Techno Press


Smart Structures and Systems   Volume 5, Number 1, January 2009, pages 95-117
DOI: http://dx.doi.org/10.12989/sss.2009.5.1.095
 
Application of time series based damage detection algorithms to the benchmark experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan
Hae Young Noh, K. Krishnan Nair, Anne S. Kiremidjian and C-H. Loh

 
Abstract     [Full Text]
    In this paper, the time series based damage detection algorithms developed by Nair, et al. (2006) and Nair and Kiremidjian (2007) are applied to the benchmark experimental data from the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan. Both acceleration and strain data are analyzed. The data are modeled as autoregressive (AR) processes, and damage sensitive features (DSF) and feature vectors are defined in terms of the first three AR coefficients. In the first algorithm developed by Nair, et al. (2006), hypothesis tests using the t-statistic are applied to evaluate the damaged state. A damage measure (DM) is defined to measure the damage extent. The results show that the DSF\'s from the acceleration data can detect damage while the DSF from the strain data can be used to localize the damage. The DM can be used for damage quantification. In the second algorithm developed by Nair and Kiremidjian (2007) a Gaussian Mixture Model (GMM) is used to model the feature vector, and the Mahalanobis distance is defined to measure damage extent. Additional distance measures are defined and applied in this paper to quantify damage. The results show that damage measures can be used to detect, quantify, and localize the damage for the high intensity and the bidirectional loading cases.
 
Key Words
    structural health monitoring; damage diagnosis; autoregressive model; hypothesis test; Gaussian mixture model.
 
Address
Hae Young Noh, K. Krishnan Nair and Anne S. Kiremidjian; Department of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USA
C-H. Loh; National Taiwan University, Taipei, Taiwan
 

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