Operational modal analysis (OMA) is to extract the dynamic characteristics of structures based on vibration responses of structures without considering the excitation measurement. In this study both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of a test structure ( a 3- story steel frame subjected to a series of earthquake and white noise excitations back to back) using both input and output response data or output only measurement and identify the damage location. For the modal-based identification, the multi-variant autoregressive model (MV-AR model) is used to identify the dynamic characteristics of structure. The MV-AR model parameters are then used to develop the vectors of autoregressive model and Mahalanobis distance, and then to identify the damage features and locate the damage. From the signal-based feature identification two damage features will be discussed: (1) the enhancement of time-frequency analysis of acceleration responses, and (2) WPT based energy damage indices. Discussion on the correlation of the extract local damage features from measurements with the global damage indices, such as null-space and subspace damage indices, is also made.
Chin-Hsiung Loh, Chuan-Kai Chan, and Chung-Hsien Lee, "Application of time-series-based damage detection algorithms to structures under ambient excitations," Proc. SPIE 9803, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2016, 98031Q (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 22, 2016; Published: 20 April 2016); https://doi.org/10.1117/12.2218654.
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Study of self-shadowing effect as a simple means to realize nanostructured thin films and layers with special attentions to birefringent obliquely deposited thin films and photo-luminescent porous silicon