Quantitative Characterization Model of Macroscopic Mechanical Properties of Recycled Concrete Based on Porosity and Pore Sizes - Haiyu Chen; Fuwei Xu (Advances in Materials Science and Engineering)
In consideration of the impact of porosity and pore sizes on the macroscopic mechanical parameters (compressive strength and elastic modulus) of recycled concrete, an investigation was conducted on the macroscopic mechanical properties of the recycled concrete with varying replacement rates of recycled coarse aggregates. By utilizing nuclear magnetic resonance technology to scan the porosity and pore structure of the recycled concrete, an analysis was performed on the correlation between the macroscopic mechanical properties of the recycled concrete and pore sizes (most available pore size, mean pore size, and critical pore size). The regression analysis using the least square method was carried out on test data. A quantitative characterization model of macroscopic mechanical properties of the recycled concrete between porosity and pore sizes (most available pore size, mean pore size, and critical pore size) was established. This model was compared with existing models of the strength-pore structure. Research results indicated that with the increasing replacement rate for the recycled coarse aggregate, both porosity and pore sizes in the recycled concrete increased, and its macroscopic mechanical properties (compressive strength and elastic modulus) continuously decreased. The correlation analysis revealed a strong relationship between porosity and pore sizes (most available pore size, mean pore size, critical pore size, and the macroscopic mechanical properties (compressive strength and elastic modulus)) of the recycled concrete. In comparison to existing models of the strength-pore structure, the established quantitative characterization model provided a more precise depiction of the relationship of the macroscopic mechanical properties of the recycled concrete between porosity and pore sizes, resulting in enhanced accuracy in prediction outcomes.