Smart Structures and Systems

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Damage detection of shear buildings through structural mass-stiffness distribution

  • Liang, Yabin (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Li, Dongsheng (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Song, Gangbing (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Zhan, Chao (Faculty of Infrastructure Engineering, Dalian University of Technology)
  • Received : 2015.12.16
  • Accepted : 2016.09.16
  • Published : 2017.01.25
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Abstract

For structural damage detection of shear buildings, this paper proposes a new concept using structural element mass-stiffness vector (SEMV) based on special mass and stiffness distribution characteristics. A corresponding damage identification method is developed combining the SEMV with the cross-model cross-mode (CMCM) model updating algorithm. For a shear building, a model is assumed at the beginning based on the building's distribution characteristics. The model is updated into two models corresponding to the healthy and damaged conditions, respectively, using the CMCM method according to the modal parameters of actual structure identified from the measured acceleration signals. Subsequently, the structural SEMV for each condition can be calculated from the updated model using the corresponding stiffness and mass correction factors, and then is utilized to form a new feature vector in which each element is calculated by dividing one element of SEMV in health condition by the corresponding element of SEMV in damage condition. Thus this vector can be viewed as a damage detection feature for its ability to identify the mass or stiffness variation between the healthy and damaged conditions. Finally, a numerical simulation and the laboratory experimental data from a test-bed structure at the Los Alamos National Laboratory were analyzed to verify the effectiveness and reliability of the proposed method. Both simulated and experimental results show that the proposed approach is able to detect the presence of structural mass and stiffness variation and to quantify the level of such changes.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

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