## 6 Conclusions  
This paper presented a hierarchical clustering algorithm to differentiate AE sources during the accelerated corrosion process of RC prisms and RC beams. The clustering algorithm is based on the cross-correlation coefficient which is a measure for the similarity between two signals. Dissimilarity (1 minus cross-correlation coefficient) is used as a virtual distance measure to group signals into clusters. The algorithm is applied on two sample scales: RC prisms and RC beams.  
On the scale of RC prisms, it was verified that the algorithm is able to distinguish different clusters based on a user-defined dissimilarity threshold. It allows differentiating macro-cracking from micro-cracking and corrosion. In addition, electromagnetic interference can be distinguished, and subsequently filtered. However, analysis of AE signals obtained from several dummy samples revealed that further differentiation between micro-cracking, corrosion, absorption and hydration is not possible as characteristic features of these signals are overlapping. Signals are therefore much alike. Alternatives to cross-correlation can be investigated. Alternatively, supervised learning algorithms may be applied based on the data sets obtained on dummy samples. Still, the algorithm is proven capable of assessing the onset of corrosion-induced cracking. Results were confirmed by crack measurements and a decrease in peak and center frequency, which was previously shown to be a reliable indicator to pinpoint the onset of macro-cracking.  
Results were scaled up towards corroding RC beams. Two test series were investigated which vary in position of the corrosion zone and AE sensor layout. In each case, the algorithm is able to distinguish signals originating from macro-cracking and gives an early warning before the crack reaches the surface. The used accelerated corrosion setups aim at obtaining high corrosion levels in a limited time frame which might not be representative for natural corrosion processes on-site. Future work will focus on further validation of the clustering algorithm on samples with slower corrosion rates. Additionally, the effect of sensor type and acquisition settings should be further investigated and verified whether similar findings are observed.  
**Author Contributions:** Conceptualization, C.V.S. and E.V.; methodology, C.V.S. and E.V.; software, C.V.S.; validation, C.V.S.; formal analysis, C.V.S.; investigation, C.V.S.; resources, E.V.; data curation, C.V.S.; writing--original draft preparation, C.V.S.; writing--review and editing, C.V.S. and E.V.; visualization, C.V.S.; supervision, E.V.; project administration, E.V.; funding acquisition, C.V.S. and E.V. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was performed within the framework of project C24/17/042 \"Multi-scale assessment of residual structural capacity of deteriorating reinforced concrete structures\", supported by Internal Funds KU Leuven. Moreover, The financial support by FWO-Flanders for the postdoctoral mandate of [PERSON] (Grant no. 12 ZD221N) is gratefully acknowledged.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.