3D FE analysis for buckling behavior of longitudinal reinforcement embedded in LRS FRP-confined concrete under axial compression. (15th May 2023) - Bai, Yu-Lei; Yang, Hong-Long; Mohammadi, Mohsen; Dai, Jian-Guo
Highlights: A modified concrete damaged plasticity model is proposed considering the deformation and mechanical characteristics of LRS FRP-confined concrete. The 3D finite element analysis of longitudinal reinforcement buckling in LRS FRP-confined RC columns is realized. The effect of longitudinal reinforcement buckling on member is illustrated by the FEA results. The factors affecting the buckling of longitudinal reinforcement are studied, and an empirical formula for determining the failure of members by the buckling of longitudinal reinforcement is proposed. Abstract: Large rupture strain (LRS) FRPs have great potential to be used for seismic retrofit of RC columns because of its high rupture strain (i.e., > 5%) that provides better confinement effects. However, in FRP-confined RC columns, the longitudinal reinforcements may buckle before the LRS FRP rupture, especially in the case of large stirrup spacing. This study aims to present a 3D finite element (FE) model for simulating the buckling effect of longitudinal reinforcements in LRS FRP-confined RC columns. A modified concrete damaged plasticity model (CDPM) is proposed to consider the deformation and mechanical characteristics of LRS FRP-confined concrete. Removal of failed concrete elements is considered to facilitate the buckling of longitudinal reinforcements in LRS FRP-confined RC columns. The FE analysis accuracy was verified by comparisons with test results. The factors affecting the buckling of longitudinal reinforcements were discussed through an extensive parametric study. Empirical formulas for the stress and strain values at which the longitudinal reinforcement starts to soften were developed. An empirical formula for the ultimate axial strain of LRS FRP-confined RC columns determined by longitudinal reinforcement buckling was also obtained by regression analysis.