A new method to identify bridge bearing damage based on Radial Basis Function Neural Network

  • Chen, Zhaowei (State Key Laboratory of Traction Power, Southwest Jiaotong University) ;
  • Fang, Hui (Electric Power Research Institute, State Grid Chongqing Electric Power Co.) ;
  • Ke, Xinmeng (Locomotive Vehicle Department, Zhengzhou Railway Vocational and Technical College) ;
  • Zeng, Yiming (Locomotive and Car Research Institute, China Academy of Railway Sciences)
  • Received : 2016.05.21
  • Accepted : 2016.10.21
  • Published : 2016.11.25


Bridge bearings are important connection elements between bridge superstructures and substructures, whose health states directly affect the performance of the bridges. This paper systematacially presents a new method to identify the bridge bearing damage based on the neural network theory. Firstly, based on the analysis of different damage types, a description of the bearing damage is introduced, and a uniform description for all the damage types is given. Then, the feasibility and sensitivity of identifying the bearing damage with bridge vibration modes are investigated. After that, a Radial Basis Function Neural Network (RBFNN) is built, whose input and output are the beam modal information and the damage information, respectively. Finally, trained by plenty of data samples formed by the numerical method, the network is employed to identify the bearing damage. Results show that the bridge bearing damage can be clearly reflected by the modal information of the bridge beam, which validates the effectiveness of the proposed method.


Supported by : Southwest Jiaotong University


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