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Volume 52 Issue 8
Aug.  2022
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Article Contents
FENG Heng, GAO Bin, GUO Huiyong. Nonlinear Damage Identification of Transmission Towers Based on AR/ARCH Model Conversion Distance and Its Experimental Research[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(8): 57-63. doi: 10.13204/j.gyjzG21071316
Citation: FENG Heng, GAO Bin, GUO Huiyong. Nonlinear Damage Identification of Transmission Towers Based on AR/ARCH Model Conversion Distance and Its Experimental Research[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(8): 57-63. doi: 10.13204/j.gyjzG21071316

Nonlinear Damage Identification of Transmission Towers Based on AR/ARCH Model Conversion Distance and Its Experimental Research

doi: 10.13204/j.gyjzG21071316
  • Received Date: 2021-07-13
    Available Online: 2022-12-01
  • The safety of engineering steel structures such as transmission towers is often affected by cracks and other damages. Such damages often exhibit nonlinear characteristics of variable stiffness in structural vibration. In order to solve this kind of nonlinear damage detection problem, a nonlinear damage identification method based on AR/ARCH model conversion distance was presented. Firstly, the basic theory of AR/ARCH hybrid model was described, and the order determination and parameter estimation methods of modeling were given. Then, the time-varying stiffness characteristics of nonlinear damage were described, and the nonlinear damage identification strategy based on second-order variance index was given. On this basis, the relations between AR/ARCH model residual and condition variance and nonlinear damage was analyzed, and a conversion distance index based on AR/ARCH model was proposed. Finally, the related experimental research was carried out, and the three-layer frame experiment was used to verify the effectiveness of the index, and the damage identification experiment of the transmission tower model was also conducted. The experimental and calculation results showed that the conversion distance index based on AR/ARCH model was obviously superior to the traditional second-order variance index, and it had stronger nonlinear damage identification capability and reliability.
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