American Society of Civil Engineers


Multivariate Statistical Approach to Structural Damage Detection


by Zengrong Wang, (Ph.D. Candidate, Dept. of Civil Engineering, National Univ. of Singapore, #07-03, Block E1A, 1 Engineering Dr. 2, Singapore 117576, Singapore.) and K. C. G. Ong, (corresponding author), (Associate Professor and Deputy Head, Dept. of Civil Engineering, National Univ. of Singapore, #07-03, Block E1A, 1 Engineering Dr. 2, Singapore 117576, Singapore E-mail: cveongkc@nus.edu.sg)

Journal of Engineering Mechanics, Vol. 136, No. 1, January 2010, pp. 12-22, (doi:  http://dx.doi.org/10.1061/(ASCE)0733-9399(2010)136:1(12))

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Document type: Journal Paper
Abstract: The issue of structural damage detection is addressed through an innovative multivariate statistical approach in this paper. By invoking principal component analysis, the vibration responses acquired from the structure being monitored are represented by the multivariate data of the sample principal component coefficients (PCCs). A damage indicator is then defined based on a multivariate exponentially weighted moving average control chart analysis formulation, involving special procedures to allow for the effects of the estimated parameters and to determine the upper control limits in the control chart analysis for structural damage detection applications. Also, a data shuffling procedure is proposed to remove the autocorrelation probably present in the obtained sample PCCs. This multivariate statistical structural damage detection scheme can be applied to either the time domain responses or the frequency domain responses. The efficacy and advantages of the scheme are demonstrated by the numerical examples of a five-story shear frame and a shear wall as well as the experimental example of the I-40 Bridge benchmark.


ASCE Subject Headings:
Damage
Assessment
Monitoring
Statistics
Structural dynamics
Data analysis
Methodology

Author Keywords:
Structural health monitoring
Statistics
Structural dynamics
Data analysis
Methodology