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Correction: Khan et al. Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models. <i>Polymers</i> 2022, <i>14</i>, 2270.

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Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model.

Muhammad Nasir Amin1, Mudassir Iqbal2,3, Babatunde Abiodun Salami4

  • 1Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Hofuf 31982, Al-Ahsa, Saudi Arabia.

Polymers
|June 10, 2022
PubMed
Summary

Fiber-reinforced plastic (FRP) rebars offer corrosion resistance. Gene expression programming accurately predicted FRP rebar bond strength, identifying key factors like diameter and embedment length for optimal performance.

Keywords:
FRPGEP modellingbond strengthconcrete compressive strengthconcrete cover to bar diameter ratioparametric study

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Structural Engineering

Background:

  • Mild steel rebars corrode, necessitating alternative materials like fiber-reinforced plastic (FRP) rebars.
  • FRP rebars exhibit distinct bond characteristics compared to steel due to their unique stress-strain behavior.
  • Accurate prediction of bond strength (BS) is crucial for the effective application of FRP rebars in concrete structures.

Purpose of the Study:

  • To investigate and model the bond strength (BS) of fiber-reinforced plastic (FRP) rebars in concrete.
  • To utilize non-linear capabilities of gene expression programming (GEP) for predicting FRP rebar bond strength.
  • To identify the significant parameters influencing the bond strength of FRP rebars.

Main Methods:

  • A dataset of 273 bond strength samples for FRP rebars was analyzed.
  • Gene expression programming (GEP) was employed to develop predictive models for bond strength.
  • Models were evaluated using coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE).
  • Parametric analysis was conducted to determine the influence of various factors on bond strength.

Main Results:

  • The M11 model, developed using GEP with 30 chromosomes, 9 head size, and 5 genes, demonstrated superior accuracy in predicting bond strength.
  • The optimal model achieved high accuracy with R² values of 0.925 (training) and 0.9285 (testing), indicating close agreement between experimental and predicted results.
  • Bar diameter (d), concrete-cover-to-bar-diameter ratio (c/d), and embedment-length-to-bar-diameter ratio (l/d) were identified as significant factors influencing bond strength.

Conclusions:

  • Gene expression programming provides a robust method for predicting the bond strength of FRP rebars.
  • The developed GEP model accurately captures the complex relationship between influencing factors and bond strength.
  • Design parameters such as FRP rebar diameter (>10 mm), l/d ratio (>12), and c/d ratio significantly impact bond performance, guiding future structural design with FRP rebars.