Artificial neural network Approach for Ductility Computation of Flexural RC Members

Ductility of structure is an essential property of structures responding inelastically during severs shaking, such as earthquake. As a result, the inertial forces imposed to the structures can be decreased. Several forms of ductility are often considered, these include curvature and displacement ductility. The calculation of the accurate values of ductility of members is usually complicated and therefore a direct and accurate approach to obtain such value is necessary needed particularly in seismic regions. In this paper, one method is considered to calculate the flexural curvature ductility ratio of reinforced concrete (RC) sections. In this approach to calculate curvature ductility factor of RC beams was presented by using artificial neural network (ANN).To investigate the performance and accuracy of the ANN method, fifteen high strength concrete (HSC) beams were casted and tested under bending and also the available results of thirty eight beams were selected from the literature. Based on the obtained experimental results a comparison was made experimental results and artificial neural network, and it was shown that a goodagreement is available

Artificial neural network Approach for Ductility Computation of Flexural RC Members Keywords:

Concrete RC beams , Ductility , Experimental (HSC) tests , Artificial neural network