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Experimental Investigation on Statistical Moment-based Structural Damage Detection Method

Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China; ceylxu{at}polyu.edu.hk

J. Zhang

Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

J.C. Li

Faculty of Engineering, University of Technology Sydney, Australia

Y. Xia

Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China

Although vibration-based structural damage detection methods have demonstrated various degrees of success, the damage detection of civil structures still remains as a challenging task. The main obstacles include the insensitivity to local damage and the high sensitivity to measurement noise. A new structural damage detection method based on the statistical moments of dynamic responses of a structure has been recently proposed by the authors, and the numerical study manifested that the proposed method is sensitive to local structural damage but insensitive to measurement noise. The experimental investigation on this method is presented in this article. Three shear building models with and without damage were built and subjected to ground motions generated by a shaking table. The displacement and acceleration responses of each building model at each floor were recorded. The recorded ground motion and building responses as well as identified structural damping ratios were then used to identify damage locations and severities using the statistical moment-based damage detection method. The identified damage locations and severities were compared with the theoretical values. The comparison is found satisfactory, and the method proposed is effective and feasible.

Key Words: damage detection • statistical moment • experimental investigation • sensitivity • noise

This version was published on November 1, 2009

Structural Health Monitoring, Vol. 8, No. 6, 555-571 (2009)
DOI: 10.1177/1475921709341011


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