Strength Optimization of Nanocomposite Cementitious Materials Using Nanoscale Modifications

Represent Volume Element (RVE) is broadly used by investigators to control the properties of nano-cementitious materials. This paper focuses on analyzing a set of RVE data and proposes parametric equations for determining the compressive and flexural strength characteristics (σc and σf). Primarily, a parametric study is performed with RVE analysis. The essential design parameters are used to fit rational equations. The RVE data are also applied to train artificial neural networks of σc and σf. RVE, neural networks, and rational equations are correlated. Regression equations are validated with the experimental study. SEM, TEM, XRD, and FTIR results are carried out in the microscopic and mechanical analysis for carbon nanofiber cement composites, which can lift their strength, constancy, integrity, and density and reinforce the composite microstructure. Lastly, the Pareto-optimal design results are presented with a multi-objective optimization problem.