Compatibility of sustainable geopolymer based on artificial neural network
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41062-024-01632-0.pdf
Reference21 articles.
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2. Bai M, Zhang Z, Cao K, Li H, He C (2023) Prediction of compressive strength of fly ash-slag based geopolymer paste based on multi-optimized artificial neural network. Mater 16(3):1090. https://doi.org/10.3390/ma16031090
3. Henigal A, Elbeltgai E, Eldwiny M, Serry M (2016) Artificial Neural Network Model for Forecasting Concrete Compressive Strength and Slump in Egypt. J Al-Azhar Univ Eng Sect 11(39):435–446. https://doi.org/10.21608/auej.2016.19445
4. Huynh AT et al (2020) A machine learning-assisted numerical predictor for compressive strength of geopolymer concrete based on experimental data and sensitivity analysis. Appl Sci 10(21):1–16. https://doi.org/10.3390/app10217726
5. John SK, Cascardi A, Nadir Y, Aiello MA, Girija K (2021) A new artificial neural network model for the prediction of the effect of molar ratios on compressive strength of fly ash-slag geopolymer mortar. Adv in Civil Eng 2021:1–17. https://doi.org/10.1155/2021/6662347
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