Real-time structural damage assessment using artificial neural networks and antiresonant frequencies

Shock and Vibration

Volumn 2014, Issue , 2014, Pages

  Meruane, V ^a   Mahu, J ^a  

^a UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GLOBAL OPTIMIZATION; NEURAL NETWORKS;

ANTIRESONANT FREQUENCY; DYNAMIC CHARACTERISTICS; GLOBAL OPTIMIZATION ALGORITHM; NEURAL NETWORK PARAMETERS; PRIMARY CONTRIBUTION; REAL-TIME HEALTH MONITORING; STRUCTURAL DAMAGE ASSESSMENTS; VIBRATION-BASED DAMAGE;

DAMAGE DETECTION;

EID: 84898045889     PISSN: 10709622     EISSN: None     Source Type: Journal    
DOI: 10.1155/2014/653279     Document Type: Article

References (39)

1. V. Meruane and W. Heylen, "Damage detection with parallel genetic algorithms and operational modes," Structural Health Monitoring, Vol. 9, no. 6, pp. 481-496, 2010.
2. V. Meruane and W. Heylen, "An hybrid real genetic algorithm to detect structural damage using modal properties," Mechanical Systems and Signal Processing, Vol. 25, no. 5, pp. 1559-1573, 2011.
3. R. Perera and R. Torres, "Structural damage detection via modal data with genetic algorithms," Journal of Structural Engineering, Vol. 132, no. 9, pp. 1491-1501, 2006.
4. B. Kouchmeshky, W. Aquino, J. C. Bongard, and H. Lipson, "Co-evolutionary algorithm for structural damage identification using minimal physical testing," International Journal for Numerical Methods in Engineering, Vol. 69, no. 5, pp. 1085-1107, 2007
5. A. Teughels, G. de Roeck, and J. A. K. Suykens, "Global optimization by coupled local minimizers and its application to FE model updating," Computers and Structures, Vol. 81, no. 24-25, pp. 2337-2351, 2003.
6. C. Valdés-González and J. González-Pérez, "Identification of structural damage in a vehicular bridge using artificial neural networks," Structural Health Monitoring, Vol. 10, no. 1, pp. 33-48, 2011.
7. B. Sahoo and D. Maity, "Damage assessment of structures using hybrid neuro-genetic algorithm," Applied Soft Computing Journal, Vol. 7, no. 1, pp. 89-104, 2007
8. S. Arangio and J. L. Beck, "Bayesian neural networks for bridge integrity assessment," Structural Control and Health Monitoring, Vol. 19, no. 1, pp. 3-21, 2012.
9. A. Messina, A. Jones, and E. J. Williams, "Damage detection and localisation using natural frequency changes," in Proceedings of the 1st Conference on Identification in Engineering Systems, pp. 67-76, Swansea, UK, 1996.
10. R. M. Lim and D. J. Ewins, "Model updating using FRF data," in Proceedings of the 15th International Seminar on Modal Analysis, pp. 141-163, Leuven, Belgium, 1990.
11. N. Stubbs, J. T. Kim, and K. Topole, "An efficient and robust algorithm for damage localization in offshore platforms," in Proceedings of the ASCE 10th Structures Congress, pp. 543-546, San Antonio, Tex, USA, 1992.
12. S. F. Masri, A. W. Smyth, A. G. Chassiakos, T. K. Caughey, and N. F. Hunter, "Application of neural networks for detection of changes in nonlinear systems," Journal of Engineering Mechanics, Vol. 126, no. 7, pp. 666-676, 2000.
13. P. Castellini and G. M. Revel, "Experimental technique for structural diagnostic based on laser vibrometry and neural networks," Shock and Vibration, Vol. 7, no. 6, pp. 381-397, 2000.
14. C. Zang and M. Imregun, "Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection," Journal of Sound and Vibration, Vol. 242, no. 5, pp. 813-827, 2001.
15. 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.
16. O. S. Salawu, "Detection of structural damage through changes in frequency: a review," Engineering Structures, Vol. 19, no. 9, pp. 718-723, 1997.
17. A. H. Gandomi, M. G. Sahab, A. Rahaei, and M. S. Gorji, "Development in mode shape-based structural fault identification technique," World Applied Sciences Journal, Vol. 5, no. 1, pp. 29-38, 2008.
18. J. L. Zapico, M. P. González, and K. Worden, "Damage assessment using neural networks," Mechanical Systems and Signal Processing, Vol. 17, no. 1, pp. 119-125, 2003.
19. C. B. Yun, J. H. Yi, and E. Y. Bahng, "Joint damage assessment of framed structures using a neural networks technique," Engineering Structures, Vol. 23, no. 5, pp. 425-435, 2001.
20. A. K. Pandey, M. Biswas, and M. M. Samman, "Damage detection from changes in curvature mode shapes," Journal of Sound and Vibration, Vol. 145, no. 2, pp. 321-332, 1991.
21. M. Sahin and R. A. Shenoi, "Vibration-based damage identification in beam-like composite laminates by using artificial neural networks," Proceedings of the Institution of Mechanical Engineers C, Vol. 217, no. 6, pp. 661-676, 2003.
22. W. D'Ambrogio and A. Fregolent, "Use of antiresonances for robust model updating," Journal of Sound and Vibration, Vol. 236, no. 2, pp. 227-243, 2000.
23. E. J. Williams and A. Messina, "Applications of the multiple damage location assurance criterion," Key Engineering Materials, Vol. 167, pp. 256-264, 1999.
24. M. Dilena and A. Morassi, "The use of antiresonances for crack detection in beams," Journal of Sound and Vibration, Vol. 276, no. 1-2, pp. 195-214, 2004.
25. Y. Bamnios, E. Douka, and A. Trochidis, "Crack identification in beam structures using mechanical impedance," Journal of Sound and Vibration, Vol. 256, no. 2, pp. 287-297, 2002.
26. T. Inada, Y. Shimamura, A. Todoroki, and H. Kobayashi, "Development of the two-step delamination identification method by resonant and anti-resonant frequency changes," Key Engineering Materials, Vol. 270-273, no. 3, pp. 1852-1858, 2004.
27. D. Wang and H. Zhu, "Wave propagation based multi-crack identification in beam structures through anti-resonances information," Key Engineering Materials, Vol. 293-294, pp. 557-564, 2005.
28. V. Meruane and W. Heylen, "Structural damage assessment with antiresonances versus mode shapes using parallel genetic algorithms," Structural Control and Health Monitoring, Vol. 18, no. 8, pp. 825-839, 2011.
29. M. A. Arbib, The Handbook of Brain Theory and Neural Networks, Bradford Book, 2003.
30. M. F. Møller, "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, Vol. 6, no. 4, pp. 525-533, 1993.
31. M. Beale and H. Demuth, Neural Network Toolbox, Version 7.0.3 Release, The MathWorks, 2012.
32. D. W. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," Journal of the Society for Industrial and Applied Mathematics, Vol. 11, no. 2, pp. 431-441, 1963.
33. M. I. Friswell, J. E. T. Penny, and S. D. Garvey, "A combined genetic and eigensensitivity algorithm for the location of damage in structures," Computers and Structures, Vol. 69, no. 5, pp. 547-556, 1998.
34. J. J. Lee, J. W. Lee, J. H. Yi, C. B. Yun, and H. Y. Jung, "Neural networks-based damage detection for bridges considering errors in baseline finite element models," Journal of Sound and Vibration, Vol. 280, no. 3-5, pp. 555-578, 2005.
35. K. D. Hjelmstad and S. Shin, "Damage detection and assessment of structures from static response," Journal of Engineering Mechanics, Vol. 123, no. 6, pp. 568-576, 1997.
36. S. V. Barai and P. C. Pandey, "Vibration signature analysis using artificial neural networks," Journal of Computing in Civil Engineering, Vol. 9, no. 4, pp. 259-265, 1995.
37. J. E. Mottershead, "On the zeros of structural frequency response functions and their sensitivities," Mechanical Systems and Signal Processing, Vol. 12, no. 5, pp. 591-597, 1998.
38. L. Prechelt, "Early stopping-but when?" in Neural Networks: Tricks of the Trade, Vol. 1524 of Lecture Notes in Computer Science, pp. 55-69, Springer, New York, NY, USA, 1998.
39. M. H. Richardson and D. L. Formenti, "Parameter estimation from frequency response measurements using rational fraction polynomials," in Proceedings of the 1st International Modal Analysis Conference, pp. 167-181, 1982. Tecnológico