Advances in Modeling Surface Chloride Concentrations in Concrete Serving in the Marine Environment: A Mini Review

• Published in: Buildings (Basel) Vol. 14; no. 6; p. 1879
• Main Authors: Zhao, Ruiqi, Li, Chunfeng, Guan, Xuemao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Analysis; Chloride; Chloride ions; Chlorides; Concrete; concrete corrosion; Concrete structures; Convection; Corrosion; Corrosion and anti-corrosives; Corrosion mechanisms; Coupling; Data acquisition; Data entry; Durability; Empirical analysis; Erosion mechanisms; Exponential functions; Formulations; Ion concentration; Learning algorithms; Li Chunfeng; Machine learning; Marine corrosion; Marine environment; Research methodology; Research methods; Seawater; surface chloride concentration; China; United States > US
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings14061879

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.