XIANG Zehui, ZHOU Jie, NIU Jiangang, et al. Mechanical properties of square concrete short columns strengthened by concrete canvas and CFRP strips under axial compression[J]. Acta Materiae Compositae Sinica, 2022, 39(10): 4824-4838. DOI: 10.13801/j.cnki.fhclxb.20211115.006
Citation: XIANG Zehui, ZHOU Jie, NIU Jiangang, et al. Mechanical properties of square concrete short columns strengthened by concrete canvas and CFRP strips under axial compression[J]. Acta Materiae Compositae Sinica, 2022, 39(10): 4824-4838. DOI: 10.13801/j.cnki.fhclxb.20211115.006

Mechanical properties of square concrete short columns strengthened by concrete canvas and CFRP strips under axial compression

  • Through the axial compression test of concrete square columns which strengthened by concrete canvas (CC) and carbon fiber reinforced polymer (CFRP) strips, the influences of restraint rate, width and spacing of CFRP, and number of layers on the mechanical property of the concrete square column were studied. The failure form, bearing capacity, energy consumption capacity and deformation capacity of the reinforced concrete square column were analyzed. The research results show that the addition of CC can alleviate the stress concentration at the corners, significantly increase the deformation ability of the specimen, and improve the failure form of the column. The influence of width and spacing of the strips on the bearing capacity and energy dissipation capacity of the specimen is attributed to the restraint rate of the fiber strips. With the increase of the strip restraint rate and the number of CFRP layers, the load-bearing capacity and energy consumption capacity of the specimens continue to increase. When the fiber restraint ratio is 0.5, the bearing capacity is the largest when the width and spacing are 50 mm. Based on the experimental research, a theoretical analysis of the effective constrained area change was carried out, and the role of CC in joint reinforcement was obtained, and the axial compression bearing capacity model was established. The error analysis shows that the model has high prediction accuracy.
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