  Kriging is an efficient machine-learning tool, which allows to obtain an
approximate response of an investigated phenomenon on the whole parametric
space. Adaptive schemes provide a the ability to guide the experiment yielding
new sample point positions to enrich the metamodel. Herein a novel adaptive
scheme called Monte Carlo-intersite Voronoi (MiVor) is proposed to efficiently
identify binary decision regions on the basis of a regression surrogate model.
The performance of the innovative approach is tested for analytical functions
as well as some mechanical problems and is furthermore compared to two
regression-based adaptive schemes. For smooth problems, all three methods have
comparable performances. For highly fluctuating response surface as encountered
e.g. for dynamics or damage problems, the innovative MiVor algorithm performs
very well and provides accurate binary classification with only a few
observation points.
