Investigating the effects of superplasticizer and recycled plastics on the compressive strength of cementitious composites using neural networks.

The potential application of neural network (NN) models to estimate the compressive strength (CS) of cementitious composites under a variety of experimental settings and cement mixes was investigated. The data were extensively collected from previous literature, and the bootstrap resampling tests were applied to estimate the statistics of the parameter correlations. We find that the NN model that involves the coarse and fine natural aggregates (CA and FA), superplasticizer (SP) and recycled plastics (RP) as the features can accurately predict the CS (R2 ∼ 0.9), without the need to specify the type of SP and the structure of RP in advance. The developed NN model holds promise for revealing the global dependency of CS on these parameters. It suggested that increasing 100 kg/m3 of CA could increase CS by ∼4 MPa, but the usage of CA more than 700 kg/m3 could negatively affect CS. How the CS varying with FA is apparently nonlinear. Within the optimum limit, adding 1 kg/m3 of SP could enhance the CS by ∼2 MPa. Contrarily, additional 1 kg/m3 of RP results in a decrease of ∼0.2 MPa of CS. The mixture-type independent models developed here would broaden our understanding of the global influential-sensitivity among these variables and help save cost and time in the industrial applications.