Data-Driven Prediction of FRP Confinement Efficiency in Concrete Columns: Incorporating Corner Radius and Aspect Ratio Effects

Document Type : Original Article

Authors

1 Department of Civil Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran

2 Department of Civil Engineering, University of Minho, Azurém, Guimarães, Portugal

Abstract

This study develops a regression-based, design-oriented model for predicting the ultimate axial compressive strength of concrete columns fully confined with fiber-reinforced polymer (FRP) jackets. The model is formulated to be applicable to circular, square, and rectangular cross-sections within a single unified framework. Confinement effectiveness in non-circular sections is explicitly accounted for through two continuous geometric correction factors: one defined by the ratio of corner radius to section dimension and another defined by the cross-sectional aspect ratio. These factors modify the effective lateral confining pressure to reflect the progressive reduction in confinement efficiency as the section deviates from circularity. The model is calibrated and validated using a comprehensive experimental database comprising 1,723 FRP-confined concrete column tests, covering unconfined concrete compressive strengths from 6.6 to 204 MPa, FRP elastic moduli ranging from 13.6 to 657 GPa, and rectangular section aspect ratios up to 2.0. By construction, the formulation satisfies essential physical boundary conditions, including recovery of unconfined concrete strength in the absence of FRP confinement and a smooth, continuous transition in predicted strength between circular and non-circular cross-sections. The proposed model provides a general-purpose and physically consistent tool suitable for the design and assessment of FRP-confined concrete columns with varying cross-sectional geometries.

Keywords

Main Subjects

References

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Civil Engineering and Applied Solutions
Volume 2, Issue 3
July 2026
Pages 1-15

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History

  • Receive Date: 10 December 2025
  • Revise Date: 06 January 2026
  • Accept Date: 29 January 2026
  • First Publish Date: 30 January 2026

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