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

ABSTRACT: 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 shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive expe

SUBMITTER: Hosseinzadeh M 

PROVIDER: S-EPMC10497559 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature