Browse > Article
http://dx.doi.org/10.12989/sss.2015.15.1.057

A two-stage and two-step algorithm for the identification of structural damage and unknown excitations: numerical and experimental studies  

Lei, Ying (School of Architecture and Civil Engineering, Xiamen University)
Chen, Feng (School of Architecture and Civil Engineering, Xiamen University)
Zhou, Huan (School of Architecture and Civil Engineering, Xiamen University)
Publication Information
Smart Structures and Systems / v.15, no.1, 2015 , pp. 57-80 More about this Journal
Abstract
Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.
Keywords
Extended Kalman filter; two-stage; two-step; system identification; structural damage detection; unknown excitation; least-squares estimation;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
연도 인용수 순위
1 Yun, G.J., Lee, S.G., Carletta, J. and Nagayama, T. (2011), "Decentralized damage identification using wavelet signal analysis embedded on wireless smart sensors", Eng. Struct., 33(7), 2162-2172.   DOI
2 Zhang, H.D. and Han, Q.H. (2013), "Numerical investigation of seismic performance of large span single-layer latticed domes with semi-rigid joints", Struct. Eng. Mech., 48(1), 57-75.   DOI
3 Zhang, Q., Jankowski, L. and Duan Z. (2012), "Simultaneous identification of excitation time histories and parameterized structural damages", Mech. Syst. Signal Pr., 33,56-68.   DOI
4 Azam, S.E. and Mariani S. (2007), "Unscented Kalman filtering for nonlinear structural dynamics", Nonlinear Dynam., 49(1-2), 131-150;   DOI
5 Bernal, D. and Beck, J. (2004), "Special section: phase I of the IASC-ASCE structural health monitoring benchmark", J. Eng. Mech. - ASCE, 130(1), 1-127.   DOI
6 Chen, H.P. (2008), "Application of regularization methods to damage detection in large scale plane frame structures using incomplete noisy modal data", Eng. Struct., 30(1), 3219-3227.   DOI
7 Hsu, T.Y., Huang, S.K., Lu, K.C. Loh, C.H., Wang, Y. and Lynch, J.P. (2011), "On-line structural damage localization and quantification using wireless sensors", Smart Mater. Struct., 20(10), 105025.   DOI
8 Fan, W. and Qiao, P.Z. (2011), "Vibration-based damage identification methods: A review and comparative study", Struct. Health Monit., 10(1), 83-111.   DOI
9 Feng, M.Q. (2009), "Application of structural health monitoring in civil infrastructure", Smart Struct. Syst., 5(4), 469-482.   DOI   ScienceOn
10 Hoshiya, M. and Sutoh, A. (1993), "Kalman filter-finite element system method in identification", J. Eng. Mech. - ASCE, 119(2), 197-210.   DOI
11 Huang, H.W., Yang, J.N. and Zhou L. (2010), "Adaptive quadratic sum-squares error with unknown inputs for damage identification of structures", Struct. Control Health Monit., 17(4), 404-426.   DOI
12 Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "The phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech. - ASCE, 130(1), 3-15.   DOI
13 Julier, S., Uhlmann, J. and Durrant-Whyte, H.F. (2010), "A new method for the nonlinear transformation of means and covariances in filters and estimators", IEEE T. Automat. Contr., 45(3), 477-482;
14 Karayannis, C.G., Favvata, M.J. and Kakaletsis, D.J. (2011), "Seismic behaviour of infilled and pilotis RC frame structures with beam-column joint degradation effect", Eng. Struct., 10, 821-2831.
15 Katkhudat, H.N., Dwairi, H.M. and Shatarat, N. (2010), "System identification of steel framed structures with semi-rigid connections", Struct. Eng. Mech., 34(3), 351-366.   DOI   ScienceOn
16 Lee, K.J. and Yun, C.B. (2008), Parameter identification for nonlinear behavior of RC bridge piers using sequential modified extended Kalman filter. Smart Structures and Systems, 4(3), 319-342.   DOI   ScienceOn
17 Kim, J.H., Kim, K.Y. and Sohn, H. (2013), "Data-driven physical parameter estimation for lumped mass structures from a single point actuation test", J. Sound Vib., 332(18), 4390-4402   DOI
18 Kim, J.H. and Lynch, J.P. (2012), "Subspace system identification of support-excited structures-part I: theory and black-box system identification", Earthq. Eng. Struct. D., 41(15), 2235-2251.   DOI
19 Koh, C.G., See, L.M. and Balendra, T. (1991), "Estimation of structural parameters in time domain: a substructure approach", Earthq. Eng. Struct. D., 20(8), 787-801.   DOI
20 Lei, Y., Jiang, Y.Q. and Xu, Z.Q. (2012), "Structural damage detection with limited input and output measurement signals", Mech. Syst. Signal Pr., 28, 229-243.   DOI
21 Lei, Y., Lai, Z.L., Liu, L.J., Tan, Y.L. and Wang X. J. (2011), "A new type wireless sensor network for distributed structural damage detection", Proceedings of the 1st Middle East Conference on Smart Monitoring, Assessment and Rehabilitation of Civil Structures, Feb. 8-10, Dubai UAE.
22 Lei, Y., Liu, C., Jiang. Y.Q. and Mao, Y.K. (2013), "Substructure based structural damage detection with limited input and output measurements", Smart Struct. Syst., 12(6), 619-640.   DOI
23 Li, G.Q., Shi, W.L. and Xiao Y. (2007), "State of the art of research on semi-rigid composite beam-to-column joints", Progress Steel Build. Struct., 9(4), 11-22.
24 Lynch, J.P. (2007), "An overview of wireless structural health monitoring for civil structures", Philos. T. R. Soc. A, 365, 345-372.   DOI
25 Li, J.C., Dackermann, U., Xu, Y.L. (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.   DOI
26 Liu, X., Escamilla-Ambrosio, P.J. and Lieven, N.A. (2009), "Extended Kalman filtering for the detection of damage in linear mechanical structures", J. Sound Vib., 325, 1023-1046.   DOI
27 Lu Z.R. and Law, S.S. (2007), "Identification of system parameters and input force from output only", Mech. Syst. Signal Pr., 21(5), 2099-2111.   DOI
28 Mariani, S. and Ghisi, A. (2007), "Unscented Kalman filtering for nonlinear structural dynamics", Nonlinear Dynam., 49(1-2), 131-150;   DOI
29 Ou, J.P. and Li, H. (2010), "Structural health monitoring in mainland China: Review and future trends", Struct. Health Monit., 9(3), 219-231.   DOI
30 Park, J.W., Sim, S.H. and Jung, H.J. (2013), "Wireless sensor network for decentralized damage detection of building structures", Smart Struct. Syst., 12(3-4), 399-414.   DOI   ScienceOn
31 Ren, W.X., Lin, Y.Q. and Fang, S.E. (2011), "Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory", Smart Mater. Struct., 20(11),115009.   DOI
32 Sim, S.H., Spencer, B.F., Jr. and Zhang, M. (2010), "Automated decentralized modal analysis using smart sensors", Struct. Control Health Monit., 17(8), 872-894.   DOI
33 Wan E.A. and Van der Merwe, R. (2001), The unscented Kalman filter, Kalman Filtering and Neural Networks, (Ed. Haykin, S.), Wiley.
34 Sirca, Jr. G.F. and Adeli, H. (2012), "System identification in structural engineering", Scientia Iranica A., 19 (6), 1355-1364   DOI   ScienceOn
35 Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W. and Nadler, B.R. (2003), A review of structural health monitoring literature: 1996-2001, Los Alamos National Laboratory Report LA-13976-MS.
36 Spencer, Jr. B.F., Ruiz-Sandova, M.E. and Kurata, N. (2004), "Smart sensing technology: opportunities and challenges", Struct. Control Health Monit., 11(4), 349-368.   DOI
37 Weng, J.H., Loh, C.H. and Yang, J.N. (2009), "Experimental study of damage detection by data-driven subspace identification and finite-element model updating", J. Struct. Eng. - ASCE, 135(12), 1533-1544.   DOI
38 Wu, Z.S., Xu, B. and Yokoyama, K. (2002), "Decentralized parametric damage detection based on neural network", Comput. -Aided Civil Infrastruct. Eng., 17(3), 175-184.   DOI
39 Wu, Z.S., Xu, B. and Harada, T. (2003), "Review on structural health monitoring for infrastructures", J. Appl. Mech. - JSCE , 6, 1043-1054.   DOI
40 Xia, Y. (2011), System Identification and Damage Detection of Nonlinear Structures, PhD Dissertation, Department of Civil and Environmental Engineering, University of California, Irvine, CA.
41 Xu, B., He, J., Rovekamp, R. and Dyke, S.J. (2012), "Structural parameters and dynamic loading identification form incomplete measurements: approach and validation", Mech. Syst. Signal Pr., 28, 244-257.   DOI   ScienceOn
42 Yang, J.N., Pan, S.W. and Huang, H.W. (2007), "An adaptive extended Kalman filter for structural damage identification II: unknown inputs", Struct. Control Health Monit., 14(3), 497-521.   DOI
43 Xu, B., Song, G.B. and Masri, S.F. (2012), "Damage detection for a frame structure model using vibration displacement measurement", Struct. Health Monit., 11(3), 281-292.   DOI
44 Yang, J.N., Huang, H.W. and LIN S.L. (2006), "Sequential non-linear least-square estimation for damage identification of structures", Int. J. Nonlinear Mech., 41, 124-140.   DOI
45 Yang, J.N. and Huang, H.W. (2009), "Adaptive quadratic sum-squares error for structural damage identification", J. Eng. Mech. - ASCE, 135(2), 67-77.   DOI
46 Yi, T.H., Li, H.N. and Gu M. (2011), "Optimal sensor placement for structural health monitoring based on multiple optimization strategies", Struct. Des. Tall Spec., 20(7), 881-900.   DOI
47 Yi, T.H., Li, H.N. and Sun, H.M. (2013), "Multi-stage structural damage diagnosis method based on "energy-damage" theory", Smart Struct. Syst., 12(3-4), 345-361.   DOI
48 Yuen, K.V., Liang, P.F. and Kuok, S.C. (2013), "Online estimation of noise parameters for Kalman filter", Struct. Eng. Mech., 47(3), 361-381.   DOI
49 Yun, C.B. and Min, J.Y. (2011), "Smart sensing, monitoring, and damage detection for civil infrastructures", J. Civil Eng. - KSCE, 15(1), 1-14.
50 Yun, C.B., Lee, J.J. and Koo, K.Y. (2011), "Smart structure technologies for civil infrastructures in Korea: recent research and applications", Struct. Infrastruct. E., 7(9), 673-688.   DOI