| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Abed, Farid
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (4/4 displayed)
- 2024Numerical Analysis of the Ultimate Bearing Capacity of Strip Footing Constructed on Sand-over-Clay Sedimentcitations
- 2022Prediction of columns with GFRP bars through Artificial Neural Network and ABAQUScitations
- 2021Reliability analysis of strength models for short-concrete columns under concentric loading with FRP rebars through Artificial Neural Networkcitations
- 2021Microstructure and Mechanical Property Evaluation of Dune Sand Reactive Powder Concrete Subjected to Hot Air Curingcitations
Places of action
| Organizations | Location | People |
|---|
article
Prediction of columns with GFRP bars through Artificial Neural Network and ABAQUS
Abstract
<p>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</p>