Stress-Strain Behavior of Geomaterials in Loading Reversal Simulated by Time-Delay Neural Networks

The effectiveness of time-delay artificial neural networks (TDANNs) as highly nonlinear mapping tools for simulation of hysteresis stress-strain (-) response of geomaterials under repeated reversal loading is investigated. A nonlinear recursive simulator containing the developed TDANNs was designed to enable forecasting of complete sigma-epsilon curves from the knowledge of only the initial sigmaepsilon condition of the tested material. Theoretically-obtained data were used to derive the TDANNs, and simulations were performed to validate the proposed approach. Advantages, limitations, and method of enhancement of the approach are presented.