Continuous and Rapid Detection Methods for Segregation in Asphalt Mixture Paving

About the project

Segregation in asphalt mixtures, where coarse aggregates become separated from fine aggregates, leads to non-uniform pavement surfaces with reduced density and durability. This deficiency significantly impacts the performance of asphalt pavements, often resulting in premature failures such as raveling, cracking, and potholes. Identification of segregation during or immediately after asphalt paving operations is crucial for mitigating these potential issues, ensuring higher-quality, longer lasting, and more durable pavements while minimizing future repair needs.

Historically, segregation detection has relied on visual inspection methods or density measurements, which are both time-consuming and susceptible to errors. Recent advances in real-time monitoring and continuous inspection technologies, such as infrared imaging, Ground Penetrating Radar (GPR), continuous density and macrotexture measurement, and machine learning-driven analysis present opportunities for detecting segregation as it occurs. These innovations promise to improve the detection process, allowing for more immediate interventions that preserve pavement quality and minimize costs.

Segregation is a leading cause of premature asphalt pavement failure. Thermal, density, and gradation inconsistencies create weak areas in pavements that deteriorate faster and cost more to maintain. The purpose of this study is to explore, identify, and validate advanced technologies for detecting and quantifying segregation in asphalt pavements both during and immediately following paving operations. The focus will be on the development and implementation of continuous, real-time detection methods that facilitate immediate corrective actions and improve the overall quality and longevity of pavements.

Project team

Principal investigators

Co-Principal investigators

Project manager

Project monitor

Members