硫酸盐腐蚀环境作用下混凝土力学性能劣化是多阶段演化的动态过程, 不同的物理化学反应在不同时期主导材料性能的渐进式退化, 准确预测力学性能劣化对混凝土耐久性的研究至关重要。在腐蚀混凝土力学性能预测方法中, 传统回归方法易受高维复杂数据的影响而产生过拟合现象, 进而降低其泛化能力, 而数值模拟方法需要依赖复杂假设和海量力学参数, 计算成本高且不确定性大。此外, 工程实践中存在长期监测数据匮乏、加速试验与实际环境存在动力学差异等, 进一步加剧了力学性能预测的不确定性。机器学习可通过非线性映射建模实现多变量耦合分析, 有效整合材料配比、环境参数等多微观、宏观参数, 进而提升腐蚀混凝土力学性能预测的普适性与准确性。本文首先阐述了硫酸盐腐蚀作用下混凝土材料腐蚀机理, 归纳了已有力学性能退化机器学习预测方法研究进展, 梳理了现有预测模型所对应数据集的瓶颈, 最后展望了机器学习预测模型的未来的研究方向。

The deterioration of the mechanical properties of concrete under sulfate attack is a multi-stage and dynamic evolution process, in which different physicochemical reactions dominate the progressive degradation at different stages. Accurate prediction of mechanical degradation is thus essential for durability assessment. Traditional regression-based prediction methods are susceptible to overfitting when dealing with high-dimensional and heterogeneous datasets, resulting in reduced generalization capability. Numerical simulation methods, on the other hand, rely on complex assumptions and a large number of mechanical parameters, which leads to high computational cost and significant uncertainty. In engineering practice, the lack of long-term monitoring data and the kinetic discrepancies between accelerated tests and real environments further exacerbate prediction uncertainty. Machine learning enables nonlinear modeling and multivariate coupling analysis, effectively integrating micro-and macro-level parameters such as material composition and environmental conditions, thereby improving the accuracy and robustness of mechanical property prediction for sulfate-corroded concrete. This paper first reviews the corrosion mechanisms of concrete under sulfate attack, summarizes the research progress of machine-learning-based prediction methods for mechanical degradation, and identifies the major bottlenecks of existing datasets. Finally, future research directions for machine-learning prediction models are discussed.