Buy article PDF
The purchased file will be sent to you
via email after the payment is completed.
US$ 35
|
Computers and Concrete Volume 23, Number 1, January 2019 , pages 49-60 DOI: https://doi.org/10.12989/cac.2019.23.1.049 |
|
|
|
Determining the shear strength of FRP-RC beams using soft computing and code methods |
||
Gunnur Yavuz
|
||
| Abstract | ||
| In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum R2≈0.97). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters. | ||
| Key Words | ||
| internal FRP bar; reinforced concrete; beam; shear strength; artificial neural network | ||
| Address | ||
| Gunnur Yavuz: Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey | ||