Structural Engineering and Mechanics

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Nonlinear damage detection using higher statistical moments of structural responses

  • Yu, Ling (College of Civil Engineering and Architecture, China Three Gorges University) ;
  • Zhu, Jun-Hua (MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University)
  • Received : 2014.10.23
  • Accepted : 2015.03.08
  • Published : 2015.04.25
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Abstract

An integrated method is proposed for structural nonlinear damage detection based on time series analysis and the higher statistical moments of structural responses in this study. It combines the time series analysis, the higher statistical moments of AR model residual errors and the fuzzy c-means (FCM) clustering techniques. A few comprehensive damage indexes are developed in the arithmetic and geometric mean of the higher statistical moments, and are classified by using the FCM clustering method to achieve nonlinear damage detection. A series of the measured response data, downloaded from the web site of the Los Alamos National Laboratory (LANL) USA, from a three-storey building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the performance of the new nonlinear damage detection method. The effectiveness and robustness of the new proposed method are finally analyzed and concluded.

Keywords

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