Abstract for presentation (Poster or Podium) with a Paper in the Conference Proceedings
Highway Pavements
Kaustav Chatterjee, Master of Technology (he/him/his)
Graduate Research Assistant
Oklahoma State University
Stillwater, OK, United States
Kaustav Chatterjee, Master of Technology (he/him/his)
Graduate Research Assistant
Oklahoma State University
Stillwater, OK, United States
David Vivanco, PE
Asphalt Branch Manager
Oklahoma Department of Transportation
Edmond, Oklahoma, United States
Joshua Q. Li, PhD, PE
Associate Professor Williams Professor
Oklahoma State University
Stillwater, OK, United States
Xue Yang, na
PhD student
Oklahoma State University
Stillwater, Oklahoma, United States
Joshua Q. Li, PhD, PE
Associate Professor Williams Professor
Oklahoma State University
Stillwater, OK, United States
The Oklahoma Department of Transportation (ODOT) has embarked on a multi-phase journey to implement Balanced Mix Design (BMD) over several years. This endeavor commenced Phase I in 2018 with four shadow projects, followed by the proof of concept stage (Phase 2) with 11 pilot projects, and dozens of additional projects in Phase 3 and Phase 4 for long-term evaluation and implementation. The objective of this project is to evaluate the field performance of BMD mixes currently laid in Oklahoma. The cutting-edge 3D laser imaging technology was leveraged to gather pavement condition data from BMD sites, and the pavement field performance metrics, including percentage cracking, International Roughness Index (IRI), rut depth, and mean profile depth, were calculated and obtained. Three types of analysis were conducted to understand the BMD performance. Our investigation commenced with a comparative analysis, utilizing hypothesis testing to evaluate the performance of traditional Superpave mixes in direct comparison to BMD mixes. Additionally, within the realm of BMD mixes, the performance variations of different mix constituents were tested and compared. Subsequently, stepwise statistical regression analysis and machine learning-based decision tree methodologies were developed to probe for the most significant factors affecting field performance. The findings of this study are anticipated to provide insights into the successful implementation of BMD and also contribute to the broader understanding of pavement design and performance.