Evaluates compressive strength of concrete with recycled aggregates, suggesting sustainable solutions.
It is known that a significant portion of global carbon dioxide (CO2) emissions originate from concrete production. However, construction and demolition activities result in a considerable amount of construction and demolition waste (CDW). The proper recycling of CDW is important in terms of conserving natural resources and ensuring sustainability. A significant amount of recycled concrete aggregate (RCA) is obtained from the recycling of CDW. Many researchers have contributed to reducing carbon emissions by conducting studies on RCA. However, the fact that recycled aggregates (RAs) are obtained from different construction wastes is the biggest obstacle to generalizing the studies in the literature. This study aims to identify machine learning (ML) models that can reliably predict the compressive strength of concrete produced with recycled concrete aggregates (RCAs) and to evaluate the impacts of their use. In this study, keywords (15) obtained from articles (7953) selected from Web of Science were searched in the Scopus database. The selected studies (397) were analyzed using VOSviewer (version 1.6.20) software to identify leading institutions, countries, authors, sources, fields, gaps, challenges, and trends related to the use of recycled aggregate in concrete. This study not only has a theoretical structure but also makes a significant contribution to the literature by offering practical recommendations for field applications. This is the most important feature that distinguishes this study from other research. This study also promotes the use of RAs in concrete to reduce CO2 emissions and encourages its sustainable use in the construction sector.
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Yusuf Tahir ALTUNCI (2026) studied this question.
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