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An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste.

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Title: An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste.
Authors: Khuat, Duy Dung1,2, Nguyen, Dam Duc2, Nguyen, May Huu1,3, Pham, Binh Thai1,3, Nakarai, Kenichiro1,2, nakarai@hiroshima-u.ac.jp
Source: Buildings (2075-5309); Feb2026, Vol. 16 Issue 3, p674, 27p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191609260
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PubTypeId: academicJournal
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  Data: An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191609260
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        Value: 10.3390/buildings16030674
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      – Code: eng
        Text: English
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        PageCount: 27
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      – TitleFull: An Interpretable Hybrid Machine Learning Approach for Predicting the Compressive Strength of Internal-Curing Concrete Incorporating Recycled Roof-Tile Waste.
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            NameFull: Nguyen, Dam Duc
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              Text: Feb2026
              Type: published
              Y: 2026
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