Torsion and Shear Flow of Hollow Reinforced Concrete Beams Retrofitted with Carbon Fiber-Reinforced Polymer Sheets

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Title: Torsion and Shear Flow of Hollow Reinforced Concrete Beams Retrofitted with Carbon Fiber-Reinforced Polymer Sheets

Author(s): Yail J. Kim and Abdulaziz Alqurashi

Publication: Structural Journal

Volume: 122

Issue: 4

Appears on pages(s): 35-49

Keywords: carbon fiber-reinforced polymer (CFRP); hollow sections; rehabilitation; strengthening; torsion.

DOI: 10.14359/51743305

Date: 7/1/2025

Abstract:
This paper presents the torsional behavior of hollow reinforced concrete beams strengthened with carbon fiber-reinforced polymer (CFRP) U-wraps. Test parameters involve variable wall thickness in the section and the width and spacing of the externally bonded CFRP sheets. An experimental program is conducted with 27 beams (three unstrengthened and 24 strengthened) to examine their capacities, shear flows, and force distributions when incorporating a ratio of 0.27 to 0.46 between the areas of the hollow and gross cross sections. The stiffness and capacity of the test beams are dominated by the wall thickness, and the effectiveness of CFRP strengthening becomes pronounced as the void of the beams decreases. The presence of CFRP redistributes internal shear forces in the cross section, which is facilitated by narrowing the spacing of the U-wraps. The effective zone of CFRP retrofit is positioned near the outer boundary of the strengthened section. Regarding crack control, multiple discrete U-wraps with narrow spacings outperform wide U-wraps with enlarged spacings. While the location of a shear-flow path is dependent upon the wall thickness, the width of the U-wraps controls the effective shear-flow area of the beams. The size of the void is related to the stress levels of internal reinforcing components, including yield characteristics. Transverse stirrups are the principal load-bearing element for the unstrengthened beams; however, the reliance of the stirrups is reduced for the strengthened beams because the U-wraps take over portions of the torsional resistance. Through a machine learning approach combined with stochastic simulations, design recommendations are proposed.

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