Prediction of compressive strength of concretes containing micro silica subject to carbonation using neural network
Subject Areas :
Ali
Delnavaz
1
(Department of civil and surveying engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
Keywords: Compressive Strength, Concrete, Silica Fume, Carbonation, Artificial Neural Network,
Abstract :
Concrete materials are exposed to special weather conditions, corrosion and significant damage. For this purpose, the effect of 28-day compressive strength changes on the samples studied in this study was investigated by considering the simultaneous effect of chloride ion penetration and carbonation phenomenon. For this reason, in the first case, the samples are exposed to carbon dioxide once and then to chloride ions. In the latter case, only samples under the influence of chloride infiltration are examined. To make the samples, which include 9 mixing designs, three water-to-cement ratios of 0.35, 0.4 and 0.5 and three percent of 0%, 7% and 10% silica fume have been used. Finally, an optimal model is introduced to predict the compressive strength of concrete containing micro silica exposed to carbonation using artificial neural network. Also, a relation for estimating compressive strength based on the ratio of water to cement and the amount of silica is presented.
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