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Using genetic programming to model the bond strength of GFRP bars in concrete under the effects of design guidelines

Using genetic programming to model the bond strength of GFRP bars in concrete under the effects... This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy.Design/methodology/approachAssessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures.FindingsUltimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.Originality/valueThe model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering Computations Emerald Publishing

Using genetic programming to model the bond strength of GFRP bars in concrete under the effects of design guidelines

Chuang, Ying-Ji; Tsai, Hsing-Chih
Engineering Computations , Volume 38 (5): 19 – Jun 30, 2021
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0264-4401
DOI
10.1108/ec-05-2020-0258
Publisher site
See Article on Publisher Site

Abstract

This paper aims to use a derivative of genetic programming to predict the bond strength of glass fiber-reinforced polymer (GFRP) bars in concrete under the effects of design guidelines. In developing bond strength prediction models, this paper prioritized simplicity and meaningfulness over extreme accuracy.Design/methodology/approachAssessing the bond strength of GFRP bars in concrete is a critical issue in designing and building reinforced concrete structures.FindingsUltimately, the equation of a linear form of a particular design guideline was suggested as the optimal prediction model. Improvements to the current design guidelines suggested by this model include setting a 1.31 magnification and considering the effects of the three significant parameters of bar diameter (db), minimum cover-to-bar diameter (C/db) and development length to bar diameter (l/db) under an acceptable root mean square error accuracy of around 2 MPa. Furthermore, the model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.Originality/valueThe model suggests that the original influence parameter of concrete compressive strength (fc) may be removed from bond strength calculations.

Journal

Engineering Computations – Emerald Publishing

Published: Jun 30, 2021

Keywords: Genetic programming; Design guidelines; Bond strength of GFRP bars in concrete; Functional mapping

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