Material behavior modeling with multi-output support vector regression

Abstract

Based on neural network material-modeling technologies, a new paradigm, called multi-output support vector regression, is developed to model complex stress/strain behavior of materials. The constitutive information generally implicitly contained in the results of experiments, i.e., the relationships between stresses and strains, can be captured by training a support vector regression model within a unified architecture from experimental data. This model, inheriting the merits of the neural network based models, can be employed to model the behavior of modern, complex materials such as composites. Moreover, the architectures of the support vector regression built in this research can be more easily determined than that of the neural network. Therefore, the proposed constitutive models can be more conveniently applied to finite element analysis and other application fields.As an illustration, the behaviors of concrete in the state of plane stress under monotonic biaxial loading and compressive uniaxial cycle loading are modeled with the multi-output and single-output support regression respectively. The excellent results show that the support vector regression provides another effective approach for material modeling.