Statistical Clustering and Times Series Analysis for Bridge Monitoring Data
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 . 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  to carefully selecting measurement points based on specific bridge knowledge . However, they are either inapplicable for interrelated nature data sets, or using too much mechanical knowledge in its process.
KeywordsTime Series Analysis Canonical Correlation Statistical Cluster Structural Health Monitoring Joint Probability Density Function
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- 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
- 3.Hrdle, W., Simar, L.: Applied multivariate statistical analysis, 2nd edn. Springer (2007)Google Scholar
- 5.Jolliffe, I.T.: Principal component analysis, 2nd edn. Springer (2002)Google Scholar
- 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.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.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.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.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.Lingyun, Y., Schopf, J.M., Dumitrescu, C.L., Foster, I.: Statistical Data Reduction for Efficient Application Performance Monitoring. CCGRID (2006)Google Scholar
- 14.Zhang, Q.W.: Statistical damage identification for bridges using ambient vibration data, pp. 476–485. Elsevier (2006)Google Scholar