Abdulkadir Karaci1,*, Hasbi Yaprak2, Osman Ozkaraca3, Ilhami Demir4, Osman Simsek5
CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.1, pp. 207-228, 2019, DOI:10.31614/cmes.2019.04216
Abstract In this study, deep-neural-network (DNN)- and artificial-neural-network (ANN)-based models along with regression models have been developed to estimate the pressure, bending and elongation values of ground-brick (GB)-added mortar samples. This study is aimed at utilizing GB as a mineral additive in concrete in the ratios 0.0%, 2.5%, 5.0%, 7.5%, 10.0%, 12.5% and 15.0%. In this study, 756 mortar samples were produced for 84 different series and were cured in tap water (W), 5% sodium sulphate solution (SS5) and 5% ammonium nitrate solution (AN5) for 7 days, 28 days, 90 days and 180 days. The developed DNN models have three inputs… More >
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