Journal of Concrete Structures and Materials

Journal of Concrete Structures and Materials

Analytical Model for Predicting Maximum Compressive Strength of Concrete Columns with Partial Confinement with FRP Sheets

Document Type : Original Article

Authors
1 Master of Science, Department of Civil Engineering, University of Mazandaran, Babolsar
2 Associated professor, Department of Civil Engineering, University of Mazandaran, Babolsar
3 PhD Candidate, Department of Civil Engineering, University of Mazandaran, Babolsar, Iran
4 PhD Candidate, ISISE, Department of Civil Engineering, University of Minho, Azurém 4800-058 Guimarães, Portugal
Abstract
Several analytical models have been suggested to determine the maximum compressive strength of concrete columns confined by fiber-reinforced polymer (FRP) full confinement arrangements. However, the capability and reliability of such models need further assessment for the case of partially FRP-confined concrete columns. In this study, a new model is provided to predict the maximum compressive strength of concrete columns with partial confinement which is also applicable to full confinement ones. For unifying the model for full and partial confining systems, based on the confinement mechanism in the case of partially confined concrete, a new definition is proposed for the concept of ‘confinement efficiency factor’ considering the distributions of lateral confining pressure and effective confinement area. Subsequently, a reduction factor is considered in terms of the confinement pressure generated by FRP strips. To find the best-fit model parameters obtained from regression analysis and evaluate the model predictive performance, a compressive experimental dataset comprising 1699 test specimens with full and partial confinement systems has been collected. The performance of the proposed model is assessed with experimental results based on statistical indicators. Based on the values predicted by the model and the results reported by experiments, Mean Value (MV), Coefficient of Variation (CoV), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2) were achieved as 0.977, 0.211, 0.154, 0.259, and 0.864, respectively. Furthermore, the performance of the proposed model is evaluated with other models available in the literature. The archived statistical indicators demonstrate the capability and accuracy of the proposed model and its better performance compared to other models.
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Subjects


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  • Receive Date 26 May 2024
  • Revise Date 08 September 2024
  • Accept Date 16 December 2024