Abstract：Research purposes: It has significant engineering value and research meaning to do research on intelligent diagnosis methods of a hybrid girder cable － stayed bridge． Taking Tianjin Hebei Street hybrid girder cable － stayed bridge as the engineering background， based on artificial neural networks， the method of hierarchical damage identification which is suitable to hybrid girder cable － stayed bridge is presented: the damaged substructure and damaged steel girder substructural components can be detected by using Probablistic Neural Network ( PNN) and Radial Basis Function ( RBF) Neural Network individually． Furthermore，an combined static and dynamic damage sensitive index which is suitable in the second step is presented，a RBF Neural Network model is constituted and used to simulate three damage conditions，i． e． single damage and double or three damages which occurred simultaneously．
Research conclusions: The identification results show that: ( 1) The proposed hierarchical damage identification method has a identified precision and efficiency，it is suitable to intelligent diagnosis process of hybrid girder cable － stayed bridges． ( 2) The combined static － dynamic damage identification index is also sensitive to cable － stayed hybrid girder bridges． ( 3) The identified precision for single damage cases is nearly to 100% ． ( 4) For double and triple damage cases individually，the identified precision is nearly to 82． 61% and 78． 3% ．
Liu Qiong， Huang Caiping， Dang Zhijie， etc． Experimental Research on the Load Carrying Performance of Steel － concrete Junction in Box Girder of Cable － stayed Bridge ［J］． Journal of Railway Engineering Society，2010( 9) : 46 － 49．
Li Zhongxian，Yang Xiaoming，DingYang． Research on Substructural Damage Identification of Large Cable － stayed Bridges using Artificial Neural Networks［J］． Earthquake Engineering and Engineering Vibration， 2003( 3) : 93 － 99．
Zhuo Yi． Health Monitoring System and Intelligent Diagnosis Methods of a Long － Span Cable － Stayed Hybrid Girder Bridge［D］． Tianjin: Tianjin University， 2007．
Tian Jingwen，Gao Meijuan． Research and Application of Artificial Neural Network Algorithm［M］． Beijing: Beijing Institute of Technology Press，2006．
［6］ 范立础． 桥梁抗震［M］． 上海: 同济大学出版社，1997． Fan Lichu． Seismic Design of Bridges［M］． Shanghai: Tongji Univerisity Press，1996．
［7］ Cawley P，Adams R D． The Location of Defects in Structures From Measurements of Natural Frequencies ［J］． Journal of Strain Analysis，1979( 2) : 49 －57．
［8］ Kaminski P C． The Approximate Location of Damage Through the Analysis of Natural Frequencies with Artificial Neural Networks ［J］． Journal of Process Mechanical Engineering，1995( 209) : 117 － 123．
［9］ 朱劲松，肖汝诚． 大跨度 PC 斜拉桥结构快速分析的神经网络模型研究［J］． 中国铁道科学，2007( 1) : 58 －64．
Zhu jinsong， Xiao Rucheng． Research on Neural Network Model to Structural Simulation of Large － span PC Cable － stayed Bridges［J］． China Railway Science， 2007( 1) : 58 － 64．
2011, Vol. 28 
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