Nonlinear damage detection using higher statistical moments of structural responses

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 manner 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-story building structure considering the environmental variety as well as different nonlinear damage cases, are analyzed and used to assess the effectiveness and robustness of the new nonlinear damage detection method. Some valuable conclusions are made and related issues discussed as well.