RSM and ANN modelling of the mechanical properties of self-compacting concrete with silica fume and plastic waste as partial constituent replacement

Abstract

In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) was used to predict the mechanical properties of self-compacting concrete (SCC) with silica fume as partial cement replacement and Polyethylene terephthalate (PET) solid waste as partial sand replacement. PET plastic was varied between 0 and 20 wt% while the silica fume was varied between 0 and 40 wt%. The parameters investigated were the compressive strength, tensile strength and impact strength of SCC. The RSM model was fairly accurate (R2 ≥ 0.92) in predicting the mechanical properties. The model was statistically significant (p-value < 0.5) and did not possess any prediction bias. The ANN model was able to capture the variability of the data as evidenced by the good R2 threshold (R2 > 0.93) for training, testing and validation. Parity plots revealed that both the ANN and RSM models do not have any prediction bias. However, the ANN model is superior because of its higher accuracy and the use of admixtures enhanced the workability suitability for dataset. The 3D microstructural analysis showed that the interfacial adhesion between the aggregates and the cementitious materials reduced at increased partial replacement leading to a decrease in the strength.