Evolutionary AI Predicts Strength of Glass-Powder Geopolymers for Lower-Carbon Concrete
bioengineer.orgResearchers in Morocco used an evolutionary AI method to predict the compressive strength of geopolymers made from fly ash and waste glass powder. They trained a differential evolution-optimized XGBoost model on 429 experimental records from 11 studies and reached an R-squared of 0.9913 on unseen data. The model found that sodium hydroxide concentration matters most, and that too much silica from glass can actually weaken the mix. Practical guidance from the analysis: keep glass replacement below 20%, use around 12M activator, and cure at 60 to 80 C for about 24 hours. This matters because cement production accounts for roughly 5 to 8% of global CO2 emissions. Geopolymers offer a lower-carbon alternative, but mix design has been hard to predict. Machine learning tools like this can speed up formulation and help turn waste glass into a useful construction material. The next step is moving from lab results to commercial production, where cost, feedstock consistency, and curing energy will decide whether the approach really scales.
