Compressive Strength of Geopolymer Concrete Prediction Using Machine Learning Methods

The implementation of machine learning methods as one of the artificial intelligence technologies has allowed bringing the construction process to a new qualitative level. Significant interest in these methods is observed in predictive modeling of the building materials’ properties. In the scientific field of innovative concretes, limitations exist regarding the disclosure of intelligent algorithms’ capabilities to predict material properties when altering specific chemical elements and process parameters. This article focuses on seven machine learning techniques that are used to solve the issue in forecasting geopolymer concrete’s compressive strength: from the simplest, such as Linear Regression, to more complex and modern methods, including the TabPFNv2 generative transformer model. The dataset was formed based on 204 datasets available in the public domain, including the author’s experimental data. The leading machine learning features were selected: blast-furnace granulated slag (kg/m3); NaOH molarity; NaOH content in the alkaline activator (%); Na2SiO3 content in the alkaline activator (%); fiber type; fiber dosage (%); and curing temperature (°C). The MAE, RMSE, MAPE metrics and the R2 determination coefficient were used to evaluate the prediction quality. The kNN method (MAE = 0.37, RMSE = 0.63, MAPE = 1.62%, R2 = 0.9996) and TabPFNv2 (MAE = 0.46, RMSE = 0.64, MAPE = 1.39%, R2 = 0.9996) presented the highest accuracy in predicting compressive strength, as assessed by the chosen parameters. If computing resources are limited and interpretability is required, it is recommended to use the CatBoost or Random Forest algorithms; if a graphics processing unit and a small dataset are available, it is advisable to use TabPFN; if there is no need for manual parameter adjustment, H2O AutoML is suitable.