Development of Pavement Condition Prediction Model for Airport Runway to Enhance Operational Availability: The Case of Mtwara Airport

Airport runway pavements constitute critical infrastructure components requiring systematic maintenance management to ensure operational safety and efficiency. This study developed a pavement condition prediction model for airport runways to enhance operational availability, using Mtwara Airport as a case study. The research employed a mixed-methods approach involving comprehensive surveys of 70 aviation professionals, including civil engineers, technicians, managers, artisans, and pilots, to identify and quantify factors affecting runway pavement deterioration. Using Relative Importance Index (RII) analysis, twelve critical factors were systematically evaluated and ranked based on their impact on pavement condition. Construction quality emerged as the most significant factor (RII = 0.726), followed by aircraft weight (RII = 0.714) and subgrade strength (RII = 0.703). A multiple regression model was subsequently developed, incorporating the eight highest-importance factors, achieving strong predictive capability with R² = 0.828, explaining 82.8% of the variance in pavement condition. The model equation demonstrated statistical significance (F = 33.756, p < 0.001). Key limitations identified include systematic upward bias for poor to moderate pavement conditions, indicating the model's optimal performance for good to excellent conditions and highlighting areas requiring future calibration enhancement. Despite these limitations, comprehensive validation results confirm the model's practical utility for proactive maintenance planning. The study provides airport managers with a quantitative tool for data-driven decisions regarding resource allocation and improving runway operational availability, representing a significant advancement in pavement management for tropical developing country contexts.