A modified wavelet energy rate-based damage identification method for steel bridges

Document Type : Article

Authors

1 International Institute for Urban Systems Engineering, Southeast University, Nanjing 210096, China, Mechanical Engineering and ASME Fellow, California Polytechnic State University, San Luis Obispo, California, USA

2 State University of New York, Buffalo, NY 14260, USA

3 International Institute for Urban Systems Engineering, Southeast University, Nanjing 210096, China, Nanjing Zhixing Information Technology Company Nanjing, China

4 International Institute for Urban Systems Engineering, Southeast University, Nanjing 210096, China.

Abstract

Strain is sensitive to damage, especially in steel structures. But traditional strain gauge does not fit bridge damage identification because it only provides the strain information of the point where it is set up. While traditional strain gauges suffer from its drawbacks, long-gage FBG strain sensor is capable of providing the strain information of a certain range, which all the damage information within the sensing range can be reflected by the strain information provided by FBG sensors. The wavelet transform is a new way to analyze the signals, which is capable of providing multiple levels of details and approximations of the signal. In this paper, a wavelet packet transform-based damage identification is proposed for the steel bridge damage identifications numerically and with experimental experiment to validate the proposed method. The strain data obtained via long-gage FBG strain sensors are transformed into a modified wavelet packet energy rate index first to identify the location and severity of damage. The results of numerical simulations show that the proposed damage index is a good candidate which is capable of identifying both the location and severity of damage under noise effect.

Keywords

Main Subjects


References
1. Padgett, J.E. and Tapia, C. Sustainability of natural
hazard risk mitigation: Life cycle analysis
of environmental indicators for bridge infrastructure",
J. Infrastructure Systems, 19(4), pp. 395-
3228 M. Noori et al./Scientia Iranica, Transactions B: Mechanical Engineering 25 (2018) 3210{3230
408 (2013). https://doi.org/10.1061/(ASCE)IS.1943-5
55X.0000138.
2. He, C., Xing, J., Li, J., Qian, W., and Zhang, X. A
new structural damage identi cation method based on
wavelet packet energy entropy of impulse response",
The Open Civil Engineering Journal, 9, pp. 570-576
(2015).
3. Balageas, D., Fritzen, C.P., and Guemes, A., Structural
Health Monitoring, Wiley-ISTE, London, pp. 493,
ISBN: 978-1-905209-01-9 (2006).
4. Jiang, S.F., Wu, S.Y., and Dong, L.Q. A timedomain
structural damage detection method based
on improved multiparticle swarm coevolution optimization
algorithm", J. Mathematical Problems in
Engineering, 2014, Article ID 232763, 11 pages,
http://dx.doi.org/10.1155/2014/232763
5. Shi, Z.Y., Law, S.S., and Zhang, L.M. Structural
damage detection from modal strain energy change",
J. Engineering Mechanics, 126(12), pp. 1216-1223
(2000).
https://doi.org/10.1061/(ASCE)0733-9399(2000)126:
12(1216).
6. Liu, Y., Fard, M.Y., and Chattopadhyay, A. Damage
assessment of CFRP composites using a timefrequency
approach", J. Intelligent Material Systems
and Structures, 23(4), pp. 397-413 (2012). DOI:
https://doi.org/10.1177/1045389X11434171
7. Bu, J.Q., Law, S.S., and Zhu, X.Q. Innovative bridge
condition assessment from dynamic response of a
passing vehicle", J. Engineering Mechanics - ASCE,
132(12), pp. 1372-1379 (2006).
https://doi.org/10.1061/(ASCE)0733-9399(2006)132:
12(1372).
8. McGetrick, P.J. and Kim, C.W. Wavelet based damage
detection approach for bridge structures utilising
vehicle vibration", In Proceedings of 9th German
Japanese Bridge, Symposium, GJBS09 (2012).
9. Gonzalez, A., Obrien, E.J., and McGetrick, P.J. Identi
cation of damping in a bridge using a moving instrumented
vehicle", J. Sound and Vibration, 331(18), pp.
4115-4131 (2012).
https://doi.org/10.1016/j.jsv.2012.04.019.
10. Mallikarjuna Reddya, D., and Swarnamani, S. Damage
detection and identi cation in structures by spatial
wavelet based approach", International Journal of
Applied Science and Engineering, 10(1), pp. 69-87
(2012).
11. Wang, HF., Noori, M., and Zhao, Y. A waveletbased
damage identi cation for large crane structures",
Sixth World Conference on Structural Control and
Monitoring, pp.15-17 (2014).
DOI: 10.13140/2.1.2810.7207
12. Hera, A. and Hou, Z. Application of wavelet approach
for ASCE structural health monitoring benchmark
studies", J. Engineering Mechanics, 130(1), pp.
96-104 (2004). https://doi.org/10.1061/(ASCE)0733-
9399(2004)130:1(96).
13. Gokdag, H. and Kopmaz, O. A new damage detection
approach for beam-type structures based on the combination
of continuous and discrete wavelet transforms",
J. Sound and Vibration, 324(3), pp. 1158-1180 (2009).
https://doi.org/10.1016/j.jsv.2009.02.030.
14. Hester, D. and Gonzalez, A. A wavelet-based damage
detection algorithm based on bridge acceleration response
to a vehicle", J. Mechanical Systems and Signal
Processing, 6(7), pp. 145-166 (2011).
https://doi.org/10.1016/j.ymssp.2011.06.007
15. Zhao, Y., Noori, M., Altabey, W.A., and Beheshti-
Aval, S.B. Mode shape based damage identi cation
for a reinforced concrete beam using wavelet coecient
di erences and multi-resolution analysis", J. Structural
Control & Health Monitoring, 25(1), pp. 1-41 (2017).
https://doi.org/10.1002/stc.2041
16. Zhao, Y., Noori, M., and Altabey, W.A. Damage
detection for a beam under transient excitation via
three di erent lagorithms", J. Structural Engineering
and Mechanics, 64(6), pp. 803-817 (2017).
DOI: https://doi.org/10.12989/sem.2017.64.6.803.
17. Identi cation of hysterically degrading structures
using the Bouc-Wen-Baber-Noori (BWBN) model",
J.F. Silva Gomes and S.A. Meguid, Editors, Proceedings
IRF2018: 6th International Conference Integrity-
Reliability-Failure, Lisbon/Portugal, 22-26 July 2018,
Publ. INEGI/FEUP, ISBN: 978-989-20-8313-1 (2018).
18. Reda Taha, M.M., Noureldin, A., Lucero, J.L., and
Baca, T.J. Wavelet transform for structural health
monitoring: a compendium of uses and features", J.
Structural Health Monitoring, 5(3), pp. 267-295 (2006).
DOI: https://doi.org/10.1177/1475921706067741.
19. Nguyen, K.V. and Tran, H.T. Multi-cracks detection
of a beam-like structure based on the on-vehicle vibration
signal and wavelet analysis", J. Sound and
Vibration, 329(21), pp. 4455-4465 (2010).
https://doi.org/10.1016/j.jsv.2010.05.005.
20. Lee, S.G., Yun, G.J., and Shang, S. Reference-free
damage detection for truss bridge structures by continuous
relative wavelet entropy method", J. Struct.
Health Monit, 145(2), pp. 28-45 (2014).
DOI: https://doi.org/10.1177/1475921714522845.
21. Li, S. and Wu, Z. Characterization of long-gauge
ber optic sensors for structural identi cation, smart
structures and materials", International Society for
Optics and Photonics, SPIE 5765, pp. 564-575 (2005).
DOI: 10.1117/12.606367
22. Horiguchi, T., Kurashima, T., and Tateda, M. Tensile
strain dependence of Brillouin frequency shift in silica
optical bers", Photonics Technology Letters, IEEE,
1(5), pp. 107-108 (1989).
DOI: 10.1109/68.34756.
23. Suzhen, L. and Zhishen, W. Structural health monitoring
strategy based on distributed ber optic sensing",
J. Struct. Health Monit, 6(2), pp. 133-143 (2007).
DOI: https://doi.org/10.1177/1475921706072078.
M. Noori et al./Scientia Iranica, Transactions B: Mechanical Engineering 25 (2018) 3210{3230 3229
24. Dakin, J.P., Distributed Optical Fiber Sensor, International
Society for Optics and Photonics, SPIE 1797,
pp. 76-108 (1993).
25. Lau, K.T., Yuan, L., and Zhou, L.M. Strain monitoring
in FRP laminates and concrete beams using
FBG sensors", J. Composite Structures, 51(1), pp. 9-
20 (2001).
https://doi.org/10.1016/S0263-8223(00)00094-5.
26. Schulz, W.L., Conte, J.P., and Udd, E. Real-time
damage assessment of civil structures using ber grating
sensors and modal analysis", 9th Annual International
Symposium on Smart Structures and Materials.
International Society for Optics and Photonics, SPIE
4696, pp. 228-237 (2002).
27. Wei-Xin, R. and Sun, Z. Structural damage identi
cation by using wavelet entropy", J. Engineering
Structures, 30(10), pp. 2840-2849 (2008).
https://doi.org/10.1016/j.engstruct.2008.03.013.
28. Ovanesova, A.V. and Suarez, L.E. Applications of
wavelet transforms to damage detection in frame structures",
J. Engineering Structures, 26(1), pp. 39-49
(2004).
https://doi.org/10.1016/j.engstruct.2003.08.009.
29. Yongchao, Y. and Nagarajaiah, S. Blind identi cation
of damage in time-varying system using independent
component analysis with wavelet transform", J. Mechanical
Systems and Signal Processing, 27, pp. 3-20
(2012).
https://doi.org/10.1016/j.ymssp.2012.08.029.
30. Jian-Gang, H., Ren, W., and Sun, Z. Wavelet packet
based damage identi cation of beam structures", International
Journal of Solids and Structures, 42(26),
pp. 6610-6627 (2005).
https://doi.org/10.1016/j.ijsolstr.2005.04.031.
31. Yen, G.G. and Lin, K.C. Wavelet packet feature
extraction for vibration monitoring", J. Industrial
Electronics, IEEE Transactions on, 47(3), pp. 650-667
(2000).
DOI: 10.1109/41.847906.
32. Sun, Z., Chang, C.C. Structural damage assessment
based on wavelet packet transform", J. Structural
Engineering, 128(10), pp. 1354-1361 (2002).
https://doi.org/10.1061/(ASCE)0733-9445(2002)128:
10(1354).
33. Han, J.G., Ren, W.X., and Sun, Z.S. Wavelet packet
based damage identi cation of beam structures", Int.
J. Solids and Structures, 42(26), pp. 6610-6627 (2005).
https://doi.org/10.1016/j.ijsolstr.2005.04.031.
34. Maosen, C. and Pizhong, Q. Integrated wavelet
transform and its application to vibration mode shapes
for the damage detection of beam-type structures", J.
Smart Materials and Structures, IOP, 17(5), pp. 222-
232 (2008).
https://doi.org/10.1088/0964-1726/17/5/055014.