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CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.

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Title: CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.
Authors: Yilmaz, Yildiran1 (AUTHOR) yildiran.yilmaz@erdogan.edu.tr, Çakmak, Talip2 (AUTHOR), Ustabaş, İlker2 (AUTHOR)
Source: Scientific Reports. 11/13/2025, Vol. 15 Issue 1, p1-18. 18p.
Database: Academic Search Ultimate
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  Data: CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.
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  Data: <searchLink fieldCode="JN" term="%22Scientific+Reports%22">Scientific Reports</searchLink>. 11/13/2025, Vol. 15 Issue 1, p1-18. 18p.
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        Value: 10.1038/s41598-025-23515-9
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        Text: English
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      – TitleFull: CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments.
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              Text: 11/13/2025
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