Damage Severity Quantification Using Wavelet Packet Transform and Peak Picking Method
Publication: Practice Periodical on Structural Design and Construction
Volume 27, Issue 1
Abstract
This article aims to present the results of an investigation on detection and quantification of damage location and severity for steel structures using wavelet packet transform for denoising the initial signals, in combination with a peak picking technique, to capture the modal parameters of the structures. In the peak picking technique, as an extended version of the basic frequency domain method, modes can be identified from the spectral density when having a white noise input and a damped structural model. Using the spectral density matrix, this method estimates the modes by a singular value decomposing. Consequently, this decomposition results in a single-degree-of-freedom identification of the system for each singular value. In other words, in terms of a proposed two-step algorithm, the modal parameters of the structures are identified by decomposing their free vibration responses. Because the decomposed signal could has the same amount of energy as the main one, it can be utilized in the peak picking technique to obtain the modal parameters. In the next stage, the obtained modal parameters are compared with those obtained from auto-regressive moving average with eXogenous input (ARMAX) method to control the accuracy of the proposed methodology. Finally, the Minokowski method is employed to determine the structural damage intensity. Moreover, the precision of the proposed algorithm was measured against the results of a famous experimental benchmark structural model using modal characteristics.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abu-Hamdeh, N. H., K. Daqrouq, and F. Mebarek-Oudina. 2021. “Simulation and analysis with wavelet transform technique and the vibration characteristics for early revealing of cracks in structures.” In Mathematical problems in engineering, 1–16. London: Hindawi. https://doi.org/10.1155/2021/6626232.
Alonso, F. J., and D. R. Salgado. 2008. “Analysis of the structure of vibration signals for tool wear detection.” Mech. Syst. Sig. Process. 22 (3): 735–748. https://doi.org/10.1016/j.ymssp.2007.09.012.
Black, C. J., and C. E. Ventura. 1998. “Blind test on damage detection of a steel frame structure.” In Proc., 16th Int. Modal Analysis Conf. Society for Experimental Mechanics, 623–629. Berlin: SEM.
Cao, M. S., G. G. Sha, Y. F. Gao, and W. Ostachowicz. 2017. “Structural damage identification using damping: A compendium of uses and features.” Smart Mater. Struct. 26 (4): 043001. https://doi.org/10.1088/1361-665X/aa550a.
Ceravolo, R., G. Coletta, G. Miraglia, and F. Palma. 2021. “Statistical correlation between environmental time series and data from long-term monitoring of buildings.” Mech. Syst. Sig. Process. 152 (May): 107460. https://doi.org/10.1016/j.ymssp.2020.107460.
Ching, J., and J. L. Beck. 2004. “Bayesian analysis of the phase II IASC–ASCE structural health monitoring experimental benchmark data.” J. Eng. Mech. 130 (10): 1233–1244. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:10(1233).
Chopra, A. K. 2012. Dynamics of structures: Theory and applications to earthquake engineering. 4th ed. Hoboken, NJ: Prentice Hall.
Das, S., and P. Saha. 2018. “Structural health monitoring techniques implemented on IASC–ASCE benchmark problem: A review.” J. Civ. Struct. Health Monit. 8 (4): 689–718. https://doi.org/10.1007/s13349-018-0292-5.
Do, N. T., and M. Gül. 2020. “A time series based damage detection method for obtaining separate mass and stiffness damage features of shear-type structures.” Eng. Struct. 208 (Apr): 109914. https://doi.org/10.1016/j.engstruct.2019.109914.
Gislason, G. P., Q. Mei, and M. Gül. 2019. “Rapid and automated damage detection in buildings through ARMAX analysis of wind induced vibrations.” Front. Built Environ. 5 (Feb): 1–15. https://doi.org/10.3389/fbuil.2019.00016.
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.
He, X. H., X. G. Hua, Z. Q. Chen, and F. L. Huang. 2011. “EMD-based random decrement technique for modal parameter identification of an existing railway bridge.” Eng. Struct. 33 (4): 1348–1356. https://doi.org/10.1016/j.engstruct.2011.01.012.
Hera, A., and Z. Hou. 2004. “Application of wavelet approach for ASCE structural health monitoring benchmark studies.” J. Eng. Mech. 130 (1): 96–104. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(96).
Johnson, E. A., H. F. Lam, L. S. Katafygiotis, and J. L. Beck. 2004. “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data.” J. Eng. Mech. 130 (1): 3–15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3).
Khademian, F., H. Naderpour, and M. K. Sharbatdar. 2020. “Structural damage detection of reinforced concrete shear walls subject to consequent earthquakes.” SN Appl. Sci. 2 (1): 92. https://doi.org/10.1007/s42452-019-1899-9.
Kiyani, K., J. Raeisi, and A. Heidari. 2019. “Time-frequency localization of earthquake record by continuous wavelet transforms.” Comput. Eng. Phys. Model. 2 (2): 49–61. https://doi.org/10.22115/CEPM.2019.193015.1065.
Kopsaftopoulos, F. P., and S. D. Fassois. 2010. “Vibration based health monitoring for a lightweight truss structure: Experimental assessment of several statistical time series methods.” Mech. Syst. Sig. Process. 24 (7): 1977–1997. https://doi.org/10.1016/j.ymssp.2010.05.013.
Lhermitte, S., J. Verbesselt, W. W. Verstraeten, and P. Coppin. 2011. “A comparison of time series similarity measures for classification and change detection of ecosystem dynamics.” Remote Sens. Environ. 115 (12): 3129–3152. https://doi.org/10.1016/j.rse.2011.06.020.
Lim, C., M. McAleer, and J. C. H. Min. 2009. “ARMAX modelling of international tourism demand.” Math. Comput. Simul 79 (9): 2879–2888. https://doi.org/10.1016/j.matcom.2008.08.010.
Mohebi, B., O. Yazdanpanah, F. Kazemi, and A. Formisano. 2021. “Seismic damage diagnosis in adjacent steel and RC MRFs considering pounding effects through improved wavelet-based damage-sensitive feature.” J. Build. Eng. 33 (Jan): 101847. https://doi.org/10.1016/j.jobe.2020.101847.
Naderpour, H., A. Ezzodin, A. Kheyroddin, and G. Ghodrati Amiri. 2017. “Signal processing based damage detection of concrete bridge piers subjected to consequent excitations.” J. Vibroeng. 19 (3): 2080–2089. https://doi.org/10.21595/jve.2015.16474.
Naderpour, H., and P. Fakharian. 2016. “A synthesis of peak picking method and wavelet packet transform for structural modal identification.” KSCE J. Civ. Eng. 20 (7): 2859–2867. https://doi.org/10.1007/s12205-016-0523-4.
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 (1–2): 349–368. https://doi.org/10.1016/j.jsv.2005.06.016.
Nie, Z., E. Guo, and H. Ma. 2019. “Structural damage detection using wavelet packet transform combining with principal component analysis.” Int. J. Lifecycle Perform. Eng. 3 (2): 149. https://doi.org/10.1504/IJLCPE.2019.100337.
Oh, C. K., and J. L. Beck. 2018. “A Bayesian learning method for structural damage assessment of phase I IASC-ASCE benchmark problem.” KSCE J. Civ. Eng. 22 (3): 987–992. https://doi.org/10.1007/s12205-018-1290-1.
Pandey, A. K., and M. Biswas. 1994. “Damage detection in structures using changes in flexibility.” J. Sound Vib. 169 (1): 3–17. https://doi.org/10.1006/jsvi.1994.1002.
Park, B.-H., and K.-J. Kim. 1989. “Vector ARMAX modeling approach in multi-input modal analysis.” Mech. Syst. Sig. Process. 3 (4): 373–387. https://doi.org/10.1016/0888-3270(89)90044-7.
Peña, D., and I. Sánchez. 2005. “Multifold predictive validation in ARMAX time series models.” J. Am. Stat. Assoc. 100 (469): 135–146. https://doi.org/10.1198/016214504000000610.
Sakellariou, J. S., and S. D. Fassois. 2017. “Global identification of stochastic dynamical systems under different pseudo-static operating conditions: The functionally pooled ARMAX case.” Mech. Syst. Sig. Process. 82 (Jan): 32–55. https://doi.org/10.1016/j.ymssp.2016.05.002.
Shahrokhinasab, E., N. Hosseinzadeh, A. Monirabbasi, and S. Torkaman. 2020. “Performance of image-based crack detection systems in concrete structures.” J. Soft Comput. Civ. Eng. 4 (1): 127–139. https://doi.org/https://doi.org/10.22115/SCCE.2020.218984.1174.
Shahsavari, V., L. Chouinard, and J. Bastien. 2017. “Wavelet-based analysis of mode shapes for statistical detection and localization of damage in beams using likelihood ratio test.” Eng. Struct. 132 (Feb): 494–507. https://doi.org/10.1016/j.engstruct.2016.11.056.
Sharbatdar, M. K., S. R. H. Vaez, G. G. Amiri, and H. Naderpour. 2011. “Seismic response of base-isolated structures with LRB and FPS under near fault ground motions.” Procedia Eng. 14 (Jan): 3245–3251. https://doi.org/10.1016/j.proeng.2011.07.410.
Spanos, N. A., J. S. Sakellariou, and S. D. Fassois. 2020. “Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure.” Struct. Health Monit. 19 (3): 736–750. https://doi.org/10.1177/1475921719862487.
Tang, H., J. Chen, and G. Dong. 2015. “Dynamic linear models-based time series decomposition and its application on bearing fault diagnosis.” J. Vib. Control 21 (5): 975–988. https://doi.org/10.1177/1077546313492556.
Tatsis, K., V. Dertimanis, Y. Ou, and E. Chatzi. 2020. “GP-ARX-based structural damage detection and localization under varying environmental conditions.” J. Sensor Actuator Networks 9 (3): 41. https://doi.org/10.3390/jsan9030041.
Yazdanpanah, O., A. Formisano, M. Chang, and B. Mohebi. 2021. “Fragility curves for seismic damage assessment in regular and irregular MRFs using improved wavelet-based damage index.” Measurement 182 (Sep): 109558. https://doi.org/10.1016/j.measurement.2021.109558.
Yazdanpanah, O., B. Mohebi, and M. Yakhchalian. 2020a. “Seismic damage assessment using improved wavelet-based damage-sensitive features.” J. Build. Eng. 31 (Sep): 101311. https://doi.org/10.1016/j.jobe.2020.101311.
Yazdanpanah, O., B. Mohebi, and M. Yakhchalian. 2020b. “Selection of optimal wavelet-based damage-sensitive feature for seismic damage diagnosis.” Measurement 154 (Mar): 107447. https://doi.org/10.1016/j.measurement.2019.107447.
Zhou, K., D. Lei, J. He, P. Zhang, P. Bai, and F. Zhu. 2021. “Single micro-damage identification and evaluation in concrete using digital image correlation technology and wavelet analysis.” Constr. Build. Mater. 267 (Jan): 120951. https://doi.org/10.1016/j.conbuildmat.2020.120951.
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Received: Feb 19, 2021
Accepted: Aug 11, 2021
Published online: Oct 4, 2021
Published in print: Feb 1, 2022
Discussion open until: Mar 4, 2022
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