Predicting the Temperature Regime in Hardening Massive Monolithic Walls Using CatBoost Gradient Boosting

Thermal cracking due to hydration heat in massive monolithic walls poses a significant risk, but traditional prediction methods are often too complex for rapid engineering assessments. This study aims to develop machine learning models to predict the maximum temperature and center-to-surface temperature difference in hardening massive walls, considering variable heat exchange, concrete hardening rate, and formwork curing time. A dataset of 855,360 numerical experiments was collected by solving the transient heat conduction equation using the finite element method (FEM), varying wall thickness, initial and ambient temperatures, heat transfer coefficient, hardening rate, curing time, and heat release. CatBoost gradient boosting regression models were trained and validated to predict both output parameters. The models achieved high accuracy with coefficients of determination exceeding 0.99 for both targets, mean absolute percentage errors of 0.2% for maximum temperature and 3% for temperature difference. Feature importance analysis revealed that heat release dominates both predictions (35–43% importance), followed by wall thickness. The developed CatBoost models enable rapid, accurate prediction of thermal regimes in massive monolithic walls without time-consuming finite element simulations, offering a practical tool for assessment of early cracking risk temperature indicators during construction.