Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming
Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is co...
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Vilnius Gediminas Technical University
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doaj-43b199bee41f4209afc795927ea1f1532021-07-02T06:12:50ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052019-08-0110.3846/jcem.2019.10798Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programmingYasmin Murad0Ahmed Ashteyat1Rozan Hunaifat2University of Jordan, Queen Rania str., Amman, JordanUniversity of Jordan, Queen Rania str., Amman, JordanUniversity of Jordan, Queen Rania str., Amman, Jordan Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests. First published online 30 August 2019 https://mma.vgtu.lt/index.php/JCEM/article/view/10798bond strengthgene expression programmingFRPconcretelarge data base |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yasmin Murad Ahmed Ashteyat Rozan Hunaifat |
spellingShingle |
Yasmin Murad Ahmed Ashteyat Rozan Hunaifat Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming Journal of Civil Engineering and Management bond strength gene expression programming FRP concrete large data base |
author_facet |
Yasmin Murad Ahmed Ashteyat Rozan Hunaifat |
author_sort |
Yasmin Murad |
title |
Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming |
title_short |
Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming |
title_full |
Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming |
title_fullStr |
Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming |
title_full_unstemmed |
Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming |
title_sort |
predictive model to the bond strength of frp-to-concrete under direct pullout using gene expression programming |
publisher |
Vilnius Gediminas Technical University |
series |
Journal of Civil Engineering and Management |
issn |
1392-3730 1822-3605 |
publishDate |
2019-08-01 |
description |
Gene expression programming (GEP) is used in this research to develop an empirical model that predicts the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets under direct pull out. Therefore, a large and reliable database containing 770 test specimens is collected from the literature. The gene expression programming model is developed using eight parameters that predominantly control the bond strength. These parameters are concrete compressive strength, maximum aggregate size, fiber reinforced polymer (FRP) tensile strength, FRP thickness, FRP modulus of elasticity, adhesive tensile strength, FRP length, and FRP width. The model is validated using the experimental results and a statistical assessment is implemented to evaluate the performance of the proposed GEP model. Furthermore, the predicted bond results, obtained using the GEP model, are compared to the results obtained from several analytical models available in the literature and a parametric study is conducted to further ensure the consistency of the model by checking the trends between the input parameters and the predicted bond strength. The proposed model can reasonably predict the bond strength that is most fitting to the experimental database compared to the analytical models and the trends of the GEP model are in agreement with the overall trends of the analytical models and experimental tests.
First published online 30 August 2019
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topic |
bond strength gene expression programming FRP concrete large data base |
url |
https://mma.vgtu.lt/index.php/JCEM/article/view/10798 |
work_keys_str_mv |
AT yasminmurad predictivemodeltothebondstrengthoffrptoconcreteunderdirectpulloutusinggeneexpressionprogramming AT ahmedashteyat predictivemodeltothebondstrengthoffrptoconcreteunderdirectpulloutusinggeneexpressionprogramming AT rozanhunaifat predictivemodeltothebondstrengthoffrptoconcreteunderdirectpulloutusinggeneexpressionprogramming |
_version_ |
1721337565495164928 |