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Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete

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Abstract

The bond strength between GFRP bars and concrete is one of the most important aspects in reinforced concrete structures and is generally affected by several factors. In this study, experimental data of 159 notched, hinged, splice and inverted hinged beam specimens from an existing database in the literature were used to develop artificial neural network (ANN) and genetic programming (GP). The data used in modeling are arranged in a format of seven input parameters that cover the bar position, bar surface, bar diameter (d b), concrete compressive strength (f c), minimum cover to bar diameter ratio (C/d b), bar development length to bar diameter ratio (l/d b) and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bar and bar diameter (A tr/snd b). The MAE of testing data was found to be less than 1.06 and 0.76 MPa for the proposed ANN and GP models, respectively. Moreover, the study concluded that the proposed ANN and GP models predict the bond strength of GFRP bars in concrete better than the multi-linear regression model and existing building code equations. A parametric analysis was also conducted using the developed ANN and GP models to establish the trend of the main influencing variables on the bond capacity. Many of the assumptions made by the bond design methods are predicted by the developed models; however, few are inconsistent with the developed models’ predictions.

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Abbreviations

A tr :

The area of transverse reinforcement (mm2)

C :

Minimum concrete cover (mm)

d b :

Bar diameter (mm)

d sc :

Smallest distance from the closest concrete surface to the center of the developed bar or two- thirds spacing of the developed bars, d cs ≤2.5d b (mm)

\(f^{\prime}_{\text{c}}\) :

Concrete compressive strength (MPa)

K 1 :

Bar location factor (in CSA code)

K 2 :

Concrete density factor (in CSA code)

K 3 :

Bar size factor (in CSA code)

K 4 :

Bar fiber factor (in CSA code)

K 5 :

Bar surface profile factor (in CSA code)

l :

Development length (mm)

n :

Number of developed bar

s :

Transverse reinforcement spacing (mm)

τ b :

Bond strength in the developed bar (MPa) that is defined as the maximum horizontal shear force per unit area of the bar perimeter

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran

    E. M. Golafshani, A. Rahai & M. H. Sebt

Authors
  1. E. M. Golafshani
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  2. A. Rahai
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  3. M. H. Sebt
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Correspondence to E. M. Golafshani.

Appendix

Appendix

See Table 3.

Table 3 Properties of experimental database of 159 specimens and bond strength predictions [7, 36–50]
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Golafshani, E.M., Rahai, A. & Sebt, M.H. Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete. Mater Struct 48, 1581–1602 (2015). https://doi.org/10.1617/s11527-014-0256-0

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