Modeling Hysteretic Deteriorating Behavior Using Generalized Prandtl Neural Network
Publication: Journal of Engineering Mechanics
Volume 141, Issue 8
Abstract
In this paper, a new kind of activation function using a particular combination of stop and play operators is proposed and used in a feedforward neural network to improve its learning capability in the identification of nonlinear hysteretic material behavior with both stiffness and strength degradation. The new neuron and neural network are referred to as a deteriorating stop and generalized Prandtl neural network, respectively. To show the generality of the proposed neural network, it is trained on several data sets generated by various mathematical models of material hysteresis with and without deterioration as well as on a set of experimental data with very high nonlinearity, including severe damage. In each case, the training is successful, and the generalized Prandtl neural network response precision is very high. Also, using the proposed neural network, a neuro-modeler is designed and used in the dynamic analysis of a one-story shear frame under seismic loads with severe damage. A comparison of the results shows that the generalized Prandtl neural network type of the neuro-modeler is more successful than the previously proposed Prandtl neural network type.
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Published In
Journal of Engineering Mechanics
Volume 141 • Issue 8 • August 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Nov 15, 2013
Accepted: Jan 5, 2015
Published online: Apr 29, 2015
Published in print: Aug 1, 2015
Discussion open until: Sep 29, 2015
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