## 2 Background
### Asset Health Index  
The Asset Health Index (AHI) is defined as a quantitative measure that characterizes the condition of an asset and its proximity to the end of its useful life, according to standard EN 13306:2017 [40]. This index allows for the evaluation of the state of assets at a specific moment and usually relies on historical values to parameterize metrics that define intervention thresholds. Since its inception, the AHI has evolved to include multiple indicators and modifiers reflecting operational, maintenance, and reliability conditions, offering a more comprehensive framework for decision-making in asset management [41]. The concept of AHI originates in the British telecommunications sector, where it was used to assess risk based on the condition of electrical assets. Initially, its methodology incorporated modifiers such as geographic location, load, and historical conditions observed [42]. This approach has been expanded to include aspects related to reliability, such as operations and preventive and corrective maintenance, to provide more realistic representations of asset health [41]. These improvements have been fundamental in enabling comparisons between assets and prioritizing investments in maintenance and renovation. Figure 2: Architectures: (**a**) RAMI 4.0 [9]; (**b**) IIRA [4]. Regarding its application, AHIs have proven useful in various industrial and infrastructural sectors. For example, as has been developed in [43], they have been used in the management of bridges [44; 45], roads [46; 47], power transmission lines [48], and transformers [49]. However, many of these applications have primarily depended on sporadic inspections or qualitative evaluations by experts, limiting their ability to dynamically capture the real-time conditions of assets. This reliance on historical and qualitative data restricts the potential of AHIs to predict failures or anticipate maintenance needs in complex systems [48; 50]. One of the main current limitations of AHIs is their inability to integrate real-time data from IoT sensors, which could provide a more accurate and timely perspective on asset degradation [51; 52]. Furthermore, traditional approaches tend to focus on individual components or simple systems, failing to fully capture interactions among multiple dimensions affecting more complex systems. This challenge is exacerbated when a lifecycle perspective is not adopted, reducing the utility of AHIs as predictive tools for long-term planning [53; 54]. Recently, methodologies have been developed to address these limitations through the incorporation of real-time data and the use of IoT technologies. These innovations include dynamic factors, such as real-time operational load, which allow for a more accurate reflection of operational conditions' impact on asset health. Additionally, integrating sensor data with maintenance records and information systems has enabled the creation of more comprehensive and adaptive models [43]. These improvements not only enhance the AHI's ability to predict failures but also facilitate the optimization of maintenance strategies and the efficient allocation of resources in complex systems. Structural Health Monitoring (SHM) in bridges highlights the need to overcome the limitations of traditional methods, such as visual inspections, which are ineffective at detecting early-stage damage in aging infrastructure [45]. Recent advances in SHM techniques, including Operational Modal Analysis (OMA) and automation through advanced algorithms, have improved damage identification. However, the significant influence of environmental and operational factors (EOCs) on structural responses requires statistical normalization to isolate damage indicators. Despite these advances, industrial implementation of SHM remains limited due to the lack of standardized solutions integrating heterogeneous sensors and processing large volumes of data.