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Computers and Concrete Volume 24, Number 5, November 2019 , pages 469-488 DOI: https://doi.org/10.12989/cac.2019.24.5.469 |
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Predicting the shear strength of reinforced concrete beams using Artificial Neural Networks |
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Panagiotis G. Asteris, Danial J. Armaghani, George D. Hatzigeorgiou, Chris G. Karayannis and Kypros Pilakoutas
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| Abstract | ||
| In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation. | ||
| Key Words | ||
| neural networks; heuristic algorithm; reinforced concrete beams; stirrups; soft computing; shear strength | ||
| Address | ||
| Panagiotis G. Asteris: Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece Danial J. Armaghani: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam George D. Hatzigeorgiou: School of Science and Technology, Hellenic Open University, Parodos Aristotelous 18, GR-26335, Patras, Greece Chris G. Karayannis: Department of Civil Engineering, Democritus University of Thrace, Xanthi, 67100, Greece Kypros Pilakoutas: Department of Civil and Structural Engineering, University of Sheffield, Sheffield, United Kingdom | ||