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

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.