Boundary damage identification of a two-storey framed structure utilising frequency response functions and artificial neural networks

MOVIC 2010 - 10th International Conference on Motion and Vibration Control, Proceedings

Volumn , Issue , 2010, Pages

  Dackermann, Ulrike ^a   Li, Jianchun ^a   Samali, Bijan ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Artificial neural networks; Damage identification; Frequency response functions; Neural network ensembles; Principal component analysis; Structural health monitoring

Indexed keywords

DAMAGE DETECTION; DATA REDUCTION; FREQUENCY RESPONSE; MODAL ANALYSIS; NUMERICAL METHODS; PALMPRINT RECOGNITION; PATTERN RECOGNITION SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING; SPURIOUS SIGNAL NOISE; STRUCTURAL DYNAMICS; STRUCTURAL FRAMES; STRUCTURAL HEALTH MONITORING;

CIVIL ENGINEERING STRUCTURES; DAMAGE IDENTIFICATION; FREQUENCY RESPONSE FUNCTIONS; INDIVIDUAL CHARACTERISTICS; NEURAL NETWORK ENSEMBLES; PATTERN RECOGNITION PROBLEMS; STIFFNESS AND DAMPING; VIBRATION BASED DAMAGE IDENTIFICATIONS;

NEURAL NETWORKS;

EID: 85119102339     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. ANSYS INC (2007a) ANSYS. release 11.0.
2. ANSYS INC (2007b) ANSYS Workbench. release 11.0.
3. BISHOP, C. M. (1994) Neural networks and their applications. Review of Scientific Instruments, 65, 1803-1832.
4. CADA-X (1996) Modal analysis manual. LMS International. Belgium.
5. FANG, X. & TANG, J. (2005) Frequency response based damage detection using principal component analysis. Proceedings of the IEEE International Conference on Information Acquisition. Hong Kong and Macau, China.
6. HANSEN, L. K. & SALAMON, P. (1990) Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993 -1001.
7. HUYNH, D., HE, J. & TRAN, D. (2005) Damage location vector: A non-destructive structural damage detection technique. Computers and Structures, 83, 2353-2367.
8. MARWALA, T. & HUNT, H. E. M. (1999) Fault identification using finite element models and neural networks Mechanical Systems and Signal Processing, 13, 475-490.
9. MASRI, S. F., SMYTH, A. W., CHASSIAKOS, A. G., CAUGHEY, T. K. & HUNTER, N. F. (2000) Application of neural networks for detection of changes in nonlinear systems. Journal of Engineering Mechanics-ASCE, 126, 666-676.
10. PEARSON, K. (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559-572.
11. SOLLICH, P. & KROGH, A. (1996) Learning with ensembles: how over fitting can be useful. Advances in Neural Information Processing Systems 8. Denver, CO, US, MIT Press.
12. THE MATHWORKS, I. (2009) Communications Toolbox 4. User's Guide.
13. TRENDAFILOVA, I. & HEYLEN, W. (1998) Fault localization in structures from remote FRF measurements - influence of the measurement points. Proceedings of the 23rd International Conference on Noise and Vibration. Leuven, Belgium.
14. WHITE, P. R., TAN, M. H. & HAMMOND, J. K. (2006) Analysis of the maximum likelihood, total least squares and principal component approaches for frequency response function estimation. Journal of Sound and Vibration, 290, 676-689.
15. XIN, Y. & YONG, L. (1998) Making use of population information in evolutionary artificial neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 28, 417-425.
16. ZANG, C. & IMREGUN, M. (2001) Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration, 242, 813-827.
17. ZHOU, Z.-H., WU, J. & TANG, W. (2002) Ensembling neural networks: many could be better than all. Artificial Intelligence, 137, 239-263.