Corrosion monitoring of prestressed concrete structures by using topological analysis of acoustic emission data

Abstract

The potential of topological data analysis (TDA) to aid acoustic emission (AE) in revealing early signs of corrosion in prestressed concrete is investigated. The AE generated by corrosion is quantified in terms of features, including cumulative energy, cumulative number of hits, and peak frequency. Shape (i.e. topological) characteristics of the AE datacloud, which may embed corrosion-related information, are then studied quantitatively using TDA. The proposed method was evaluated in accelerated corrosion testing of a prestressed concrete specimen. AE was recorded non-invasively on the concrete surface, with more than 600 000 hits observed over 118 cycles of accelerated corrosion (spanning 206 d). The large AE datacloud contained holes, which opened and closed near distinct points in the corrosion process. These holes were quantified using TDA, and shown to correlate with and provide early indications of certain corrosion mechanisms. The results highlight the potential of TDA to aid in extracting corrosion information from AE data. Further, with TDA aiding traditional AE monitoring, there is potential for early and reliable indication of concrete cracking, prior to the appearance of external visual signs.