This paper addresses signal processing and applicability of local damage detection algorithms on a 1/15 scale model of the Yeongjong Bridge floor system. As many as 48 channels of dynamic strain data were collected, corresponding to strain gauge placements on the in-situ structure. Model excitation was induced via a series of impact hammer strikes. Twelve different damage cases are examined including connection loosening, chord removal, saw-cuts on the deck plate, and bearing removal simulating hanger failure. Signal anomaly index (SAI) representing the amount of change in frequency response is adopted to detect the presence and type of damage along with a series of neural network classifiers. It is found that SAI and ANN are promising tools for detecting local damage in steel bridges.


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    Title :

    Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck


    Additional title:

    KSCE J Civ Eng


    Contributors:

    Published in:

    Publication date :

    2003-07-01


    Size :

    8 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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