Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates

By Abdelkader Hammoudi, K. Moussaceb, Cherif Belebchouche, F. Dahmoune. Construction and Building Materials, 2019

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Abstract This study aims at predicting and modeling the 7; 28 and 56 days compressive strength of a concrete containing concrete’s recycled coarse aggregates and that, for different range of cement content and slump. To achieve this, the response surface methodology (RSM) and the artificial neural networks (ANN) approaches were used for three variable processes modeling (cement content in the range of 300 to 400 kg/m3, percentage of recycled coarse aggregate from 0 to 100% and slump from 5 to 12 ± 1 cm). The results indicate that the compressive strength of recycled concrete at 7, 28 and 56 days is strongly influenced by the cement content, %RCA and slump (p

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/37745613ff9854afcc94fdcd317998c1939b1c6f