Predictive Modelling and Explainability of Metakaolin Concrete Compressive Strength: Benchmarking TabPFN Against Boosted Ensemble and Kernel-Based Algorithms
- Authors
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Oluwafemi Omotayo
Department of Civil Engineering, Federal University of Technology, Akure
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- Keywords:
- Array, Array, Array, Array, Array
- Abstract
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Predicting concrete compressive strength accurately, particularly in supplementary cementitious material-based systems, remains a critical challenge despite the advances in modelling techniques in the past decade. This is due to concrete’s highly nonlinear physicochemical interactions. The study therefore evaluates how effectively TabPFN, a transformer-based probabilistic foundation model, compares with support vector regression (SVR), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost), in predicting metakaolin (MK) concrete compressive strength. A dataset of 328 records was curated from 14 literature studies on MK concrete compressive strength, processed, analysed and used in training the selected models. Specific input features in the dataset include oxide compositions of metakaolin; the water-binder ratio; the percentage MK addition; the cement, MK, fine aggregate, coarse aggregate, and water contents, and the curing age, while the target was the compressive strength. Bayesian hyperparameter optimization was carried out on SVR, XGBoost, and CatBoost using Optuna SearchCV, while TabPFN was implemented without hyperparameter tuning. The models’ performance was measured via mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R2), and benchmarked. The results revealed that TabPFN demonstrated the highest predictive accuracy with MAE, MSE, RMSE, R2 values of 2.27, 13.54, 3.68, and 0.98. respectively, outperforming the benchmark models despite its untuned configuration. SHAP analysis revealed that curing age and water content were the most dominant contributors to the compressive strength prediction. The findings demonstrate the robustness of TabPFN for concrete property prediction in sustainable cementitious systems.
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