Mechanical properties of multi-recycled aggregate concrete under combined compression-shear loading

The mechanical properties and strength failure criteria of multi-recycled aggregate concrete (multi-RAC) under combined compression and shear loading states are investigated in this paper. The peak shear strength, peak shear displacement, and failure patterns are compared under different regeneration cycles and normal compressive stress ratios. The results reveal that both the peak shear strength and peak shear displacement increase with the increased normal stress ratio. The shear failure pattern with higher severity corresponds to more spalling powder and debris deposited on the shear fracture surface. When the regeneration cycles of coarse aggregate increase, the peak shear strength decreases and the descending trend become more evident with the higher vertical compressive stress ratio. Under the normal compressive stress, contact friction strength is the dominant component of peak shear strength among the cohesive strength, contact friction strength, and shear dilation strength. Based on different stress expressions, three compression-shear failure criterion models considering the regeneration cycles of coarse aggre-gate under planar stress state were established for RAC. The stress invariance failure criterion model and octahedral stress failure criterion model in quadratic parabolic functional form can provide high prediction accuracies.

Automated summary

The mechanical properties and strength failure criteria of multi-recycled aggregate concrete (multi-RAC) under combined compression and shear loading states were investigated. The study found that both the peak shear strength and peak shear displacement increased with the increased normal stress ratio. The study also established three compression-shear failure criterion models for RAC considering the regeneration cycles of coarse aggregate under planar stress state, and the stress invariance failure criterion model and octahedral stress failure criterion model provided high prediction accuracies.