Utilization Of Metaheuristic-based Regression Analysis To Calculate The Modified High-performance Concrete's Compressive Strength

Different regression analytics were used to provide a unique approach to testing the compressive strength (CS) of high-performance concrete (HPC) made with blast furnace slag and fly ash. In this study, it was employed the equilibrium optimizer (EO) and the arithmetic optimization algorithm (AOA) to identify key regression method variables (i.e., Support vector regression (SVR)) which could be adjusted to improve performance. The suggested approaches were created utilizing 1030 tests, eight inputs (aggregates, primary mix designs, admixtures, and curing age), and the CS as the forecasting objective. The results were then compared to those in the corpus of already published scientific literature. Estimation outcomes point to the potential benefit of combining EO-SVR with AOA-SVR analysis. The AOA-SVR displayed significantly better R2 (0.9874 and 0.993) and lower RMSE values as compared to the EO-SVR. Comparing the data demonstrates how much better the created AOA-SVR is than anything that has previously been reported. Overall, the suggested technique for determining the CS of HPC augmented with fly ash and blast furnace slag may be used using the AOA-SVR analysis.