Browse > Article
http://dx.doi.org/10.12989/eas.2013.5.3.359

Dynamic state estimation for identifying earthquake support motions in instrumented structures  

Radhika, B. (Department of Civil Engineering, Indian Institute of Science)
Manohar, C.S. (Department of Civil Engineering, Indian Institute of Science)
Publication Information
Earthquakes and Structures / v.5, no.3, 2013 , pp. 359-378 More about this Journal
Abstract
The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.
Keywords
dynamic state estimation; particle filters; force identification; earthquake support motions;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Radhika, B. and Manohar, C.S. (2010), "Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis", Prob. Eng. Mech., 25(4), 393-405.   DOI   ScienceOn
2 Radhika, B. and Manohar, C.S. (2012), "Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures", Prob. Eng.Mech., 30, 89-103.   DOI   ScienceOn
3 Radhika, B. (2012), "Monte Carlo simulation based response estimation and model updating in nonlinear random vibrations", PhD dissertation, Indian Institute of Science, Bangalore, India.
4 Ristic, B., Arulampalam, S. and Gordon, N. (2004), Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, Boston.
5 Schon, T. and Gustafsson, F. (2005), "Marginal particle filters for mixed linear/nonlinear state space models", IEEE Transactions on Signal Proce., 53(7), 2279-2288.   DOI   ScienceOn
6 Shi, T., Jones, N.P. and Ellis, J.H. (2000), "Simultaneous estimation of system and input parameters from output measurements", J. Eng. Mech. ASCE, 126(7), 746-753.   DOI   ScienceOn
7 Stansby, P.K.,Worden, K. and Tomlinson, G.R. (1992),"Improved wave force classification using system identification", Application. Ocean Res.,14 107-118.   DOI   ScienceOn
8 Sugiyama, T., Ishii, T. and Kaneko, M. (1995), "Effects of seismic wave propagation on a longand narrow building: - body wave and surface wave propagation", Comput.Geotech., 17, 547-64.   DOI   ScienceOn
9 Vanmarcke, E. H. and Fenton, G.A. (1991), "Conditioned simulation of local fields of earthquake ground motion", Struct. Safety,10, 247-64.   DOI   ScienceOn
10 Wang, M.L. and Kreitinger, T.J. (1994), "Identification of force from response data of a nonlinear system", Soil Dyn. Earthq. Eng., 13, 267-280.   DOI   ScienceOn
11 Wang, D. and Haldar, A. (1994),"Element level system identification with unknown input", J. Eng. Mech., 120(1), 159-176.   DOI   ScienceOn
12 Wen, Y.K. (1989), "Methods of random vibration for inelastic structures", ASME Appl. Mech. Rev., 42(2), 39-52.   DOI
13 Worden, K., Stansby, P.K., Tomlinson, G.R. and Billings, S.A. (1994), "Identification of nonlinear wave forces", J. Fluids Struct., 8, 19-71.   DOI   ScienceOn
14 Xu, B., He, J., Rovekamp, R. and Dyke, S.J. (2012), "Structural parameters and dynamic loading identification from incomplete measurements: Approach and validation", Mech. Syst. Signal Pr., 28, 244-257.   DOI   ScienceOn
15 Yuen, K.V. (2010), Bayesian methods for structural dynamics and civil engineering, John Wiley & Sons, Singapore.
16 Yun, C.B. and Shinozuka, M. (1980), "Identification of nonlinear structural dynamic systems", J. Struct. Mech., 8, 187-203.   DOI   ScienceOn
17 Zerva, A., Shinozuka, M. (1991), "Stochastic differential ground motion", Struct. Safety., 10,129-43.   DOI   ScienceOn
18 Zhang, E., Antoni, J. and Feissel, P. (2012), "Bayesian force reconstruction with an uncertain model", J. Sound Vib., 331, 798-814.   DOI   ScienceOn
19 Cappe, O., Moulines, E. and Ryden, T. (2005), Inference in hidden Markov Models, Springer, New York.
20 Chen, J. and Li, J. (2004), "Simultaneous identification of structural parameters and input time history from output-only measurements", Comput. Mech., 33, 365-374.   DOI   ScienceOn
21 Ching, J., Beck, J.L. and Porter, K.A. (2006), "Bayesian state and parameter estimation of uncertain dynamical systems", Prob. Eng. Mech., 21, 81-96.   DOI   ScienceOn
22 Ching, J. and Beck, J.L. (2007), "Real-time reliability estimation for serviceability limit states in structures with uncertain dynamic excitation and incomplete output data", Prob. Eng. Mech., 22, 50-62.   DOI   ScienceOn
23 Der Kiureghian, A. (1996), "A coherency model for spatially varying ground motions", Earthq. Eng. Struct. Dyn., 25, 99-111.   DOI
24 Doucet,A., de Freitas, N. and Gordon, N. (2001), Sequential monte carlo methods in practice, Springer, New York.
25 Gordon, N.J., Salmond, D.J. and Smith, A.F. (1993), "Novel approach to nonlinear/non-Gaussian Bayesian state estimation", IEE Proceedings-F, 140, 107-113.
26 Harichandran, R.S. and Vanmarcke , E.H. (1986), "Stochastic variation of earthquake groundmotion in space and time", J. Eng. Mech., 112, 154-74.   DOI   ScienceOn
27 Hoshiya, M. and Saito, E. (1984), "Structural identification by extended Kalman filter", ASCE Eng. Mech., 110(12), 1757-1770.   DOI   ScienceOn
28 Jankowski, R. and Walukiewicz, H. (1997), "Modeling of two-dimensional random fields", Prob. Eng. Mech., 12, 115-21.   DOI   ScienceOn
29 Jazwinski, A.H. (1970), Stochastic processes and filtering theory, Academic Press, New York.
30 Jankowski, R. (2012), "Non-linear FEM analysis of pounding-involved response of buildings under non-uniform earthquake excitation", Eng. Struct., 37, 99-105.   DOI   ScienceOn
31 Kameda, H. and Morikawa, H. (1994), "Conditioned stochastic processes for conditionalrandom fields", J. Eng. Mech., 120, 855-875.   DOI   ScienceOn
32 Kloeden, P.E. and Platen, E. (1992), Numerical solution of stochastic differential equations, Springer, Berlin.
33 Kreitinger, T.J., Wang, M.L. and Schreyer, H.L. (1992), "Non-parametric force identification from structural response", Soil Dyn. Earthq. Eng., 11, 269-277.   DOI   ScienceOn
34 Lourens, E., Reynders, E., De Roeck, G., Degrande, G. and Lombaert, G. (2012), "An augmented Kalman filter for force identification in structural dynamics", Mech. Systems and Signal Processing, 27, 446-460.   DOI   ScienceOn
35 Maybeck, P.S. (1979), Stochastic Models, Estimation, and Control, Volume 1, Academic Press, New York.
36 Maybeck, P.S. (1982), Stochastic Models, Estimation, and Control, Volume 2, Academic Press, New York.
37 Meirovitch, L. (2007), Elements of vibration analysis, Tata McGraw-Hill, New Delhi.
38 Nasrellah, H.A. and Manohar, C.S. (2011a), "Finite element based Monte Carlo filters for structural system identification", Prob. Eng. Mech., 26, 294-307.   DOI   ScienceOn
39 Nasrellah, H.A. and Manohar, C.S. (2011b), "Particle filters for structural system identification using multiple test and sensor data: A combined computational and experimental study", Struct. Control and Health Monitoring, 18(1), 99-120.