Statistical results of feature variables.

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 R2 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 R2 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 R2 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.

水泥28天抗压强度是评估水泥品质的核心指标。为克服人工检测固有的时长滞后问题，本文提出了一种基于Transformer特征提取器与XGBoost元学习器的水泥28天强度融合预测方法。该方法首先通过Transformer嵌入层对理化多源强度变量进行编码，随后利用多头注意力机制计算注意力分数以动态分配权重。随后，借助XGBoost的梯度提升树结构与正则化技术，提升了小样本场景下水泥强度预测模型的鲁棒性。最后，本文采用水泥厂实测的28天强度检测数据对所提方法进行了验证。结果表明，相较于未进行特征提取的模型，应用Transformer特征提取后，回归模型的决定系数（R²）提升了5.62%，均方根误差（RMSE）降低了22.33%。此外，相较于其他小样本模型，XGBoost在5折交叉验证（CV）中取得了0.93的最高平均R²值，其训练效率、抗噪声鲁棒性以及特征缺失处理能力均优于其他元学习器。相较于其他方法，TF-XGBoost在25次蒙特卡洛（MC）交叉验证中取得了0.94的最高平均R²值，拟合效果最优。本文所提方法具备更高的预测精度、更优的泛化能力与更强的稳定性，为小样本场景下的水泥28天强度预测提供了全新解决方案。