Experiments and predictive modeling of optimized fiber-reinforced concrete columns having FRP rebars and hoops

Abstract

This research studies the structural performance of glass fiber-reinforced polymer (glass-FRP) reinforced concrete (RC) columns incorporating glass hybrid fibers (GFC) and steel rebars RC columns having steel hybrid fibers (SFC) using experiments, artificial neural networks (NN), and theoretical modeling. A set of 18 circular concrete columns, each with a diameter of 300 mm and a height of 1200 mm, was constructed and axially loaded to failure. Glass fibers and steel fibers were incorporated to produce hybrid fiber-RC (HFRC). Nine samples were manufactured with glass-FRP reinforcement, while the remaining nine were manufactured with steel reinforcement. According to the findings, the GFC columns had lower axial strengths (ASs) up to 20%, and higher ductility indices up to 26% than the SFC columns. Both GFC and SFC columns showed the same influence of eccentric loading in the form of a decrease in AS. To develop a novel NN model, a database of 275 specimens of glass-FRP-RC columns was gathered from previous studies. To achieve an optimum model, the NN model was calibrated for different numbers of neurons in the hidden layers (HLs). A novel theoretical equation for determining the AS of GFC columns was also suggested. The proposed theoretical and NN models showed average discrepancies of 3.2 and 1.9% from the test results of both GFC and SFC columns.

Keywords:

• Composite columns
• artificial neural networks
• glass-FRP
• ductility
• theoretical model

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through the large group research program under grant number R.G.P. 2/112/43.

Disclosure statement

The authors have no conflict of interest.

Data availability statement

Some or all data will be available upon request from the corresponding author.

Additional information

Funding

This work was supported by Deanship of Scientific Research at King Khalid University [grant number R.G.P. 2/112/43].