Structural Damage Prognosis on Truss Bridges with End Connector Bolts
Publication: Journal of Engineering Mechanics
Volume 143, Issue 3
Abstract
The successful development of structural damage prognosis (SDP) capability requires the further development and integration of many technology areas, such as hardware, software, modeling capabilities, and so forth. By combining the damage-sensitive feature extraction, the higher statistical moments of structural responses and the fuzzy c-means (FCM) clustering algorithm with the traditional time-series analysis-based SDP method, an integrated method is proposed for the SDP of a truss bridge model with end connector bolts under environmental and operational variability simulated in laboratory. The theoretical formulation of the integrated method is developed first. A six-bay truss bridge model is then designed and fabricated in laboratory for assessing the performance of the proposed method. Various damage cases are simulated by partially or completely loosening some end connecter bolts of the truss bridge under the environmental variability. The performance of the integrated SDP method is assessed based on the time-series acceleration response data measured at 16 ball nodes of the truss bridge model in each reference state and test state. The illustrated results show that the proposed integrated SDP method can effectively identify the damages of a truss bridge with end connector bolts. The introduction of higher statistical moments can provide a beneficial supplement to the traditional damage-sensitive indexes.
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Acknowledgments
This research is jointly supported by National Natural Science Foundation of China (51278226 and 50978123) and the Project of Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory (Nos. 9140C030605140C03015 and ZHD201207).
References
Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithm, Plenum Press, New York.
Box, G., and Jenkins, G. (1976). Time series analysis: Forecasting and control, Prentice Hall, Englewood Cliffs, NJ.
Carden, E. P., and Brownjohn, J. M. (2008). “ARMA modelled time-series classification for structural health monitoring of civil infrastructure.” Mech. Syst. Signal Process., 22(2), 295–314.
Carden, E. P., and Fanning, F. (2004). “Vibration-based condition monitoring: A review.” Struct. Health Monit., 3(4), 355–377.
Chen, L. J., and Yu, L. (2013). “Structural nonlinear damage identification algorithm based on time series ARMA/GARCH model.” Adv. Struct. Eng., 16(9), 1597–1610.
da Silva, S., Dias Júnior, M., Lopes Junior, V., and Brennan, M. J. (2008). “Structural damage detection by fuzzy clustering.” Mech. Syst. Signal Process., 22(7), 1636–1649.
Doebling, S. W. and Farrar, C. R. (1997). “Using statistical analysis to enhance modal-based damage identification.” Proc., DAMAS 97: Structural Damage Assessment Using Advanced Signal Processing Procedures, Univ. of Sheffield, Sheffield, U.K., 199–210.
Doebling, S. W., Farrar, C. R., and Prime, M. B. (1998). “A summary review of the vibration-based damage identification methods.” Shock Vibr. Digest, 30(2), 91–105.
Fan, W., and Qiao, P. Z. (2011). “Vibration-based damage identification methods: A review and comparative study.” Struct. Health Monit., 10(1), 83–111.
Farrar, C. R., and Lieven, N. A. J. (2007). “Damage prognosis: The future of structural health monitoring.” Phil. Trans. R. Soc. A, 365(1851), 623–632.
Figueiredo, E., Park, G., Figueiras, J., Farrar, C. R., and Worden, K. (2009). “Structural health monitoring algorithm comparisons using standard data sets.”, Los Alamos National Laboratory, Los Alamos, NM.
Fugate, M. L., Sohn, H., and Farrar, C. R. (2001). “Vibration-based damage detection using statistical process control.” Mech. Syst. Signal Process., 15(4), 707–721.
Gul, M., and Catbas, F. N. (2009). “Statistical pattern recognition for structural health monitoring using time series modelling: Theory and experimental verifications.” Mech. Syst. Signal Process., 23(7), 2192–2204.
Gul, M., and Catbas, F. N. (2011). “Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering.” J. Sound Vib., 330(6), 1196–1210.
Lu, Y., and Gao, F. (2005). “A novel time-domain auto-regressive model for structural damage diagnosis.” J. Sound Vibr., 283(3–5), 1031–1049.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mattson, S., and Pandit, S. (2006). “Statistical moments of autoregressive model residuals for damage localization.” Mech. Syst. Sig. Process., 20(3), 627–645.
Nair, K. K., Kiremidjian, A. S., and Law, K. H. (2006). “Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure.” J. Sound Vibr., 291(1–2), 349–368.
Omenzetter, P., and Brownjohn, J. M. (2006). “Application of time series analysis for bridge monitoring.” Smart Mater. Struct., 15(1), 129–138.
Sim, S. H., Spencer, B. F., and Nagayama, T. (2011). “Multimetric sensing for structural damage detection.” J. Eng. Mech., 22–30.
Sohn, H., and Farrar, C. R. (2001). “Damage diagnosis using time series analysis of vibration signals.” Smart Mater. Struct., 10(3), 446–451.
Sohn, H., Farrar, C. R., Hemez, F. M., Shunk, D. D., Stinemates, D. W., and Nadler, B. R. (2004). “A review of structural health monitoring literature: 1996-2001.”, Los Alamos, NM.
Wang, Z. R., and Ong, K. C. G. (2010). “Multivariate statistical approach to structural damage detection.” J. Eng. Mech., 12–22.
Worden, K., Farrar, C. R., Manson, G., and Park, G. (2007). “The fundamental axioms of structural health monitoring.” Proc. R. Soc. London A: Math. Phys. Eng. Sci., 463(2082), 1639–1664.
Xu, Z. D., and Wu, K. Y. (2012). “Damage detection for space truss structures based on strain mode under ambient excitation.” J. Eng. Mech., 1215–1223.
Yan, Y. J., Cheng, L., Wu, Z. Y., and Yam, L. H. (2007). “Development in vibration-based structural damage detection technique.” Mech. Syst. Signal Process., 21(5), 2198–2211.
Yao, R., and Pakzad, S. N. (2012). “Autoregressive statistical pattern recognition algorithms for damage detection in civil structures.” Mech. Syst. Signal Process., 31, 355–368.
Yao, R., and Pakzad, S. N. (2014). “Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration.” Smart Mater. Struct., 23(2), 025007.
Yu, L., and Lin, J. C. (2015). “Cloud computing-based time series analysis for structural damage detection.” J. Eng. Mech., C4015002.
Yu, L., and Zhu, J. H. (2015). “Nonlinear damage detection using higher statistical moments of structural responses.” Struct. Eng. Mech., 54(2), 221–237.
Yu, L., Zhu, J. H., and Yu, L. L. (2013). “Structural damage detection in a truss bridge model using fuzzy clustering and measured FRF data reduced by principal component projection.” Adv. Struct. Eng., 16(1), 207–218.
Zhang, Q. W. (2007). “Statistical damage identification for bridges using ambient vibration data.” Comput. Struct., 85(7–8), 476–485.
Zhou, L. R., Yan, G. R., Wang, L., and Ou, J. P. (2013). “Review of benchmark studies and guidelines for structural health monitoring.” Adv. Struct. Eng., 16(7), 1187–1206.
Zugasti, E., Gómez González, A., Anduaga, J., Arregui, M. A., and Martínez, F. (2012). “Null space and auto regressive damage detection: A comparative study.” Smart Mater. Struct., 21(8), 085010.
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Published In
Journal of Engineering Mechanics
Volume 143 • Issue 3 • March 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Dec 29, 2014
Accepted: Oct 30, 2015
Published online: Feb 25, 2016
Discussion open until: Jul 25, 2016
Published in print: Mar 1, 2017
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