Performance Evaluation of Compressive Strength Models for SRP and SRG-Confined Concrete Columns

Document Type : Regular Paper

Authors

1 Department of Civil Engineering, University of Birjand, Birjand, Iran

2 Detarrment of Civil Engineering, Research Group of Novel Technologies in Civil Engineering, University of Birjand, Birjand, Iran

Abstract

This paper focused on confining effects of externally bonded composites with polymer and grout matrices equipped with steel fibers, respectively named steel reinforced polymer (SRP) and steel reinforced grout (SRG) composites, as novel and effective methods of strengthening structures. To achieve this goal, an experimental database including 13 and 10 concrete columns with square cross-sections respectively confined by SRP and SRG composites was compiled from a recent empirical study. Moreover, after a comprehensive review and conducting a trial and error process of 45 existing models for estimating the relative compressive strength of confined concrete columns, six models for the SRP-confined concrete columns and six models for SRG-confined concrete columns with square cross-sections were selected and their performance was evaluated by comparing the Pearson correlation coefficient (R) and the mean absolute percentage error (MAPE) criteria. The results illustrated that for the SRP-confined concrete columns with square cross-sections, the selected model from the CNR-DT200 standard with respectively R and MAPE values of 0.7671 and 7.39% outperformed other selected SRP models. On the other hand. for the SRG-confined concrete columns with square cross-sections, the selected model from the research work of Isleem et al. with respectively R and MAPE values of 0.4405 and 18.45% surpassed the other selected SRP models. As most of the proposed models to estimate the relative compressive strength of confined concrete columns were suggested for the fiber-reinforced polymer (FRP) composites rather than the textile-reinforced mortar (TRM) composites, the overall comparison showed that all the selected SRP models outperformed the selected SRG models.

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History

  • Receive Date: 22 May 2023
  • Revise Date: 03 July 2023
  • Accept Date: 07 July 2023

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