## 6 Conclusions and Areas for Further Research  
This study focuses on maintenance digitization, highlighting the gap between technological advancements like IoT platforms and digital twins and their practical implementation. The proposed framework creates a unique digital representation of the asset, akin to concepts like the Asset Administration Shell (AAS), while promoting a model-based approach for asset and maintenance management. The practical aspects of maintenance management take center stage in the framework, highlighting the need to integrate advanced digitization solutions with a focus on IoT platforms and digital twins. By prioritizing the development of asset-related models and advocating for their integration within a comprehensive digitalization architecture, this study provides a structured approach that prevents the isolation of models, reduces extra costs, shortens implementation times, and mitigates interoperability issues. As a use case, a notable contribution of this study is the consideration of an AHI model, addressing the limitation of many IoT/cloud networks that primarily focus on short-term decisions. This model connects real-time monitoring with medium- and long-term planning, including End-of-Life (EOL) management. The integration of the AHI model emerges as an interesting component in digitization, facilitating knowledge management within advanced asset digitization models. This study addresses challenges with infrastructure assets lacking historical data by integrating monitoring variables with factors influencing asset degradation. The adaptable model enables refinement through real degradation processes and emphasizes detailed data recording and analysis. The results of this study demonstrate the effectiveness of integrating IoT-based monitoring with the AHI model for asset management. However, incorporating uncertainty and sensitivity analyses could further enhance the framework's robustness. Uncertainty analysis would evaluate how variations in input data, such as sensor inaccuracies and environmental changes, affect AHI outputs, while sensitivity analysis would identify key parameters influencing decisions. These additions would improve predictive reliability and provide greater confidence in recommendations. Future work could employ advanced computational methods, such as Monte Carlo simulations, to address these aspects and optimize maintenance strategies. In summary, this study provides a framework for effective maintenance digitization, emphasizing practical implementation, model-based perspectives, and the strategic integration of an Asset Health Index model, ultimately contributing to the advancement of digitization practices in the field of maintenance management. While our research represents a step forward in the field of asset digitalization and management, there are several avenues for future exploration. These include further refinement of model integration techniques, empirical validation of the proposed framework in diverse industrial contexts, and strategies for scaling the framework across different organizational settings and industry sectors. In order to do so, a positive factor is that the proposed framework is designed with modularity and flexibility to ensure its applicability to a broad range of asset types and operational contexts. For instance, for assets with shorter lifecycles, such as equipment in manufacturing plants, the framework could incorporate dynamic monitoring parameters and more frequent updates to the Asset Health Index(AHI). Similarly, for highly complex systems, such as chemical plants or power grids, the IoT network design could integrate redundant monitoring systems to ensure continuous data flow and robustness under critical conditions. Also, although this study focuses on bridges, the underlying methodology is inherently scalable.