Cluster-Driven Predictive Model for Asphalt Pavement Maximum Temperature in Tropical Airport

The majority of runways are constructed using flexible pavement surfaced with Hot Mix Asphalt (HMA). The performance of these materials is significantly influenced by temperature due to their viscoelastic nature. Understanding the maximum temperature profile in the HMA layer is essential for evaluating pavement load-bearing capacity and durability. Therefore, this study aimed to present a robust model for predicting maximum pavement temperature distributions based on direct measurements from 13 strategically selected airports in the tropical region of Indonesia. Data was collected using the Airside Pavement Sensing System (AirPaSS), a monitoring device that integrated solar-powered energy management, automated data transmission, and multi-depth thermocouple sensors, providing real-time and accurate temperature measurements. By using hierarchical clustering, airports were categorized into three clusters based on air temperature, pavement temperature, and elevation, enabling precise and cluster-specific material design. The result showed that the predictive model incorporating linear and logarithmic regression achieved high accuracy, with Root Mean Squared Error (RMSE) values ranging from 0.91°C to 2.01°C and Adjusted R² values between 0.76-0.91. This model offered a practical solution for predicting HMA layer temperature at any depth. The results provided valuable information for performance-based grading systems with significant implications for improving infrastructure resilience in tropical and similar climatic regions. Doi: 10.28991/CEJ-2025-011-03-01 Full Text: PDF