Prediction of the compressive strength of environmentally friendly concrete using artificial neural network

• Autorzy: Kulisz Monika , Kujawska Justyna , Aubakirova Zulfiya , Zhairbaeva Gulnaz , Warowny Tomasz

Identyfikatory

DOI

10.35784/acs-2022-29

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

ANN; compressive strength; RCA; MLP; RBF

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 4
• Strony: 68--81
• Opis fizyczny: Bibliogr. 35 poz., fig., tab.

Twórcy

autor

Kulisz Monika

• Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprise, Poland

• https://orcid.org/0000-0002-8111-2316

autor

Kujawska Justyna

• Lublin University of Technology, Faculty of Environmental Engineering, Department of Biomass and Waste Conversion into Biofuels, Poland

• https://orcid.org/0000-0002-4809-2472

autor

Aubakirova Zulfiya

• D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, Kazakhstan

• https://orcid.org/0000-0003-0322-7692

autor

Zhairbaeva Gulnaz

• D. Serikbayev East Kazakhstan Technical University, School of Architecture and Construction, Kazakhstan

• https://orcid.org/0000-0002-5102-397X

autor

Warowny Tomasz

• Lublin University of Technology, Faculty of Management, Department of Quantitative Methods in Management, Poland

• https://orcid.org/0000-0002-1500-9812

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