Traffic-informed Pavement Performance Prediction and Maintenance Prioritization Using the Pavement Condition Index: A Case Study from Afghanistan

Vollständiger Abstract

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Road infrastructure serves as the primary medium of economic and agricultural transportation in developing countries. Afghanistan’s district roads play a critical role in connecting rural production centers to markets; however, systematic maintenance is often hindered by inadequate funding, lack of structural monitoring, and climatic deterioration. This research develops pavement condition assessment and deterioration prediction models using Pavement Condition Index (PCI), pavement age, characteristic rebound deflection (Dc), and Average Annual Daily Traffic (AADT). Multiple regression models were generated based on recorded field data across six district road sections in Herat Province. Regression coefficients demonstrated high goodness of fit (R² = 0.964 – 0.992), indicating reliable predictive capability. Based on PCI-based prioritization, maintenance strategies were ranked and scheduled under budget constraints. Results indicate that Sections D01 and D02 urgently require rehabilitation due to structural fatigue and distress propagation, while other sections can be preserved through preventive maintenance. This study provides a scientifically supported PMS framework for Afghan district roads, enabling optimized resource allocation, deterioration forecasting, and long-term serviceability planning.