- Tytuł:
- Prediction of the compressive strength of environmentally friendly concrete using artificial neural network
- Autorzy:
-
Kulisz, Monika
Kujawska, Justyna
Aubakirova, Zulfiya
Zhairbaeva, Gulnaz
Warowny, Tomasz - Powiązania:
- https://bibliotekanauki.pl/articles/38432692.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Tematy:
-
ANN
compressive strength
RCA
MLP
RBF - Opis:
- 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.
- Źródło:
-
Applied Computer Science; 2022, 18, 4; 68-81
1895-3735
2353-6977 - Pojawia się w:
- Applied Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł