PREDICTING OF TORSIONAL STRENGTH OF REINFORCED CONCRETE BEAMS USING ARTIFICIAL NEURAL NETWORK | ||
| Iraqi journal of mechanical and material engineering | ||
| Article 1, Volume 12, Issue 4, December 2012, Pages 694-708 | ||
| Author | ||
| Abdalkader A. Mohammed | ||
| Abstract | ||
| In this paper, the artificial neural networks (ANNs) model in predicting the torsional strength of reinforced concrete (RC) beams is done. Experimental data of 85 rectangular RC beams under pure torsion from an existing database in the literature were used to develop ANN model. The input parameters affecting the torsional strength were selected as dimensions of beams, spacing of stirrups, dimensions of closed stirrups, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. A back propagation neural network (BPNN) with the log-sigmoid activation function is adopted due to its accuracy of prediction. In addition to the ANN model is compared with well-known the building codes provisions for the design of RC beams under pure torsion. The study shows that the ANN models give reasonable predictions of the ultimate torsional strength of RC beams better than existing equations for torsion | ||
| Keywords | ||
| Artificial Neural Network; Back Propagation; Reinforced concrete beam; torsional strength | ||
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