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Kriging is an efficient machine-learning tool, which allows to obtain anapproximate response of an investigated phenomenon on the whole parametricspace. Adaptive schemes provide a the ability to guide the experiment yieldingnew sample point positions to enrich the metamodel. Herein a novel adaptivescheme called Monte Carlo-intersite Voronoi (MiVor) is proposed to efficientlyidentify binary decision regions on the basis of a regression surrogate model.The performance of the innovative approach is tested for analytical functionsas well as some mechanical problems and is furthermore compared to tworegression-based adaptive schemes. For smooth problems, all three methods havecomparable performances. For highly fluctuating response surface as encounterede.g. for dynamics or damage problems, the innovative MiVor algorithm performsvery well and provides accurate binary classification with only a fewobservation points.

An innovative adaptive kriging approach for efficient binary classification of mechanical problems