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Proceedings Paper

Damage detection system of a real steel truss bridge by neural networks
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Paper Abstract

The damage detection system of a real steel truss bridge was developed to identify the location and severity of the damaged members. At first, the loading test was performed to characterize the real bridge. The real steel truss bridge was measured by electrical strain gages and accelerometers when the train passed. The measured strains and acceleration were used to refine the stiffness and the mass of the finite element model. The damage scenario, that can be happened in the real situation, was simulated by the refined finite element model. The damage localization was implemented to classify the damaged part in the bridge by the neural networks. The neural network was constructed as two steps: at 1st step, the half-span, which had some damages occurred, was found, and at 2nd step, the severest abnormal part in the total 8 parts of the real bridge was detected. The learned neural network was verified by the used data.

Paper Details

Date Published: 20 April 2000
PDF: 12 pages
Proc. SPIE 3988, Smart Structures and Materials 2000: Smart Systems for Bridges, Structures, and Highways, (20 April 2000); doi: 10.1117/12.383151
Show Author Affiliations
Man-Yong Choi, Korea Research Institute of Standards and Science (South Korea)
Il-Bum Kwon, Korea Research Institute of Standards and Science (South Korea)


Published in SPIE Proceedings Vol. 3988:
Smart Structures and Materials 2000: Smart Systems for Bridges, Structures, and Highways
S.-C. Liu, Editor(s)

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