Modeling Hysteretic Deteriorating Behavior Using Generalized Prandtl Neural Network

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.