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

ABSTRACT: 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

SUBMITTER: Ekprasert J 

PROVIDER: S-EPMC10660538 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature