Signal-Based Acoustic Emission Clustering for Differentiation of Damage Sources in Corroding Reinforced Concrete Beams

Corrosion in reinforced concrete (RC) structures is a major durability issue that requires attention in terms of monitoring, in order to assess the degraded condition and reduce financial costs for maintenance and repair. The acoustic emission (AE) technique has been found to be useful to monitor damage due to steel corrosion in RC. However, further development of monitoring protocols is still necessary towards on-site application. In this paper, a hierarchical clustering algorithm based on cross-correlation is developed and applied to automatically distinguish damage sources during the corrosion process. The algorithm is verified on dummy samples and corroding RC prisms. It is able to distinguish two clusters of which the first one containing AE signals due to corrosion, absorption, hydration, and micro-cracking, and the second one AE signals due to macro-cracking. Electromagnetic interference can be distinguished as a third cluster and filtered subsequently. Due to overlapping characteristics, further differentiation of the first cluster is not possible. Afterwards, the algorithm is scaled up to two sets of RC beams, one set with a uniform corrosion zone, and the other set with a local corrosion zone. In addition, on this sample scale, the algorithm is able to successfully differentiate macro-cracking from corrosion and micro-cracking. It can therefore serve as an additional tool to assess the extent of corrosion-induced damage.

Automated summary

In this study, a hierarchical clustering algorithm based on cross-correlation was developed to automatically distinguish damage sources during the corrosion process. The algorithm was verified and found to be successful in differentiating macro-cracking from corrosion and micro-cracking at the sample scale, offering potential for assessing the extent of corrosion-induced damage.