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<p>To optimize the mix design of frost-resistant concrete for cold-region infrastructures and establish a probabilistic reliability evaluation method for its long-term durability, this study takes the ternary composite system consisting of fly ash, ground granulated blast-furnace slag (GGBS) and defoamer as the research subject. A total of 360 rapid freeze–thaw cycle tests were performed on five batches of concrete labelled H0-H4 to systematically investigate the evolution laws of mass loss rate, relative dynamic elastic modulus, compressive strength degradation and micro-pore structure throughout the freeze–thaw process. Multi-scale characterization techniques including X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to quantitatively analyze the influence mechanisms of micro parameters such as porosity, pore size distribution and air void spacing factor on the macroscopic frost resistance of concrete. By integrating backpropagation (BP) neural network, XGBoost machine learning model, Wiener stochastic process and Weibull distribution theory, an integrated framework coupling freeze–thaw damage evolution and service life prediction of concrete was established, which quantitatively revealed the individual and coupled regulatory effects of each component on concrete frost durability. Test results demonstrate that the ternary composite mixture H0 delivers the optimal frost durability. After 360 freeze–thaw cycles, all its macroscopic performance indicators outperform other batches significantly, owing to its dense hydration gel network, abundant closed micro-fine air voids and uniformly distributed pore structure. Model comparison reveals that the BP neural network achieves higher accuracy in single-point performance prediction, yet it cannot predict long-term damage evolution trends or conduct probabilistic service life assessment. The two-stage Wiener–Weibull framework proposed in this work effectively addresses this limitation and identifies defoamer as the core regulator mitigating freeze–thaw damage propagation. The prediction results show that the average freeze–thaw service life of mixture H0 reaches 812 cycles, 21.0% longer than that of mixture H4 with the worst anti-freezing performance. This research provides theoretical support and analytical references for the mix proportion optimization and long-term durability assessment of frost-resistant concrete applied in cold regions with large temperature differences.</p>

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Microscopic pore characteristics governing macroscopic freeze thaw durability and service life prediction of ternary blended concrete

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Xiaoling Zhong,
  • Shiqiang Yin,
  • Xueshuai Liu,
  • Chen Zhang,
  • Shibo Dong

摘要

To optimize the mix design of frost-resistant concrete for cold-region infrastructures and establish a probabilistic reliability evaluation method for its long-term durability, this study takes the ternary composite system consisting of fly ash, ground granulated blast-furnace slag (GGBS) and defoamer as the research subject. A total of 360 rapid freeze–thaw cycle tests were performed on five batches of concrete labelled H0-H4 to systematically investigate the evolution laws of mass loss rate, relative dynamic elastic modulus, compressive strength degradation and micro-pore structure throughout the freeze–thaw process. Multi-scale characterization techniques including X-ray diffraction (XRD) and scanning electron microscopy (SEM) were utilized to quantitatively analyze the influence mechanisms of micro parameters such as porosity, pore size distribution and air void spacing factor on the macroscopic frost resistance of concrete. By integrating backpropagation (BP) neural network, XGBoost machine learning model, Wiener stochastic process and Weibull distribution theory, an integrated framework coupling freeze–thaw damage evolution and service life prediction of concrete was established, which quantitatively revealed the individual and coupled regulatory effects of each component on concrete frost durability. Test results demonstrate that the ternary composite mixture H0 delivers the optimal frost durability. After 360 freeze–thaw cycles, all its macroscopic performance indicators outperform other batches significantly, owing to its dense hydration gel network, abundant closed micro-fine air voids and uniformly distributed pore structure. Model comparison reveals that the BP neural network achieves higher accuracy in single-point performance prediction, yet it cannot predict long-term damage evolution trends or conduct probabilistic service life assessment. The two-stage Wiener–Weibull framework proposed in this work effectively addresses this limitation and identifies defoamer as the core regulator mitigating freeze–thaw damage propagation. The prediction results show that the average freeze–thaw service life of mixture H0 reaches 812 cycles, 21.0% longer than that of mixture H4 with the worst anti-freezing performance. This research provides theoretical support and analytical references for the mix proportion optimization and long-term durability assessment of frost-resistant concrete applied in cold regions with large temperature differences.