Novel Optimized Support Vector Regression Networks for Estimating Fresh and Hardened Characteristics of SCC

Fly ash-containing concrete has only been the subject of a small amount of research focused on forecasting the hardened concrete qualities. So little research has been done to predict the characteristics of self-compacting concrete in both its fresh and hardened states (SCC). Using support vector regression (SVR), it is planned to construct networks for estimating SCC's before and after hardening attributes. The goal of this research is to identify the SVR technique's critical parameters utilizing Henry gas solubility optimization ( HGSO) and particle swarm optimization ( PSO). SCC's fresh-phase characteristics include the slump flow, V-funnel test, and L-box test, whereas its hardened-phase features involve the strength of the compressive. The outcomes show tremendous promise for all assessed qualities in the assessment and development sections. In terms of development and assessment, it was clear that the presented networks have an excellent value. In other words, it signifies that the correlation between the real and anticipated characteristics of SCC from hybrid systems is satisfactory, which reflects superlative precision in the process of approximation and development. Overall, the HGSO-SVR model beats PSO-SVR, showing the capacity of the algorithm to choose the most effective parameters for the method under examination.