Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete

Abstract

The use of recycled concrete aggregate to produce new concrete can assist the sustainability in construction industry. However, the mechanical properties of this type of aggregate should be precisely investigated before its using in different applications. The elastic modulus of concrete is one of the most important design parameters in many construction applications. Because of various mix designs, the existing formulas for the elastic modulus of concrete cannot be used for recycled aggregate concrete (RAC). In recent years, there have been a few attempts for predicting the elastic modulus of RAC, especially, with various types of artificial intelligence (AI)methods: In this paper, three automatic regression methods, namely, genetic programming (GP), artificial bee colony programming (ABCP) and biogeography-based programming (BBP) were used for estimating the elastic modulus of RAC. Performances of the different automatic regression models were compared with each other. Moreover, the sensitivity analysis was performed to assess the trend of the elastic modulus as a function of effective input parameters used for developing the different automatic regression models. Overall, the results show that GP, ABCP, and BBP can be used as reliable algorithms for prediction of the elastic modulus of RAC. In addition, the water absorption of the mixed coarse aggregate and the ratio of the fine aggregate to the total aggregate were found as two of the most effective parameters affecting the elastic modulus of RAC.