Accurate corrosion assessment of reinforced concrete (RC) structures is expected to improve the service life and durability of structures. However, traditional evaluation methods rely on simple regression and assumption models, which are easy to lead to unreliable evaluation results. The time-consuming and complex calculations in corrosion assessment are particularly suitable for machine learning (ML) and have already been deeply affected by the application of existing ML algorithms. The review analyzes recent ML methods for corrosion assessment of RC structures. These algorithms have recently had a significant impact on the estimation of the corrosion process, significant mechanical properties and durability of RC structures. In addition, some challenges that have emerged in corrosion evaluation and could be solved by ML algorithm are discussed critically. Through the detailed analysis of the challenges and future directions, researchers and engineers related industry will gain vital insight on the sustainable durability design of RC structures.

钢筋混凝土（RC）结构的准确腐蚀评估有望提高结构的使用寿命和耐久性。然而，传统的评价方法依赖于简单的回归和假设模型，容易导致评价结果不可靠。腐蚀评估中耗时且复杂的计算特别适用于机器学习 (ML)，并且已经深受现有 ML 算法应用的影响。该评论分析了最近的ML 方法用于钢筋混凝土结构的腐蚀评估。这些算法最近对腐蚀过程的估计、重要的机械性能和 RC 结构的耐久性产生了重大影响。此外，还批判性地讨论了腐蚀评估中出现的一些可以通过ML算法解决的挑战。通过对挑战和未来方向的详细分析，相关行业的研究人员和工程师将获得对 RC 结构可持续耐久性设计的重要见解。