Cyclic behavior of materials is complex and difficult to model. A combination of hardening rules in classical plasticity is one possibility for modeling this complex material behavior. Neural network (NN) constitutive models have been shown in the past to have the capability of modeling complex material behavior directly from the results of material tests. In this paper, we propose a novel approach for NN-based modeling of the cyclic behavior of materials. The proposed NN material model uses new internal variables that facilitate the learning of the hysteretic behavior of materials. The same approach can also be used in modeling of the hysteretic behavior of structural systems or structural components under cyclic loadings. The proposed model is shown to be superior to the earlier versions of NN material models. Although the earlier versions of the NN material models were effective in capturing the multi-axial material behavior, they were only tested under cyclic uni-axial state of stress. The proposed NN material model is capable of learning the hysteretic behavior of materials under even non-uniform stress state in multi-dimensional stress space. The performance of the proposed model is demonstrated through a series of examples using actual experimental data and simulated testing data.
|Original language||English (US)|
|Number of pages||23|
|Journal||International Journal for Numerical Methods in Engineering|
|State||Published - Jan 22 2008|
All Science Journal Classification (ASJC) codes
- Numerical Analysis
- Applied Mathematics