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

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