Structural Engineering and Mechanics

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Predictive model for the shear strength of concrete beams reinforced with longitudinal FRP bars

  • Alzabeebee, Saif (Department of Roads and Transport Engineering, University of Al-Qadisiyah) ;
  • Dhahir, Moahmmed K. (Institute of Concrete Structures, Technical University Dresden) ;
  • Keawsawasvong, Suraparb (Department of Civil Engineering, Thammasat School of Engineering, Thammasat University)
  • Received : 2022.06.07
  • Accepted : 2022.07.17
  • Published : 2022.10.25
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Abstract

Corrosion of steel reinforcement is considered as the main cause of concrete structures deterioration, especially those under humid environmental conditions. Hence, fiber reinforced polymer (FRP) bars are being increasingly used as a replacement for conventional steel owing to their non-corrodible characteristics. However, predicting the shear strength of beams reinforced with FRP bars still challenging due to the lack of robust shear theory. Thus, this paper aims to develop an explicit data driven based model to predict the shear strength of FRP reinforced beams using multi-objective evolutionary polynomial regression analysis (MOGA-EPR) as data driven models learn the behavior from the input data without the need to employee a theory that aid the derivation, and thus they have an enhanced accuracy. This study also evaluates the accuracy of predictive models of shear strength of FRP reinforced concrete beams employed by different design codes by calculating and comparing the values of the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R2), and percentage of prediction within error range of ±20% (a20-index). Experimental database has been developed and employed in the model learning, validation, and accuracy examination. The statistical analysis illustrated the robustness of the developed model with MAE, RMSE, 𝜇, 𝜎, R2, and a20-index of 14.6, 20.8, 1.05, 0.27, 0.85, and 0.61, respectively for training data and 10.4, 14.1, 0.98, 0.25, 0.94, and 0.60, respectively for validation data. Furthermore, the developed model achieved much better predictions than the standard predictive models as it scored lower MAE, RMSE, and 𝜎, and higher R2 and a20-index. The new model can be used in future with confidence in optimized designs as its accuracy is higher than standard predictive models.

Keywords

References

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