Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions

Predicting FRP-to-concrete bond strength (FRP-CBS) under diverse exposure conditions is an intricate task influenced by multiple variables. Yet, existing pertinent models have several limitations. Accordingly, this study proposes a novel data driven machine learning (ML) methodology to predict the F...

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Bibliographic Details
Main Authors: Aman Kumar, Harish Chandra Arora, Prashant Kumar, Nishant Raj Kapoor, Moncef L. Nehdi
Format: Article
Language:English
Published: Elsevier 2024-03-01
Series:Developments in the Built Environment
Subjects:
FRP
Concrete
Bond strength
Durability
Machine learning
XGBoost
Online Access:http://www.sciencedirect.com/science/article/pii/S266616592300193X

Internet

http://www.sciencedirect.com/science/article/pii/S266616592300193X

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