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This paper proposes a Response Surface Methodology (RSM) based Genetic Algorithm (GA) using MATLAB ® to assess and optimize the thermal and fluidity of high strength concrete (HSC). The overall heat transfer coefficient, slump-spread flow and T 50 time was defined as thermal and fluidity properties of high strength concrete . In addition to above mentioned properties, a 28-day compressive strength of 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 method was carried out by obtaining quadratic models using RSM. Having excessive or low ratio of water to binder provides lower overall heat transfer coefficient. Moreover, T 50 time of high strength concrete decreased with the increasing of water to binder ratio and the percentage of superplasticizer content . Results show that RSM based GA is effective in determining optimal mixture ratios of HSC .

an international journal of optimization and control: theories and applications/e-an international journal of optimization and control: theories and applications

Assessment and optimization of thermal and fluidity properties of high strength concrete via genetic algorithm