Novel hybrid HGSO optimized supervised machine learning approaches to predict the compressive strength of admixed concrete containing fly ash and micro-silica

Abstract

Using machine learning models to provide a reliable and accurate model to predict the compressive strength of high-performance concrete helps save the time-cost and financial cost of concrete casting. On the other hand, applying admixtures such as fly ash and silica fume in the concrete structure to replace cement helps diminish carbon dioxide emissions. In the present study, a support vector machine-based regression was considered to overcome the difficulties of compressive strength, which is intensified with a modern mix design of high-performance concrete. The reliability and accuracy of the model were enhanced by providing an optimal structure by employing novel Henry's gas solubility optimization (HGSO) and particle swarm optimization (PSO) algorithms. The comparative study aimed to prove that the model optimized with Henry's gas solubility algorithm has a higher potential in predicting compressive strength. The obtained OBJ values for HGSO based model and PSO-based model of 1.4156 and 1.5419, respectively, confirmed the higher accuracy of HGSO based model.