关键词: 混凝土, 抗压强度预测, 骨料级配, 卷积神经网络, Stacking深度集成模型, SHAP分析

Abstract: Accurate prediction of concrete compressive strength plays a significant role in quality control during construction. Previous predictive models largely focused on the influence of initial mix proportions, but neglected the impact of aggregate gradation, derived features, and its interpretability. This study develops a stacking-based deep ensemble model for predicting compressive strength, which holistically considers these two factors to improve predictive accuracy and interpretability. This novel model uses three widely used ensemble learning algorithms and a Convolutional Neural Network (CNN) as heterogeneous base learners to leverage diversity and heterogeneity among these algorithms. To improve the tree-based models that are usually too sensitive to hyperparameters and limited by their capacity for high-dimensional feature extraction, we integrate CNN with a channel attention mechanism, thereby enhancing its feature representation capability. A Multi-Layer Perceptron (MLP) incorporating an attention mechanism is adopted as a robust meta-learner to mitigate overfitting risks. Leveraging the SHAP (Shapley Additive explanation) framework, we examine systematically the critical features of concrete strength prediction and their interactive effects. Experimental results show our new model, through considering aggregate gradation and derived features comprehensively, achieves a 27.53% improvement in the accuracy of compressive strength predictions. Using a SHAP analysis, we have identified the dominant drivers of the model－water-to-binder ratio, water content, fly-ash-to-water ratio, cement content, and the mass fraction of aggregates in the size range of 31.5-40 mm. This study improves predictive accuracy and sheds light on the understanding of core parameters governing concrete strength through interpretable analysis, helping intelligent concrete management.

Key words: concrete, compressive strength prediction, aggregate gradation, convolutional neural network, Stacking-based deep ensemble model, SHAP analysis