Improved Artificial Bee Colony based Compressive Strength Prediction and Optimization in Concrete Samples

The usage of Silica Fume (SF) and Fly Ash (FA) materials are stated in concrete sample design and it used numerous industrial applications because of the benefit of these materials. Through, the proper usage of correct mixes is a significant factor according to concrete weight and strength. Hence, this paper proposed an Improved Artificial Bee Colony (IABC) algorithm to estimate the concrete CS through laboratory experiments. The database contains SF, FA, Total Cementitious Material (TCM), Age of Samples (AS) and High-Rate Water Reducing Agent (HRWRA) is utilized for estimate and forecast concrete Compressive Strength (CS). The proposed IABC formed a connection among above mentioned parameters. The IABC based on whale dynamic selection probability and search are reached an optimal solution and stable operate reasonable and state ability configuration. In the innovative technique, every parameter influence on CS is defined. Lastly, the IABC algorithm is used for optimal conditions of concrete samples compressive strength is presented and it is able to progress connection which is serve as better replacement for empirical models.