Integrated system identification and reliability evaluation of stochastic building structures

J. Zhang, You Lin Xu, J. Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)

Abstract

System identification and reliability evaluation play a significant role in structural health monitoring to ensure the serviceability and safety of existing structures. Although the development of system identification methods has attained much attention and some degree of maturity, reliability evaluation of existing structures still remains a challenging problem especially when uncertainties in measurement data and inherent randomness, which are inevitably involved in civil structures, are considered. In this regard, this paper presents a framework for integrated system identification and reliability evaluation of stochastic building structures. Two algorithms are proposed to respectively evaluate component reliability and system reliability of stochastic building structures by combining a statistical moment-based system identification method and a probability density evolution equation-based reliability evaluation method. System identification is embedded in the procedure of reliability evaluation of a stochastic building structure. The uncertainties in both the structure and the external excitation are considered. Numerical examples show that the structural component and system reliabilities of a three-story shear building structure with three damage scenarios can be effectively evaluated by the proposed methods.
Original languageEnglish
Pages (from-to)528-538
Number of pages11
JournalProbabilistic Engineering Mechanics
Volume26
Issue number4
DOIs
Publication statusPublished - 1 Oct 2011

Keywords

  • Component reliability
  • Reliability evaluation
  • Stochastic structures
  • System identification
  • System reliability

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Ocean Engineering
  • Aerospace Engineering
  • Civil and Structural Engineering
  • Mechanical Engineering
  • Statistical and Nonlinear Physics
  • Condensed Matter Physics

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