Predicting the shear strength of reinforced concrete beams using artificial neural networks

Date

2004-05-01

Author

Mansour, MY
Dicleli, Murat
Lee, JY
Zhang, J

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The application of artificial neural networks (ANNs) to predict the ultimate shear strengths of reinforced concrete (RC) beams with transverse reinforcements is investigated in this paper. An ANN model is built, trained and tested using the available test data of 176 RC beams collected from the technical literature. The data used in the ANN model are arranged in a format of nine input parameters that cover the cylinder concrete compressive strength, yield strength of the longitudinal and transverse reinforcing bars. the shear-span-to-effective-depth ratio, the span-to-effective-depth ratio, beam's cross-sectional dimensions, and the longitudinal and transverse reinforcement ratios. The ANN model was found to predict the ultimate shear stress well within the range of input parameters considered. The average value of the experimental shear strength to predicted shear strength ratios of the 176 specimens is 1.003. The ANN shear strength predicted results were also compared to those obtained using building codes' empirical equations and various-compatibility aided softened truss model theories. The results show that ANNs have strong potential as a feasible tool for predicting the ultimate shear strength of RC beams with transverse reinforcement within the range of input parameters considered. Finally, the ANN model was used to show that it could perform parametric studies to evaluate the effects of some of the inputs parameters on the chosen output.

Subject Keywords

Civil and Structural Engineering

URI

https://hdl.handle.net/11511/47819

Journal

ENGINEERING STRUCTURES

DOI

https://doi.org/10.1016/j.engstruct.2004.01.011

Collections

Department of Engineering Sciences, Article

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Citation Formats

M. Mansour, M. Dicleli, J. Lee, and J. Zhang, “Predicting the shear strength of reinforced concrete beams using artificial neural networks,” ENGINEERING STRUCTURES, pp. 781–799, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/47819.