| Autor: |
Alacali, Sema, Akkaya, Hasan Cem, Sengun, Kadir, Arslan, Guray |
| Předmět: |
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| Zdroj: |
Neural Computing & Applications; Sep2024, Vol. 36 Issue 25, p15515-15544, 30p |
| Abstrakt: |
Fiber-reinforced polymers (FRP) have been widely used in shear strengthening applications of reinforced concrete (RC) beams. The accurate prediction of the FRP contribution to the shear strength of beams is essential for reliable design. Gene expression programming (GEP) has been widely utilized because it reliably expresses complex relationships between experimental variables. In this study, three new GEP models are proposed for three different strengthening configurations of FRP such as fully-wrapping, U wrapping, and side-bonding to predict the FRP contribution to shear strength. These models are developed using the most comprehensive database containing a total of 811 strengthened beams (350 fully-wrapped, 328 U-wrapped, and 133 side-bonded. Many variables have been considered in the proposed GEP models, including those that have been experimentally effective but are often neglected in existing literature equations, such as the shear span-to-effective depth ratio (a / d) and the stirrup ratio ( ρ w ). Additionally, the reliability of existing equations in the literature and the proposed GEP models for predicting the FRP contribution to shear strength was statistically evaluated. As a result of this evaluation, the proposed GEP models for each strengthening configuration of FRP yielded the most accurate statistical results, with the lowest coefficient of variation (COV), and the highest coefficient of correlation (R). [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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