TY - JOUR
T1 - Advanced damage detection technique by integration of unsupervised clustering into acoustic emission
AU - Behnia, Arash
AU - Chai, Hwa Kian
AU - GhasemiGol, Mohammad
AU - Sepehrinezhad, Alireza
AU - Mousa, Ahmad A.
N1 - Funding Information:
The authors would like to acknowledge the Ministry of Higher Education (MOHE) for the provision of financial support for this project under HIR Grant No. UM.C/HIR/MOHE/ENG/54. Thanks are also extended to Mr. Sreedharan A/l V. Kraman and Ms. Maria Bagherifaez of the University of Malaya for their pivotal role in the conducted laboratory testing and also Dr. Shahaboddin Shamshirband for his advices.
Funding Information:
The authors would like to acknowledge the Ministry of Higher Education (MOHE) for the provision of financial support for this project under HIR Grant No. UM.C/HIR/MOHE/ENG/54. Thanks are also extended to Mr. Sreedharan A/l V. Kraman and Ms. Maria Bagherifaez of the University of Malaya for their pivotal role in the conducted laboratory testing and also Dr. Shahaboddin Shamshirband for his advices.
Publisher Copyright:
© 2018 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Acoustic emission
KW - Damage detection
KW - Non-destructive testing
KW - Structural health monitoring
KW - Torsional loading
KW - Unsupervised pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85050495939&partnerID=8YFLogxK
U2 - 10.1016/j.engfracmech.2018.07.005
DO - 10.1016/j.engfracmech.2018.07.005
M3 - Article
AN - SCOPUS:85050495939
VL - 210
SP - 212
EP - 227
JO - Engineering Fracture Mechanics
JF - Engineering Fracture Mechanics
SN - 0013-7944
ER -