Prediction of columns with GFRP bars through Artificial Neural Network and ABAQUS

Afaq Ahmad, Aiman Aljuhni, Usman Arshid, Mohamed Elchalakani, Farid Abed

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


The objective of this study is to compare the conventional models used for estimating the ultimate response of Concrete Columns with Glass Fiber Reinforced Polymers (GFRPs) bars i.e., Current Design Codes (CDCs), proposed equations by different researcher (EQs) and non-conventional problem solver i.e., Artificial Neural Network (ANN). For this purpose, a database of 108 samples of Concrete Columns with GFRP bars under concentric loading, with detail information collected from the previous studies. including the details of the critical parameters. The ANN model (i.e FRP-SC-4) results for axial load values having R = 0.94 exhibited closer results to the experimental values as compared to counterpart CDCs and EQs. Furthermore, Finite Element Analysis (FEA) is used to valid the ANN prediction, for the selected cases. The FEA results was in a good agreement of numerical results with the experimental results and ANN results

Original languageEnglish
Pages (from-to)247-255
Number of pages9
Publication statusPublished - Jun 2022


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