Combining GCN with attribute mathematical theory to improve attribute recognition of recycled coarse aggregate

Abstract

Recycled coarse aggregate exhibits significant heterogeneity in its physical and chemical properties, along with complex interdependencies among various feature attributes. These characteristics often lead to challenges in achieving high accuracy in attribute recognition, highlighting the urgent need for a structured and intelligent analytical framework. To address this issue, this study proposes a novel hybrid approach that integrates graph convolutional networks (GCN) with attribute mathematics theory, aiming to enhance feature representation and improve recognition performance. The method begins by constructing a multi-dimensional attribute graph based on the physicochemical properties of recycled coarse aggregate, capturing the intrinsic correlations among different features. A multilayer GCN is then employed to extract deep-level, globally coupled feature representations. Subsequently, attribute mathematics theory is applied to simplify and logically abstract the output features through membership functions and covering operators, enabling effective feature selection and dimensionality reduction. The refined feature set is finally fed into a discriminant classifier to achieve accurate attribute recognition. Experimental results demonstrate the superiority of the proposed fusion model over traditional machine learning methods such as SVM, random forest, and MLP. The model achieves average recognition accuracies and recall rates of 0.89 and 0.89 across eight material categories, and 0.90 and 0.89 across seven particle size ranges, respectively. Five-fold cross-validation yields an average accuracy between 0.889 and 0.918, with a low standard deviation of 0.012, indicating strong stability and generalization performance. Moreover, the feature simplification strategy achieves an average feature reduction rate of 0.67 while retaining 0.92 of the original information. These results confirm that the proposed GCN–attribute mathematics framework significantly enhances the attribute recognition capability of recycled coarse aggregate, offering a robust and efficient solution for intelligent identification in sustainable construction materials research.