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PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: B.H.V. Topping and Y. Tsompanakis
Predictions of Hysteresis Energy Dissipation in Steel Reinforcing Bars using Artificial Neural Networks
J.A. Abdalla and R.A. Hawileh
Department of Civil Engineering, American University of Sharjah, United Arab Emirates
J.A. Abdalla, R.A. Hawileh, "Predictions of Hysteresis Energy Dissipation in Steel Reinforcing Bars using Artificial Neural Networks", in B.H.V. Topping, Y. Tsompanakis, (Editors), "Proceedings of the First International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 32, 2009. doi:10.4203/ccp.92.32
Keywords: artificial neural network, dissipated energy, low-cycle, fatigue life.
The hysteresis energy dissipated in steel reinforcing bars under low-cycle fatigue load is an important parameter in predicting their fatigue life. The fatigue life of steel reinforcing bars and therefore the amount of hysteresis energy dissipated, directly influences the level of damage in reinforced concrete buildings under seismic load. In this study artificial neural networks (ANNs) are used to predict the hysteresis energy dissipated in steel reinforcing bars. Although an ANN has been used before to predict the fatigue life of steel reinforcing bars and other metals [1,2,3], to the best of the authors' knowledge no study has been conducted to predict the hysteresis energy dissipated in steel reinforcing bars under cyclic loading using anANN. Therefore, an ANN is used to predict the hysteresis energy dissipated in steel reinforcing bars subject to low-cycle fatigue. The input parameters for the network include the total strain amplitude and the strain ratio. The output of the ANN is the energy dissipated in the first cycle, the energy dissipated in the average cycles and the total energy dissipated in all cycles to failure. The data used to train and test the ANN are the results of experiments conducted by the authors for different types of steel reinforcing bars, subjected to variable strain amplitudes beyond yield.
The ANN was trained using the experimental data and then tested using the test data to predict the desired hysteresis energy. The ANN predicted and experimentally measured or desired energy values were compared and the accuracy of the ANN was evaluated. It is concluded that:
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