  To evaluate the cyclic behavior under different loading conditions using the
kinematic and isotropic hardening theory of steel, a Chaboche viscoplastic
material model is employed. The parameters of a constitutive model are usually
identified by minimization of the distance between model response and
experimental data. However, measurement errors and differences in the specimens
lead to deviations in the determined parameters. In this article, the Choboche
model is used and a stochastic simulation technique is applied to generate
artificial data which exhibit the same stochastic behavior as experimental
data. Then the model parameters are identified by applying an estimation using
Bayes's theorem. The Gauss-Markov-Kalman filter using functional approximation
is introduced and employed to estimate the model parameters in the Bayesian
setting. Identified parameters are compared with the true parameters in the
simulation, and the efficiency of the identification method is discussed. In
the end, the effect of the load path on parameter identification is
investigated.
