Smart Structures and Systems

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Structural damage identification with power spectral density transmissibility: numerical and experimental studies

  • Li, Jun (Department of Civil Engineering, Curtin University) ;
  • Hao, Hong (Department of Civil Engineering, Curtin University) ;
  • Lo, Juin Voon (School of Civil and Resource Engineering, The University of Western Australia)
  • Received : 2014.02.05
  • Accepted : 2014.05.05
  • Published : 2015.01.25
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Abstract

This paper proposes a structural damage identification approach based on the power spectral density transmissibility (PSDT), which is developed to formulate the relationship between two sets of auto-spectral density functions of output responses. The accuracy of response reconstruction with PSDT is investigated and the damage identification in structures is conducted with measured acceleration responses from the damaged state. Numerical studies on a seven-storey plane frame structure are conducted to investigate the performance of the proposed damage identification approach. The initial finite element model of the structure and measured acceleration measurements from the damaged structure are used for the identification with a dynamic response sensitivity-based model updating method. The simulated damages can be identified accurately without and with a 5% noise effect included in the simulated responses. Experimental studies on a steel plane frame structure in the laboratory are performed to further verify the accuracy of response reconstruction with PSDT and validate the proposed damage identification approach. The locations of the introduced damage are detected accurately and the stiffness reductions in the damaged elements are identified close to the true values. The identification results demonstrated the accuracy of response reconstruction as well as the correctness and efficiency of the proposed damage identification approach.

Keywords

Acknowledgement

Grant : Development of a Self-powered Wireless Sensor Network from Renewable Energy for Integrated Structural Health Monitoring and Diagnosis

Supported by : Australian Research Council

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