𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks

✍ Scribed by R. Perera; M. Barchín; A. Arteaga; A. De Diego

Publisher
Elsevier Science
Year
2010
Tongue
English
Weight
1001 KB
Volume
41
Category
Article
ISSN
1359-8368

No coin nor oath required. For personal study only.

✦ Synopsis

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 analysis from experimental data corresponding to specific configurations which makes very difficult to capture the real interrelation among the involved parameters. To avoid this, an artificial neural network has been developed to predict the shear strength of concrete beams reinforced with this method from previous tests. Furthermore, a parametric study has been carried out to determine the influence of some beam and external reinforcement parameters on the shear strength with the purpose of reaching more reliable designs. Finally, some modifications of the design expressions are proposed and checked with experimental results.

📜 SIMILAR VOLUMES

Prediction of intermediate crack debondi
Prediction of intermediate crack debonding failure in FRP-strengthened reinforced concrete beams
✍ Luciano Ombres 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 540 KB

The present paper addresses with intermediate crack (IC) debonding failure modes in FRP-strengthened reinforced concrete beams; a non-linear local deformation model, derived from a cracking analysis based on slip and bond stress, is adopted to predict the stresses and strains distribution at failure

Artificial intelligence techniques for p
Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement
✍ Ricardo Perera; Angel Arteaga; Ana De Diego 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 567 KB

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

Neural network based approach for determ
Neural network based approach for determining the shear strength of circular reinforced concrete columns
✍ Naci Caglar 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 748 KB

The objective of this study is to investigate the adequacy of neural networks (NN) as a quicker, more secure and more robust method to determine the shear strength of circular reinforced concrete columns. In the application of the NN model, a multilayer perceptron (MLP) with a back-propagation (BP)

A unified approach for the static and dy
A unified approach for the static and dynamic analyses of intermediate debonding in FRP-strengthened reinforced concrete beams
✍ Ricardo Perera; Daniel Bueso-Inchausti 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 519 KB

A spectral method is proposed for solving static and dynamic problems in FRP-strengthened reinforced concrete beams in a unified way. In order to appropriately simulate the debonding failure a mechanical model considering nonlinear stress-strain relationships for concrete and steel is used. The FRP-

Discussion on “Predicting the shear stre
Discussion on “Predicting the shear strength of reinforced concrete beams using artificial neural networks” by M.Y. Mansour, M. Dicleli, J.Y. Lee, J. Zhang [Eng Struct 26 (2004) 781–799]
✍ A.H. Gandomi; A.H. Alavi 📂 Article 📅 2009 🏛 Elsevier Science 🌐 English ⚖ 368 KB

The paper reporting the study by the authors considers the use of artificial neural networks (ANNs) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements. Because of some paradoxes in the results, the proposed ANN model has almost no reliability. Th