Browse > Article
http://dx.doi.org/10.12989/sss.2013.12.6.619

Substructure based structural damage detection with limited input and output measurements  

Lei, Y. (Department of Civil Engineering, Xiamen University)
Liu, C. (Department of Civil Engineering, Xiamen University)
Jiang, Y.Q. (Department of Civil Engineering, Xiamen University)
Mao, Y.K. (Department of Civil Engineering, Xiamen University)
Publication Information
Smart Structures and Systems / v.12, no.6, 2013 , pp. 619-640 More about this Journal
Abstract
It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.
Keywords
structural identification; structural damage detection; substructure approach; extended Kalman estimator; least-squares estimation; unknown inputs;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Bernal, D. and Beck, J. (Eds). (2004), "Special section: phase I of the IASC-ASCE structural health monitoring benchmark", J. Eng. Mech. - ASCE, 130(1), 1-127.   DOI   ScienceOn
2 Chang, F.K. (Ed.) (2009, 2011), Proceedings of the 6th, 7th and the 8th International Workshops on Structural Health Monitoring, Stanford University, Stanford, CA, CRC Press, New York.
3 Chen, J and Li, J. (2004), "Simultaneous identification of structural parameters and input time history from output-only measurements", Comput. Mech., 33(5), 365-374.   DOI   ScienceOn
4 Ghanem, R.G. and Shinozuka, M. (1995), "Structural system identification I: theory", J. Eng. Mech. - ASCE, 121(2), 255-264.   DOI   ScienceOn
5 Glaser, S.D., Li, H., Wang, M.L., Ou, J.P. and Lynch, J.P. (2007), "Sensor technology innovation for the advancement of structural health monitoring: a strategic program of US-China research for the next decade", Smart Struct. Syst., 3(2), 221-244.   DOI   ScienceOn
6 Hoshiya, M. and Saito, E. (1984), "Structural identification by extended Kalman filter", J. Eng. Mech.- ASCE, 110(12), 1757-1771.   DOI   ScienceOn
7 Hou, J.L., Jankowski, L. and Ou, J.P. (2011), "A substructural isolation method for local structural health monitoring", Struct. Health Monit ., 18, 601-618.   DOI   ScienceOn
8 Hsieh, C.S. and Chen, F.C. (1999), "Optimal Solution of the Two-Stage Kalman Estimator", IEEE T. Automat.Contr., 44(1), 194-199.   DOI   ScienceOn
9 Huang, H.W. and Yang, J.N. (2008), "Damage identification of substructure for local health monitoring", Smart Struct. Syst., 4(6), 795-807.   DOI   ScienceOn
10 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   ScienceOn
11 Kathuda, H., Martinez, R. and Hladar, A. (2005), "Health assessment at local level with unknown input excitation", J. Struct. Eng.- ASCE, 131(6), 956-965.   DOI   ScienceOn
12 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
13 Koh, C.G. and Shankar K. (2003), "Substructural identification method without interface measurement", J. Eng. Mech. - ASCE, 129(7), 769-776.   DOI   ScienceOn
14 Law, S.S. and Yong, D. (2011), "Substructure methods for structural condition assessment", J. Sound Vib., 330(5), 3606-3619.   DOI   ScienceOn
15 Law, S.S., Zhang, K. and Duan, Z.D. (2011), "Structural damage detection from coupling forces between substructures under support excitation", Eng. Struct., 32(8), 2221-2228.
16 Meier, U., Havaranek, B. and Motavalli M. (Eds.) (2009), Proceedings of the 4th International Conference on structural health monitoring of intelligent infrastructures, Zurich.
17 Lei, Y., Lei, J.Y. and Song, Y. (2007), "Element level structural damage detection with limited observations and with unknown inputs", Proceedings of the SPIE's Conference on Health Monitoring of Structural and Biological Systems, 6532, 65321X1-X9, San Diego, CA, USA.
18 Lee, K.J. and Yun, C.B. (2008),"Parameter identification for nonlinear behavior of RC bridge piers using sequential modified extended Kalman filter", Smart Struct. Syst., 4(3), 319-342.   DOI   ScienceOn
19 Ling, X.L. and Haldar, A. (2004), "Element level system identification with unknown input with rayleigh damping", J. Eng. Mech. - ASCE, 130(8), 877-885.   DOI   ScienceOn
20 Tee, K.F., Koh, C.G. and Quek, S.T. (2009), "Numerical and experimental studies of a substructural identification strategy", Struct. Health Monit., 8(5), 397-410.   DOI   ScienceOn
21 Trinh, T.N. and Koh, C.G. (2011), "An improved substructural identification strategy for large structural systems", Struct. Health Monit., Article first published online, 25 MAY 2011 DOI: 10.1002/stc.463.   DOI   ScienceOn
22 Weng, S., Xia,Y., Xu, Y.L. and Zhu, H.P. (2011), "Substructure based approach to finite element model updating", Comput. Struct., 89(9-10), 772-782.   DOI   ScienceOn
23 Wu, Z.S., Xu, B. and Harada, T. (2003), "Review on structural health monitoring for infrastructures", J. Appl. Mech. - JSCE, 6, 1043-1054.   DOI
24 Xu, B. (2005), "Time domain substructural post-earthquake damage diagnosis methodology with neural networks", Lecture Note Comput. Sci., 3611, 520-529,
25 Yang, J.N., Pan S. and Huang, H.W. (2007), "An adaptive extended Kalman filter for structural damage identification II: unknown inputs", Struct. Health Monit., 14(3), 497-521.   DOI   ScienceOn
26 Xu, B., Rovekamp, J.H,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