Development of Pavement Condition Index and Roughness Index Relationship for Optimal Pavement Maintenance Strategies: A Case Study of Johnstone Road. - Bogere, David (Kabale University, 2025)
Pavement maintenance is crucial for ensuring road safety, reducing congestion, and minimizing repair costs. However, determining the optimal timing and strategy for pavement maintenance remains a challenge. This study investigates the correlation between the Pavement Condition Index (PCI) and Roughness Index (RI) to develop a numerical model for describing the relationship of indices for pavement maintenance decision-making. Using statistical analysis and data visualization techniques, a significant correlation was found between PCI and RI, with a coefficient of determination (R²) value of 0.47 means that about 47% of the variation in PCI can be explained by the IRI values using this model. While this suggests that the model is capturing a significant amount of the relationship between PCI and IRI, there is still room for improvement, as about 53% of the variance in PCI is not explained by the model. The R² value is simply the square of the correlation coefficient (r) when you're dealing with a simple linear regression model. Since the PCI is a measure of road pavement conditions (on a scale typically ranging from 0 to 100), an RMSE of 7.77 means that the model's predictions for PCI are, on average, about 7 to 8 PCI units off from the actual value. The study recommends using PCI and RI data as inventory data for decision-making, prioritizing pavements with PCI values ≥50.3 and RI values ≤ 5.12 m/km for maintenance. Regular monitoring of PCI and RI values is also recommended to ensure timely maintenance and prevent costly repairs. This research benefits the Uganda National Roads Authority, Ministry of Works and Transport, and Local Government Department of Works, providing a framework for optimal pavement maintenance strategies.