Machine learning algorithms in the environmental corrosion evaluation of reinforced concrete structures-A review

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.

Automated summary

This review analyzes recent machine learning methods for corrosion assessment of reinforced concrete structures and discusses some challenges in corrosion evaluation. These methods have significant impact on the estimation of corrosion process, mechanical properties, and durability of structures, providing valuable insights for researchers and engineers in the field.