Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Piezoelectric impedance based damage detection in truss bridges based on time frequency ARMA model

    Access Status
    Fulltext not available
    Authors
    Fan, X.
    Li, Jun
    Hao, Hong
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Fan, X. and Li, J. and Hao, H. 2016. Piezoelectric impedance based damage detection in truss bridges based on time frequency ARMA model. Smart Structures and Systems. 19 (3): pp. 501-523.
    Source Title
    Smart Structures and Systems
    DOI
    10.12989/sss.2016.18.3.501
    ISSN
    1738-1584
    School
    Department of Civil Engineering
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DE140101741
    URI
    http://hdl.handle.net/20.500.11937/46156
    Collection
    • Curtin Research Publications
    Abstract

    Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

    Related items

    Showing items related by title, author, creator and subject.

    • Impedance resonant frequency sensitivity based structural damage identification with sparse regularization: experimental studies
      Fan, X.; Li, Jun; Hao, Hong (2019)
      Electromechanical impedance (EMI) based structural health monitoring methods have been successfully applied to various engineering fields. However, the studies on damage quantification using EMI based techniques are still ...
    • Using sparse regularization and impedance sensitivity for structural damage detection
      Fan, X.; Li, Jun; Hao, Hong (2017)
      © 2017 International Society for Structural Health Monitoring of Intelligent Infrastrucure. All rights reserved. Electromechanical impedance (EMI) based structural damage detection methods have been widely developed in ...
    • Identification of Minor Structural Damage Based on Electromechanical Impedance Sensitivity and Sparse Regularization
      Fan, X.; Li, Jun; Hao, Hong; Ma, S. (2018)
      © 2018 American Society of Civil Engineers. This paper proposes a structural damage identification approach based on model updating with electromechanical impedance sensitivity and the sparse regularization technique to ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.