Development of a decision-making framework to prioritize expressway pavement sections for large-scale repairs

This study presents a structured and data-driven framework for selecting expressway pavement sections for large-scale repairs. In many countries such as Korea, where most expressways are already constructed and aging, long-term maintenance planning has become more critical than new construction. Existing repair decisions often rely on heuristic and project-level approaches, which lack consistency and scalability. To address these challenges, this framework links unit-level condition assessment with continuous section-level planning through a combination of expert judgment and predictive modeling. It extends the remodeling index (RMI), which was previously developed, by incorporating quantitative predictions of pavement deterioration to improve decision making. Using 100-m units, the method quantifies urgency and aggregates these units spatially into homogeneous deteriorated sections. A systematic prioritization and adjustment procedure follows, ensuring feasibility based on structural, operational, and economic constraints. The framework consists of three main steps: (1) assessing unit-level urgency using RMI; (2) identifying homogeneous sections through spatial logic; and (3) prioritizing and adjusting sections for large-scale repairs. A national-scale application to the Korean expressway system demonstrates its practical utility. The approach supports proactive, scalable repair planning and is broadly applicable to other infrastructure systems with comprehensive condition data and long-term rehabilitation needs.