Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions.

ABSTRACT: The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay of various influencing factors and guide mix design for improved compressive strength and sustainability. Ensemble methods and symbolic regression are promising approaches for this task due to their complementary strengths and solving challenges associated with repeated experiments in the laboratory. Choosing machine learning predictions over repeated, expensive, and time-consuming experiments in research projects, such as optimizing the utilization of metakaolin in pre-cured geopolymer concrete, presents a paradigm shift in how data-driven insights can revolutionize material development. The integration of ensemble and symbolic regression models enables resea

SUBMITTER: Onyelowe KC 

PROVIDER: S-EPMC11865618 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature