### Research Gaps and Questions  
For complex technical systems like offshore platforms, traditional maintenance management approaches such as Risk-Based Inspection (RBI) and Reliability-Centred Maintenance (RCM) have been fundamental in enhancing the effectiveness of inspection and maintenance planning [5]. These methodologies provide structured frameworks for prioritising maintenance activities based on risk assessments and the equipment's criticality [6]. However, their implementation often relies heavily on expert judgment and historical data, which may not effectively capture the dynamic nature of corrosion progression.  
Recent advancements in artificial intelligence (AI) have significantly transformed corrosion prediction and assessment capabilities [7]. AI-based systems can process and analyse vast inspection data [6], enabling more accurate predictions of corrosion progression patterns [8]. These systems overcome traditional limitations by providing data-driven insights for maintenance decision-making, reducing dependency on subjective assessments [9]. Emerging research indicates that combining RBI/RCM methodologies with AI-driven predictive capabilities offers promising results in maintenance optimisation. For instance, computational intelligence has been successfully applied to transformer maintenance [10], and AI-enhanced RCM systems have significantly improved maintenance efficiency [11]. Comprehensive reviews of the evolution of power system maintenance suggest that this integrated approach represents the future of maintenance strategies [12]. However, despite these advances in the power sector, similar integrated approaches have not been adequately explored or implemented in the oil and gas industry, particularly for offshore facilities [13].  
Research has highlighted integrating advanced technologies to enhance decision support in infrastructure management. Digital twins, combining monitoring and simulation, are proposed for risk management in underground gas storage [14] and lifecycle optimisation of civil infrastructure [15]. These advancements aim to improve infrastructure management's sustainability, safety, and efficiency [16]. However, research has not adequately addressed the specific application of these advanced technologies in the context of offshore platforms, particularly in corrosion maintenance and planning for anti-corrosive painting [17]. Despite advances in managing onshore infrastructure, a significant gap exists in integrating 3D CAD/CAE models with decision support systems for offshore platforms [18]. This research gap emphasises the need to develop integrated technological solutions that specifically address the challenges of maintenance and integrity management on offshore platforms, combining the power of machine learning models and optimisation algorithms tailored to this unique environment.  
The current research presents a notable gap in addressing the specific application of these advanced technologies in offshore platforms, particularly regarding maintenance planning and the optimisation of anti-corrosive painting [19]. The growing demand for more efficient and risk-based maintenance strategies [20], aimed at reducing the high costs associated with corrosion [21] and improving the reliability of assets in this critical sector, remains a pressing need [21]. Given this scenario, innovative solutions that optimise the inspection and maintenance process in offshore installations, considering the particularities and challenges of this sector, are needed. The Design Science Research (DSR) methodology emerges as a promising approach to addressing this problem, focusing on designing and developing artefacts that solve relevant and complex problems [22].  
Given this complex scenario, this study aimed to answer the following research question (RQ):  
RQ: How can the DSR approach contribute to developing innovative solutions for maintenance challenges in the offshore sector?