Yan Niu

TL;DR: In this article, the vector autoregressive models (ARV) method is adopted in the operational modal analysis (OMA) for this TV tower, and the identified natural frequencies, damping ratios and mode shapes are presented and compared with the available results from some other research groups which used different methods, e.g., the data-driven stochastic subspace identification (SSI-DATA) method, the enhanced frequency domain decomposition (EFDD) algorithm, and an improved modal identification method based on NExT-ERA technique.

TL;DR: In this article, an Operational Modal Analysis (OMA) for the Guangzhou New TV Tower (GNTVT) is performed using the Vector AutoRegressive models (ARV) method.

TL;DR: The proposed concept lays the ground for future research in the field of SHM for utilizing knowledge-based methods in correlation with SHM algorithms and analysis of feedbacks obtained from sensors, engineering expertise and users former experience.

TL;DR: In this article, a steady-state Kalman filter based unknown input estimator is adopted to solve the above mentioned ill-posed inverse problem by fulfilling some stability criteria, which are actually some requirements on sensor type, sensor number and sensor position.