Sequential damage detection approaches for beams using time-modal features and artificial neural networks

Journal of Sound and Vibration

Volumn 323, Issue 1-2, 2009, Pages 451-474

  Park, Jae Hyung ^a   Kim, Jeong Tae ^a   Hong, Dong Soo ^a   Ho, Duc Duy ^a   Yi, Jin Hak ^b  

^a Pukyong National University   (South Korea)

^b Korea Research Institute of Ships and Ocean Engineering   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ACCELERATION SIGNALS; ARTIFICIAL NEURAL NETWORKS; CROSS-COVARIANCE FUNCTIONS; DAMAGE ESTIMATIONS; DAMAGE MONITORING; DETECTION APPROACHES; FEATURE-BASED; FREE-FREE BEAMS; LABORATORY TESTS; LOADING PATTERNS; MODAL STRAIN ENERGIES; MODE SHAPES; NUMERICAL TESTS; SECOND PHASE; SEQUENTIAL APPROACHES; SIMPLY SUPPORTED BEAMS; TIME DOMAINS;

BACKPROPAGATION; DAMAGE DETECTION; SENSOR NETWORKS; STRAIN ENERGY; STRUCTURAL ANALYSIS;

NEURAL NETWORKS;

EID: 64449086980     PISSN: 0022460X     EISSN: 10958568     Source Type: Journal    
DOI: 10.1016/j.jsv.2008.12.023     Document Type: Article

References (27)

1. Barai S.V., and Pandey P.C. Vibration signature analysis using artificial neural networks. Journal of Computing in Civil Engineering, ASCE 9 4 (1995) 259-265
2. Bendat J.S., and Piersol A.G. Random Data Analysis and Measurement Procedures (1991), Wiley, Singapore
3. Brinker R., Zhang L., and Andersen P. Modal identification of output-only systems using frequency domain decomposition. Smart Materials and Structures 10 (2001) 441-445
4. Catbas F.N., and Aktan A.M. Condition and damage assessment: issues and some promising indices. ASCE Journal of Structural Engineering 128 8 (2002) 1026-1036
5. Y. Chen, M.Q. Feng, Condition assessment of bridge sub-structure by vibration monitoring, C.B. Yun, B.F. Spencer, (Eds.), Second International Workshop on Advanced Smart Materials and Smart Structures Technology, 21-24 July 2005, Gyeongju, Korea, pp. 651-676.
6. Doebling S.W., Farrar C.R., and Prime M.B. A summary review of vibration-based damage identification methods. Shock and Vibration Digest 30 2 (1998) 91-105
7. Gao Y., and Spencer B.F. Damage localization under ambient vibration using changes in flexibility. Journal of Earthquake Engineering and Earthquake Vibration 1 1 (2002) 136-144
8. Hao H., and Xia Y. Vibration-based damage detection of structures by genetic algorithm. Journal of Computing in Civil Engineering 16 3 (2002) 222-229
9. Kim J.T., and Stubbs N. Model uncertainty impact and damage-detection accuracy in plate-girder. ASCE Journal of Structural Engineering 121 10 (1995) 1409-1417
10. Kim J.T., Ryu Y.S., Cho H.M., and Stubbs N. Damage identification in beam-type structures: frequency-based method vs mode-shape-based method. Engineering Structures 25 (2003) 57-67
11. J.T. Kim, J.H. Park, H.S. Do, Y.H. Lee, Real-time damage monitoring using output-only acceleration data, Asia-Pacific Workshop on Structural Health Monitoring, 2-4 December 2006, Yokohama, Japan.
12. Kim J.T., Park J.H., Yoon H.S., and Yi J.H. Vibration-based damage detection in beams using genetic algorithms. Smart Structures and Systems 3 3 (2007) 263-280
13. Ko J.M., Sun Z.G., and Ni Y.Q. Multistage identification scheme for detecting damage in cable-stayed Kap Shui Mun Bridge. Engineering Structures 24 (2002) 857-868
14. Lee J.J., Lee J.W., Yi J.H., Yun C.B., and Jung J.Y. Neural networks-based damage detection for bridges considering errors in baseline finite element models. Journal of Sound and Vibration 280 3 (2005) 555-578
15. Lee J.J., and Yun C.B. Two-step approaches for effective bridge health monitoring. Structural Engineering and Mechanics 23 1 (2006) 75-95
16. Maia N.M.M., Silva J.M.M., and Almas E.A.M. Damage detection in structures: from mode shape to frequency response function methods. Mechanical System and Signal Processing 17 3 (2003) 489-498
17. MIDAS Information Technology, MIDAS/Civil 2006 Trial Version 〈http://eng.midasuser.com/〉.
18. National Instruments, Data Sheet of NI 4472 Series 〈http://www.ni.com/pdf/products/us/3sv414-416.pdf〉.
19. National Instruments, NI PXI-8186/8187 User Manual 〈http://www.ni.com/pdf/manuals/370747c.pdf〉.
20. National Instruments, Labview 7.1 User Manual 〈http://www.ni.com/labview/〉.
21. Ni Y.Q., Wang B.S., and Ko J.M. Constructing input vectors to neural networks for structural damage identification. Smart Materials and Structures 11 (2002) 825-833
22. Qu W.L., Chen W., and Xiao Y.Q. A two-step approach for joint damage diagnosis of framed structures using artificial neural networks. Structural Engineering and Mechanics 16 5 (2003) 581-595
23. Szewczyk Z.P., and Hajela P. Damage detection in structures based on feature-sensitive neural networks. ASCE Journal of Computing in Civil Engineering 8 2 (1994) 163-178
24. Wu X., Ghaboussi J., and Garret Jr. J.H. Use of neural networks in detection of structural damage. Computers and Structures 42 4 (2001) 649-659
25. Yang J.N., Pan S., and Huang H. An adaptive extended Kalman filter for structural damage identifications II: unknown inputs. Structural Control and Health Monitoring 14 3 (2007) 497-521
26. Yi J.H., and Yun C.B. Comparative study on modal identification methods using output-only information. Structural Engineering and Mechanics 17 3-4 (2004) 445-466
27. Yun C.B., and Bhang E.Y. Joint damage assessment of framed structures using neural networks technique. Engineering Structures 23 5 (2001) 425-435