An efficient machine learning approach for predicting concrete chloride resistance using a comprehensive dataset

• Published in: Scientific reports Vol. 13; no. 1; pp. 15024 - 33
• Main Authors: Hosseinzadeh, Maedeh, Mousavi, Seyed Sina, Hosseinzadeh, Alireza, Dehestani, Mehdi
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.09.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/166/986; 639/301/1023/1025; Algorithms; Chloride; Chlorides; Classification; Concrete; Datasets; Diffusion coefficient; Fly ash; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Neural networks; Risk reduction; Science; Science (multidisciplinary); Silica; Slag; Water content

By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete sh...