28-day cement strength prediction via transformer-based feature extraction and XGBoost

Abstract

The 28-day compressive strength of cement is a key indicator for assessing cement quality. To overcome the time delays inherent in manual testing, this paper proposed a 28-day cement strength fusion prediction method based on a Transformer feature extractor and an XGBoost meta-learner. This method first encoded the physicochemical multi-source strength variables through the Transformer embedding layer, then calculated the attention scores using the multi-head attention mechanism to allocate weights dynamically. Next, XGBoost’s gradient boosting tree structure and regularization techniques were employed to enhance the robustness of the cement strength prediction model in small-sample scenarios. Finally, the method was validated using real-world 28-day strength testing data from cement plants. The results indicated that, compared to the model without feature extraction, the regression model’s R 2 increased by 5.62%, and its RMSE decreased by 22.33% after applying Transformer feature extraction. Furthermore, when compared with other small-sample models, XGBoost achieved the highest average R 2 of 0.93 in 5-fold cross-validation (CV). Its training efficiency, robustness to noise, and ability to handle feature missingness outperformed other meta-learners. Compared to other methods, TF-XGBoost achieved the highest average R 2 of 0.94 in 25 Monte Carlo (MC) CVs, providing the best fit. The method proposed in this paper demonstrates higher accuracy, better generalization, and greater stability, offering a new approach for the prediction of cement 28-day strength with small sample sizes.