Use of explainable symbolic regression approaches for predicting nanomaterial-enhanced concrete performance

Abstract The incorporation of nanomaterials in concrete improves mechanical strength, durability, and resistance to environmental effects, presenting a sustainable approach for modern construction. This research employs symbolic regression approaches, namely Gene Expression Programming (GEP) and Multi Expression Programming (MEP), to forecast the compressive strength of nano enhanced (nano TiO 2 and nano SiO 2 ) concretes. The developed models were trained and validated using a comprehensive experimental database and evaluated through multiple statistical metrics. Based on the comparative performance metrics, the MEP model clearly outperformed the GEP model, achieving higher predictive accuracy (R 2 = 0.954), lower error values (RMSE = 5.427 MPa, MAE = 4.596 MPa, MAPE = 10.40 %), and stronger reliability (NSE = 0.953) compared to the GEP model (R 2 = 0.914). Model performance was illustrated through Taylor’s diagram. Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots were used to examine feature importance and interaction effects, showing that concrete age, cement, slag, and nano silica enhance strength, whereas higher water content and fine aggregate proportions reduce it. These results highlight the potential of MEP-based modeling to optimize mix design and promote the sustainable use of nanomaterials and supplementary cementitious materials (SCMs) in concrete, offering valuable guidance for sustainable construction.