Abstract
Identification of dynamic characteristics of structures is a desired objective for existing infrastructure and has been accounted as a serious challenge for civil engineers. In this research, a structural identification method is proposed, which is capable of identifying dynamics of structures using sensor data inside vehicles passing over a bridge. The methodology utilizes a special type of identification algorithm facilitated by Expectation Maximization (STRIDEX) that is capable of identifying systems using mobile data networks. In this study, it is assumed that the mobile sensor measurements are the accelerations inside rigid vehicles and are primarily a mixtures of accelerations caused by the road roughness and bridge dynamic acceleration. With this regard, a stochastic State-Space model represents the equation of motion for a linear dynamic vehicle-bridge system consisting of an impure input. The observation vector is treated as a linear mixture of two sources that are not known. Therefore, the problem turns to a Blind Source Separation (BSS) procedure that is aiming to draw out the bridge vibrations from the mixture. An algorithm called Second Order Blind Identification (SOBI) has been utilized for source separation and validated using simulation. The entire algorithm, including both SOBI and STRIDEX acting together, could successfully identify natural frequencies and mode shapes of a numerical bridge model.
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References
Pakzad, S.N., Fenves, G.L., Kim, S., Culler, D.E.: Design and implementation of scalable wireless sensor network for structural monitoring. J. Inf. Syst. 14(1), 89–101 (2008)
Swartz, R.A., Lynch, J.P.: Strategic network utilization in a wireless structural control system for seismically excited structures. J. Struct. Eng. 135(5), 597–608 (2009)
Kim, J., Lynch, J.P.: Experimental analysis of vehiclebridge interaction using a wireless monitoring system and a two-stage system identification technique. Mech. Syst. Signal Process. 28, 3–19 (2012)
Cho, S., et al.: Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses. Smart Struct. Syst. 6(5–6), 461–480 (2010)
Dorvash, S., Pakzad, S.N., Cheng, L.: An iterative modal identification algorithm for structural health monitoring using wireless sensor networks. Earthq. Spectra. 29(2), 339–365 (2013)
Sohn, H., Farrar, C.R., Hemez, F.M., Czarnecki, J.J.: A Review of Structural Health Review of Structural Health Monitoring Literature 1996–2001. Los Alamos National Laboratory, Los Alamos, NM (2002)
Farrar, C.R.: Structural health monitoring: technological advances to practical implementations. Proc. IEEE. 104(8), 1508–1512 (2016)
Juang, J.-N.: Applied System Identification, 1st edn. Prentice Hall, New York (1994)
Peeters, B., De Roeck, G.: One-year monitoring of the Z 24-bridge: environmental effects versus damage events. Earthq. Eng. Struct. Dyn. 30(2), 149–171 (2001)
Matarazzo, T.J., Pakzad, S.N.: Direct state-space models for time-varying sensor networks. Proc. 10th Int. Structural Health Monitoring 2015. 7, 59–65 (2015)
Chang, M., Asce, S.M., Pakzad, S.N., Asce, A.M.: Observer Kalman filter Identification for output-only systems using interactive structural modal identification toolsuite. J. Bridg. Eng. 19, 1–11 (2014)
Chang, M., Pakzad, S.N.: Observer Kalman filter identification for output-only systems using interactive structural modal identification toolsuite. J. Bridg. Eng. 19(5), 4014002 (2013)
Juang, J.-N., Pappa, R.S.: An eigensystem realization algorithm for modal parameter identification and model reduction. J. Guid. 8(5), 620–627 (1985)
Van Overschee, P., De Moor, B.L.: Subspace Identification for Linear Systems: Theory—Implementation—Applications. Springer Science & Business Media, Berlin (2012)
Matarazzo, T.J., Pakzad, S.N.: Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput. Civ. Infrastruct. Eng. 33(1), 4–20 (2018)
Matarazzo, T.J., Pakzad, S.N.: STRIDE for structural identification using expectation maximization: iterative output-only method for modal identification. J. Eng. Mech. 142(4), (2016)
Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications, vol. 97. Springer, Cham (2011)
Oja, E., Hyva, A.: Independent component analysis: algorithms and applications. Neural Netw. 13, 411–430 (2000)
Kerschen, G.Ã., Poncelet, F., Golinval, J.: Physical interpretation of independent component analysis in structural dynamics. Mech. Syst. Signal Process. 21, 1561–1575 (2007)
Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 45(2), 434–444 (1997)
Poncelet, F., Kerschen, G., Golinval, J., Verhelst, D.: Output-only modal analysis using blind source separation techniques. Mech. Syst. Signal Process. 21, 2335–2358 (2007)
Shinozuka, M., Deodatis, G.: Simulation of stochastic processes by spectral representation. Appl. Mech. Rev. 44(4), 191–204 (1991)
Matarazzo, T.J., Pakzad, S.N.: Truncated physical model for dynamic sensor networks with applications in high-resolution mobile sensing and BIGDATA. J. Eng. Mech. 142(5), 4016019 (2016)
González, A., O’brien, E.J., Li, Y.-Y., Cashell, K.: The use of vehicle acceleration measurements to estimate road roughness. Veh. Syst. Dyn. 46(6), 483–499 (2008)
Kong, X., Cai, C.S., Kong, B.: Numerically extracting bridge modal properties from dynamic responses of moving vehicles. J. Eng. Mech. 142(2011), 4016025 (2016)
Huang, D., Wang, T.-L.: Impact analysis of cable-stayed bridges. Comput. Struct. 43(5), 897–908 (1992)
McNeill, S.: Blind Modal Identification (BMID) toolbox. MATLAB (2011)
McNeill, S.I., Zimmerman, D.C.: A framework for blind modal identification using joint; approximate diagonalization: Mechanical Systems and Signal Processing. 22(7), 1526–1548 (2008)
Chang, M., Pakzad, S.N.: Optimal sensor placement for modal Identi fi cation of bridge systems considering number of sensing nodes. J. Bridg. Eng. 19(6), 1–10 (2014)
Chang, M., Pakzad, S.N.: Optimal sensor configuration for flexible structures with multi-dimensional mode shapes. Smart Mater. Struct. 24(5), 55012 (2015)
Valeti, B., Matarazzo, T.J., Pakzad, S.N.: Experimental study on wireless mobile sensor configurations for output-only modal identification of a beam testbed. In: Sensors and Instrumentation, vol. 5, pp. 71–77. Springer, Cham (2017)
Matarazzo, T.J., Pakzad, S.N.: Sensitivity metrics for maximum likelihood system identification. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2, B4015002 (2015)
Acknowledgement
Research funding is partially provided by the National Science Foundation through Grant No. CMMI-1351537 by Hazard Mitigation and Structural Engineering program and by a grant from the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA).
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Sadeghi Eshkevari, S., Pakzad, S. (2019). Bridge Structural Identification Using Moving Vehicle Acceleration Measurements. In: Pakzad, S. (eds) Dynamics of Civil Structures, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74421-6_34
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DOI: https://doi.org/10.1007/978-3-319-74421-6_34
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