Journal of South China University of Technology (Natural Science Edition) ›› 2013, Vol. 41 ›› Issue (8): 67-73.doi: 10.3969/j.issn.1000-565X.2013.08.011

• Traffic & Transportation Engineering • Previous Articles     Next Articles

Beam Structure Finite Element Model Updating via BP Neural Network Optimized

Hu Jun- liang1 Yu Xiao- lin1 Zheng Heng- bin1 Chen Zhou1 Yan Quan- sheng1,2   

  1. 1.School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,Guangdong,China;2.State Key Laboratory of Subtropical Building Science,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2012-10-10 Revised:2013-04-26 Online:2013-08-25 Published:2013-07-01
  • Contact: 颜全胜(1968-)，男，博士，教授，主要从事大跨度桥梁稳定、斜拉桥施工控制研究． E-mail:cvqshyan@scut.edu.cn
  • About author:胡俊亮(1984-)，男，博士生，主要从事桥梁结构损伤识别与健康监测研究．E-mail:hujunliang1@126．com
  • Supported by:

    国家自然科学基金资助项目(51208208);广东省交通运输厅科技项目(科技-2012-02-024)

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Abstract:

The traditional model updating methods based on sensitivity analysis can not determine the appropriate design parameters to be updated,and most of the model updating methods independent of sensitivity analysis are complex with large computation.In this paper,a model updating method based on parameters is adopted,and a BP neural network optimized by genetic algorithm is introduced.Then,with the structural dynamic eigen vector as the input,the updated design parameters are directly obtained.The parameters are then used to obtain the structural dynamic eigen vector error of the updated finite element model,and the error minimization is taken as the objective function to implement iterative resolution,thus determining the final updated design parameters.Finally,a 3- span continuous beam model is tested.The results prove that the proposed method is accurate and effective.

Key words: beam structure, model updating, genetic algorithm, BP neural network

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