## 1 Introduction
Models reproducing degradation have been widely studied in different contexts. [PERSON] et al. [8] classify degradation models for different kinds of civil infrastructure into four basic categories and provide a review of their particular use cases. They distinguish physics-based approaches, which rely on mathematical models to calculate the mechanisms leading to degradation; knowledge-based models in cases when complexity is so high that expert knowledge must be incorporated; data-driven models; and hybrid models, as a combination of the ones previously mentioned. In connection with railway infrastructure, there are several examples of degradation or entire life cycle models of the different model types, which are applied in different contexts. The German infrastructure manager DB Netz AG, which analyses the cause-effect relationship as well, is currently considering the degradation of its infrastructure assets via various approaches. [PERSON] et al. [9] use a prediction approach to describe degradation for bridges and tracks, whereas switches, interlockings, and railway crossings are each modelled using a risk approach. In this context, specific quality indicators of different infrastructural assets are considered. In [10], [PERSON] et al. describe the prediction of failures in interlockings in Germany using regressions. [PERSON] et al. [11] use a frequency-domain approach to assess the influence of track stiffness on their degradation. [PERSON] et al. [12] implement an artificial neural network to model track degradation and to identify the key factors that have the heaviest impact on track degradation. In [13], degradation models for the different infrastructure elements relevant to line sections are developed and used to optimize maintenance strategies. However, the focus of this paper is on station infrastructure and not on line infrastructure. [PERSON] [14] investigates the parameters that influence the actual service life of railway bridges, taking into account the varying degradation of different materials. Only a few publications have dealt with the degradation of station infrastructure so far. [PERSON] et al. [15] researched the modeling of degradation functions for station infrastructure and described a data-based modeling approach in detail. The focus is on data evaluation to separate aging effects from maintenance influence. Also, concerning different prioritization approaches, there are studies in which methods for the determination of the most effective strategies are developed. In [16], factors considered in the prioritization of maintenance measures and existing methods for the prioritization are reviewed. The authors state that an analytical hierarchy process, the use of priority criteria or matrices, and failure mode and effect analysis are the most commonly applied methods to schedule maintenance. [PERSON] [17], for example, describes how equipment inspection can be integrated into a procedure with the assessment of failure consequences in operation to evaluate the total risk for the equipment used. Similarly, [PERSON] and [PERSON] [18] investigate the risk associated with failure modes of equipment. They not only use historical data but also predicted data and compare the resulting maintenance order to the existing maintenance schedule with an example of a gearbox in a steel plant. However, the modeling of complete life cycles for infrastructure depends on a multitude of infrastructure characteristics and quality indicators. These characteristics often strongly vary, depending on what kind of element is considered. This is why it is difficult to find a general model that applies to a variety of different infrastructure types and to transfer existing models to railway infrastructure assets. In addition, the assessment of the most effective maintenance strategy is dependent on external and problem-specific factors, such as feasible maintenance strategies in the investigated scenario or available data.