M., & Yusuf, S. A. (2022). Investigation of the effect of mineral additives on concrete strength using ANN. Asian Journal of Civil Engineering, 23, 405–414. https://doi.org/10.1007/s42107-022-00431-1
5. Allen, D. M. (1971). Mean square error of prediction as a criterion for selecting variables. Technometrics, 13(3), 469–475. https://doi.org/10.1080/00401706.1971.10488811s
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