Estimation of compressive strength of cement mortars; [Estimarea rezistenței la compresiune a mortarelor de ciment]
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Date
2016
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Abstract
Due to several advantages of cementitious materials especially mortars, they are widely used in construction works. It is important to determine the mechanical properties of cementitious materials to understand their behavior under different effects. In this study, Artificial Neural Networks (ANN) analysis is used to predict the 7 and 28 days compression strength values of cement mortars. Physical-mechanical properties such as flow, setting time and compressive strength of cement mortars incorporating of different chemical admixtures such as air-entraining admixture (HS), naphthalene sulfonate based (SPNS) and modified polymer (SPMP) based admixtures have been determined. The aim of the usage of combinations of air-entraining admixture with two different based superplasticizers is to form different inner structure affecting on compressive strength. All admixtures are used with three different ratios by cement weight and one of them is for overdosage effect. ANN analysis has been performed to predict the compression strength values after 7 and 28 days, in correlation with experimental part of the study. According to this view, 28 sets have been prepared with different combination of admixtures. At early ages, HS015-SPNS2.0 series had the lowest strength whereas the highest compressive strength at 28 days were obtained for HS005-SPMP0.8 series. Obtained compression strength values after 7 and 28 days have also been predicted by ANN analysis. It is stated that the established ANN model indicates a great capacity to predict the compressive strength values in the end. © 2016, Procema SA. All rights reserved.