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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 17 | Page No.: 3589-3593
DOI: 10.3923/jas.2013.3589.3593
 
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Damage Identification of Railway Simply Supported Steel Truss Bridge Based on Support Vector Machine

Jianying Ren, Mubiao Su and Qingyuan Zeng

Abstract:
When a train with one locomotive run on a 64 m railway simply supported steel truss bridge, the change percentages of the lower chord panel nodes maximum deflections and the beam end maximum horizontal displacement are calculated. The percentages are as the identification indexes and the identification models are established respectively using C-SVC and ε-SVR to identify 2 bars damage location and damage degree. The results show that when the noise level is to 5%, the damage location identification model begin to misidentify, the precision is 75% and when the noise level is 10%, the damage degree identification model results maximum mean square error is 0.0080, the minimum correlation coefficient is 90.50%. The identification models have good generalization and good anti-noise capability.
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How to cite this article:

Jianying Ren, Mubiao Su and Qingyuan Zeng, 2013. Damage Identification of Railway Simply Supported Steel Truss Bridge Based on Support Vector Machine. Journal of Applied Sciences, 13: 3589-3593.

DOI: 10.3923/jas.2013.3589.3593

URL: https://scialert.net/abstract/?doi=jas.2013.3589.3593

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