## 4 Asset Digitalization Use Case: IoT and AHI App for a Bridge Maintenance
### The Bridge Health Index Model  
The initiation of the health model hinges on the information gathered through the monitoring and digitization solution outlined in the preceding section, constituting an integral component of the entire process. However, it is crucial to view the management of the health model as an ongoing, open-ended procedure, as illustrated in Figure 8.  
Integrating the proposed IoT network is essential for processing monitoring data on the platform, allowing for the computation of indicators that represent the asset's health condition. The application of a novel methodology involves calculating the health index based on indicators, input variables, and interpretation of health and reliability modifiers. Figure 8: AHI model evolution process. Figure 7: IoT platform with information about bridge location, nodes, and sensors. Validation of the results, comparing calculated health status with expert estimates, is crucial for refining the model. In Figure 9, the reader can also appreciate the steps of the process where the different factors acting as health (temperature, radiation, humidity, etc.) or reliability modifiers (maintenance fulfillment level, etc.) are introduced. Formal equations can be found in [37]. Figure 10 outlines the comprehensive framework for the architecture of the Asset Health Index (as presented in Figure 1[13]) proposed using the six-step methodology (process) elaborated upon in [37] (see Figure 9). Figure 10: The AHI model description through the DMM framework. Figure 9: The six-step methodology presented in [37] to obtain the health index. The complete model integration with the platform involves data extraction, processing, and loading (ETL Processes), providing necessary inputs for databases (DB Processes) needed for the following:  
* Calculating the Asset Health Index to interpret the impact on the asset's health through normalized and pondered modifiers (asset class, normal life, location and load factors, age, maintenance and other incidents files, sensors, etc.). * Updating and correcting aging rate (\(\beta\)c(_t_)) at a given time _ti_ to project the health index and failure frequency over the asset lifecycle according to a given strategy for overhauls (for instance, conduct overhauls based on the AHI or the asset's age) and considering a standard failure rate when the asset is in good condition (_Std_\(\lambda\)(_t_)). * Estimating the lifecycle cost profiles assuming the cost of different maintenance operations (PM, CM, and overhaul or major maintenance). The Intelligent Asset Management systems (in AHI Processes Apps), supported by mathematical modeling, simulation modeling (Simulation App), and interpretation rules, establish thresholds for modifier calculation and formulate the health index, facilitating result representation in Business Intelligence tools (BI App/User Interface)). They also project health and cost metrics over the asset lifecycle, if needed, according to selected overhaul/replacement strategies (see Figure 8). Interpreting the Asset Health Index (AHI) directly for an individual asset facilitates decision-making at the specific asset level. This involves defining and managing thresholds established on the index to delineate states and state changes that guide the maintenance strategy, as illustrated in Table 3. When the AHI is determined at the component level or \"child asset\" level, understanding the health of the \"parent asset\" involves applying interpretation rules. These rules enable the combination of AHI interpretations for all children, providing a comprehensive overview of the parent asset's health. AHI handling complements economic calculation models like ToTex or Life Cycle Cost (LCC) models in designing data-driven decision-making processes for medium- and long-term decisions, such as planning major maintenance, replacements, and reinvestments.