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Abstract
Fiber-Reinforced Polymers (FRP) were developed as a new method over the past decades due to their many beneficial mechanical properties, and they are commonly applied to strengthen masonry structures. In this paper, the Artificial Neural Network (ANN), K-fold Cross-Validation (KFCV) technique, Multivariate Adaptive Regression Spline (MARS) method, and M5 Model Tree (M5MT) method were utilized to predict the ultimate strength of FRP strips applied on masonry substrates. The results obtained via ANN, KFCV, MARS, and M5MT were compared with the existing models. The results clearly indicate that the considered approaches have better efficiency and higher precision compared to the models available in the literature. The correlation coefficient values for the considered models (i.e., ANN, KFCV, MARS, and M5MT) are promising results, with up to 99% reliability.
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Authors (4)
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Reza Kamgar
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Houman Ebrahimpour Komleh
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app13126955
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Applied Sciences-Basel
no. 13,
ISSN: 2076-3417 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Kamgar R., Komleh H. E., Jakubczyk-Gałczyńska A., Jankowski R.: Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond// Applied Sciences-Basel -,iss. 12 (2023), s.1-21
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/app13126955
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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