• Hector Jensen, Federico Santa Maria Technical University
  • Jianbing Cheng
  • Marcos Valdebenito
  • Ioannis Kougioumtzoglou
  • Dixiong Yang

Dynamical loading acting over structural systems can be seldom characterized as deterministic due to inherent uncertainty associated with its duration, intensity, and temporal and spatial behavior. In such scenario, probability theory allows modeling such uncertainty by resorting to stochastic processes, that capture randomness and correlation structure of time-dependent loads. While such framework for modeling dynamic loading is widely accepted, its practical application becomes an extremely challenging task as usually, the performance of structural systems is characterized by means of highly refined numerical models. These issues prevent the direct application of most approaches for uncertainty propagation. Two possible means for performing uncertainty propagation for large scale dynamical models are resorting to advanced analytical and simulation methods and/or applying reduced order (or surrogate) models. On one hand, advanced analytic and simulation methods offer the opportunity of quantifying uncertainty in terms of probability density functions or excursion probabilities with a reduced number of system analyses. Reduced order models are capable of capturing - up to some extent - the dependence of the performance of a structural system with respect to uncertain parameters while demanding considerably less numerical effort than the full model.
The aim of this mini-symposium is addressing the very latest development on approaches for advanced analytic and simulation methods as well as reduced order (and surrogate) models for uncertainty quantification in stochastic structural dynamics. The scope of the mini-symposium is broad, as it includes different types of structural problems (linear or nonlinear); analytic and numerical methodologies for stochastic response analysis; development and application of surrogate models; and practical applications of methods for uncertainty quantification in stochastic dynamics. Both theoretical developments and practical applications involving dynamical systems of engineering interest are particularly welcomed in this session.

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