View Important Policies and System Requirements for this course
Interested in registering 5 or more engineers for a course? Contact us for information and rates.
INSTRUCTORS:
Geoffrey M. Rowe
Abhary Eleyedath, Ph.D
Mehdi Sadeghi
Hong Lang, Ph.D
Purpose and Background
These presentations were recorded at the International Airfield & Highway Pavements Conference 2025.
The Use of the Rheology to Describe the Asphalt Fracture Behavior Occurring in the Poker Chip Test (18 minutes)
This presentation explored how rheological principles can be applied to interpret fracture behavior in the poker chip test, a method originally developed for roofing applications. The speaker reviewed historical insights from Huckelman’s 1966 study and examined whether a brittle-to-ductile transition could be identified and correlated to binder stiffness. Experimental procedures included rheological characterization using the Glover-Rowe parameter and poker chip testing at various temperatures. The team developed a methodology to define loading times precisely, accounting for initial slack and adjusting analysis to ensure accurate results. Findings showed a strong correlation between fracture behavior and binder stiffness, supporting rheology as a valid predictive framework. The analysis confirmed that fracture transitions occur near the stiffness values historically associated with cracking susceptibility. This insight can improve the interpretation of asphalt binder performance and inform material selection for both paving and roofing applications.
Effect of Confinement on Dynamic Modulus of Porous Asphalt Mixtures (8 minutes)
This study investigated how different levels of confinement affect the dynamic modulus (E*) of porous asphalt mixtures. Two mix types modified open graded friction course (MOGFC) and modified asphalt stabilized drainage course (MASDC) were evaluated using both confined and unconfined conditions. Testing was performed under a range of temperatures, frequencies, and confining pressures (10, 20, and 30 PSI) using AMPT equipment. Results showed that E* values increased with confinement, particularly at higher temperatures and lower frequencies, with MOGFC generally showing higher stiffness due to its lower air void content. Pavement simulations using KENPAVE revealed that increases in confined E* could improve pavement design life by up to 91%. This demonstrates the critical role of confinement in influencing mixture performance and supports the development of more cost-effective, durable porous pavement designs.
Enhancing Porous Friction Course Design: Incorporating Warm Mix Asphalt and Performance-Based Design (11 minutes)
This presentation examined the use of warm mix asphalt (WMA) and performance-based testing as alternatives to traditional hot mix asphalt (HMA) with fibers in porous friction courses (PFCs). The study assessed four mixes for key performance indicators including cracking resistance, abrasion loss, permeability, moisture susceptibility, rutting resistance, and drain-down. WMA mixes demonstrated higher cracking resistance and comparable drain-down and permeability, but exhibited reduced rutting and moisture resistance, particularly without fibers. Long-term aging tests showed that performance gaps in abrasion resistance narrowed over time. The results suggest that fiberless WMA can be a viable, more sustainable alternative under certain conditions, supporting a shift toward performance-based mix design rather than prescriptive specifications.
Efficient Prediction of Asphalt Concrete Mix Design Based on Performance Tests (16 minutes)
This study presented a machine learning-based approach to improve the efficiency of asphalt concrete mix design by predicting mix components that meet desired performance criteria. Drawing from over 18,000 iFIT and 8,000 HWTT samples, the model was trained to optimize aggregate gradation and binder content using desired flexibility index (FI) and rutting depth (RD) as targets. A two-step framework was developed—first predicting mix parameters using gradient boosting, then refining them using a genetic algorithm to ensure performance alignment. The resulting tool, deployed as a web-based application, allows users to input mix characteristics and receive optimized designs with predicted performance. The tool aims to reduce trial-and-error in mix design while promoting consistency and accuracy. The research highlights how AI and big data can transform traditional empirical processes in pavement engineering.
Benefits and Learning Outcomes
Upon completion of this course, you will be able to:
- Explain how rheological properties, such as binder stiffness, can be used to characterize fracture behavior in asphalt materials using the poker chip test.
- Describe the effect of lateral confinement on the dynamic modulus and design life of porous asphalt pavement systems.
- Discuss the performance trade-offs when substituting warm mix asphalt for hot mix asphalt with fibers in porous friction course designs.
- Identify how machine learning and performance test data can be integrated to optimize asphalt concrete mix design.
Assessment of Learning Outcomes
Students' achievement of the learning outcomes will be assessed via a short post-test assessment (true-false, multiple choice, and/or fill in the blank questions).
Who Should Attend?
- Transportation Engineers
- Transportation Professionals
- Materials engineers
- Consultants & Contractors
- Academics & Researchers
- Young Professionals & Students
How to Earn Your CEUs/PDHs and Receive Your Certificate of Completion
To receive your certificate of completion, you will need to complete a short post-test and receive a passing score of 70% or higher within 1 year of purchasing the course.
How do I convert CEUs to PDHs?
1.0 CEU = 10 PDHs [Example: 0.1 CEU = 1 PDH]