Statistical Clustering and Times Series Analysis for Bridge Monitoring Data

  • Man Nguyen
  • Tan Tran
  • Doan Phan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)


The process of implementing a damage detection strategy for bridges is referred to as Bridge Health Monitoring (BHM). The BHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage-sensitive features from these measurements, and the statistical analysis of these features to determine the current state of the system’s health [12]. Therefore, the achieved data from attached sensors would be very huge in dimensions, would make researchers confused in further examinations on data bridge. There have been many approaches to solve the BHM sensors reduction problem, range from univariate analysis between couples of variables [13] to carefully selecting measurement points based on specific bridge knowledge [7]. However, they are either inapplicable for interrelated nature data sets, or using too much mechanical knowledge in its process.


Time Series Analysis Canonical Correlation Statistical Cluster Structural Health Monitoring Joint Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Farrar, C.R., Keith, W.: An introduction to structural health monitoring. In: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, vol. 365(1851), pp. 303–315 (2007)Google Scholar
  2. 2.
    Eastment, H.T., Krzanowski, W.J.: Cross-Validatory Choice of the Number of Components from a Principal Component Analysis. Technometrics 24(1), 73–77 (1982)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hrdle, W., Simar, L.: Applied multivariate statistical analysis, 2nd edn. Springer (2007)Google Scholar
  4. 4.
    Hotelling, H.: Relations Between Two Sets of Variates. Biometrika 28(3-4), 321–377 (1936)zbMATHCrossRefGoogle Scholar
  5. 5.
    Jolliffe, I.T.: Principal component analysis, 2nd edn. Springer (2002)Google Scholar
  6. 6.
    Ljung, L.: System identification: theory for the user. Prentice Hall, Englewood Cliffs (1987)zbMATHGoogle Scholar
  7. 7.
    Papadimitriou, C.: Optimal sensor placement methodology for parametric identification of structural systems. Journal of Sound and Vibration 278(4-5), 923–947 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Rytter, A.: Vibration based inspection of civil engineering structures. Ph.D. Dissert., Department of Building Technology & Structural Engineering. Aalborg University, Denmark (1993)Google Scholar
  9. 9.
    Sithole, M.M., Ganeshanandam, S.: Variable selection in principal component analysis to preserve the underlying multivariate data structure. In: ASC XII 12th Australian Stats Conference, Monash University, Melbourne (1994)Google Scholar
  10. 10.
    Hoon, S., Allen, D.W., Worden, K., Farrar, C.R.: Statistical damage classification using sequential probability ratio test. Structural Health Monitoring, 57–74 (2003)Google Scholar
  11. 11.
    Hoon, S., Worden, K., Farrar, C.R.: Statistical Damage Classification under Changing Environmental and Operational Conditions. Journal of Intelligent Materials Systems and Structures (2007)Google Scholar
  12. 12.
    Hoon, S., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W., Nadler, B.R., Czarnecki, J.J.: A Review of Structural Health Monitoring Literature: 1996-2001. Structural Health Monitoring. Los Alamos National Laboratery Report (2004)Google Scholar
  13. 13.
    Lingyun, Y., Schopf, J.M., Dumitrescu, C.L., Foster, I.: Statistical Data Reduction for Efficient Application Performance Monitoring. CCGRID (2006)Google Scholar
  14. 14.
    Zhang, Q.W.: Statistical damage identification for bridges using ambient vibration data, pp. 476–485. Elsevier (2006)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  • Man Nguyen
    • 1
  • Tan Tran
    • 1
  • Doan Phan
    • 1
  1. 1.University of Technology, VNU-HCMHo Chi Minh CityViet Nam

Personalised recommendations