Incremental dynamic analysis: A nonlinear stochastic dynamics perspective

Ketson R.M. dos Santos, Ioannis A. Kougioumtzoglou, André T. Beck

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

An efficient stochastic incremental dynamic analysis (IDA) methodology for nonlinear/hysteretic oscillators is developed by resorting to nonlinear stochastic dynamics concepts and tools such as stochastic averaging and statistical linearization. Specifically, modeling the excitation as a nonstationary stochastic process possessing an evolutionary power spectrum (EPS), an approximate closed-form expression is derived for the parameterized oscillator response amplitude probability density function (PDF). In this regard, an IDA surface is determined providing the PDF of the engineering demand parameter (EDP) for a given intensity measure (IM) value. In contrast to a computationally expensive Monte Carlo simulation (MCS) based determination of the IDA surface, the methodology developed herein determines the EDP PDF at minimal computational cost. Further, an approximate closed-form expression is derived for the parameterized nonlinear oscillator response EPS as well; thus, a conceptually novel IDA surface is determined where the EDP relates to the nonlinear oscillator response EPS. The stochastic IDA framework can account for physically realistic excitation models possessing not only time-varying intensities but time-varying frequency contents as well. A bilinear/hysteretic single-degree-of-freedom oscillator is considered as a numerical example, whereas comparisons with pertinent MCS data demonstrate the accuracy and efficiency of the developed stochastic IDA methodology.

Original languageEnglish (US)
Article number06016007
JournalJournal of Engineering Mechanics
Volume142
Issue number10
DOIs
StatePublished - Oct 1 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 American Society of Civil Engineers.

Keywords

  • Incremental dynamic analysis
  • Nonlinear system
  • Performance-based engineering
  • Statistical linearization
  • Stochastic averaging
  • Stochastic dynamics
  • Stochastic process

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