Prediction of compressive strength of recycled aggregate concrete using artificial neural networks: Fid-Bau Portal

Prediction of compressive strength of recycled aggregate concrete using artificial neural networks

Duan, Z.H. / Kou, S.C. / Poon, C.S.

Graphical abstract The proposed 14-16-1 ANNs model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 168 sets of available test data based on many different concrete mix-designs used. The predicted results were compared with the experimentally determined results. Display Omitted Highlights ► Maximum size, absorption and density of aggregates can reflect the quality of RA. ► ANN has a fairly high accuracy on predicting the strength of RAC. ► The performance of ANN model may be improved with more parameters considered.

Abstract Recycled aggregates are substantially different in composition and properties compared with natural aggregates, leading it hard to predict the performance of recycled aggregate concrete and design their mix proportions. This paper aims to show the possible applicability of artificial neural networks (ANNs) to predict the compressive strength of recycled aggregate concrete. ANN model is constructed, trained and tested using 146 available sets of data obtained from 16 different published literature sources. The ANN model developed used 14 input parameters that included: the mass of water, cement, sand, natural coarse aggregate, recycled coarse aggregate used in the mix designs, water to cement ratio of concrete, fineness modulus of sand, water absorption of the aggregates, saturated surface-dried (SSD) density, maximum size, and impurity content of recycled coarse aggregate, the replacement ratio of recycled coarse aggregate by volume, and the coefficient of different concrete specimen. The ANN model, run in a Matlab platform, was used to predict the compressive strength of the recycled aggregate concrete. The results show that ANN has good potential to be used as a tool for predicting the compressive strength of recycled aggregate concrete prepared with varying types and sources of recycled aggregates.