Real-Time Optimization of Discrete Element Models for Studying Asphalt Mixture Compaction Characteristics at the Meso-Scale

To enhance the quality of asphalt mixture compaction, the compaction mechanism, particularly at the meso-scale, must be thoroughly understood. Researchers have employed various technologies to study the compaction process. Among these, discrete element modeling (DEM) has been widely adopted due to its effectiveness in addressing particle-scale problems. However, improving simulation accuracy while maintaining computational efficiency remains a challenge. Therefore, this study aims to establish and optimize a DEM model for asphalt mixture gyratory compaction by integrating real-time laboratory SmartKli^® sensing data. The Kalman filter was implemented as the fusion algorithm to enable real-time calibration. Two SmartKli sensors were positioned at different locations within specimens during both the laboratory Superpave gyratory compaction (SGC) test and DEM simulation to investigate the particle rotation characteristics. The results showed that the upper layer exhibited a higher degree of compaction than the lower layer. After calibration, particle rotation in the optimized DEM model more closely matched the laboratory SmartKli sensing data. Additionally, the changing trends of relative rotation for both SmartKli particles and coarse aggregate particles improved. The SmartKli simulation ball effectively represented the rotational behavior of its surrounding coarse aggregates, making its kinematic responses a reliable predictor of asphalt mixture compaction characteristics at the meso-scale.