Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks
Mahmoud, T., Elnady, A., Elkafrawy, M., Abdeltawab, S. (2019). Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks. EKB Journal Management System, 1(1), 13-24. doi: 10.21608/jisse.2019.19772.1015
Tamer Elsayed Mahmoud; Ahmed Elnady; Mostafa Elkafrawy; Shaimaa Abdeltawab. "Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks". EKB Journal Management System, 1, 1, 2019, 13-24. doi: 10.21608/jisse.2019.19772.1015
Mahmoud, T., Elnady, A., Elkafrawy, M., Abdeltawab, S. (2019). 'Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks', EKB Journal Management System, 1(1), pp. 13-24. doi: 10.21608/jisse.2019.19772.1015
Mahmoud, T., Elnady, A., Elkafrawy, M., Abdeltawab, S. Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks. EKB Journal Management System, 2019; 1(1): 13-24. doi: 10.21608/jisse.2019.19772.1015

Prediction of Shear Behavior of Fiber Reinforced Concrete Beams Using Artificial Neural Networks

Journal of International Society for Science and Engineering
Article 3, Volume 1, Issue 1, September 2019, Page 13-24  XML PDF (965.09 K)
Document Type: Original Article
DOI: 10.21608/jisse.2019.19772.1015
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Authors
Tamer Elsayed Mahmoud email orcid 1; Ahmed Elnady2; Mostafa Elkafrawy2; Shaimaa Abdeltawab3
1Engineering division National Research Centre
2Structural Engineering Department, Faculty of Engineering, Cairo University.
3M.Sc. student Cairo university
Abstract
Due to the shortage of clear equations in the building codes that explain shear strength for fiber reinforced concrete (FRC) beams; there is a need to develop a numerical approach that can be used to predict shear behavior in FRC. The main objective of this research is to develop an artificial Neural Network (ANN) that can predict shear strength and simplify its use through developing a Graphic User Interface (GUI). Moreover, shear behavior in fiber reinforced concrete beams (FRCBs) is quantified by compressive strength of concrete, longitudinal steel, size effect, fiber's type, content and aspect ratio. The research methodology is based on collecting experimental results of technical investigations carried out to predict shear behavior in FRCBs. ANN aims at reducing the amount of computing time required in the numerous iterations involving structural analysis and experimental work. For this, two back-propagation neural networks have been experimented by MATLAB program; their types have been fitting (1st network) and pattern recognition (2nd network) which are used to classify failure of FRC beams into 6 categories. Through simulation study, the optimum architectures for the individual ANNs have been determined. The training algorithms used feed forward back propagation. The ANNs model has been assessed in comparison with exact values and deduces a good correlation with it. Finally, a software program is developed as an evaluation system for predicting resistance of FRC beams to shear forces, and to expect the failure pattern in order to avoid its occurrence.
Keywords
shear resistance; ANN; fiber reinforced concrete beams; GUI
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