Techno Press

Structural Engineering and Mechanics   Volume 54, Number 2, April7 2015, pages 221-237
Nonlinear damage detection using higher statistical moments of structural responses
Ling Yu and Jun-Hua Zhu

Abstract     [Full Text]
    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.
Key Words
    structural damage detection; nonlinear damage detection; time series analysis; higher statistical moments
Ling Yu: College of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, China
Ling Yu and Jun-Hua Zhu: MOE Key Lab of Disaster Forecast and Control in Engineering, Jinan University, Guangzhou 510632, China
Science and Technology on Reliability Physics and Application Technology of Electronic Component Laboratory, Guangzhou 510610, China

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