Eliminating Temperature Effects in Damage Detection for Civil Infrastructure Using Time Series Analysis and Autoassociative Neural Networks
Publication: Journal of Aerospace Engineering
Volume 32, Issue 2
Abstract
Temperature effects may mask the variation in structural properties or responses due to damage by causing equally or even larger changes in structures, resulting in false positive or false negative detections. These temperature effects should be eliminated during the process of damage detection; however, the complexity of operating civil structures makes it difficult to separate those influences from structural damage using closed form solutions or parametric approaches. In this study, a new damage detection approach based on autoassociative neural networks (AANNs) is proposed to detect the structural damage in bridges by eliminating the temperature effects. First, time series analysis–based damage features extracted from undamaged structure under varying temperature effects only are used to train the AANN. The trained neural networks were then fed by damage features with both damage and temperature effects. The results show that the proposed method can detect and locate the damage by tracking the prediction errors of the AANN under varying temperature effects.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The first author of this paper, Mr. Haiyang Zhang, was sponsored by China Scholarship Council (No. 201508180011).
References
Askegaard, V., and P. Mossing. 1988. Long term observation of RC-bridge using changes in natural frequency,. Oslo, Norway: Nordic Concrete Federation.
Bernal, D., and J. Beck. 2004. “Preface to the special issue on phase I of the IASC-ASCE structural health monitoring benchmark.” J. Eng. Mech. 130 (1): 1–2. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(1).
Deraemaeker, A., E. Reynders, G. De Roeck, and J. Kullaa. 2008. “Vibration-based structural health monitoring using output-only measurements under changing environment.” Mech. Syst. Sig. Proc. 22 (1): 34–56. https://doi.org/10.1016/j.ymssp.2007.07.004.
Fan, W., and P. Qiao. 2011. “Vibration-based damage identification methods: A review and comparative study.” Struct. Health Monit. 10 (1): 83–111. https://doi.org/10.1177/1475921710365419.
Fanning, P. J., and E. P. Carden. 2001. “Auto-regression and statistical process control techniques applied to damage indication in telecommunication masts.” In Vol. 204 of Proc., Key Engineering Materials. Zürich, Switzerland: Trans Tech Publications.
Farahani, R. V., and D. Penumadu. 2016. “Damage identification of a full-scale five-girder bridge using time-series analysis of vibration data.” Eng. Struct. 115 (May): 129–139. https://doi.org/10.1016/j.engstruct.2016.02.008.
Farrar, C. R., and D. A. Jauregui. 1998. “Comparative study of damage identification algorithms applied to a bridge: II. Numerical study.” Smart Mater. Struct. 7 (5): 720–731. https://doi.org/10.1088/0964-1726/7/5/014.
Fu, Y., and J. T. DeWolf. 2001. “Monitoring and analysis of a bridge with partially restrained bearings.” J. Bridge Eng. 6 (1): 23–29. https://doi.org/10.1061/(ASCE)1084-0702(2001)6:1(23).
Giraldo, D. F., S. J. Dyke, and J. M. Caicedo. 2006. “Damage detection accommodating varying environmental conditions.” Struct. Health Monit. 5 (2): 155–172. https://doi.org/10.1177/1475921706057987.
Gu, J., M. Gul, and X. Wu. 2017. “Damage detection under varying temperature using artificial neural networks.” Struct. Control Health Monit. 24 (11): e1998. https://doi.org/10.1002/stc.1998.
Gul, M. 2009. “Investigation of damage detection methodologies for structural health monitoring.” Ph.D. dissertation, Dept. of Civil, Environmental and Construction Engineering, Univ. of Central Florida.
Gul, M., and F. N. Catbas. 2011. “Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering.” J. Sound Vib. 330 (6): 1196–1210. https://doi.org/10.1016/j.jsv.2010.09.024.
Huang, H. B., T. H. Yi, and H. N. Li. 2015. “Sensor fault diagnosis for structural health monitoring based on statistical hypothesis test and missing variable approach.” J. Aerosp. Eng. 30 (2): B4015003. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000572.
Khanukhov, K. M., V. Polyak, G. Avtandilyan, and P. Vizir. 1986. “Dynamic elasticity modulus for low-carbon steel in the climatic temperature range.” Strength Mater. 18 (7): 917–920. https://doi.org/10.1007/BF01522693.
Ko, J., and Y. Ni. 2005. “Technology developments in structural health monitoring of large-scale bridges.” Eng. Struct. 27 (12): 1715–1725. https://doi.org/10.1016/j.engstruct.2005.02.021.
Kopsaftopoulos, F., and S. Fassois. 2013. “A functional model based statistical time series method for vibration based damage detection, localization, and magnitude estimation.” Mech. Syst. Signal Process. 39 (1): 143–161. https://doi.org/10.1016/j.ymssp.2012.08.023.
Kostić, B., and M. Gül. 2017. “Vibration-based damage detection of bridges under varying temperature effects using time-series analysis and artificial neural networks.” J. Bridge Eng. 22 (10): 04017065. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001085.
Kramer, M. A. 1991. “Nonlinear principal component analysis using autoassociative neural networks.” AIChE J. 37 (2): 233–243. https://doi.org/10.1002/aic.690370209.
Kullaa, J. 2002. “Elimination of environmental influences from damage-sensitive features in a structural health monitoring system.” In Proc., 1st European Workshop on Structural Health Monitoring, 742–749. Lancaster, PA: DEStech Publications.
Kullaa, J. 2005. “Damage detection under a varying environment using the missing data concept.” In Proc., 5th Int. Workshop on Structural Health Monitoring, 12–14. Stanford, CA: Stanford Univ.
Li, J., U. Dackermann, Y. L. Xu, and B. Samali. 2011. “Damage identification in civil engineering structures utilizing PCA-compressed residual frequency response functions and neural network ensembles.” Struct. Control Health Monit. 18 (2): 207–226. https://doi.org/10.1002/stc.369.
Liu, C., and J. T. DeWolf. 2007. “Effect of temperature on modal variability of a curved concrete bridge under ambient loads.” J. Struct. Eng. 133 (12): 1742–1751. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:12(1742).
Ljung, L. 1987. “System identification: Theory for the user. Englewood Cliffs, NJ: Prentice-Hall.
Ljung, L. 1998. “System identification.” In Signal analysis and prediction, 163–173. New York: Springer.
Macdonald, J. H., and W. E. Daniell. 2005. “Variation of modal parameters of a cable-stayed bridge identified from ambient vibration measurements and FE modelling.” Eng. Struct. 27 (13): 1916–1930. https://doi.org/10.1016/j.engstruct.2005.06.007.
Moser, P., and B. Moaveni. 2011. “Environmental effects on the identified natural frequencies of the Dowling Hall footbridge.” Mech. Syst. Signal Process. 25 (7): 2336–2357. https://doi.org/10.1016/j.ymssp.2011.03.005.
Nair, K. K., A. S. Kiremidjian, and K. H. Law. 2006. “Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure.” J. Sound Vib. 291 (1): 349–368. https://doi.org/10.1016/j.jsv.2005.06.016.
Ni, Y., K. Fan, G. Zheng, and J. Ko. 2005a. “Automatic modal identification and variability in measured modal vectors of a cable-stayed bridge.” Struct. Eng. Mech. 19 (2): 123–139.https://doi.org/10.12989/sem.2005.19.2.123.
Ni, Y., X. Hua, K. Fan, and J. Ko. 2005b. “Correlating modal properties with temperature using long-term monitoring data and support vector machine technique.” Eng. Struct. 27 (12): 1762–1773. https://doi.org/10.1016/j.engstruct.2005.02.020.
Omenzetter, P., and J. M. W. Brownjohn. 2006. “Application of time series analysis for bridge monitoring.” Smart Mater. Struct. 15 (1): 129–138. https://doi.org/10.1088/0964-1726/15/1/041.
Peeters, B., and G. De Roeck. 2001. “One-year monitoring of the Z 24-bridge: Environmental effects versus damage events.” Earthquake Eng. Struct. Dyn. 30 (2): 149–171. https://doi.org/10.1002/1096-9845(200102)30:2%3C149::AID-EQE1%3E3.0.CO;2-Z.
Sabeur, H., H. Colina, and M. Bejjani. 2007. “Elastic strain, Young’s modulus variation during uniform heating of concrete.” Mag. Concr. Res. 59 (8): 559–566. https://doi.org/10.1680/macr.2007.59.8.559.
Sadhu, A., and B. Hazra. 2013. “A novel damage detection algorithm using time-series analysis-based blind source separation.” Shock Vib. 20 (3): 423–438. https://doi.org/10.1155/2013/237805.
Samali, B., U. Dackermann, and J. Li. 2012. “Location and severity identification of notch-type damage in a two-storey steel framed structure utilising frequency response functions and artificial neural network.” Adv. Struct. Eng. 15 (5): 743–757. https://doi.org/10.1260/1369-4332.15.5.743.
Sohn, H., K. Worden, and C. R. Farrar. 2001. “Novelty detection under changing environmental conditions.” In Proc., Smart Structures and Materials 2001: Smart Systems for Bridges, Structures, and Highways, 108–118. Bellingham, WA: SPIE.
Sohn, H., K. Worden, and C. R. Farrar. 2002. “Statistical damage classification under changing environmental and operational conditions.” J. Intell. Mater. Syst. Struct. 13 (9): 561–574. https://doi.org/10.1106/104538902030904.
Tatani, K., and Y. Nakamura. 2003. “Dimensionality reduction and reproduction with hierarchical NLPCA neural networks-extracting common space of multiple humanoid motion patterns.” In Proc., IEEE Int. Conf. on Robotics and Automation, 1927–1932. Piscataway, NJ: IEEE.
Udwadia, F. E. 2005. “Structural identification and damage detection from noisy modal data.” J. Aerosp. Eng. 18 (3): 179–187. https://doi.org/10.1061/(ASCE)0893-1321(2005)18:3(179).
Wang, S., and L. Wan. 2013. “Global structural stiffness and random vibration response of one-story frame and its damage effect.” J. Aerosp. Eng. 28 (1): 04014045. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000355.
Yan, A. M., G. Kerschen, P. De Boe, and J. C. Golinval. 2005a. “Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis.” Mech. Syst. Signal Process. 19 (4): 847–864. https://doi.org/10.1016/j.ymssp.2004.12.002.
Yan, A. M., G. Kerschen, P. De Boe, and J. C. Golinval. 2005b. “Structural damage diagnosis under varying environmental conditions—Part II: Local PCA for non-linear cases.” Mech. Syst. Signal Process. 19 (4): 865–880. https://doi.org/10.1016/j.ymssp.2004.12.003.
Yao, R., and S. N. Pakzad. 2013. “Damage and noise sensitivity evaluation of autoregressive features extracted from structure vibration.” Smart Mater. Struct. 23 (2): 025007. https://doi.org/10.1088/0964-1726/23/2/025007.
Żak, G., J. Obuchowski, A. Wyłomańska, and R. Zimroz. 2014. “Application of ARMA modelling and alpha-stable distribution for local damage detection in bearings.” Diagnostyka 15 (3): 3–10.
Zhang, J., P. Li, and Z. Wu. 2013. “A new flexibility-based damage index for structural damage detection.” Smart Mater. Struct. 22 (2): 025037. https://doi.org/10.1088/0964-1726/22/2/025037.
Zhou, H., Y. Ni, and J. Ko. 2010. “Constructing input to neural networks for modeling temperature-caused modal variability: Mean temperatures, effective temperatures, and principal components of temperatures.” Eng. Struct. 32 (6): 1747–1759. https://doi.org/10.1016/j.engstruct.2010.02.026.
Zhou, H., Y. Ni, and J. Ko. 2011. “Eliminating temperature effect in vibration-based structural damage detection.” J. Eng. Mech. 137 (12): 785–796. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000273.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 32 • Issue 2 • March 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Oct 4, 2017
Accepted: Aug 30, 2018
Published online: Jan 4, 2019
Published in print: Mar 1, 2019
Discussion open until: Jun 4, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.