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    Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations

    Access Status
    Fulltext not available
    Authors
    Li, Jun
    Hao, Hong
    Chen, Z.
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Li, J. and Hao, H. and Chen, Z. 2015. Damage Identification and Optimal Sensor Placement for Structures under Unknown Traffic-Induced Vibrations. Journal of Aerospace Engineering. B4015001.
    Source Title
    Journal of Aerospace Engineering
    DOI
    10.1061/(ASCE)AS.1943-5525.0000550
    ISSN
    1943-5525
    School
    Department of Civil Engineering
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/DE140101741
    URI
    http://hdl.handle.net/20.500.11937/17356
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a damage-identification and optimal sensor-placement approach for structures under unknown traffic-induced vibrations. Response reconstruction is performed for structures under traffic-induced vibrations to formulate the relationship between different sets of time-domain responses. Measured acceleration responses from a structure under traffic-induced ground motions are used for damage identification with a sensitivity-based iterative model updating method. Numerical simulations on a seven-story planar frame structure are conducted to verify the performance of the proposed approach. Damage identification is conducted based on the initial finite-element model of the structure and the measured responses from the damaged state under traffic-induced vibrations. Structural elemental stiffness factors are iteratively updated until the reconstructed responses match the measured ones as closely as possible. A two-phase optimal sensor-placement scheme is proposed for better response reconstruction and subsequent damage identification. Optimal sensor placement is investigated to identify the best locations for response reconstruction and sensitivity-based damage identification. With the use of optimal sensor locations, the introduced damages can be identified effectively and accurately with nearly exact damage severity estimation and very small false positives and false negatives under a 5% noise effect.

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