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Structural Monitoring and Maintenance
  Volume 3, Number 4, December 2016, pages 315-333
open access

Damage identification for high-speed railway truss arch bridge using fuzzy clustering analysis
Bao-Ya Cao, You-Liang Ding, Han-Wei Zhao and Yong-Sheng Song

    This study aims to perform damage identification for Da-Sheng-Guan (DSG) high-speed railway truss arch bridge using fuzzy clustering analysis. Firstly, structural health monitoring (SHM) system is established for the DSG Bridge. Long-term field monitoring strain data in 8 different cases caused by high-speed trains are taken as classification reference for other unknown cases. And finite element model (FEM) of DSG Bridge is established to simulate damage cases of the bridge. Then, effectiveness of one fuzzy clustering analysis method named transitive closure method and FEM results are verified using the monitoring strain data. Three standardization methods at the first step of fuzzy clustering transitive closure method are compared: extreme difference method, maximum method and non-standard method. At last, the fuzzy clustering method is taken to identify damage with different degrees and different locations. The results show that: non-standard method is the best for the data with the same dimension at the first step of fuzzy clustering analysis. Clustering result is the best when 8 carriage and 16 carriage train in the same line are in a category. For DSG Bridge, the damage is identified when the strain mode change caused by damage is more significant than it caused by different carriages. The corresponding critical damage degree called damage threshold varies with damage location and reduces with the increase of damage locations.
Key Words
    railway bridge; steel truss arch; structural health monitoring; damage identification; fuzzy clustering; finite element analysis
Bao-Ya Cao, You-Liang Ding and Han-Wei Zhao: The Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China
Yong-Sheng Song: Jinling Institute of Technology, Nanjing 211169, China

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