Evaluate and Predict the Resist Electric Current and Compressive Strength of Concrete Modified with GGBS and Steelmaking Slag Using Mathematical Models

Concrete is a highly adaptable composite material that is widely used in construction. Steelmaking produces byproducts such as ferrous steel slag and Ground Granulated Blast Furnaces (GGBS). When molten steel is separated from impurities in steelmaking furnaces, it forms. Steel slag recovery is environmentally friendly since it conserves resources and uses less landfill space. To increase mechanical properties, notably compressive strength (CS) and electrical resistivity, steel slag was often used in concrete as a partial substitute for normal and crushed stone aggregate (ER). Early mechanical discoveries are now essential to the design of buildings. A popular non-destructive method for assessing the microstructure and quality of concrete is electrical resistivity (ER). A mathematical model is necessary to understand how steel slag, used as a partial replacement, affects concrete's electrical resistivity and compressive strength. So, 134 pieces of literature data were obtained and evaluated. The modeling technique evaluated the curing period (3-90 days), cement content (92-469.8 kg/m(3)), water-to-cement ratio (0.3-0.75), fine aggregate (620-773.3 kg/m(3)), and steel slag content (0-365 kg/m(3)) as the most important factors that affect the CS and ER of concrete with steel slag substitution. According to published data from several previous studies, all-steel slag percentages increased CS and reduced ER. This study offered a Multi Logistic Regression (MLR), an Artificial Neural Network (ANN), and a Full Quadratic (FQ) model for forecasting the CS and ER of concrete with steel slag aggregate substitution. According to statistical analysis, the ANN model predicted the CS and ER of concrete modified with steel slag replacement better than the other models (MLR, M5P-tree, FQ). It has a higher coefficient of determination of 0.992 for ER and 0.993 for compressive strength (CS). It has a small root mean square error (RMSE) of 1.39 MPa and resistance of 28.82 & OHM; m for CS and ER of concrete. The relationship between the ER and CS was well predicted using Logistic Power and Exponential association models.

Automated summary

Using steel slag as an aggregate in concrete can enhance its compressive strength and electrical resistivity, resulting in environmental benefits. A mathematical model was developed to analyze the influence of factors such as steel slag content and water-to-cement ratio on the performance of concrete. An Artificial Neural Network (ANN) model was utilized to predict the compressive strength and electrical resistivity of concrete with steel slag aggregate, yielding accurate results.