JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Operational modal analysis of reinforced concrete bridges using autoregressive model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Smart Structures and Systems
  • Volume 17, Issue 6,  2016, pp.1017-1030
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2016.17.6.1017
 Title & Authors
Operational modal analysis of reinforced concrete bridges using autoregressive model
Park, Kyeongtaek; Kim, Sehwan; Torbol, Marco;
 Abstract
This study focuses on the system identification of reinforced concrete bridges using vector autoregressive model (VAR). First, the time series output response from a bridge establishes the autoregressive (AR) models. AR models are one of the most accurate methods for stationary time series. Burg`s algorithm estimates the autoregressive coefficients (ARCs) at p-lag by reducing the sum of the forward and the backward errors. The computed ARCs are assembled in the state system matrix and the eigen-system realization algorithm (ERA) computes: the eigenvector matrix that contains the vectors of the mode shapes, and the eigenvalue matrix that contains the associated natural frequencies. By taking advantage of the characteristic of the AR model with ERA (ARMERA), civil engineering can address problems related to damage detection. Operational modal analysis using ARMERA is applied to three experiments. One experiment is coupled with an artificial neural network algorithm and it can detect damage locations and extension. The neural network uses a specific number of ARCs as input and multiple submatrix scaling factors of the structural stiffness matrix as output to represent the damage.
 Keywords
system identification;autoregressive model;Burg`s algorithm;eigen-system realization algorithm;
 Language
English
 Cited by
1.
Vision-Based Natural Frequency Identification Using Laser Speckle Imaging and Parallel Computing, Computer-Aided Civil and Infrastructure Engineering, 2018, 33, 1, 51  crossref(new windwow)
 References
1.
Allemang, R.J. (2003), "The modal assurance criterion - Twenty years of use and abuse", J. Sound Vib., 37(8):,14-23.

2.
Brincker, R., Zhang, L.M. and Andersen, P. (2000), "Modal identification from ambient responses using frequency domain decomposition", Imac-Xviii: A Conference on Structural Dynamics, Vols 1 and 2, Proceedings, 4062, 625-630.

3.
Brockwell, P. J. and Davis, R.A. (2002), Introduction to time series and forecasting, Springer. New York.

4.
Hearn, G. and Testa, R. B. (1991), "Modal-analysis for damage detection in structures", J. Struct. Eng. - ASCE, 117(10), 3042-3063. crossref(new window)

5.
Juang, J.N. and Pappa, R.S. (1985), "An eigensystem realization-algorithm for modal parameter-identification and model-reduction", J. Guid. Control Dynam., 8(5), 620-627. crossref(new window)

6.
Lee, H., Grosse, R., Ranganath, R. and Ng, A.Y. (2011), "Unsupervised learning of hierarchical representations with convolutional deep belief networks", Communications of the Acm, 54(10), 95-103.

7.
McKenna, F. (2011), "OpenSees: A framework for earthquake engineering simulation", Comput. Sci. Eng., 13(4), 58-66.

8.
Mohanty, P. and Rixen, D.J. (2004), "A modified Ibrahim time domain algorithm for operational modal analysis including harmonic excitation", J. Sound Vib., 275(1-2), 375-390. crossref(new window)

9.
Neumaier, A. and Schneider, T. (2001), "Estimation of parameters and eigenmodes of multivariate autoregressive models", Acm T. Math. Software, 27(1), 27-57. crossref(new window)

10.
Omenzetter, P., Brownjohn, J.M.W. and Moyo, P. (2003), "Application of time series analysis for bridge health monitoring", Struct. Health Monit. Intell. Infrast., 1-2, 1073-1080.

11.
Priestley, M. B. (1981), Spectral analysis and time series, Academic Press. London ; New York.

12.
Torbol, M., Kim, S. and Shinozuka, M. (2013), "Long term monitoring of a cable stayed bridge using DuraMote", Smart Struct. Syst., 11(5), 453-476. crossref(new window)

13.
Vu, V.H., Thomas, M., Lakis, A.A. and Marcouiller, L. (2011), "Operational modal analysis by updating autoregressive model", Mech. Syst. Signal Pr., 25(3), 1028-1044. crossref(new window)

14.
Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M. and Xu, F. (2013), "A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification", Smart Struct. Syst., 12(3-4), 363-379. crossref(new window)

15.
Ye, X. W., Ni, Y.Q., Wong, K.Y. and Ko, J.M. (2012), "Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data", Eng. Struct., 45, 166-176. crossref(new window)

16.
Yun, C.B. and Bahng, E.Y. (2000), "Substructural identification using neural networks", Comput. Struct., 77(1), 41-52. crossref(new window)

17.
Yun, Y.A.N. (2004), "Design of structure optimization with APDL", J. East China Jiaotong Univ., 21(4), 52-55.