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Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
Highlights RCA percentage, cement content and slump were used as parameters to build CCD plan. The RSM and ANN models were used for predicting compressive strength at 7, 28 and 56 days. The prediction model based on ANN exhibits higher precision on the prediction compared with the RSM model.
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 < 0.01). It is found that the compressive strength at 7, 28 and 56 days decreases from 22.62 to 18.56, 34.91 to 28.70 and 37.77 to 32.26 respectively with increasing in RCA from 0 to 100% at middle levels of cement content and slump. The results in statistical terms; relative percent deviation (RDP), mean squared error (MSE), root mean square error (RMSE), determination coefficient (R2) and adjusted coefficient (R2 adj), reveals that the both approaches ANN and RSM are a powerful tools for the prediction of the compressive strength. Furthermore, ANN and RSM models are very well correlated with experimental data. However, artificial neural network model shows better accuracy.
Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
Highlights RCA percentage, cement content and slump were used as parameters to build CCD plan. The RSM and ANN models were used for predicting compressive strength at 7, 28 and 56 days. The prediction model based on ANN exhibits higher precision on the prediction compared with the RSM model.
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 < 0.01). It is found that the compressive strength at 7, 28 and 56 days decreases from 22.62 to 18.56, 34.91 to 28.70 and 37.77 to 32.26 respectively with increasing in RCA from 0 to 100% at middle levels of cement content and slump. The results in statistical terms; relative percent deviation (RDP), mean squared error (MSE), root mean square error (RMSE), determination coefficient (R2) and adjusted coefficient (R2 adj), reveals that the both approaches ANN and RSM are a powerful tools for the prediction of the compressive strength. Furthermore, ANN and RSM models are very well correlated with experimental data. However, artificial neural network model shows better accuracy.
Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
Hammoudi, Abdelkader (author) / Moussaceb, Karim (author) / Belebchouche, Cherif (author) / Dahmoune, Farid (author)
Construction and Building Materials ; 209 ; 425-436
2019-03-11
12 pages
Article (Journal)
Electronic Resource
English
Recycled Aggregates Concrete Compressive Strength Prediction Using Artificial Neural Networks (ANNs)
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Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
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