Bibliographic Details
| 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] |
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| Database: |
Engineering Source |
