Structural Performance of Strengthening of High-Performance Geopolymer Concrete Columns Utilizing Different Confinement Materials: Experimental and Numerical Study

Abstract

The objective of this study was to investigate the effectiveness of different confinement materials in strengthening geopolymer concrete (GP) columns subjected to axial compression loading. This research encompassed both experimental and numerical analyses. The experimental phase involved testing seven circular GP columns, while the numerical phase involved developing 3D finite element (FE) models using ABAQUS software. The primary focus of this study was to assess the impact of using outer and inner steel tubes, as well as an outer polyvinyl chloride (PVC) tube and a carbon-fiber-reinforced polymer (CFRP) sheet. To validate the FE models, the experimental results were utilized for comparison. The findings of this study revealed that the outer steel tube provided superior confinement effects on the GP column’s concrete core compared to the PVC tube and CFRP sheet. The axial capacities of the columns confined with steel, PVC, and CFRP materials were observed to increase by 254.7%, 43.2%, and 186%, respectively, in comparison to the control specimens. Furthermore, the utilization of all confinement materials significantly enhanced the absorbed energy and ductility of the columns. The FE models demonstrated a reasonably close match to the experimental results in terms of load–displacement curves and deformation patterns. This correspondence between the numerical predictions and experimental data confirmed the reliability of the FE models and their suitability for generating further predictions. In summary, this study contributes to the field by exploring the efficacy of various confinement materials in strengthening GP columns. The results highlight the superior performance of the outer steel tube and demonstrate the positive influence of PVC and CFRP materials on enhancing the structural behavior of the columns. The validation of the FE models further supports their reliability and their potential for future predictions in similar scenarios.