Estimation of the Ultimate Strength of FRP Strips-to-Masonry Substrates Bond
Source abstract
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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Research context
OpenAlex topics: Structural Behavior of Reinforced Concrete · Concrete Corrosion and Durability · Innovative concrete reinforcement materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil Engineering, Faculty of Engineering, Shahrekord University, Shahrekord 88186-34141, Iran
Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman 76169-13439, Iran
Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland
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