MODELING AND OPTIMIZATION OF STANDARD CONCRETE CONTAINING GRANULE BLAST FURNACE SLAG: A GENE EXPRESSION MODELING BASED MULTI-RESPONSE WEIGHTED NON-LINEAR PROGRAMMING APPLICATION - Şimşek, Barış (International Journal of Industrial Engineering: Theory, Applications and Practice, 2018)
In this study, Gene expression models have been obtained for prediction of slump flow, 3rd compressive strength, 7th compressive strength and 28th compressive strength of normal weight concrete containing granule blast furnace slag using GeneXproTools software package version 5.0 with data collected by quality control laboratory. Optimal mixture proportions of normal weight concrete containing granule blast furnace slag was determined by multi-response weighted non-linear programming by means of using non-linear gene expression models via Matlab version 2015a. The results demonstrate that gene expression programming is more successful in terms of predicting the properties of normal weight concrete containing granule blast furnace slag according to fuzzy modeling and artificial neural network. It is concluded that proposed gene expression models based multi response weighted non-linear programming methodology is the first application that is effective in order to determine optimal mixture proportions. In addition, this methodology provides possibility of optimal use of resources such as raw materials for laboratory personnel without the need to additional experiments causing a loss of time.