## 4 Conclusions  
A semi-supervised SHM approach has been presented, which is capable of detecting and localizing damages in spatially discrete strain data of a real structure. This is achieved by solely learning the pristine structure's behavior under load from synthetic strain data, which is derived from simulations of a corresponding numerical model. The SHM approach is independent of environmental variables, like varying loading scenarios. There is no need to train the method with a specific damage characteristic, in contrast to conventional supervised ML approaches in SHM that require labeled damage data [13; 14; 15]. The localization of the damage is possible due to the sensor-wise architecture of the method and the known grid setup. This approach also illustrates one of the challenges of strain-based SHM methods quite well when it comes to monitoring larger structures. Damages or discontinuities in a structure only affect the strain field in their local vicinity. This implies a lower bound on the number of sensors and their density required on the structure [4]. The FOSs have shown considerable potential in this context due to their spatial resolution and their ability to be embedded in or applied to complex composite structures [1; 3]. Compared to conventional strain gauges, they are well suited for denser sensor layouts and can be tailored more effectively to the geometry and loading characteristics of the monitored structure. This enables the optimization of sensor grid design to improve sensitivity and coverage [10; 28]. Since a regular, evenly spaced sensor grid layout is not a mandatory requirement of the SHM approach presented in this work, applying FOS technology represents a promising direction for future research. In the context of data-driven SHM, however, the ability to measure the strain field alone is not sufficient. For SHM Levels 2 and above, supervised learning approaches are typically required, which in turn depend on labeled damage data at the training stage. This data is typically rarely available [4; 29]. As shown in this work, the utilization of synthetic training data mitigates this problem to some extent, but there is much more potential. Recent research focuses on approaches like federated learning [30; 31; 32] or population-based SHM [4; 33]. The latter leverages transfer learning techniques, specifically domain adaption, to increase the available data by moving from a single structure to a population of structures. Through the functional abstraction of structures, the diagnosis of a data-poor  
Figure 12: Damage localization by interpolation of the residuals \(\delta_{j}\) of the larger experimental damage state under the load case 814. The true damage is marked by the red, dash-lined circle. The black dots indicate the sensor locations. target structure is enabled when there is a data-rich source structure similar enough to the initial one. Promising results have been reported in applications such as rotary machine monitoring, using transfer learning techniques like domain-adaption and fine-tuning [34]. These techniques present an interesting direction of further research, as they promise to improve the matching of physical and numerical models. Conceptualization, F.F. and C.K. ; methodology, F.F. and C.K. ; validation, F.F. and C.K. ; investigation, F.F. and C.K. ; writing--original draft preparation, F.F. ; writing--review and editing, F.F. and C.K. ; visualization, F.F. ; supervision, C.K. and M.S. ; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript. This work has been supported by the COMET-K2 Center of the Linz Center of Mechatronics (LCM), funded by the Austrian federal government and the federal state of Upper Austria. Not applicable. The data presented in this study are available upon request from the corresponding author. Author [PERSON] was employed by the company Linz Center of Mechatronics GmbH.