Reliability sensitivity and optimization of linear dynamical systems subject to Gaussian excitation

M. A. Valdebenito, H. A. Jensen, G. I. Schuëller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Although Reliability-based Optimization (RBO) is a sound approach for design in engineering, its application has remained limited in the past due to high numerical costs associated with its solution process. In view of this fact, this contribution proposes a framework for solving most efficiently a particular class of RBO problems, namely the weight minimization of linear structures subject to dynamic Gaussian excitation. The associated reliability problem is solved by means of an efficient simulation technique while the optimization problem is addressed applying a gradient-based scheme. In order to apply this optimization scheme, an efficient procedure for estimating reliability sensitivity is applied. This procedure consists in introducing local approximations of functions related with reliability and structural performance. An example involving a two degree-of-freedom system subject to a stochastic ground acceleration illustrates the efficiency and effectiveness of the proposed framework.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Structural Dynamics, EURODYN 2011
EditorsG. Lombaert, G. Muller, G. De Roeck, G. Degrande
PublisherUniversity of Southampton, Institute of Sound Vibration and Research
Pages2953-2959
Number of pages7
ISBN (Electronic)9789076019314
StatePublished - 2011
Event8th International Conference on Structural Dynamics, EURODYN 2011 - Leuven, Belgium
Duration: 4 Jul 20116 Jul 2011

Publication series

NameProceedings of the 8th International Conference on Structural Dynamics, EURODYN 2011

Conference

Conference8th International Conference on Structural Dynamics, EURODYN 2011
Country/TerritoryBelgium
CityLeuven
Period4/07/116/07/11

Keywords

  • First excursion probability
  • Gaussian process
  • High dimensions
  • Reliability-based Optimization
  • Sensitivity analysis

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