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A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers

  • Mohammad Omidalizarandi ORCID logo EMAIL logo , Ralf Herrmann , Boris Kargoll ORCID logo , Steffen Marx , Jens-André Paffenholz ORCID logo and Ingo Neumann
Published/Copyright: June 9, 2020
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Abstract

Today, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively.

Funding statement: The research presented was carried out within the scope of the collaborative project “Spatio-temporal monitoring of bridge structures using low cost sensors” with ALLSAT GmbH, which was supported by the German Federal Ministry for Economic Affairs and Energy (BMWi) and the Central Innovation Programme for SMEs (Grant ZIM Kooperationsprojekt, ZF4081803DB6).

Acknowledgment

The authors would like to acknowledge ALLSAT GmbH for providing the low-cost sensors used in the experiments. In addition, the authors would also like to acknowledge Eva Kemkes, M. Sc. (ALLSAT GmbH) and Dmitri Diener, M. Sc. (GIH) for their assistance in data acquisition within the experiments.

References

[1] Dawson B. Vibration condition monitoring techniques for rotating machinery. Shock Vib Dig 1976; 8(12): 3.10.1177/058310247600801203Search in Google Scholar

[2] Alvandi A, Cremona C. Assessment of vibration-based damage identification techniques. J Sound Vib 2006; 292(1): 179–202.10.1016/j.jsv.2005.07.036Search in Google Scholar

[3] Wenzel H. Health monitoring of bridges. United Kingdom: John Wiley and Sons Ltd., 2009.10.1002/9780470740170Search in Google Scholar

[4] Peeters B, Maeck J, De Roeck G. Vibration-based damage detection in civil engineering: excitation sources and temperature effects. Smart Mater Struct 2001; 10(3): 518–27.10.1088/0964-1726/10/3/314Search in Google Scholar

[5] Rohrmann RG, Baessler M, Said S, Schmid W, Ruecker WF. Structural causes of temperature affected modal data of civil structures obtained by long time monitoring. Proc. 18th Int. Modal Analytical Conf. IMAC 18, San Antonio, Tex., 1–7, 2000.Search in Google Scholar

[6] Duff K, Hyzak M. Structural monitoring with GPS. https://www.fhwa.dot.gov/publications/publicroads/97spring/_gps.cfm (Accessed 2 November 2018).Search in Google Scholar

[7] Roberts GW, Meng X, Dodson AH. Integrating a global positioning system and accelerometers to monitor the deflection of bridges. J Surv Eng 2004; 130(2): 65–72.10.1061/(ASCE)0733-9453(2004)130:2(65)Search in Google Scholar

[8] Neitzel F, Niemeier W, Weisbrich S, et al. Investigation of low-cost accelerometer, terrestrial laser scanner and ground-based radar interferometer for vibration monitoring of bridges. Proc of the 6th European Workshop on Structural Health Monitoring, 542–51, 2012.Search in Google Scholar

[9] Psimoulis PA, Stiros SC. Measuring deflections of a short-span railway bridge using a robotic total station. J Bridge Eng 2013; 18(2): 182–5.10.1061/(ASCE)BE.1943-5592.0000334Search in Google Scholar

[10] Pagiatakis SD. Stochastic significance of peaks in the least-squares spectrum. J Geodesy 1999; 73(2): 67–78.10.1007/s001900050220Search in Google Scholar

[11] Pytharouli SI, Stiros SC. Spectral analysis of unevenly spaced or discontinuous data using the Normperiod code. Comput Struct 2008; 86(1–2): 190–6.10.1016/j.compstruc.2007.02.022Search in Google Scholar

[12] Ehrhart M, Lienhart W. Monitoring of civil engineering structures using a state-of-the-art image assisted total station. J Appl Geodesy 2015: 9(3): 174–82.10.1515/jag-2015-0005Search in Google Scholar

[13] Heylen W, Lammens S, Sas P. Modal analysis theory and testing. Leuven, Belgium: Katholieke Universiteit Leuven, 1997.Search in Google Scholar

[14] Guillaume P, De Troyer T, Devriendt C, De Sitter G. OMAX–a combined experimental-operational modal analysis approach. Proc of ISMA2006 international conference on noise and vibration engineering, Leuven, Belgium, 2985–96, September 2006.Search in Google Scholar

[15] Brandt A. Noise and vibration analysis: signal analysis and experimental procedures. Chichester, UK: John Wiley and Sons Ltd., 2011.10.1002/9780470978160Search in Google Scholar

[16] Zhang G, Tang B, Tang G. An improved stochastic subspace identification for operational modal analysis. Measurement 2012; 45(5): 1246–56.10.1016/j.measurement.2012.01.012Search in Google Scholar

[17] Parloo E. Application of frequency-domain system identification techniques in the field of operational modal analysis. PhD thesis, Belgium: Department of Mechanical Engineering, Vrije Universiteit Brussel, 2003.Search in Google Scholar

[18] Reynders E. System identification methods for (operational) modal analysis: review and comparison. Arch Comput Method Eng 2012; 19(1): 51–124.10.1007/s11831-012-9069-xSearch in Google Scholar

[19] Peeters B, Vanhollebeke F, Van der Auweraer H. Operational PolyMAX for estimating the dynamic properties of a stadium structure during a football game. Proc of the IMAC, Vol. 23, Orlando, FL, USA, January 2005.Search in Google Scholar

[20] Peeters B, Van der Auweraer H. PolyMAX: a revolution in operational modal analysis. 1st International Operational Modal Analysis Conference, Copenhagen, Denmark, April 2005.Search in Google Scholar

[21] James GH, Carne TG, Lauffer JP. The natural excitation technique (NExT) for modal parameter extraction from operating structures. Modal Anal 1995; 10(4): 260–77.Search in Google Scholar

[22] Van Overschee P, De Moor B. Subspace algorithms for the stochastic identification problem. Automatica 1993; 29(3): 649–60.10.1109/CDC.1991.261604Search in Google Scholar

[23] Hermans L, Van der Auweraer H. Modal testing and analysis of structures under operational conditions: industrial applications. Mech Sys Signal Process 1999; 13(2): 193–216.10.1006/mssp.1998.1211Search in Google Scholar

[24] Peeters B. System identification and damage detection in civil engineering, PhD thesis, Belgium: Department of Civil Engineering, K. U. Leuven, 2000.Search in Google Scholar

[25] Fan J, Zhang Z, Hua H. Data processing in subspace identification and modal parameter identification of an arch bridge. Mech Sys Signal Process 2007; 21(4): 1674–89.10.1016/j.ymssp.2006.07.010Search in Google Scholar

[26] Reynders E, Pintelon R, De Roeck G. Uncertainty bounds on modal parameters obtained from stochastic subspace identification. Mech Sys Signal Process 2008; 22(4): 948–69.10.1016/j.ymssp.2007.10.009Search in Google Scholar

[27] Magalhaes F, Cunha A, Caetano E. Online automatic identification of the modal parameters of a long span arch bridge. Mech Sys Signal Process 2009; 23(2): 316–29.10.1016/j.ymssp.2008.05.003Search in Google Scholar

[28] Boonyapinyo V, Janesupasaeree T. Data-driven stochastic subspace identification of flutter derivatives of bridge decks. J Wind Eng Ind Aerod 2010; 98(12): 784–99.10.1016/j.jweia.2010.07.003Search in Google Scholar

[29] Brincker R, Zhang L, Andersen P. Modal identification of output-only systems using frequency domain decomposition. Smart Mater Struct 2001; 10(3): 441–5.10.1088/0964-1726/10/3/303Search in Google Scholar

[30] Ibrahim SR. Efficient random decrement computation for identification of ambient responses. Proc of the International Modal Analysis Conference – IMAC, Orlando, FL, 2001, 698–703.Search in Google Scholar

[31] Lardies J, Gouttebroze S. Identification of modal parameters using the wavelet transform. Int J Mech Sci 2002; 44(11): 2263–83.10.1016/S0020-7403(02)00175-3Search in Google Scholar

[32] Guillaume P, Verboven P, Vanlanduit S. Frequency-domain maximum likelihood identification of modal parameters with confidence intervals. Proc of ISMA 23, Noise and Vibration Engineering, Belgium: K. U. Leuven, 1998.Search in Google Scholar

[33] Golub GE, Van Loan CF. Matrix computations. The Johns Hopkins University Press, 2013.10.56021/9781421407944Search in Google Scholar

[34] Bendat JS, Piersol AG. Engineering applications of correlation and spectral analysis. New York: Wiley-Interscience, 1980.Search in Google Scholar

[35] Kang C, Bode M, Wenner M, Marx S. Experimental and numerical investigations of rail behaviour under compressive force on ballastless track systems. Eng Struct 2019; 197: 1–13. DOI:10.1016/j.engstruct.2019.109413.Search in Google Scholar

[36] Diederley J, Herrmann R, Marx S. Ermüdungsversuche an großformatigen Betonprobekörpern mit dem Resonanzprüfverfahren. Beton- Stahlbetonbau 2018; 113(8): 589–97. DOI:10.1002/best.201800010.Search in Google Scholar

[37] Cuéllar P, Mira P, Pastor M, Fernández Merodo JA, Baeßler M, Rücker W. A numerical model for the transient analysis of offshore foundations under cyclic loading. Comput Geotech 2014; 59: 75–86.10.1016/j.compgeo.2014.02.005Search in Google Scholar

[38] Nerger D, Hille F, Moosavi R, Grunwald M, Redmer B, Kühn T, Hering M, Bracklow F. Improved tomographic investigation for impact damage characterization. Proc of the 25th SMiRT conference (SMiRT 25), Charlotte, 4–9 August 2019.Search in Google Scholar

[39] Kargoll B, Omidalizarandi M, Paffenholz JA, Neumann I, Kermarrec G, Alkhatib H. Bootstrap tests for model selection in robust vibration analysis of oscillating structures. Proc of the 4th Joint International Symposium on Deformation Monitoring (JISDM), Athens, 15–17 May 2019.Search in Google Scholar

[40] PCB piezoelectric accelerometer. PCB piezotronics MTS systems corporation, https://www.pcb.com/resources/technical-information/introduction-to-accelerometers (accessed 17 September 2019).Search in Google Scholar

[41] Omidalizarandi M, Neumann I, Kemkes E, Kargoll B, Diener D, Rüffer J, Paffenholz JA. MEMS based bridge monitoring supported by image-assisted total station. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 833–42, Karaj, 12–14 October 2019.10.5194/isprs-archives-XLII-4-W18-833-2019Search in Google Scholar

[42] Shin EH, El-Sheimy N. A new calibration method for strapdown inertial navigation systems. Z Vermess 2002; 127: 1–10.Search in Google Scholar

[43] KUKA youBot. KUKA youBot User Manual. Document revision 1.01. ftp://ftp.youbot-store.com/manuals/KUKA-youBot_UserManual.pdf (25 November 2019).Search in Google Scholar

[44] Kargoll B, Omidalizarandi M, Loth I, Paffenholz JA, Alkhatib H. An iteratively reweighted least-squares approach to adaptive robust adjustment of parameters in linear regression models with autoregressive and t-distributed deviations. J Geodesy 2018; 92(3): 271–97.10.1007/s00190-017-1062-6Search in Google Scholar

[45] Marple SL. Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987, pp. 373–8.Search in Google Scholar

[46] Jiang X, Adeli H. Pseudospectra, MUSIC and dynamic wavelet neural network for damage detection of highrise buildings. Int J Numer Meth Eng 2007; 71(5): 606–29.10.1002/nme.1964Search in Google Scholar

[47] Amezquita-Sanchez JP, Adeli H. A new music-empirical wavelet transform methodology for time–frequency analysis of noisy nonlinear and non-stationary signals. Digit Signal Process 2015; 45: 55–68.10.1016/j.dsp.2015.06.013Search in Google Scholar

[48] Torr PH, Zisserman A. MLESAC: A new robust estimator with application to estimating image geometry. Comput Vis Image Und 2000; 18(1): 138–56.10.1006/cviu.1999.0832Search in Google Scholar

[49] Cheynet E. Operational modal analysis with automated SSI-COV algorithm (https://www.mathworks.com/matlabcentral/fileexchange/69030-operational-modal-analysis-with-automated-ssi-cov-algorithm), MATLAB Central File Exchange. Retrieved February 7, 2020.Search in Google Scholar

[50] Omidalizarandi M, Kargoll B, Paffenholz JA, Neumann I. Accurate vision-based displacement and vibration analysis of bridge structures by means of an image-assisted total station. Adv Mech Eng 2018; 10(6): 1687814018780052.10.1177/1687814018780052Search in Google Scholar

[51] Alkhatib H, Kargoll B, Paffenholz JA. Further results on a robust multivariate time series analysis in nonlinear models with autoregressive and t-distributed errors, Time Series Analysis and Forecasting. ITISE 2017. Rojas I, Pomares H, Valenzuela O (eds.). Contributions to statistics, Cham: Springer, 25–38, 2018.10.1007/978-3-319-96944-2_3Search in Google Scholar

[52] Nassar S, Schwarz KP, EL-Sheimy N, Noureldin A. Modeling inertial sensor errors using autoregressive (AR) models. Navigation 2004; 51(4): 259–68.10.1002/j.2161-4296.2004.tb00357.xSearch in Google Scholar

[53] Hargreaves GI. Interval analysis in MATLAB. Numerical Analysis Report No. 416, Manchester Centre for Computational Mathematics, The University of Manchester, ISSN 1360-725, 2002.Search in Google Scholar

[54] Kargoll B, Omidalizarandi M, Alkhatib H, Schuh WD. Further results on a modified EM algorithm for parameter estimation in linear models with time-dependent autoregressive and t-distributed errors. International Work-Conference on Time Series Analysis, 323–37, Cham: Springer, 2017.10.1007/978-3-319-96944-2_22Search in Google Scholar

[55] Rump SM. INTLAB–interval laboratory. Tibor Csendes, editor, developments in reliable computing, Dordrecht: Kluwer Academic Publishers, 77–104, 1999.10.1007/978-94-017-1247-7_7Search in Google Scholar

[56] Omidalizarandi M, Paffenholz JA, Neumann I. Automatic and accurate passive target centroid detection for applications in engineering geodesy. Surv Rev 2019; 51(367): 318–33.10.1080/00396265.2018.1456001Search in Google Scholar

[57] Cheynet E, Jakobsen JB, Snæbjörnsson J. Damping estimation of large wind-sensitive structures. Procedia engineering 2017; 199: 2047–53.10.1016/j.proeng.2017.09.471Search in Google Scholar

[58] Herrmann R. Dataset: Reference Vibration Measurement of Mensa Bridge Hannover. DOI:https://doi.org/10.25835/0081614.Search in Google Scholar

Received: 2020-02-19
Accepted: 2020-05-28
Published Online: 2020-06-09
Published in Print: 2020-07-26

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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