LI Zhenjun, LIU Xi, ZHAO Chenyu, et al. Pore structure and mechanical properties of steel fiber reinforced geopolymer recycled aggregate concrete[J]. Acta Materiae Compositae Sinica, 2024, 41(10): 5516-5526. DOI: 10.13801/j.cnki.fhclxb.20240023.002
Citation: LI Zhenjun, LIU Xi, ZHAO Chenyu, et al. Pore structure and mechanical properties of steel fiber reinforced geopolymer recycled aggregate concrete[J]. Acta Materiae Compositae Sinica, 2024, 41(10): 5516-5526. DOI: 10.13801/j.cnki.fhclxb.20240023.002

Pore structure and mechanical properties of steel fiber reinforced geopolymer recycled aggregate concrete

  • To study the pore characteristics and macroscopic performance of steel fiber reinforced geopolymer recycled aggregate concrete (SFGRC), the internal pore structure, mechanical properties and shrinkage performance of SFGRC were tested. The influences of recycled aggregate content and calcium silicon ratio on the pore structure, strength, stress-strain curve shape and characteristic parameters of concrete were analyzed. Based on fractal theory, a correlation model between pore structure and the macroscopic performance of SFGRC was established. The research results indicate that recycled aggregate significantly increases the porosity and harmful pore proportion of SFGRC and deteriorates its mechanical properties. The high ground granulated blast furnace slag (GGBS) content refines the pore structure of SFGRC, increasing the complexity of the material's pore size and spatial distribution. The GGBS and recycled aggregate significantly increase the shrinkage rate of SFGRC. The pore structure of SFGRC exhibits obvious fractal characteristics, with fractal dimensions ranging from 2.623 to 2.731. It strongly correlates with pore structure characteristic parameters and mechanical properties, which can effectively evaluate the pore structure characteristics of SFGRC. A prediction model based on fractal dimension for characteristic parameters such as SFGRC elastic modulus, ultimate stress, ultimate strain and drying shrinkage was established using the Bayesian-MCMC (Markov chain monte carlo) method, with a goodness of fit of 0.51-0.98 and high prediction accuracy. This provides a theoretical basis for optimizing the pore structure and macroscopic performance of GRC concrete.
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