Experimental study and acoustic emission monitoring on damage mechanism of stud shear connectors

Stud shear connectors are popular connectors used in steel-concrete composite structures. Assessing the condition and damage process of studs embedded in concrete is difficult. This paper proposes to assess the condition and damage process of stud shear connectors using an acoustic emission (AE) tec...

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Bibliographic Details
Published in:Structures (Oxford) Vol. 63; p. 106344
Main Authors: Yao, Yadong, Si, Haopeng, Liu, Ke, Ni, Tianmeng, Gao, Lu
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2024
Subjects:
Acoustic emission (AE)
Failure mode
Fuzzy C-means clustering algorithm
Steel-concrete composite structure
Stud shear connectors
Fuzzy C-means clustering algorithm
Failure mode
Stud shear connectors
Steel-concrete composite structure
Acoustic emission (AE)
ISSN:2352-0124, 2352-0124
Online Access:Get full text
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Summary:Stud shear connectors are popular connectors used in steel-concrete composite structures. Assessing the condition and damage process of studs embedded in concrete is difficult. This paper proposes to assess the condition and damage process of stud shear connectors using an acoustic emission (AE) technique. Nine steel-concrete push-out specimens with stud shear connectors were tested, and AE sensors were used to monitor the damage process and explore the damage mechanism. The test results showed that the specimens failed due to stud fracture, accompanied by concrete cracks. The damage process included three primary stages featuring different characteristics of AE energy. An unsupervised machine learning algorithm called fuzzy C-means clustering was used to analyze the AE signals, and AE events were classified into three clusters, and various damage modes (i.e., concrete cracks and steel-concrete interface debonding, stud fracture, and concrete crushing) were identified based on the parameter characteristics and positions of AE events.
ISSN:2352-0124
2352-0124
DOI:10.1016/j.istruc.2024.106344