Data-driven machine learning methodology for designing slender FRP-RC columns

Article


Tarawneh, Ahmad, Almasabha, Ghassan, Saleh, Eman, Alghossoon, Abdullah and Alajarmeh, Omar. 2023. "Data-driven machine learning methodology for designing slender FRP-RC columns." Structures. 57. https://doi.org/10.1016/j.istruc.2023.105207
Article Title

Data-driven machine learning methodology for designing slender FRP-RC columns

ERA Journal ID211389
Article CategoryArticle
AuthorsTarawneh, Ahmad, Almasabha, Ghassan, Saleh, Eman, Alghossoon, Abdullah and Alajarmeh, Omar
Journal TitleStructures
Journal Citation57
Article Number105207
Number of Pages14
Year2023
PublisherElsevier
Place of PublicationUnited Kingdom
ISSN2352-0124
Digital Object Identifier (DOI)https://doi.org/10.1016/j.istruc.2023.105207
Web Address (URL)https://www.sciencedirect.com/science/article/abs/pii/S235201242301295
AbstractCurrent design guidelines for reinforced concrete (RC) with fiber-reinforced polymers (FRP) bars lack provisions for designing slender columns. Limited attempts were made to modify the moment magnification procedure to accommodate FRP-RC columns. This study proposes a novel approach for designing FRP-RC slender columns based on suggesting a simplified slenderness reduction factor to account for the slenderness (?slender). The novel reduction factor has been developed using data-driven machine learning, where the available experimental database of short and slender FRP-RC columns has been employed to train a robust generalized artificial neural network (ANN) model. The ANN model is then utilized to generate reduction-factor curves, facilitating a straightforward approach to designing slender FRP-RC columns. For design practice purposes, genetic expression programming (GEP) was used to generate a mathematical equation for calculating ?slender. The proposed ?slender is a function of concrete compressive strength, reinforcement ratio, eccentricity, and column slenderness ratio. Statistical correlation analysis indicated that implementing the ?slender eliminates the axial capacity correlation to the slenderness ratio, indicating a very good representation of the slenderness effect on the FRP-RC columns. The proposed approach revealed higher accuracy and consistent conservatism compared to the moment magnification procedure in predicting the strength of the slender FRP-RC columns.
KeywordsANN; Slender columns; FRP-RC; GEP; Machine learning; Strength curves; Reduction factor
ANZSRC Field of Research 2020400510. Structural engineering
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Byline AffiliationsHashemite University, Jordan
Centre for Future Materials
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