Behavior and modeling of superelastic shape memory alloy reinforced concrete beams

2013 ◽  
Vol 49 ◽  
pp. 893-904 ◽  
Author(s):  
Alaa Abdulridha ◽  
Dan Palermo ◽  
Simon Foo ◽  
Frank J. Vecchio
Keyword(s):  
Reinforced Concrete ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Concrete Beams ◽  
Reinforced Concrete Beams

Artificial neural network model for deflection analysis of superelastic shape memory alloy reinforced concrete beams

2010 ◽  
Vol 37 (6) ◽  
pp. 855-865 ◽  
Author(s):  
Y. I. Elbahy ◽  
M. Nehdi ◽  
M. A. Youssef
Keyword(s):  
Reinforced Concrete ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Companion Paper ◽  
Reduction Factor ◽  
Moment Of Inertia ◽  
Reinforced Concrete Beams ◽  
Rc Beams ◽  
Design Chart ◽  
Artificial Neural

The need for a new model capable of accurately predicting the deflection of shape memory alloy (SMA) reinforced concrete (RC) beams is clear from the results obtained in the companion paper. In the present paper, artificial neural networks (ANNs) are utilized to develop such a model. The objective is to create a design tool for computing a reduction factor β to be used in the calculation of the effective moment of inertia for SMA RC beams. First, a database was developed using the results obtained from the parametric study reported in the companion paper. The main factors affecting the moment of inertia have been considered. The network architecture that results in the optimum performance was selected and trained. After demonstrating the network’s ability to predict output data for unfamiliar input data, the network was used to develop a design chart that provides the reduction factor β as a function of the reinforcement ratio and the reinforcement modulus of elasticity. A design example is discussed to illustrate the advantages of using the developed design chart over existing models.

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