Analysis of fragmentation failure behavior and energy dissipation characteristics of negative-temperature curing concrete under impact loading

Abstract

In extremely cold regions, the perennial negative-temperature climate seriously affects the pore structure and hardening properties of concrete materials during curing. This study aims to reveal the damage mechanism of the negative-temperature curing environment on the fragmentation failure behavior and energy dissipation characteristics of concrete materials under dynamic loading. On this basis, the correlation between fragment characteristics and energy dissipation is established. Firstly, the dynamic fragmentation experiment is conducted on concrete specimens cured at different temperatures (−20, −15, −10, −5, and 20 °$C$) using the split Hopkinson pressure bar. Subsequently, based on the fractal theory, Weibull distribution, and low-field nuclear magnetic resonance technology to quantitatively analyze the influence of the negative-temperature curing environment on the fragmentation degree, characteristic fragment size, and microscopic pore size distribution. The intrinsic influence mechanism between microscopic pore damage and macroscopic fragmentation behavior is explained. Finally, based on the tensile crack softening failure criterion and Griffith fracture theory, a fragmentation energy dissipation model is established to analyze the energy dissipation during fragmentation. The result indicates that the negative-temperature curing environment enlarges the pore size, increases the porosity, decreases the dynamic compressive strength, and increases the fragmentation degree of concrete materials. As the curing temperature decreases, microscopic pore damage gradually accumulates, resulting in the transition from overall failure to localized failure in the concrete specimen’s dynamic failure form. The proposed fragmentation energy dissipation model can accurately calculate various energy transitions during the dynamic fragmentation process and achieves an effective prediction from fragment information to required input energy information.