Intelligent mixture design of steel fibre reinforced concrete using a support vector regression and firefly algorithm based multi-objective optimization model

Abstract

Steel fibre reinforced concrete (SFRC) is widely used in the construction concrete industry as it partakes an important role of evolving concrete technology. It consists of steel fibres of various shapes, sizes and geometries that influence the concrete mix composition and mechanical properties. However, compared to traditional concrete, it is difficult to design the mix proportions because more influencing variables need to be considered to optimise multiple properties including ultimate compressive strength, tensile or flexural strength and cost. Therefore, the present study proposes an artificial intelligence based multi-objective optimization model to enable an efficient method of finding the optimum mix design for SFRC. A large dataset including 299 instances for uniaxial compressive strength (UCS) test and 269 instances for flexural strength (FS) test were collected from previous literature. Support vector regression (SVR) model was applied to predict UCS and FS for SFRC. The hyper parameters of SVR models were tuned using a firefly algorithm (FA) and a sensitivity study was conducted to understand the importance of the inputs on the output variables for the algorithms. High correlation coefficients (0.91 for UCS and 0.85 for FS) were achieved on the test dataset. The FA-SVR model was then applied as the objective function for a developed multi-objective FA to search for the optimal SFRC mixture proportion. Pareto optimal solutions were obtained and served as a design guide to determine the optimal SFRC mixtures.