By the Choquet theorem, distributions of random closed sets can be characterized by a certain class of set functions called capacity functionals. In this paper a generalization to the multivariate case is presented, that is, it is proved that the joint distribution of finitely many random sets can be characterized by a multivariate set function being completely alternating in each component, or alternatively, by a capacity functional defined on complements of cylindrical sets. For the special case of finite spaces a multivariate version of the Moebius inversion formula is derived. Furthermore, we use this result to formulate an existence theorem for set-valued stochastic processes.
Tuned mass dampers serve the purpose of damping vibrations of structures such as earthquake-induced vibrations. In their design, two types of uncertainty are relevant: the stochastic excitation (e.g. earthquake record) and the inherent uncertainty of internal parameters of the devices themselves. This paper presents a new framework that admits the combination of stochastic processes and interval-type parameter uncertainty, modelled by random sets. The approach is applied to show how the efficiency of tuned mass dampers can be realistically assessed in the presence of uncertainty.
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