[Watch this episode on YouTube.]
In the latest episode of the AASHTO re:source Q & A Podcast, “Rolling Forward: Exploring the New Hamburg Wheel Track Proficiency Sample Program”, the hosts, with guests John Malusky and Ryan LaQuay, dive into the details of an exciting new addition to the Proficiency Sample Program (PSP). This episode is a must-listen for anyone involved in balanced mix design and testing and quality assurance, as it introduces this new sample type.
Introduction to the New Sample Type
The Hamburg Wheel Tracking test is one of the primary performance tests included in the Balanced Mix Design process to evaluate the rutting and stripping potential of an asphalt mixture. By providing the Hamburg Wheel Track proficiency sample, AASHTO re:source aims to reduce variability in test results, ensuring that all participating laboratories can achieve comparable and reliable outcomes.
Behind the Scenes: Development and Implementation
Listeners get an insider’s look at the development and implementation process of the new proficiency sample. Its implementation and development, driven by the Administrative Task Group (AASHTO re:source’s oversight group) and industry demand, highlights the meticulous efforts to create consistent sample quantities and maintain material uniformity.
With importance of collaboration and research in refining the program in mind, the team worked closely with plant producers, DOT staff, and other industry experts to develop a feasible and effective methodology. Pilot programs with several DOTs revealed issues such as confusion with sample reduction/splitting and variation during sample preparation and testing. These challenges underscore the importance of standardizing the process to achieve accurate and reliable results. The team highlights the variability in gyratory compactors and the steps taken to overcome these challenges, ensuring the Hamburg Wheel Track sample meets industry standards and needs.
The episode also explores the processes in place to prevent segregation. Segregation can lead to poor samples and inaccurate results, which is a significant concern for any testing program. From securing a hot box trailer to finding the right mix that prevents segregation, the process was complex and required significant effort. The use of a hot box trailer, typically used for pothole patching, was a key innovation. This method kept the material hot and less viscous, ensuring that the sample remained homogenous throughout the process.
The choice of mix was also crucial. Opting for a 3/8 inch or 9.5-millimeter mix with an asphalt content of approximately 5% helped maintain the mixture’s viscosity and prevent segregation. This careful selection and preparation process ensured that the material remained well-blended and consistent, providing reliable test results.
Future Implications
The episode concludes with a discussion on the future implications of the Hamburg Wheel Track Proficiency Sample. The success of this sample sets the stage for exploring opportunities within the AASHTO re:source Proficiency Sample Program. The guests express optimism that this new sample type will set a precedent for future additions to the PSP, paving the way for more standardized and reliable testing procedures in the industry relating to balanced mix design.
Final Thoughts
New proficiency samples are also in the works, including those for winter maintenance products and soil chemistry tests. These new samples aim to enhance the testing program's robustness and reliability, ensuring that industry standards continue to evolve and improve.
This episode of the AASHTO re:source Q & A Podcast is a valuable resource for anyone involved in materials testing and quality assurance. By focusing on the new Hamburg Wheel Track Proficiency Sample, the hosts provide listeners with a comprehensive understanding of its significance and potential impact on the industry. Don’t miss out on this insightful discussion—tune in to learn more about how this new sample type is set to revolutionize materials testing.
Important Note: Buzzsprout Cohost AI and Microsoft Copilot AI were used as resources when creating this post.