The research in Cement and Concrete Composites has employed three powerful machine learning algorithms (GEP, GBT, and ANN) due to their nonlinear capabilities. Specifically, the algorithms are used to predict the compressive strength of foamed concrete.

Material ratios of both water to cement and sand to cement were optimized using parametric analysis by the authors. Model performance, parametric analysis, and sensitivity analysis of variables are presented in the paper, and it has been proposed that a machine learning-based approach can be used to select the optimal foamed concrete composition.

The study revealed a strong correlation between foamed concrete density and compressive strength. Optimal algorithm parameters for the three machine learning-based models were also revealed in the author’s work. All optimized AI models yielded a strong R correlation, which reflects a strong agreement between the predicted and experimental results.