Application of genetic algorithm for modeling of dense packing of concrete aggregates

Abstract

Sequential Packing Algorithm (SPA) was developed to model the dense packing of large assemblies of particulate materials (in the order of millions). These assemblies represent the real aggregate systems of portland cement or asphalt concrete. To improve the SPA performance, the program engine was updated with a genetic algorithm (GA) search module. Multi-cell packing procedures, fine adjustment of the algorithm’s parameters, as well as implementation of GA were effective tools to optimize the computational resources, to speed-up the SPA and to pack very large volumes of spherical entities. The developed algorithm generates and visualizes dense packings corresponding to concrete aggregates. The influence of model variables on the degree of packing and the corresponding distribution of particles was analyzed. Based on the simulation results, different particle size distributions of particulate materials are correlated to their packing degree. These packings agreed well with the standard requirements and available research data. The results of the research can be applied to the optimal proportioning of concrete mixtures.