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Using artificial neural networks and non-destructive tests to predict the compressive strength of geopolymer concrete.

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Title: Using artificial neural networks and non-destructive tests to predict the compressive strength of geopolymer concrete.
Authors: Emarah, Dina A.1 (AUTHOR) dina_emarah@yahoo.com, Anwar, M.1 (AUTHOR), El-Hakem, Yasser1 (AUTHOR), Ali, Elsayed H.2 (AUTHOR)
Source: Journal of Radioanalytical & Nuclear Chemistry. Feb2026, Vol. 335 Issue 2, p1327-1346. 20p.
Subjects: Artificial neural networks, Compressive strength, Cement composites, Ultrasonic testing, Nondestructive testing, Sustainable construction, Machine learning
Abstract: Geopolymer concrete offers a sustainable alternative to Portland cement but poses challenges in compressive strength evaluation due to its complex chemistry. This study develops an artificial neural network (ANN) model using non-destructive testing methods, ultrasonic pulse velocity and Schmidt rebound hammer, as input features. A harmonized dataset of 680 samples was compiled from peer-reviewed sources. The optimized ANN achieved high accuracy (R2 = 0.956) with low prediction error. The results demonstrate a robust, non-invasive, and generalizable framework for in-situ compressive strength prediction of GPC. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Radioanalytical & Nuclear Chemistry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192418255
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Compressive+strength%22">Compressive strength</searchLink><br /><searchLink fieldCode="DE" term="%22Cement+composites%22">Cement composites</searchLink><br /><searchLink fieldCode="DE" term="%22Ultrasonic+testing%22">Ultrasonic testing</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+construction%22">Sustainable construction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
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  Data: Geopolymer concrete offers a sustainable alternative to Portland cement but poses challenges in compressive strength evaluation due to its complex chemistry. This study develops an artificial neural network (ANN) model using non-destructive testing methods, ultrasonic pulse velocity and Schmidt rebound hammer, as input features. A harmonized dataset of 680 samples was compiled from peer-reviewed sources. The optimized ANN achieved high accuracy (R2 = 0.956) with low prediction error. The results demonstrate a robust, non-invasive, and generalizable framework for in-situ compressive strength prediction of GPC. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of Radioanalytical & Nuclear Chemistry is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s10967-025-10418-2
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 1327
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Compressive strength
        Type: general
      – SubjectFull: Cement composites
        Type: general
      – SubjectFull: Ultrasonic testing
        Type: general
      – SubjectFull: Nondestructive testing
        Type: general
      – SubjectFull: Sustainable construction
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      – SubjectFull: Machine learning
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      – TitleFull: Using artificial neural networks and non-destructive tests to predict the compressive strength of geopolymer concrete.
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            NameFull: El-Hakem, Yasser
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            NameFull: Ali, Elsayed H.
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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