Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams

Machine learning-based prediction of crack width and bond-dependent coefficient (kb) in GFRP-reinforced concrete beams

Glass fiber-reinforced polymer (GFRP)-reinforced concrete (RC) flexural elements are typically designed to fulfill the serviceability criteria, including deflection and crack width. Crack width control in RC structures enhances durability, increases service life, and improves aesthetic appearance. T...

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Bibliographic Details
Main Authors: Omid Habibi, Omar Gouda, Khaled Galal
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Case Studies in Construction Materials
Subjects:
GFRP
RC structures
Crack width
Bond-dependent coefficient
Machine learning
SHAP
Online Access:http://www.sciencedirect.com/science/article/pii/S2214509525008034
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