Damage identification for high-speed railway truss arch bridge using fuzzy clustering analysis

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.

[1]  P. G. Bakir,et al.  Structural identification (St-Id) using finite element models for optimum sensor configuration and uncertainty quantification , 2014 .

[2]  Zhiye Zhao,et al.  A fuzzy system for concrete bridge damage diagnosis , 2002 .

[3]  Xue Z. Wang,et al.  Knowledge discovery from process operational data using PCA and fuzzy clustering , 2001 .

[4]  Dan M. Frangopol,et al.  Structural Damage Detection , 2010 .

[5]  Luca Podofillini,et al.  Dynamic safety assessment: Scenario identification via a possibilistic clustering approach , 2010, Reliab. Eng. Syst. Saf..

[6]  Ling Yu,et al.  Structural Damage Detection in a Truss Bridge Model Using Fuzzy Clustering and Measured FRF Data Reduced by Principal Component Projection , 2013 .

[7]  Ayaho Miyamoto,et al.  Fuzzy concrete bridge deck condition rating method for practical bridge management system , 2009, Expert Syst. Appl..

[8]  Ying-Ming Wang,et al.  A fuzzy group decision making approach for bridge risk assessment , 2007, Comput. Ind. Eng..

[9]  Peng Zhang,et al.  Unsupervised Performance Evaluation Strategy for Bridge Superstructure Based on Fuzzy Clustering and Field Data , 2013, TheScientificWorldJournal.

[10]  Saleh Zein-Sabatto,et al.  Information and decision fusion systems for aircraft Structural Health Monitoring , 2011, 2011 Proceedings of IEEE Southeastcon.

[11]  Valder Steffen,et al.  Probabilistic Neural Network and Fuzzy Cluster Analysis Methods Applied to Impedance-Based SHM for Damage Classification , 2014 .

[12]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[13]  Wei Zhang,et al.  Health state evaluation of shield tunnel SHM using fuzzy cluster method , 2015, Smart Structures.

[14]  Peng Xu,et al.  Structural health monitoring based on continuous ACO method , 2011, Microelectron. Reliab..

[15]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Antonia Papandreou-Suppappola,et al.  Time-Frequency based Classification of Structural Damage , 2007 .

[17]  Saleh Zein-Sabatto,et al.  Two-level fuzzy inference system for aircraft's structural health monitoring , 2013, 2013 Proceedings of IEEE Southeastcon.

[18]  Christian Döring,et al.  Fundamentals of Fuzzy Clustering , 2007 .

[19]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[20]  Michael J. Brennan,et al.  Structural damage detection by fuzzy clustering , 2008 .

[21]  L. Meyyappan,et al.  Fuzzy-neuro system for bridge health monitoring , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[22]  Jinping Ou,et al.  Structural Health Monitoring in mainland China: Review and Future Trends , 2010 .