## 1 Introduction  
Steel corrosion in reinforced concrete (RC) structures causes large economic losses and jeopardizes the structural safety. The process starts internally with a reduction of the steel cross section and the formation of corrosion products. These products exert a pressure on the surrounding concrete, eventually leading to concrete cracking. The process starts internally which means that damage can be observed by visual inspection after a certain time period when the damage has progressed to the surface. Electrochemical techniques such as half-cell potential, galvanostatic pulse method, and linear polarisation resistance, allow focusing on the probability, the rate, and location of ongoing corrosion processes from an early stage [1]. However, they are typically inspector-, condition-, and material-dependent [2, 3]. Moreover, only sporadic inspections can be performed as most of these techniques do not allow the continuous monitoring of the structure. Dedicated monitoring is necessary to assess the current state of these RC structures and to make well-informed decisions. The acoustic emission (AE) technique can be valuable to reach this goal as it allows monitoring the structure in a non-invasive way before damage can be observed on the surface. The application of the AE technique during the corrosion process has been widely studied in the literature. Several researchers have performed parameter-based analyses investigating cumulative AE event or energy trends [4, 5, 6, 7], individual parameters such as amplitude and duration [4, 8, 9, 10, 11], rise-angle (RA) and average frequency (AF) values [12, 13, 14, 15], b-value and Ib-value [12, 13, 14, 16], and intensity parameters such as historic and severity index [6, 17, 18, 16]. Many parameter-based AE methods provide consistent resultsand allow differentiating between cracked and uncracked samples. However, these methods are less applicable for source identification, and may be difficult to scale up towards on-site monitoring as the entire history is needed. The study of signal-based analysis of AE signals obtained during corrosion of metals has been studied extensively using advanced signal-processing methods [19; 20; 21]. However, signal-based analysis of corrosion in RC samples is still limited. The surrounding concrete layer influences the AE signals due to a different wave propagation path. Moreover, additional AE sources, such as absorption and hydration, are present in typical accelerated corrosion setups. Few researchers have studied the peak and/or center frequency of AE signals obtained during corrosion of RC samples [5; 11; 18]. However, this remains a difficult task due to the heterogeneous nature of RC and the complexity of the corrosion damage process. It has been found that absolute values for AE source characteristics should be approached carefully and the result may depend on the sensor type and acquisition settings [5]. Clustering and classification of AE signals is gaining more and more attention. In data mining, there is an important difference between these two concepts. Clustering or unsupervised learning tries to group a data set into a number of clusters, usually based on a distance measure. It can find an internal structure in the data that was not known beforehand. Examples of clustering algorithms are k-means, fuzzy c-means, self organising map, and hierarchical clustering [22]. In classification or supervised learning, new data is assigned to predefined classes based on a training set. Examples are support vector machines (SVM) and neural networks [22]. Both clustering and classification can be performed based on AE parameters or based on the entire waveform. Classification and clustering have been successfully applied on AE data in composites and in fibre reinforced concrete. [PERSON] et al. [23] combined both an unsupervised (k-means) and supervised method (k-nearest neighbours method) to distinguish signals originating from matrix cracking and decohesion during tensile tests on unidirectional glass/polyester composites.