Predicting The Compressive Strength Of Ground Granulated Blast Furnace Slag Concrete Using Artificial Neural Network

摘要

In this study, an artificial neural networks study was carried out to predict the compressive strength of ground granulated blast furnace slag concrete. A data set of a laboratory work, in which a total of 45 concretes were produced, was utilized in the ANNs study. The concrete mixture parameters were three different water-cement ratios (0.3, 0.4, and 0.5), three different cement dosages (350, 400, and 450 kg/m~3) and four partial slag replacement ratios (20%, 40%, 60%, and 80%). Compressive strengths of moist cured specimens (22 ± 2 ℃) were measured at 3,7, 28,90, and 360 days. ANN model is constructed, trained and tested using these data. The data used in the ANN model are arranged in a format of six input parameters that cover the cement, ground granulated blast furnace slag, water, hyperplasticizer, aggregate and age of samples and, an output parameter which is compressive strength of concrete. The results showed that ANN can be an alternative approach for the predicting the compressive strength of ground granulated blast furnace slag concrete using concrete ingredients as input parameters.