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Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network
Highlights ► An ANN study was carried out to predict the core compressive strength of SCC mixtures with mineral additives. ► One conventional concrete and 6 different SCC mixtures with mineral additives were prepared. ► ANN model predicts the compressive strength of concrete with R2 of 0.95. ► ANN can be an alternative approach for the predicting the core compressive strength of SCC mixtures with mineral additives.
Abstract In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.
Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network
Highlights ► An ANN study was carried out to predict the core compressive strength of SCC mixtures with mineral additives. ► One conventional concrete and 6 different SCC mixtures with mineral additives were prepared. ► ANN model predicts the compressive strength of concrete with R2 of 0.95. ► ANN can be an alternative approach for the predicting the core compressive strength of SCC mixtures with mineral additives.
Abstract In this study, an artificial neural networks study was carried out to predict the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives. This study is based on the determination of the variation of core compressive strength, water absorption and unit weight in curtain wall elements. One conventional concrete (vibrated concrete) and six different self-compacting concrete (SCC) mixtures with mineral additives were prepared. SCC mixtures were produced as control concrete (without mineral additives), moreover fly ash and limestone powder were used with two different replacement ratios (15% and 30%) of cement and marble powder was used with 15% replacement ratio of cement. SCC mixtures were compared to conventional concrete according to the variation of compressive strength, water absorption and unit weight. It can be seen from this study, self-compacting concretes consolidated by its own weight homogeneously in the narrow reinforcement construction elements. Experimental results were also obtained by building models according to artificial neural network (ANN) to predict the core compressive strength. ANN model is constructed, trained and tested using these data. The results showed that ANN can be an alternative approach for the predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives.
Predicting the core compressive strength of self-compacting concrete (SCC) mixtures with mineral additives using artificial neural network
Uysal, Mucteba (author) / Tanyildizi, Harun (author)
Construction and Building Materials ; 25 ; 4105-4111
2010-11-13
7 pages
Article (Journal)
Electronic Resource
English
| British Library Online Contents | 2011
Predicting the compressive strength of self compacting concrete using artificial neural network
| British Library Conference Proceedings | 2009