Dispersion quality of graphene nanoplatelets in cementitious materials with applications in geotechnical ground improvement, a machine learning based prediction model

Purpose This study aims to develop interpretable machine learning models using evolutionary polynomial regression (EPR) to predict UV–Vis absorbance of graphene nanoplatelet (GNP) dispersions in cementitious materials, enabling quality control and optimisation of nano-enhanced materials for geotechnical ground improvement applications. Design/methodology/approach EPR combines genetic algorithms with least squares regression to construct explicit polynomial models from experimental data. Six key parameters affecting GNP dispersion were investigated: loading, water content, superplasticizer dosage, sonication time, concentration and holding time. UV–Vis absorbance measurements provided training and validation data sets. Multi-objective optimisation balanced model accuracy against complexity, generating transparent mathematical expressions that reveal parameter significance and interactions governing dispersion quality. Findings EPR models achieved coefficient of determination values from 89