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Computers and Concrete Volume 19, Number 2, February 2017 , pages 217-226 DOI: https://doi.org/10.12989/cac.2017.19.2.217 |
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Prediction of compressive strength of lightweight mortar exposed to sulfate attack |
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Harun Tanyildizi
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| Abstract | ||
| This paper summarizes the results of experimental research, and artificial intelligence methods focused on determination of compressive strength of lightweight cement mortar with silica fume and fly ash after sulfate attack. The artificial neural network and the support vector machine were selected as artificial intelligence methods. Lightweight cement mortar mixtures containing silica fume and fly ash were prepared in this study. After specimens were cured in 20+-2oC waters for 28 days, the specimens were cured in different sulfate concentrations (0%, 1% MgSO-2 4, 2% MgSO-2 4, and 4% MgSO-2 4) for 28, 60, 90, 120, 150, 180, 210 and 365 days. At the end of these curing periods, the compressive strengths of lightweight cement mortars were tested. The input variables for the artificial neural network and the support vector machine were selected as the amount of cement, the amount of fly ash, the amount of silica fumes, the amount of aggregates, the sulfate percentage, and the curing time. The compressive strength of the lightweight cement mortar was the output variable. The model results were compared with the experimental results. The best prediction results were obtained from the artificial neural network model with the Powell-Beale conjugate gradient backpropagation training algorithm. | ||
| Key Words | ||
| cement mortar; silica fume; fly ash; compressive strength; modeling; sulfates/sulfate resistance | ||
| Address | ||
| Harun Tanyildizi: Department of Civil Engineering, Firat University 23119 Elazig, Turkey | ||