## 5 Discussion  
The triangulation of results obtained from the different case studies in this research allows for a more comprehensive and well-grounded view of the APM prototype's contributions to maintenance and painting planning in offshore platforms. By combining evidence generated through different methods, data sources, and theoretical perspectives, triangulation strengthens the credibility and robustness of the conclusions, increasing confidence in the effectiveness of the developed tool [91]. This approach provides more precise control over critical areas for structural integrity, demonstrating that the model addresses the regulatory compliance needs, optimises resources, and improves strategic planning in high-demand environments. One of the triangulation strategies applied was methodological triangulation, which compared the results obtained through different approaches, such as evaluating the per  
Figure 14: PH limit index (comparison of strategies). formance of the machine learning model for predicting corrosion progression, using the regulatory demand index and the criticality index in real-world contexts, and analysing the painting plans generated by APM, considering different optimisation strategies. The convergence of positive results obtained from these different methodologies reinforces the validity and usefulness of APM in supporting maintenance and painting planning for offshore platforms [82]. These findings are consistent with previous studies on predictive maintenance and asset management [92], highlighting the importance of optimised, multifactorial planning for operational sustainability in hostile environments [71]. Incorporating machine learning and multi-criteria optimisation in APM goes beyond traditional approaches, aligning with Reliability-Centred Maintenance (RCM) theories that emphasise the control of critical variables such as corrosion in strategic assets [6]. Another strategy employed was data triangulation, which compared information collected from various sources, such as inspection histories, regulatory demand spreadsheets, criticality assessments of the systems, and feedback from APM users. The consistency of the insights generated from these multiple data sources increases the reliability of the conclusions regarding the prototype's effectiveness [93]. For example, the triangulation of the case study results demonstrates that integrating corrosion progression prediction models, the regulatory demand index, and the criticality index in APM allows for optimised painting plans, considering multiple relevant criteria for offshore assets' integrity management. Figure 15 compares the different maintenance strategies for the painted areas. The comparison of maintenance strategies reveals compelling patterns in resource allocation and area coverage for offshore installation maintenance. The criticality strategy emerges as the most comprehensive, prioritising the most vulnerable and vital zones for structural integrity, demonstrating its effectiveness in addressing critical systems despite high resource demands. Closely following that is the PH strategy, showing strong optimisation of the available labour resources while achieving significant reductions in corrosion without specifically addressing the regulatory demands or the system's criticality. In contrast, the target strategy presents an intriguing efficiency profile, achieving the necessary maintenance objectives with notably lower resource consumption. This suggests a highly optimised approach that meets the designated corrosion control targets without exhausting the available resources. The regulatory demand strategy reflects a focused approach to the requirements of compliance, though this coverage reduces to 10,146 m\({}^{2}\) when disregarding  
Figure 15: Painted area (comparison of strategies). specific targets.