This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities.

Background: The construction industry has been seeking ways to improve productivity through digitalization, with Building Information Modeling (BIM) emerging as a key methodology. However, the existing literature remains unclear about the relationship between BIM implementation and productivity metrics across different project stages. Objective: This study aims to analyze productivity indicators applied in construction projects using BIM methodology and their alignment across project lifecycle stages, providing guidance for future research pathways in construction productivity. Methods: A comprehensive scoping review was conducted using bibliometric and content analysis methods. The study analyzed 151 documents from Scopus and Web of Science databases, supplemented by gray literature. The analysis focused on identifying productivity metrics, project types, and lifecycle stages where BIM is applied. Results: The analysis revealed that productivity indicators align better when mapped according to project lifecycle stages. Most studies (70%) focused on case studies and surveys, primarily in pre-design and construction phases. China and the United States dominated the literature (73% of publications). The research identified three main project categories: residential, commercial, and industrial/specific buildings. Productivity metrics varied by project complexity, with more complex projects showing greater emphasis on mechanical and electrical assembly team performance indicators. Conclusion: The study demonstrates that the potential of BIM for productivity enhancement remains partially untapped, particularly in the post-construction phases. While some productivity indicators receive substantial attention, others remain under-researched. The findings suggest the need for establishing standardized productivity metrics across project lifecycles and expanding BIM application beyond traditional construction phases, particularly in the operation and maintenance stages.