OPTIMIZATION OF AERATED CELLULAR CONCRETE (ACC) RECYCLING ALUMINUM WASTE USING GENETIC ALGORITHM
Keywords:
Recycling, aluminum waste, concrete, Nano, micro powder, ACC.Abstract
Eight experiments were performed on five samples of Aerated Cellular Concrete (ACC) with varying aluminum powder particle sizes [A1 (95μm), A2 (30-70μm), A3 (10-30μm), A4&A5 (96.96nm)]. which are ready at the Thermiston Najaf factory. Based on statistical data from testing findings, an ideal composite material was discovered utilizing the hybrid approach represented by genetic algorithms by carrying out three methods of crossover (1X, 2X, & multi). To characterize the composite, the fundamental data were constructed based on their characteristics. In order to prevent clusters from overlapping with one another, the evolution algorithm then creates a strategy for the genetic clustering process and offers a number of necessary clusters. One of the clustering validity measures termed "Davies-Bouldin index" as fitness function of the method that employed. Then, the two categories of qualities for each cluster: mechanical features (hardness, compression strength and impact strength, physical features (density, porosity, thermal conductivity, Total absorption ratio (%), Depth of water absorption). This article indicates that composite (37, 45 &45) represented optimal sample by employing one point, two-point crossover and multi crossover operators (1X,2X & multi) accordingly.
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