Neural network modelling for shear strength of concrete members reinforced with FRP bars |
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| Authors: | Rizwan Bashir Ashraf Ashour |
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| Affiliation: | 1. ISISE, University of Minho, Department of Civil Engineering, Campus de Azurém, 4810-058 Guimarães, Portugal;2. University of Nottingham, Department of Civil Engineering, Nottingham, United Kingdom;3. University of Minho, Department of Information Systems, Campus de Azurém, 4810-058 Guimarães, Portugal;4. ALGORITMI Centre, University of Minho, Department of Information Systems, Campus de Azurém, 4810-058 Guimarães, Portugal;1. ISEGI, Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal;2. INESC-ID, IST, Universidade Técnica de Lisboa, 1000-029 Lisboa, Portugal;1. Turkish Cooperation and Coordination Agency (T?KA), Atatürk Bulvar? No:15 Ulus Ankara, Turkey;2. Gazi University, Engineering Faculty, Civil Engineering Department, Celal Bayar Bulvar? Maltepe 06570, Ankara, Turkey;3. ?stanbul University, Engineering Faculty, Industrial Engineering Department, Avc?lar 34320, ?stanbul, Turkey;1. Department of Civil Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran;2. Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907-2051, USA |
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| Abstract: | This paper investigates the feasibility of using artificial neural networks (NNs) to predict the shear capacity of concrete members reinforced longitudinally with fibre reinforced polymer (FRP) bars, and without any shear reinforcement. An experimental database of 138 test specimens failed in shear is created and used to train and test NNs as well as to assess the accuracy of three existing shear design methods. The created NN predicted to a high level of accuracy the shear capacity of FRP reinforced concrete members.Garson index was employed to identify the relative importance of the influencing parameters on the shear capacity based on the trained NNs weightings. A parametric analysis was also conducted using the trained NN to establish the trend of the main influencing variables on the shear capacity. Many of the assumptions made by the shear design methods are predicted by the NN developed; however, few are inconsistent with the NN predictions. |
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