Sampling Sensitivity Analysis on the Pavement Condition Index for Airport Maintenance Strategies

Abstract: The assessment of pavement conditions at airports is essential for ensuring both safety and operational efficiency. As aviation traffic continues to grow globally, maintaining high-quality pavements becomes even more important, requiring large amounts of pavement condition data to support effective evaluation and management strategies. This study investigated the influence of different sampling strategies on Pavement Condition Index (PCI) results. PCI is a globally recognized and widely used index for network-level airport pavement management. The objective was to assess how varying sampling approaches impact PCI scores and maintenance decision outcome. The study also demonstrated the importance of comprehensive and continuous pavement condition assessment. Runway and taxiway surface condition surveys were conducted in a Brazilian airport using Laser Crack Measurement System (LCMS) technology. Multiple sampling methods, including random and systematic sampling, as well as a full inspection were investigated. PCI scores from 13 sampling scenarios were compared with a reference condition (control). The runway was assessed in two periods: 2021, prior to maintenance activities, and in 2022, after maintenance. Statistical analyses were performed to assess the accuracy and consistency of sampling techniques. A decision matrix analysis was used to evaluate the impact of PCI values on maintenance decision-making. Pavement sections were classified based on their PCI values and consequent required maintenance work. The results revealed that the choice of sampling technique considerably influences PCI outcome. Random sampling exhibited the largest deviations from the full inspection. Fixed-interval systematic sampling performed better in comparison. In 2021, higher variability was observed due to the poorer condition of the pavements, with PCI variation ranging from 2.2% to 16.4% across sampling scenarios. On the other hand, the 2022 survey, conducted post-maintenance, showed improved consistency with PCI variation ranging from 1.4% to 6.2%. The study found that most sampling techniques resulted in PCI values lower than the full inspection PCI, suggesting a higher density of distresses compared to full pavement surveys. ANOVA results indicated no statistically significant differences between the 13 sampling scenarios and the full inspection. However, the percentage of reconstruction varied from 35.2% to 69.5% for the runway segment in 2021, with the reference value being 54.3%. This emphasizes that sampling techniques can produce PCI values that lead to distorted maintenance decisions. In conclusion, adopting continuous pavement inspection and technological improvements can optimize pavement management and ensure effective resource allocation.