## 4 Results  
The APM prototype represents a significant advancement in integrated maintenance management for offshore platforms, addressing needs comprehensively from conceptual design to operation. This innovative system merges human resource management with technical data, benefiting companies and society. The information regarding the painting area in the system comes from the 3D CAD/CAE tool. This tool uses an identification system that includes the platform, module, sector, and specific system. This detailed categorisation allows for precise planning tailored to the particular needs of each platform component (Figure 4).  
Figure 4 shows the hierarchical structure of the offshore platform, broken down into modules, sectors, and systems to facilitate the planning process for anti-corrosive maintenance. Each module represents a large section of the platform, the sector is a module subdivision, and the system corresponds to specific components requiring maintenance. The study compared the painting plans generated by each of the four APM strategies for the platform using an inspection spreadsheet containing field-acquired corrosion data from inspectors. The input data for each strategy were identical to maintain comparability. To import the inspection spreadsheets, we selected the \"convert inspection spreadsheet\" option and inserted the path to the spreadsheet file (Figure 5). Upon selection of the \"browse\" option, APM validates the input data, and if everything is correct, it automatically infers the corrosion's evolution and proceeds to the next exploratory visualisation screen. This machine learning-based analysis uses historical inspection data to forecast how corrosion will develop over time if no maintenance interventions are performed.  
Figure 4: Visualisation of the platform created with the 3D CAD/CAE tool and items.  
Figure 5 illustrates the data loading interface of the APM system, including a selection field for inspection sheets containing information on corrosion levels and specific climatic conditions for each location. \"UN\" refers to the specific unit where the study is conducted, \"Platform\" indicates the platform's name, \"Inspection Year\" shows the year of inspection, and \"Corrosion Limit\" is the corrosion target expected to be achieved during maintenance. Each interface element is clearly labelled to guide the user through uploading and converting the inspection data required for analysis. Before the simulation strategies are configured, the user can view the platform's condition, based on four indicators: corrosion, painting area integrity, criticality, and regulatory demand (RD). The corrosion indicator presents both the current condition and the AI-predicted future state of the platform, allowing users to visualise how corrosion would progress if no maintenance interventions were implemented. This comparative view helps users understand the potential deterioration trajectory and the urgency of maintenance needs. These indicators provide the user with a general overview (entire platform) and a detailed view (stratification by modules/systems), guiding them in selecting the most appropriate strategy. Additionally, they can clearly understand the scale of resources that need mobilisation to generate an effective painting plan.  
Figure 6 provides an exploratory visualisation of the key asset indicators, such as the corrosion index, irreversibility, criticality, and regulatory demands. \"Current Average Corrosion\" represents the current level of corrosion on the platform, \"Irrevest Index\" indicates irreversibility, \"Criticality Index\" shows the criticality of the components, and \"RDs Index\" reflects the regulatory demands. This information presents the initial condition of the platform before implementing the maintenance and planning. Detailed explanations of each index and its role in the analysis allow the user to interpret the asset's condition clearly before configuring a maintenance strategy.  
Figure 5: Home screen of APM: inspection spreadsheet input.