About this Journal Submit a Manuscript Table of Contents
Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 786535, 9 pages
http://dx.doi.org/10.1155/2011/786535
Research Article

A New Procedure for Damage Assessment of Prestressed Concrete Beams Using Artificial Neural Network

Department of Civil and Structural Engineering, Annamalai University, Tamilnadu, Annamalainagar 608 002, India

Received 31 May 2011; Revised 24 August 2011; Accepted 24 August 2011

Academic Editor: Wilson Wang

Copyright © 2011 K. Sumangala and C. Antony Jeyasehar. 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. M. M. Abdel Wahab and G. Roeck, “Damage detection in bridges using modal curvatures: application to a real damage scenario,” Journal of Sound and Vibration, vol. 226, no. 2, pp. 217–235, 1999. View at Scopus
  2. Z. Zhengjie, D. W. Leon, F. S. Bruce, et al., “Structural health monitoring of precast concrete box girders using selected vibration-based damage detection methods,” Advances in Civil Engineering, vol. 2010, Article ID 280685, 21 pages, 2010.
  3. P. Cawley and R. D. Adams, “The location of defects in structures from measurements of natural frequencies,” Journal of Strain Analysis, vol. 14, no. 2, pp. 49–57, 1979. View at Publisher · View at Google Scholar
  4. H. Abdul Razak and F. C. Choi, “The effect of corrosion on the natural frequency and modal damping of reinforced concrete beams,” Engineering Structures, vol. 23, no. 9, pp. 1126–1133, 2001. View at Publisher · View at Google Scholar
  5. A. Carpinteri, S. Invernizzi, and G. Lacidogna, “In situ damage assessment and nonlinear modelling of a historical masonry tower,” Engineering Structures, vol. 27, no. 3, pp. 387–395, 2005. View at Publisher · View at Google Scholar
  6. X. Wu, J. Ghaboussi, and J. H. Garrett, “Use of neural networks in detection of structural damage,” Computers and Structures, vol. 42, no. 4, pp. 649–659, 1992.
  7. Q. Chen, Y. W. Chan, and K. Worden, “Structural fault diagnosis and isolation using neural networks based on response-only data,” Computers and Structures, vol. 81, no. 22-23, pp. 2165–2172, 2003. View at Publisher · View at Google Scholar
  8. X. Fang, H. Luo, and J. Tang, “Structural damage detection using neural network with learning rate improvement,” Computers and Structures, vol. 83, no. 25-26, pp. 2150–2161, 2005. View at Publisher · View at Google Scholar
  9. J. L. Zapico, K. Worden, and F. J. Molina, “Vibration-based damage assessment in steel frames using neural networks,” Smart Materials and Structures, vol. 10, no. 3, pp. 553–559, 2001. View at Publisher · View at Google Scholar
  10. K. V. Yuen and H. F. Lam, “On the complexity of artificial neural networks for smart structures monitoring,” Engineering Structures, vol. 28, no. 7, pp. 977–984, 2006. View at Publisher · View at Google Scholar
  11. N. Bakhary, H. Hao, and A. J. Deeks, “Damage detection using artificial neural network with consideration of uncertainties,” Engineering Structures, vol. 29, no. 11, pp. 2806–2815, 2007. View at Publisher · View at Google Scholar
  12. O. R. de Lautour and P. Omenzetter, “Prediction of seismic-induced structural damage using artificial neural networks,” Engineering Structures, vol. 31, no. 2, pp. 600–606, 2009. View at Publisher · View at Google Scholar
  13. D. Ambrosini, B Luccioni, and R. Danesi, “Theoretical–experimental determination in prestressed concrete beams,” NDT Net, vol. 5, no. 7, pp. 1–5, 2000.
  14. C. Antony Jeyasehar and K. Sumangala, “Nondestructive evaluation of prestressed concrete beams using an artificial neural network (ANN) approach,” Structural Health Monitoring, vol. 5, no. 4, pp. 313–323, 2006. View at Publisher · View at Google Scholar
  15. K. Sumangala, Damage assessment and rehabilitation of prestressed concrete rectangular beams, Ph.D. thesis, Annamalai University, Tamilnadu, India, 2005.