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<p>Over the past decades, the use of fiber-reinforced polymers (FRPs) for retrofitting existing structures has become a widespread practice. More recently, however, the use of FRP bars as the primary reinforcement in new structures—especially concrete columns—has attracted global attention. Various equations have been proposed to predict the compressive strength of columns reinforced with FRP bars. However, these equations typically estimate axial strength without directly accounting for eccentricity. In this study, additional important parameters are considered: eccentricity of axial load, types of longitudinal and transverse reinforcement, and column height. To this end, a dataset of 525 samples is compiled, and machine learning methods—including artificial neural networks (ANN), gene expression programming (GEP), the group method of data handling (GMDH), and multiple linear regression (MLR)—are employed. Among these, ANN yielded the best predictive performance with an R<sup>2</sup> value of 0.974. Using the GEP, GMDH, and MLR methods, three predictive equations are proposed. Of these, the GMDH and GEP approaches demonstrate relatively high accuracy, with R<sup>2</sup> values of 0.966 and 0.942, respectively. The proposed equations can be used to predict the strength of reinforced concrete (RC) columns under axial loading with or without eccentricity, for various cross-sectional shapes and different types of longitudinal and transverse reinforcement (steel and FRP).</p>

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Axial strength prediction of FRP reinforced concrete columns under concentric and eccentric loading using machine learning models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月26日

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  • Mohammad Haji,
  • Mohammad Sadegh Marefat,
  • Ali Kheyroddin

摘要

Over the past decades, the use of fiber-reinforced polymers (FRPs) for retrofitting existing structures has become a widespread practice. More recently, however, the use of FRP bars as the primary reinforcement in new structures—especially concrete columns—has attracted global attention. Various equations have been proposed to predict the compressive strength of columns reinforced with FRP bars. However, these equations typically estimate axial strength without directly accounting for eccentricity. In this study, additional important parameters are considered: eccentricity of axial load, types of longitudinal and transverse reinforcement, and column height. To this end, a dataset of 525 samples is compiled, and machine learning methods—including artificial neural networks (ANN), gene expression programming (GEP), the group method of data handling (GMDH), and multiple linear regression (MLR)—are employed. Among these, ANN yielded the best predictive performance with an R2 value of 0.974. Using the GEP, GMDH, and MLR methods, three predictive equations are proposed. Of these, the GMDH and GEP approaches demonstrate relatively high accuracy, with R2 values of 0.966 and 0.942, respectively. The proposed equations can be used to predict the strength of reinforced concrete (RC) columns under axial loading with or without eccentricity, for various cross-sectional shapes and different types of longitudinal and transverse reinforcement (steel and FRP).