Prediction of Electrical Resistivity of Concrete Containing Electric Arc Furnace Slag as Fine Aggregate Using Gene Expression Programming Method

In recent years, there has been a growing interest in developing sustainable concrete alternatives that reduce reliance on natural aggregates and promote waste recycling. One promising approach involves the utilization of electric arc furnace slag (EAFS) as a fine aggregate replacement. This study aims to investigate the impact of EAFS on the mechanical properties, specifically compressive strength and electrical resistivity, as well as the durability of concrete. Given the importance of accurately estimating concrete performance in the durability domain, this study explores the application of gene expression programming (GEP) to predict the electrical resistivity of concrete containing EAFS. To achieve these objectives, a series of concrete mixes were prepared with EAFS replacement levels ranging from 0% to 100% at water-to-cement ratios of 0.3, 0.4, and 0.5. Experimental results indicated a decrease in compressive strength with increasing EAFS content, particularly at higher water-to-cement ratios. Conversely, electrical resistivity decreased significantly with higher EAFS replacement levels. To enhance durability, it is recommended to incorporate a pozzolanic material alongside EAFS. The GEP models developed in this study exhibited excellent performance in predicting the electrical resistivity of concrete containing EAFS. The high correlation coefficients obtained demonstrate the model’s accuracy and reliability. An accurate outcome is achieved by the model configured with 45 chromosomes, a head size of 15, and a multiplicative linking function. Given the strong correlation between electrical resistivity and other durability properties, such as permeability and corrosion resistance, the GEP model can be a valuable tool for optimizing concrete mixtures and predicting long-term performance in sustainable construction applications.