| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Fakharian, Pouyan
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Topics
Publications (7/7 displayed)
- 2023Experimental Study on Mechanical Properties and Durability of Polymer Silica Fume Concrete with Vinyl Ester Resincitations
- 2022Shear Strength Prediction of Reinforced Concrete Shear Wall Using ANN, GMDH-NN and GEP
- 2021Shear Capacity Prediction of FRP Reinforced Concrete Beams Using Hybrid GMDH–GA
- 2021Mechanical properties of roller-compacted concrete pavement containing recycled brick aggregates and silica fumecitations
- 2020Application of Artificial Intelligence Methods to Estimate Shear Strength of Reinforced Concrete Shear Wall
- 2019Innovative Models for Prediction of Compressive Strength of FRP-Confined Circular Reinforced Concrete Columns Using Soft Computing Methodscitations
- 2018Compressive strength prediction of environmentally friendly concrete using artificial neural networkscitations
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article
Innovative Models for Prediction of Compressive Strength of FRP-Confined Circular Reinforced Concrete Columns Using Soft Computing Methods
Abstract
There are several methods for predicting experimental results such as empirical methods, elasticity and plasticity theory. Among these methods, the use of soft computing has been expanded due to good capabilities and high accuracy in predicting the target. Soft computing contains computational techniques and algorithms to provide useful solutions to deal with complex computational problems. In this study, three methods including Artificial neural networks, Group method of data handling and Gene expression programming are utilized to predict the compressive strength of columns confined with FRP. Total of 95 experimental data were selected to form the model. The height of the column, the compressive strength of unconfined concrete, the elastic modulus of FRP, the area of longitudinal steel, the yield strength of longitudinal steel and confinement pressure provided by FRP and transverse steel were considered as input parameters, while the compressive strength of FRP-confined columns was considered as the target. The proposed methods are compared with the existing models and provide great accuracy in predicting the results. Among the utilized methods, the ANN model showed the highest accuracy.