Advanced Damage Detection Technique by Integration of Unsupervised Clustering into Acoustic Emission

Arash Behnia (Lead Author), Hwa Kian Chai, Mohammad GhasemiGol, Alireza Sepehrinezhad, Ahmad A Mousa

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 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.
Original languageEnglish
Pages (from-to)212-227
JournalEngineering Fracture Mechanics
Early online date5 Jul 2018
Publication statusPublished - Apr 2019

Keywords / Materials (for Non-textual outputs)

  • Acoustic emission
  • torsional loading
  • unsupervised pattern recognition
  • damage detection
  • Non-destructive testing


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