Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete

Abstract

The use of recycled aggregate concrete (RAC) in the construction industry can help to prevent irreparable environmental damages and to mitigate the depletion rate of natural resources. However, the quality of the RAC should be investigated before its practical applications. Compressive strength of the RAC (fRAC′) is one of the most crucial design parameters, which is measured by time-consuming and cost-extensive experiments. One solution to restrict the number of experiments and achieve reliable fRAC′ estimation is through employing machine learning methods, Artificial Bee Colony Expression Programming (ABCEP) is a newly proposed automatic regression technique that is used in this study to predict the fRAC′. For comparison purposes, four extensions of artificial bee colony programming techniques (i.e., Artificial Bee Colony Programming (ABCP), quick Artificial Bee Colony Programming (qABCP), Quick semantic Artificial Bee Colony Programming (qsABCP), and Semantic Artificial Bee Colony Programming (sABCP)) were also served. To analyze the results, the average and best performances of all algorithms, regression analysis, execute run times, Wilcoxon signed-rank test, and the behavior of algorithms dealing with the local optima were investigated. The results show that the ABCEP method is the most effective technique, with the average root mean squared error of 10.36 MPa compared to 16.23 MPa, 10.82 MPa, 17.71 MPa, and 14.20 MPa for the developed ABCP-, qABCP-, qsABCP-, and sABCP-based models, respectively, in colony size of 30. In addition, the run time of this algorithm is remarkably less than the other algorithms.