Igor Grešovnik^1,2, Tadej Kodelja¹, Robert Vertnik^2,3, Bojan Senčič^3,2,3, Božidar Šarler^1,2,4

CMC-Computers, Materials & Continua, Vol.30, No.1, pp. 19-38, 2012, DOI:10.3970/cmc.2012.030.019

Abstract Artificial neural networks (ANNs) are employed as an alternative to physical modeling for calculation of the relations between the production path process parameters (melting of scrap steel and alloying, continuous casting, hydrogen removal, reheating, rolling, and cooling on a cooling bed) and the final product mechanical properties (elongation, tensile strength, yield stress, hardness after rolling, necking) of steel semi products. They provide a much faster technique of response evaluation complementary to physical modeling. The Štore Steel company process path for production of steel bars is used as an example for demonstrating the approach. The applied ANN is of a multilayer… More >

Abdulkadir Karaci^1,*, 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 >