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 ID | 211389 |
| Article Category | Article |
| Authors | Tarawneh, Ahmad, Almasabha, Ghassan, Saleh, Eman, Alghossoon, Abdullah and Alajarmeh, Omar |
| Journal Title | Structures |
| Journal Citation | 57 |
| Article Number | 105207 |
| Number of Pages | 14 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 2352-0124 |
| 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 |
| Abstract | Current 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. |
| Keywords | ANN; Slender columns; FRP-RC; GEP; Machine learning; Strength curves; Reduction factor |
| ANZSRC Field of Research 2020 | 400510. Structural engineering |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Hashemite University, Jordan |
| Centre for Future Materials |
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