Intelligent Mixture Design of Steel Fibre Reinforced Concrete Using Support Vector Machine with Improved Firefly Algorithm

The Steel Fibre Reinforced Concrete (SFRC) is majorly utilized in construction of concrete industry as that considers a significant part to evaluate the concrete technique. That has steel fibers of different sizes, shapes and geometric which influenced a concrete mixture composition and properties of mechanic. Through, compared with classical concrete, that is challenging for designing mixture ratios due to much influencing variables to optimize multi properties has compressive strength, flexural strength and cost. Hence, current research has Artificial Intelligence (AI) based Multi Objective Optimization (MMO) method for enabling effective model for identifying optimal mixture design of SFRC. In this research, Support Vector Regression (SVR) method is proposed for predicting UCS and FS in SFRC. The hyperparameters of SVR methods are tuned by Improved Firefly Algorithm (IFA) and examined the significance of inputs and output variables in algorithm. The huge correlation coefficient of 0.9142 on UCS and 0.88 on FS datasets that attained on testing datasets.