UROP Research Mentor Project Submission Portal: Submission #1313

Overall Research Project Description With the growing use of mechanistic-empirical models in pavement design, performance testing of asphalt materials has become increasingly important. These models rely on accurate performance data—particularly fatigue cracking behavior—to inform the structural design of pavements. However, traditional performance tests (e.g., uniaxial cyclic fatigue test) are time-consuming, require specialized equipment, and involve substantial material and labor costs.

This project explores an innovative, non-destructive approach using image analysis of asphalt mixtures to predict fatigue performance. By analyzing key characteristics such as aggregate gradation, orientation, and structure from specimen images, the goal is to establish correlations between visual aggregate features and fatigue cracking performance indicators. This image-based method has the potential to reduce reliance on costly mechanical testing, saving both time and materials for practitioners and accelerating the pavement design process.

During the UROP term, the student will assist in capturing and processing high-resolution images of asphalt mixture specimens, quantifying aggregate structure using image analysis software, and performing statistical or machine learning-based correlation with known fatigue performance data.