## 1 Introduction
[16] introduced a deep autoencoder-based method to detect and classify fatigue damage modes in carbon fiber-reinforced polymer (CFRP) laminates, relying solely on healthy-state data and clustering latent features for damage type differentiation. More advanced systems combine multiple data sources and learning methods. [PERSON] et al. [17; 18] applied deep learning techniques, specifically convolutional and recurrent neural networks, generative adversarial networks, and attention mechanisms, to recover missing strain data and predict pipeline deformation using long-gauge FBG sensors and multi-source monitoring. However, the application of ML in SHM also introduces its own challenges. One of the primary difficulties is the requirement for large amounts of labeled data, which is essential for training supervised learning models [4]. Considering SHM, this data also needs to sufficiently represent the pristine and damaged states of the system, under a variety of loading conditions. Thus, any potential damage needs to have a measurable influence on the available data in some form. Moreover, to evaluate damage beyond detection and localization, data samples of the damaged structure must exist at the time of training. In the context of composite structures, often safety-critical or high performance parts, acquiring such data is particularly expensive and time-consuming [2]. When only focusing on the detection and localization of damages, one way to avoid the acquisition of damage data is to utilize anomaly detection ML approaches, which only require data of a pristine structure for the classification task. This was carried out by [PERSON] et al. [19] by developing a strain-based SHM method that uses ANN to learn the correlation between the strain at a given location to the strain at neighboring locations, where the measurements are provided by a sensor grid, applied on the surface of a plate-like structure. The method is trained on experimental data of the pristine structure and tested on data of the same structure but with introduced damage. A further approach to address the data acquisition challenge is to use analytical or numerical models for the efficient generation of training data. [PERSON] et al. [20] developed a classifier, which was trained with pristine data and synthetically generated damage data, obtained from simulations of the numerical model of a structure. With this, the detection of damages in the strain data of the real structure was realized. Here as well, the relationship between strains at different locations was modeled by means of local bi-linear regressions, which are then evaluated by a random forest classifier. The strain-based damage evaluation proposed in this work combines the above mentioned approaches, as it adopts the concept of detecting deviations from the learned relationship between individual strain values provided by a sensor grid and additionally utilizes an existing framework for the generation of physics-based strain data for the efficient generation of a large amount of training samples [21]. Furthermore, these samples are only required to describe the state of a pristine structure. Based on the numerical training samples, the SHM application shall identify damages in experimental data of the real structure by comparing new data against learned characteristics of the structure's pristine state. This eliminates the need for expensive data representing the damaged structure. The described approach is also referred to as semi-supervised anomaly detection [22]. It is conceptually situated between supervised and unsupervised learning, since the model only sees samples which are known to be pristine at the training stage. Only during the monitoring stage do the input data potentially contain anomalous samples, which the model then ideally detects. A major challenge herein is the detection of an anomaly, regardless of the environmental and operational variations, e.g., different load cases or temperature changes [2,22].