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.
Experimental investigation of local damage detection on a 1/15 scale model of a suspension bridge deck
KSCE J Civ Eng
KSCE Journal of Civil Engineering ; 7 , 4 ; 461-468
2003-07-01
8 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
STUDIES OF SUSPENSION BRIDGE DECK FLUTTER INSTABILITY
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