钢纤维地聚物再生混凝土孔隙结构与力学性能试验研究

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

  • 摘要: 为了研究钢纤维地聚物再生混凝土(SFGRC)孔隙特性与宏观性能的发展规律,测试了混凝土的内部孔隙结构、力学性能与干燥收缩性能,分析了再生骨料掺量和前驱体钙硅比对混凝土孔隙结构、力学性能与收缩性能的影响规律,基于分形理论建立了SFGRC孔隙结构和宏观性能关联模型。研究结果表明:再生骨料显著增大了SFGRC的孔隙率和有害孔占比,劣化了其力学性能。高掺量矿渣细化了SFGRC的孔隙结构,加大了材料的孔径与空间分布的复杂程度。两者均加剧了SFGRC的早期干燥收缩。SFGRC的孔结构表现出明显的分形特征,其分形维数在2.623~2.731,且与孔隙结构特征参数、力学性能具有很强的相关性,能够有效评价材料孔隙结构特征。采用 Bayesian-MCMC (Markov chain monte carlo)方法建立的基于分形维数的SFGRC弹性模量、极限应力、极限应变与干燥收缩应变等特征参数的预测模型,拟合优度为0.51~0.98,且具有较高的预测精度,为优化SFGRC孔隙结构和宏观性能提供了理论依据。

     

    Abstract: 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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