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Advanced damage detection technique by integration of unsupervised clustering into acoustic emission

Behnia, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.engfracmech.2018.07.005
  3. Publisher: Elsevier Ltd , 2018
  4. Abstract:
  5. The use of acoustic emission (AE) technique for damage diagnostic is typically challenging due to difficulties associated with discrimination of events that occur during different stages of damage that take place in a material or a structure. In this study, an unsupervised kernel fuzzy c-means pattern recognition analysis and the principal component method were utilized to categorize various damage stages in plain and steel fiber reinforced concrete specimens monitored by AE technique. Enhancement of the discrimination and characterization of damage mechanisms were achieved by processing time and frequency domain data. Both domains (time and frequency) were taken into account to propose new descriptors for crack classification purposes. A cluster of AE data in three classes of Kernel Fuzzy c-means (KFCM) was obtained. The clustered data was subsequently correlated with each particular damage stage for identifying the peak frequency range corresponding to the respective damage stages. Moreover, a novel quantitative technique called Spatial Intelligent b-value (SIb) Analysis was proposed to quantify damage for each stage. © 2018 Elsevier Ltd
  6. Keywords:
  7. Acoustic emission ; Non-destructive testing ; Torsional loading ; Unsupervised pattern recognition ; Acoustic emissions ; Damage detection ; Fiber reinforced materials ; Frequency domain analysis ; Fuzzy systems ; Pattern recognition ; Principal component analysis ; Reinforced concrete ; Steel fibers ; Structural health monitoring ; Acoustic emission techniques ; Damage detection technique ; Non destructive testing ; Quantitative techniques ; Steel fiber reinforced concretes ; Torsional loadings ; Unsupervised clustering ; Unsupervised patterns ; Acoustic emission testing
  8. Source: Engineering Fracture Mechanics ; 2018 ; 00137944 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/abs/pii/S0013794418302054