Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles

Structural Control and Health Monitoring

Volumn 18, Issue 2, 2011, Pages 207-226

  Li, Jianchun ^a   Dackermann, Ulrike ^a   Xu, You Lin ^b   Samali, Bijan ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^b HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

artificial neural network; damage identification; frequency response functions; neural network ensemble; principal component analysis; structural health monitoring

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DAMAGE IDENTIFICATION; FREQUENCY RESPONSE FUNCTIONS; NEURAL NETWORK ENSEMBLES; STRUCTURAL HEALTH;

CIVIL ENGINEERING; DAMAGE DETECTION; FREQUENCY RESPONSE; GAUSSIAN NOISE (ELECTRONIC); NUMERICAL METHODS; PRINCIPAL COMPONENT ANALYSIS; STRUCTURAL HEALTH MONITORING;

NEURAL NETWORKS;

EID: 79952542622     PISSN: 15452255     EISSN: 15452263     Source Type: Journal    
DOI: 10.1002/stc.369     Document Type: Article

References (27)

1. Lifshitz JM, Rotem A,. Determination of reinforcement unbonding of composites by a vibration technique. Journal of Composite Materials 1969; 3 (3): 412-423.
2. Adams RD, Cawley P, Pye CJ, Stone BJ,. A vibration technique for non-destructively assessing the integrity of structures. Journal of Mechanical Engineering Science 1978; 20: 93-100. (Pubitemid 9393443)
3. Chen HL, Spyrakos CC, Venkatesh G,. Evaluating structural deterioration by dynamic response. Journal of Structural Engineering 1995; 121 (8): 1197-1204.
4. Trendafilova I,. A study on vibration-based damage detection and location in an aircraft wing scaled model. Applied Mechanics and Materials 2005; 3 (4): 309-314. (Pubitemid 43894477)
5. Kim J-T, Park J-H, Lee B-J,. Vibration-based damage monitoring in model plate-girder bridges under uncertain temperature conditions. Engineering Structures 2007; 29 (7): 1354-1365. (Pubitemid 46832014)
6. Salawu OS, Williams C,. Bridge assessment using forced-vibration testing. Journal of Structural Engineering 1995; 121 (2): 161-172.
7. Pandey AK, Biswas M, Samman MM,. Damage detection from changes in curvature mode shapes. Journal of Sound and Vibration 1991; 145 (2): 321-332.
8. Stubbs N, Kim J-T, Topole K., An efficient and robust algorithm for damage localization in offshore platforms. Proceedings of the ASCE Tenth Structures Congress, Antonio, TX, U.S.A., 1992; 543-546.
9. Dackermann U, Li J, Samali B., Vibration-based damage identification based on damage index method using neural network ensembles. Advances in Structural Engineering 2010; accepted.
10. Fang X, Tang J., Frequency response based damage detection using principal component analysis. Proceedings of IEEE International Conference on Information Acquisition, Hong Kong and Macau, China, 2005; 407-412.
11. Zang C, Imregun M,. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration 2001; 242 (5): 813-827. (Pubitemid 32432839)
12. Marwala T, Hunt HEM,. Fault identification using finite element models and neural networks. Mechanical Systems and Signal Processing 1999; 13: 475-490.
13. Ni YQ, Zhou XT, Ko JM,. Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks. Journal of Sound and Vibration 2006; 290 (1-2): 242-263. (Pubitemid 41802247)
14. Worden K, Manson G, Fieller NRJ,. Damage detection using outlier analysis. Journal of Sound and Vibration 2000; 229 (3): 647-667.
15. De Boe P, Golinval J-C,. Principal component analysis of a piezosensor array for damage localization. Structural Health Monitoring 2003; 2 (2): 137-144.
16. Kullaa J,. Damage detection of the Z24 bridge using control charts. Mechanical Systems and Signal Processing 2003; 17 (1): 163-170.
17. Trendafilova I, Cartmell MP, Ostachowicz W,. Vibration-based damage detection in an aircraft wing scaled model using principal component analysis and pattern recognition. Journal of Sound and Vibration 2008; 313 (3-5): 560-566.
18. Bishop CM,. Neural networks and their applications. Review of Scientific Instruments 1994; 65 (6): 1803-1832.
19. Masri SF, Smyth AW, Chassiakos AG, Caughey TK, Hunter NF,. Application of neural networks for detection of changes in nonlinear systems. Journal of Engineering Mechanics 2000; 126 (7): 666-676.
20. Xin Y, Yong L,. Making use of population information in evolutionary artificial neural networks. Systems, IEEE Transactions on Man, and Cybernetics, Part B Cybernetics 1998; 28 (3): 417-425.
21. Hansen LK, Salamon P,. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990; 12 (10): 993-1001.
22. Sollich P, Krogh A., Learning with ensembles: how overfitting can be useful. In Advances in Neural Information Processing Systems., Touretzky DS, Mozer MC, Hasselmo ME, (ed.), vol. 8. MIT Press: Denver, U.S.A., 1996; 190-196.
23. Zhou Z-H, Wu J, Tang W,. Ensembling neural networks: many could be better than all. Artificial Intelligence 2002; 137 (1-2): 239-263. (Pubitemid 34405220)
24. Pearson K,. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 1901; 2 (6): 559-572.
25. White PR, Tan MH, Hammond JK,. Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation. Journal of Sound and Vibration 2006; 290 (3-5): 676-689. (Pubitemid 43006037)
26. ANSYS. ANSYS release 10.0 Documentation. Canonburg, U.S.A., 2005.
27. Choi FC,. Assessment of the structural integrity of bridges using dynamic approaches. Ph.D. Thesis, University of Technology Sydney, Australia, 2007.