PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK
Monika KULISZ
m.kulisz@pollub.plLublin University of Technology, Faculty of Management, Department of Organisation of Enterprise (Poland)
Justyna KUJAWSKA
Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels (Poland)
Zulfiya AUBAKIROVA
D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction (Kazakhstan)
Gulnaz ZHAIRBAEVA
D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, (Kazakhstan)
Tomasz WAROWNY
Lublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management (Poland)
Abstract
The paper evaluated the possibility of using artificial neural network models for predicting the compressive strength (Fc) of concretes with the addition of recycled concrete aggregate (RCA). The artificial neural network (ANN) approaches were used for three variable processes modeling (cement content in the range of 250 to 400 kg/m3, percentage of recycled concrete aggregate from 25% to 100% and the ratios of water contents 0.45 to 0.6). The results indicate that the compressive strength of recycled concrete at 3, 7 and 28 days is strongly influenced by the cement content, %RCA and the ratios of water contents. It is found that the compressive strength at 3, 7 and 28 days decreases when increasing RCA from 25% to 100%. The obtained MLP and RBF networks are characterized by satisfactory capacity for prediction of the compressive strength of concretes with recycled concrete aggregate (RCA) addition. The results in statistical terms; correlation coefficient (R) reveals that the both ANN approaches are powerful tools for the prediction of the compressive strength.
Keywords:
ANN, the compressive strength, RCA, MLP, RBFReferences
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Authors
Monika KULISZm.kulisz@pollub.pl
Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprise Poland
Authors
Justyna KUJAWSKALublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels Poland
Authors
Zulfiya AUBAKIROVAD. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction Kazakhstan
Authors
Gulnaz ZHAIRBAEVAD. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, Kazakhstan
Authors
Tomasz WAROWNYLublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management Poland
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