Abstract & Overview
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.