Predicting 28-Day Cement Compressive Strength for Delayed Quality Control Using Early-Age Strength and a Weighted CatBoost-XGBoost Ensemble

Abstract

A data-driven and industrially applicable framework was developed to predict the 28-day compressive strength of cement using routine quality control data from the SEZA Cement Plant. Since 28-day strength is obtained only after a substantial delay, the proposed framework was designed to support earlier decision-making during production. To improve predictive capability, 7-day compressive strength was incorporated as an explanatory variable together with plant quality control parameters, thereby linking early-age mechanical response to later-age performance. Before modeling, the dataset was systematically preprocessed through column standardization, numeric type conversion, removal of incomplete observations, and elimination of age-related strength variables other than the target to avoid information leakage. For comparative evaluation, CatBoost, XGBoost, LightGBM, Random Forest, and Extra Trees were implemented. In addition, a hybrid model, termed the 'Early-Age-Strength-Supported Weighted Dual-Boosting Ensemble' (EYD-AIBE), was proposed by combining the optimized predictions of CatBoost and XGBoost using fixed weights. Model performance was assessed under both random split and time split schemes in order to evaluate not only overall pattern-learning capacity but also temporal generalizability. Hyperparameter optimization was performed for CatBoost and XGBoost using Optuna. The results showed that EYD-AIBE achieved the best performance in both evaluation settings. Under random split, it yielded R-2 = 0.535, RMSE = 0.988 MPa, and MAE = 0.785 MPa, whereas under time split, the model achieved R-2 = 0.405, RMSE = 1.148 MPa, and MAE = 0.900 MPa. These findings indicate that the proposed hybrid framework provides a feasible, reliable, and methodologically robust tool for cement quality control, production monitoring, process optimization, and early decision support.