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Theoretical and experimental modal analysis of the Guangzhou New TV Tower
Authors:WH ChenZR Lu  W LinSH Chen  YQ NiY Xia  WY Liao
Affiliation:
  • a Department of Applied Mechanics and Engineering, Sun Yat-sen University, Guangzhou 510275, PR China
  • b School of Civil Engineering, Fuzhou University, Fuzhou 350108, PR China
  • c Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Hong Kong
  • Abstract:The Guangzhou New TV Tower (GNTVT) in Guangzhou, China, is a supertall tube-in-tube structure with a total height of 600 m. A complicated structural health monitoring (SHM) system consisting of over 800 sensors has been implemented to the GNTVT for both in-construction and in-service real-time monitoring. By making use of this SHM system, the ambient vibration measurement is carried out in a continuous and long-term manner. This paper presents the analytical and experimental modal analysis of the tower and the field ambient vibration measurement at different construction stages and under different excitation conditions, particularly addressing the following issues: (1) a reduced-order FE model for the GNTVT; (2) field vibration measurement and modal parameter identification of the tower under construction and two environmental excitations (typhoon and earthquake); and (3) comparison of results under different excitation events in the time-frequency domain and correlation between natural frequencies and air temperature using linear regression analysis. The experimental dynamic characteristics of the tower can be used to update the finite element of the tower, so that the updated finite element model of the tower can be obtained, which will serve as the baseline model for future health monitoring and damage detection. They can also be used to verify the effectiveness of vibration control devices installed on the tower.
    Keywords:Supertall structure  Dynamic analysis  Structural health monitoring  Finite element method  Ambient vibration test  Modal analysis  Time-frequency analysis  Least-squares regression analysis
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