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    New Confined Concrete Stress–Strain Model for Square Double-Skin FRP-Concrete-Steel Stiffened Tubular Columns

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    PPSCFX.SCENG-1494.pdf (6.544Mb)
    Date
    2024
    Author
    Fayaz A. Sofi
    Irqab Farooq
    Hazim Wani
    Tafazul Qayoom
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    Abstract
    Double-skin tubular columns (DSTCs) are promising modern columns with concrete sandwiched between fiber-reinforced polymer (FRP) and steel skins. Typical concentrated and localized failure modes in DSTCs can be constrained or delayed when stiffened inner steel tubes are used. DSTCs with circular cross sections are better investigated in the literature than scarcely studied square shapes, especially with variations of stiffened inner steel tubes. This study examined the nonlinear confined concrete behavior in stiffened DSTCs with square-shaped glass FRP as outer skins and stiffened steel tube as inner skins. The documented test results on 26 DSTCs were used to formulate a new analysis-oriented, axial stress–strain model for confined concrete. Primary model-formulation parameters were evaluated using a genetic algorithm (GA)–driven error minimization. A detailed parametric study was then performed on square DSTCs by incorporating the proposed stress–strain constitutive relationship for confined concrete in nonlinear finite-element analysis. The variations used in the parametric study included the shape of the inner steel tube (circular versus square), unconfined concrete strength, and flat-stiffener geometry characteristics (i.e., quantity, configuration in terms of thickness versus the number of stiffeners for similar total cross-sectional area, and its cross-sectional dimensions). Analytical results were finally used to propose a new load-bearing equation for estimating the axial load capacity of square DSTCs having stiffened inner steel skins of circular or square shapes. The proposed formulation showed better accuracy (about 2% on average and 14% maximum disparity with refined ultimate axial load capacities of DSTCs used in this study) than the existing prediction models.
    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4298472
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