PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK
Kulisz M. Kujawska J. Aubakirova Z. Zhairbaeva G. Warowny T.
2022Polish Association for Knowledge Promotion
Applied Computer Science
2022#18Issue 468 - 81 pp.
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.
ANN , MLP , RBF , RCA , the compressive strength
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Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprise, Poland
Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels, Poland
D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, Kazakhstan
Lublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management, Poland
Lublin University of Technology
Lublin University of Technology
D. Serikbayev East Kazakhstan Technical University
Lublin University of Technology
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