Finite Element and Artificial Neural Network Modeling of FRP-RC Columns Under Axial Compression Loading

Abstract

The use of fiber-reinforced polymer (FRP) bars to overcome the corrosion problems in various reinforced concrete structures is now well documented in the literature. As a result, the currently available design guidelines such as North American design codes allow for using the FRP bars as alternative materials to steel bars to be incorporated into the concrete structures. In practice, hollow-core concrete columns (HCCs) are widely accepted to make a lightweight structure and reduce its cost. Due to the lack of laboratory tests, engineers may not perform a safe design of HCCs with internal FRP bars. Therefore, the presented paper has endeavored to numerically and theoretically explore using the FRP bars and spirals as internal reinforcement for HCCs and investigate the effects of several test parameters. Using the current version of Finite Element Analysis (FEA) ABAQUS (3DS, 2014), a total of 116 HCCs were simulated based on 29 specimens experimentally tested by the researchers which acted as control specimens for the FE model. The complex structural response of concrete was reasonably determined using the concrete damaged plasticity model (CDPM) and the mechanical response of the FRP rebars are considered to behave linearly up to failure with no yielding stage. The calibrated FE model can provide an excellent portrayal of the HCCs’ response. Based on the database obtained from laboratory and simulation, several Artificial Neural Network (ANN) models were further provided to predict the confined compressive load of GFRP-RC HCCs at different loading stages.