Graph-Based Modeling of Elastic Behavior in Microscale Multiphase Concrete Using a Community-Enhanced GNN

Abstract Predicting the elastic properties of multiphase concrete remains challenging due to its inherent heterogeneity and nonlinear behavior. This study proposes a community-enhanced graph neural network (CE-GNN) to predict the elastic response of four-phase cementitious materials. Three-dimensional microstructures obtained from X-ray CT images are transformed into graphs using a Mapper clustering algorithm, and community detection is incorporated into the GNN architecture to capture localized mechanical interactions. Ground-truth elastic properties are generated through finite element-based homogenization. Results show that CE-GNN achieves an R 2 of 0.97 for bulk modulus prediction and reduces the end-to-end wall-clock runtime by approximately 57%–68% compared with FEM homogenization on the same workstation, while outperforming both CNN and standard GNN baselines.