Estimation of mechanical properties of the modified high-performance concrete by novel regression models

Abstract

Using support vector regression (SVR) analytics, a novel method for evaluating the high-performance concrete (HPC) compressive strength (CS) containing fly ash (FA) and blast furnace slag (BFS) has been developed. Both Salp swarm optimization (SSA) and Grasshoppers optimization algorithm (GOA) were used in this research to look for critical SVR method variables that may be tweaked for better performance. The suggested approaches were created using 1030 trials, eight inputs (the primary component of admixtures, mix designs, curing age, and aggregates), and the CS as the forecasting goal. After that, the findings were compared to those found elsewhere in the literature. Combined SSA-SVR and GOA-SVR analysis could work exceptionally well when it comes to estimating, according to the estimation findings. The root means square error (RMSE) value for the GOA-SVR faces a remarkable increment in comparison with the SSA-SVR. The comparison resulted that the GOA-SVR delivered a higher rate of accuracy than any previous published research. At the outset, the developed GOA-SVR model might be considered a practical predictive system for the CS prediction of HPC admixed with FA and BFS.