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Online nonlinear structural damage detection using hilbert Huang transform and artificial neural networks

Vazirizade, M ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.24200/sci.2019.50657.1808
  3. Publisher: Sharif University of Technology , 2019
  4. Abstract:
  5. In order to implement a damage detection strategy and assess the condition of a structure, Structural Health Monitoring (SHM) as a process plays a key role in structural reliability. This paper aims to present a methodology for online detection of damages that may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Although analogous with EMD, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs). IMFs are employed to assess the first-mode frequency and mode shape. Afterwards, Artificial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured accelerations indicates the onset of damage. Then, another ANN method is applied to estimate the stiffness matrix. Though the first-mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to find stiffness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show the reliability of the proposed methodology for online prediction of structural damage. © 2019 Sharif University of Technology. All rights reserved
  6. Keywords:
  7. Hilbert transformation ; Moment-resisting steel frame ; Online damage detection ; Damage detection ; Inverse problems ; Mathematical transformations ; Neural networks ; Signal processing ; Steel construction ; Stiffness ; Stiffness matrix ; Structural analysis ; Structural frames ; Empirical Mode Decomposition ; Ensemble empirical mode decomposition ; Ensemble empirical mode decompositions (EEMD) ; Hilbert Huang Transformation ; Hilbert transformations ; Moment-resisting steel frames ; Structural damage detection ; Structural health monitoring (SHM) ; Algorithm ; Artificial neural network ; Decomposition analysis ; Detection method ; Inverse problem ; Methodology
  8. Source: Scientia Iranica ; Volume 26, Issue 3A , 2019 , Pages 1266-1279 ; 10263098 (ISSN)
  9. URL: http://scientiairanica.sharif.edu/article_21309.html