Multi-objective optimization of fly ash-slag based geopolymer considering strength, cost and CO2 emission: A new framework based on tree-based ensemble models and NSGA-II

Fly ash-slag based geopolymer has excellent mechanical performance with low carbon footprints, which has emerged as a promising alternative to Portland cement. The optimization of geopolymers requires trade-offs between multiple objectives (strength, cost, and CO2 emission) while considering a large number of highly nonlinear variables. To solve this multi-objective optimization (MOO) problem, this study developed a MOO model combining the tree-based ensemble learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). The MOO model was trained on a database collected from published literature with 676 mixture proportions. The chemical components as well as their contents and the curing conditions were selected as the input variables, while the uniaxial compressive strength (UCS) was the output variable. The UCS of fly ash-slag based geopolymers were modeled using the random forest regressor, extra trees regressor, gradient boosted regressor and extreme gradient boosting regressor. The results show that the gradient boosted regressor has the highest prediction accuracy with R (0.966) and RMSE (5.295 MPa) on the testing set. The analysis of the variable importance by the developed model indicates that the curing age, contents of slag and sodium silicate are more important to the strength development of the binary blended geopolymer. The MOO model developed based on the gradient-boosted regressor and NSGA-II successfully found the cost-UCS and CO2 emission-UCS Pareto fronts of the bi-objective optimization problem, as well as the cost-UCS-CO2 emission Pareto front of the tri-objective optimization problem. This developed framework improves the efficiency of geopolymer design and can be applied to the mixture optimization of other construction materials.