Mohamed Abdel-Mongy1, Mudassir Iqbal2, M. Farag3, Ahmed. M. Yosri1,*, Fahad Alsharari1, Saif Eldeen A. S. Yousef4
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505
Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >
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