This paper presents a rational model to predict the ultimate load capacity of reinforced concrete (RC) beams strengthened by a combination of longitudinal and transverse fiber reinforced polymer (FRP) composite plates/sheets (flexure and shear strengthening system). The model is based on the truss a
Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
β Scribed by Ricardo Perera; Angel Arteaga; Ana De Diego
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 567 KB
- Volume
- 92
- Category
- Article
- ISSN
- 0263-8223
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β¦ Synopsis
The prediction of the shear capacity of reinforced concrete beams retrofitted in shear by means of externally bonded FRP is very complex as demonstrate the studies carried out up to date. As alternative to the conventional methods two approaches based on artificial intelligence are proposed for the first time. Firstly, the use of neural networks as a means of predicting shear capacity without the need of using complex models and, secondly, the use of genetic algorithms as a means of determining suitably how the shear mechanism works. Predictions obtained with both approaches are compared to experimental values.
π SIMILAR VOLUMES
In the last years, a great number of experimental tests have been performed to determine the ultimate strength of reinforced concrete beams retrofitted in shear by means of externally bonded fibre-reinforced polymers (FRP). Most of design proposals for shear strengthening are based on a regression a