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Probabilistic structural damage detection approaches based on structural dynamic response moments

  • Lei, Ying (Department of Civil Engineering, Xiamen University) ;
  • Yang, Ning (Department of Instrumental and Electrical Engineering, Xiamen University) ;
  • Xia, Dandan (School of Civil & Architecture Engineering, Xiamen University of Technology)
  • Received : 2017.01.05
  • Accepted : 2017.04.13
  • Published : 2017.08.25

Abstract

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

Acknowledgement

Supported by : National Natural Science Foundation of China (NSFC)

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