Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 193284, 9 pages
http://dx.doi.org/10.1155/2014/193284
Research Article

Structural Damage Identification Based on Rough Sets and Artificial Neural Network

1Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen 518055, China
2Key Laboratory of C&PC Structures, Southeast University, Nanjing 211189, China
3State Key Laboratory of Robotics and System, Harbin Institute of Technology, Dazhi Street, Nangang District, Harbin 150001, China

Received 27 February 2014; Accepted 22 April 2014; Published 11 June 2014

Academic Editor: Hua-Peng Chen

Copyright © 2014 Chengyin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. R. Farrar, S. W. Doebling, and D. A. Nix, “Vibration-based structural damage identification,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 359, no. 1778, pp. 131–149, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. S. W. Doebling and C. R. Farrar, “The state of the art in structural identification of constructed facilities,” Tech. Rep., Los Alamos National Laboratory, Los Alamos, Mexico, 1999. View at Google Scholar
  3. S. W. Doebling, C. R. Farrar, and M. B. Prime, “A summary review of vibration-based damage identification methods,” Shock and Vibration Digest, vol. 30, no. 2, pp. 91–105, 1998. View at Google Scholar · View at Scopus
  4. C. R. Farrar and K. Worden, “An introduction to structural health monitoring,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 365, no. 1851, pp. 303–315, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, and R. N. Brett, “A review of structural health monitoring literature: 1996–2001,” Tech. Rep. LA-13976-MS, Los Alamos National Laboratory Report, 2003. View at Google Scholar
  6. K. Worden and J. M. Dulieu-Barton, “An overview of intelligent fault detection in systems and structures,” Structural Health Monitoring, vol. 3, no. 1, pp. 85–98, 2004. View at Google Scholar · View at Scopus
  7. C. R. Farrar, S. W. Doebling, P. J. Cornwell, and E. G. Straser, “Variability of modal parameters measured on the Alamosa Canyon Bridge,” in Proceedings of the 15th International Modal Analysis Conference, pp. 257–263, February 1996. View at Scopus
  8. P. Lingras, “Comparison of neofuzzy and rough neural networks,” Information Sciences, vol. 110, no. 3-4, pp. 207–215, 1998. View at Google Scholar · View at Scopus
  9. B. S. Ahn, S. S. Cho, and C. Y. Kim, “Integrated methodology of rough set theory and artificial neural network for business failure prediction,” Expert Systems with Applications, vol. 18, no. 2, pp. 65–74, 2000. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Xiang, J. Zhou, C. Li, Q. Li, and Z. Luo, “Fault diagnosis based on Walsh transform and rough sets,” Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1313–1326, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. F. E. H. Tay and L. Shen, “Fault diagnosis based on rough set theory,” Engineering Applications of Artificial Intelligence, vol. 16, no. 1, pp. 39–43, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Zhou and J. G. Yang, “The research of engine fault diagnosis based on rough sets and support vector machine,” Transactions of CSICE, vol. 24, no. 4, pp. 379–383, 2006 (Chinese). View at Google Scholar · View at Scopus
  13. J. R. Li, L. P. Khoo, and S. B. Tor, “RMINE: a rough set based data mining prototype for the reasoning of incomplete data in condition-based fault diagnosis,” Journal of Intelligent Manufacturing, vol. 17, no. 1, pp. 163–176, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Geng and Q. Zhu, “Rough set-based heuristic hybrid recognizer and its application in fault diagnosis,” Expert Systems with Applications, vol. 36, no. 2, pp. 2711–2718, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Jensen and Q. Shen, “Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches,” IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 12, pp. 1457–1471, 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. Q. Hu, D. Yu, J. Liu, and C. Wu, “Neighborhood rough set based heterogeneous feature subset selection,” Information Sciences, vol. 178, no. 18, pp. 3577–3594, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. I. Düntsch and G. Gediga, “Uncertainty measures of rough set prediction,” Artificial Intelligence, vol. 106, no. 1, pp. 109–137, 1998. View at Google Scholar · View at Scopus
  18. Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic, 1991.
  19. R. Li and Z.-O. Wang, “Mining classification rules using rough sets and neural networks,” European Journal of Operational Research, vol. 157, no. 2, pp. 439–448, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Bazan, A. Skowron, and P. Synak, “Dynamic reducts as a tool for extracting laws from decisions tables,” in Proceedings of the Symposium on Methodologies for Intelligent Systems, pp. 346–355, 1994.
  21. M. W. Craven and J. W. Shavlik, “Using neural networks for data mining,” Future Generation Computer Systems, vol. 13, no. 2-3, pp. 211–229, 1994. View at Google Scholar · View at Scopus
  22. H. Lu, R. Setiono, and H. Liu, “Effective data mining using neural networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 957–961, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. R. W. Swiniarski and L. Hargis, “Rough sets as a front end of neural-networks texture classifiers,” Neurocomputing, vol. 36, pp. 85–102, 2001. View at Publisher · View at Google Scholar · View at Scopus