Using Acoustic Emission to Monitor Failure Modes in CFRP-Strengthened Concrete Structures

Abstract

Carbon fiber–reinforced polymer (CFRP) composites have been widely used to repair and strength concrete structures. Nevertheless, the durability and long-term performance of FPR-strengthened structures are still not well understood. To this end, nondestructive techniques (NDTs) such as acoustic emission (AE) are usually adopted for the inspection and monitoring of composite structures. The objective of this study is to monitor the damage modes in CFRP-strengthened reinforced concrete structures using the AE technique together with advanced statistical analysis and pattern recognition (PR) methods. Three concrete cube specimens bonded with CFRP sheets and two full-scale RC beams before and after retrofitting were tested to acquire AE data originating from critical damage mechanisms. Because the damage mechanisms in the retrofitted RC beams are unknown a priori, a methodology based on the unsupervised k-means clustering analysis, and the supervised neural networks (NNs) were developed. By applying k-means clustering analysis, each data cluster was identified to associate with one or more damage mechanisms for the typical specimens. The NN models based on multilayer perceptron (MLP) and support vector machines (SVMs) were then created and applied to other similar samples, which show quite satisfactory performance on damage mode identification.