Advances in Materials Science and Engineering (Jan 2022)

Axial Compression Performance of Concrete-Filled Steel Tubular Columns with Different D/t Ratios

  • Preetha Achuthan,
  • Ganapathy Ganesh Prabhu,
  • George Gabriel Vimal Arokiaraj,
  • Panneerselvam Arul Sivanantham,
  • Subramanian Suthagar

DOI
https://doi.org/10.1155/2022/9170525
Journal volume & issue
Vol. 2022

Abstract

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The impacts of three diameter/thickness (D/t) ratios (21.22, 25.46, and 31.83) and concrete strengths (40 N/mm2, 50 N/mm2, and 60 N/mm2) on the strength capabilities of concrete-filled steel tubular (CFST) columns are investigated in this study. The central composite design (CCD) of the response surface methodology (RSM) was used to design the trials in order to complete the tests in a cost-effective manner. 13 (9 distinct tests) columns were evaluated according to the CCD experimental design, and the failure mode of the specimens, load–deformation behavior, and ultimate strength capacity were investigated. Concrete strength improves, resulting in a decrease in steel tube confinement on the core. Because the steel tube longitudinal compressive stress (fsl) increases as the D/t ratio lowers, the confinement is reduced by inhibiting the circumferential tensile stress (fsc). The Reynolds stress model’s, analysis of variance (ANOVA), Pareto chart, and contour plot demonstrated that the column D/t ratio, rather than the in-filled concrete strength, has a considerable impact on the CFST column’s strength capability. The proposed design models in different international codes and literature were evaluated for their effectiveness in predicting the strength capacities of CFST columns subjected to axial compression load. Using regression analysis, a simple design model was suggested to predict the axial strength capacities of CFST short columns, taking into account material strength and column shape. In comparison to other existing and suggested design models, the proposed design model of the present study delivers a more accurate and stable forecast.