Assessment and optimization of thermal and fluidity properties of high strengthconcrete via genetic algorithm

Abstract

This paper proposes a Response Surface Methodology (RSM) based Genetic Algorithm (GA) using MATLAB®to assess and optimize thethermal and fluidity of high strengthconcrete(HSC).The overall heat transfer coefficient, slump-spread flow andT50timewas defined as thermal and fluidity properties of high strengthconcrete. In addition to above mentioned properties, a28-day compressive strengthof HSC was also determined. Water to binder ratio, fine aggregate to total aggregate ratio and the percentage of super-plasticizer content was determined as effective factors on thermal and fluidity properties of HSC. GA based multi-objective optimization methodwas carried out byobtainingquadratic models using RSM. Having excessive or low ratio of water to binder provides lower overall heat transfer coefficient. Moreover, T50timeof high strengthconcretedecreased with the increasing of water to binder ratio and the percentage of superplasticizer content. Results show thatRSM based GA is effective indeterminingoptimal mixture ratios of HSC.