吉林大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (增刊2): 164-169.

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Damage identification of urban overpasses based on static strain data and neural networks optimized by genetic algorithm

GONG Ya-feng, CHENG Yong-chun, JIAO Yu-bo   

  1. College of Transportation, Jilin University, Changchun 130022, China
  • Received:2011-02-19 Online:2011-09-30 Published:2011-09-30

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Abstract:

A damage identification strategy of urban overpass bridges was proposed in this paper.The weight and bias values of neural networks were optimized by genetic algorithm,the static strain ratio was selected as the input variable of neural networks,while the vulnerable sections were considered as the research objects.The damage localization and severity identification ability was discussed based on the numerical simulation of the left auxiliary bridge of Qianjin Street overpass in Changchun.The simualtion results indicate that the proposed method can achieve the damage localization with single and multiple damage locations,the error of damage severity identification is within 3%.Moreover,the ability of interpolation is superior to extrapolation of the presented method.

Key words: bridge engineering, damage identification, neural networks optimized by genetic algorithm, urban overpass, strain ratio

CLC Number: 

  • U441.4


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