Modeling the compressive strength of concrete containing waste glass using multi-objective automatic regression

Some grades of municipal and industrial waste glass (WG) discarded in landfills can cause environmental issues. One of the efficient solutions to overcome this issue is to use WG in concrete mixtures as aggregate or supplementary cementitious materials. Modeling the compressive strength (CS) of the produced concrete using machine learning methods can provide helpful insights into the effects of WG on concrete properties. In this study, a comprehensive database of concrete containing WG (CCWG) was gathered from 24 peer-reviewed papers. Two different scenarios were considered in the selection of input variables, and a novel machine learning method, called multi-objective multi-biogeography-based programming, was used to predict the CS of CCWG. This algorithm can automatically select the effective input variables, the structure of the equations, and its coefficients. Moreover, the proposed model optimizes the precision and complexity of the developed models simultaneously. The definition of complexity in the optimization problem can help achieve different mathematical equations with various accuracies and assist users in predicting the CS of CCWG even with a limited number of optimal input variables. The results show that the proposed algorithm can introduce several equations with different accuracies, complexities, and input variables to predict the CS of CCWG.