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

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...

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Vydané v:	Scientific reports Ročník 13; číslo 1; s. 15024 - 33
Hlavní autori:	Hosseinzadeh, Maedeh, Mousavi, Seyed Sina, Hosseinzadeh, Alireza, Dehestani, Mehdi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 12.09.2023 Nature Publishing Group Nature Portfolio
Predmet:	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
ISSN:	2045-2322, 2045-2322
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