## 5 Evaluation of the Clustering Algorithm  
In the previous sections, the clustering algorithm was applied on an extensive dataset including dummy samples, RC prisms, and RC beams. AE sensors and AE acquisition settings were kept the same. However, AE setups varied for different sample sizes. On the two sample scales, it can be observed that two clusters can be obtained, which enables to distinguish macro-cracking. In this section, some important parameters and settings are discussed for application of the presented clustering algorithm on datasets obtained during accelerated corrosion in RC. First, the stability of the clustering algorithm over a range of input settings, or in other words the algorithm's sensitivity to parameter variations, is discussed. Hierarchical clustering is known to be a clustering method that requires few input settings or parameters [22]. This can be seen as one of the main advantages. Only two input parameters need to be chosen: the type of linkage, and the number of clusters (or value of the dissimilarity threshold). The type of linkage was investigated and complete linkage, which is less susceptible to noise and outliers, was found to be most appropriate for the current application. Average and single linkage resulted in one large cluster with all signals chained to each other. Once an AE signal is assigned to a cluster, the assignment is irreversible. This can be seen as a disadvantage of hierarchical clustering as signals that are incorrectly assigned to an existing cluster in the beginning cannot be moved to a different cluster afterwards. As the algorithm is used on an extensive experimental dataset, it can be observed from the presented results that the algorithm behaves similar in different cases when using complete linkage. Similar results are obtained for different corrosion levels, sample sizes, and AE setups. The number of clusters or the dissimilarity threshold should be chosen as well. In the current analysis, the requirement was found to be twofold after verification on the dummy samples and  
Figure 10: Clustering results for Beam-3 (**left**) and Beam-4 (**right**) represented as peak frequency (**top**) and center frequency (**bottom**) versus time. The period in which the surface crack occurred is indicated by the grey area. application on the two sample scales: the maximum number of clusters should be 3 when electromagnetic noise is not filtered beforehand, otherwise it should be 2, in addition, the dissimilarity threshold should be higher than 0.9. It is shown on the dummy samples that dividing the dendrogram in more clusters, no accurate distinction can be made between AE sources. This holds for the current datasets, but should be reconsidered when applying the algorithm on other datasets. Second, the correctness of the clusters themselves can be discussed, which is less straightforward for hierarchical clustering than for supervised learning algorithms. The results prove that macro-cracking can be distinguished in all presented cases. In this paper, this is validated by crack width measurements. The correctness of the clusters may be influenced by the data quality. It should be mentioned that it is important to filter the AE data first in order to use the most reliable AE signals as it is known that hierarchical clustering algorithms are less robust against noise and outliers [22]. In this paper, we applied the algorithm only on filtered localized AE events. This reduces the probability of the presence of low-frequency noise sources or badly triggered AE signals that influence the results. Still, it is shown that the algorithm is able to identify high-frequency noise signals. Third, the robustness of the algorithm when different AE sensors and AE acquisition settings are used, should be investigated in future work as this was outside the scope of the current paper. The algorithm works well for the current (extensive) datasets, AE sensors, and AE acquisition settings. It can be concluded that it is accurate for different AE setups.