  Parametric studies for dynamic systems are of high interest to detect
instability domains. This prediction can be demanding as it requires a refined
exploration of the parametric space due to the disrupted mechanical behavior.
In this paper, an efficient surrogate strategy is proposed to investigate the
behavior of an oscillator of Duffing's type in combination with an
elasto-plastic friction force model. Relevant quantities of interest are
discussed. Sticking time is considered using a machine learning technique based
on Gaussian processes called kriging. The largest Lyapunov exponent is proposed
as an efficient indicator of non-regular behavior. This indicator is estimated
using a perturbation method. A dedicated adaptive kriging strategy for
classification called MiVor is utilized and appears to be highly proficient in
order to detect instabilities over the parametric space and can furthermore be
used for complex response surfaces in multi-dimensional parametric domains.
