Critical assessment of asphalt mixture design procedures and asphalt mixture classification systems

State highway agencies (SHAs) and the asphalt industry are continually implementing new technologies, test methods, and specifications to improve asphalt mixtures and pavement performance. However, evaluating the effect of those new technologies is not an easy task. The link between mix design and pavement performance is not clear when it comes to asphalt pavements. Furthermore, a few SHAs have begun the process of implementing a cracking test and a rutting test into the mixture design process for what is termed a “balanced mix design” (BMD) approach. While there are four potential approaches, according to AASHTO PP 105 for implementing a BMD procedure, most states follow Approach A in which each mix design must meet both the volumetric requirements plus the performance test criteria. In addition, specimen preparation and testing time have limited the frequency of testing used for the purpose of BMD or acceptance testing.In consequence, this dissertation evaluated the most common adjustments made by SHAs (NCDOT in particular) to their Superpave volumetric mix design procedures, and verified the effectiveness of these adjustments through historical data. In addition, this research supported the ongoing efforts to reintegrate the mechanical tests as part of the mix design by generating performance prediction models for mixture characteristics that can be used in mechanistic-based pavement performance prediction models and highlighted the issues and challenges resulting from relying on only mixtures volumetrics.SHAs (NCDOT) mix design data from the HiCAMS database (North Carolina Department of Transportation mixtures database) as well as actual measured mixture performance data over the past years from the NCSU mixture database, were harvested and analyzed. The job mix formulas (JMFs) from the HiCAMS database were compiled and compared over a span of 20 years, focusing on changes implemented over the years and their effects on volumetrics. The analysis presented in this dissertation investigated how these changes have affected asphalt mixture designs with respect to composition and performance. The extracted job mix formulas were compared based on their volumetric and constituent composition. In addition, the effect of Ndesign on asphalt content and VMA as well as the sensitivity of calculated VMA values to the variability in Gsb measurements, was evaluated. The performance characteristics of mixtures with the same classifications were compared, focusing on showing the challenges when trying to relate the volumetric properties of asphalt mixtures with performance indicators. A compelling case that volumetrics-only mix design has limitations and a case study showing how the mixtures reported volumetric properties can be deceiving were presented and discussed. Different levels of mixture design and performance characteristics prediction models were developed utilizing Artificial Neural Network (ANN) modeling techniques. Mainly, three different ANN prediction models were developed and presented. The first model was a mathematical-based ANN model that has the ability to leverage state DOT mix design data to predict the Superpave optimum binder content for given mix properties. A detailed framework for the model development was presented as well. The second was a new ANN based prediction model for dynamic modulus |E*| from the basic mix design information that included the recycled binder content. The third was a series of ANN frameworks and models for predicting the cyclic fatigue test analysis results, specifically D R , Sapp C11, and C12. The findings presented in this dissertation suggest that SHAs should consider restructuring their mixture classification system to be based on mixture performance rather than on volumetric design and demonstrate the need for mechanical mixture evaluation. In addition, this dissertation demonstrated the potential use of the ANN models for aiding and simplifying the material evaluation process while highlighting and assessing the challenges related to their use.