Akademik Veri Yönetim Sistemi
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY - TRANSACTIONS OF CIVIL ENGINEERING, cilt.47, sa.5, ss.1-20, 2023 (SCI-Expanded)
The utilization of supplementary cementitious materials obtained from industrial by-products or wastes is one of the most
effective ways to minimize the costs as well as environmental impact associated with cement production. This work investigated
the effects of the replacement of Portland cement (PC) with (25, 30, 35 and 40%) fly ash (FA) and (5, 10, 15, and 20%)
silica fume (SF) by weight as binary and ternary blends on the compressive strength (fc) and flexural strength (fft) of selfcompacting
mortars (SCMs) at 28 and 91 curing days. Extreme learning machine (ELM), support vector regression (SVR),
artificial neural network (ANN), and decision tree (DT) models were devised to predict these strengths of SCMs containing
high-volume mineral admixture (HVMA). The selected input variables were the number of curing days, water-cementitious
material (W/CM), PC, FA, SF, and sand contents, while the fc and fft were the output variables. ANOVA results show that the
curing time was the most effective parameter for determining both strengths. The results also indicated that ELM achieved
superior performance for the prediction of fc and fft of SCMs with HVMA compared to SVR, ANN, and DT due to having
the highest coefficient of determination values of 0.9802 for both strengths.