Size Effect Analysis of Axial Compressive Mechanical Behavior of CFRP-Confined RAC Short Columns Based on a Three-Dimensional Mesoscopic Finite Element Method

Existing research on the axial compressive performance and size effect of carbon fiber-reinforced polymer (CFRP)-confined recycled aggregate concrete (RAC) short columns mainly relies on macroscopic experimental analysis, lacking research methods capable of reflecting the heterogeneous characteristics of materials and mesoscopic damage evolution mechanisms. Accordingly, a three-dimensional mesoscale finite element method was adopted in this study to establish a five-phase RAC mesoscopic model, including natural aggregates, old mortar, old interfacial transition zones (ITZs), new mortar, and new interfacial transition zones. Different from existing studies, predominantly based on macroscopic experiments or empirical models, this paper focuses on revealing the coupled effects of the recycled aggregate replacement ratio, the number of CFRP confinement layers, and specimen size. A total of 48 specimens were designed, covering four specimen sizes, four recycled coarse aggregate replacement ratios, and three CFRP confinement layers. The effects of these parameters on failure modes, stress–strain relationships, and size effect were systematically analyzed. The results indicate that the peak stress decreases significantly with the increase in the recycled coarse aggregate replacement ratio; the increase in CFRP layers markedly improves both the bearing capacity and post-peak bearing capacity retention rate; the ultimate stress generally declines as the specimen size increases, which highlights the pronounced size effect of CFRP-confined RAC short columns. Based on peak parameters and normalization analysis, a simplified stress–strain model was established: the goodness of fit R2 of the ascending branch is 0.98565, and the goodness of fit for the descending branch parameters are Rβ2 = 0.9655 and Rγ2 = 0.9350. Compared with existing models, the proposed model achieves a low prediction error of only 1.5–6.9%, demonstrating superior prediction accuracy. It can accurately describe the complete compressive process of CFRP-confined RAC short columns and provide a mesoscopic mechanistic basis for engineering design.