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Machine learning models for predicting carbonation depth in fly ash concrete: performance and interpretability insights.

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Title: Machine learning models for predicting carbonation depth in fly ash concrete: performance and interpretability insights.
Authors: Khan, Arslan Qayyum1, arskhan@fiu.edu, Muhammad, Syed Ghulam2, 70113345@student.uol.edu.pk, Raza, Ali2, 70110981@student.uol.edu.pk, Pimanmas, Amorn3, amorn.pi@ku.th
Source: Journal of Road Engineering; Mar2026, Vol. 6 Issue 1, p74-90, 17p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 193279049
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Machine learning models for predicting carbonation depth in fly ash concrete: performance and interpretability insights.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Road+Engineering%22">Journal of Road Engineering</searchLink>; Mar2026, Vol. 6 Issue 1, p74-90, 17p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=193279049
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.jreng.2025.07.002
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 74
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      – TitleFull: Machine learning models for predicting carbonation depth in fly ash concrete: performance and interpretability insights.
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            NameFull: Khan, Arslan Qayyum
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            NameFull: Muhammad, Syed Ghulam
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            NameFull: Raza, Ali
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 6
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            – TitleFull: Journal of Road Engineering
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