Crack detection and damage evaluation of asymmetrical steel-reinforced concrete frame nodes using acoustic emission technology

ABSTRACT

Detecting cracks in steel-reinforced concrete (SRC) frame nodes is crucial for ensuring structural safety. This study employs acoustic emission (AE) technology to monitor the growth of concrete structural defects in real time. The aim is to establish a sensitive assessment method based on AE parameters to accurately assess the damage stages of SRC frame nodes by considering the complex crack extension mechanism in SRC structures. An experimental study of the AE characteristics of SRC frame nodes under low-cycle loading was performed. The crack evolution process of SRC frame nodes was monitored and analysed using the AE parameters. The results showed that ringing count is the AE parameter more sensitive to the damage of the SRC frame node. The damage evaluation method was established based on the variation coefficient analysis of the AE parameters. Considering the sudden change in the growth rate of the damage index, the damage evolution process of the SRC frame nodes can be accurately divided into three stages: initial crack compaction, rapid crack expansion, and crack penetration. These stages are consistent with the mechanical properties of SRC frame nodes.

KEYWORDS:

• Acoustic emission
• damage assessment
• steel-reinforced concrete frame nodes
• variation coefficient analysis

Acknowledgments

This research has been partially supported by the National Natural Science Foundation of China (grant number 52378494), Education Foundation of Fujian Province (grant number JAT210287), Open Fund of Fujian Key Laboratory of Digital Simulations for Coastal Civil Engineering (grant number DSCEOF-2202), and Fujian Province Guiding Science and Technology Plan Project (grant number 2021H0032).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China, grant number: 52378494; Education Foundation of Fujian Province, grant number: JAT210287; Open Fund of Fujian Key Laboratory of Digital Simulations for Coastal Civil Engineering, grant number: DSCEOF-2202; Fujian Province Guiding Science and Technology Plan Project, grant number: 2021H0032.