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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.
