Cluster analysis and damage identification for FRP/steel-confined RC column using AE technique

This article presents the damage evaluation and pattern recognition for the newly proposed fiber reinforced polymer (FRP)/steel-confined reinforced concrete columns. The interaction of FRP material, steel tube, and reinforced concrete lead to complex damage mechanisms and invisible damage modes. The prevailing acoustic emission (AE) technique was applied to monitor the damage process and detect the sheltered damages under cyclic loading. Characteristic AE parameters, such as energy and duration, were extracted to disclose the damage evolution and evaluate the damage state. Three typical damage stages were identified. The fuzzy C-means (FCM) algorithm and particle swarm optimization (PSO) algorithm were applied as efficient clustering tools to discriminate different damage signals of FRP/steel-confined RC columns. Five types of damage mechanisms were identified and illustrated based on the statistical analysis of typical AE features. Furthermore, typical damage waveforms were extracted, the frequency content of each damage signal was discussed on the basis of wavelet transform.

Automated summary

This article investigates the damage evaluation and pattern recognition for the newly proposed FRP/steel-confined reinforced concrete columns, using AE technology to monitor the damage process and FCM and PSO algorithms for clustering analysis of different damage signals. Five damage mechanisms were identified, and typical damage waveforms were extracted for frequency content analysis.