Using Mixture Design Data and Existing Prediction Models to Evaluate the Potential Performance of Asphalt Pavements
Description
Several ways exist to improve pavement performance over time. One suggestion is to tailor the asphalt pavement mix design according to certain specified specifications, set up by each state agency. Another option suggests the addition of modifiers that are known to improve pavement performance, such as crumb rubber and fibers. Nowadays, improving asphalt pavement structures to meet specific climate conditions is a must. In addition, time and cost are two crucial settings and are very important to consider; these factors sometimes play a huge role in modifying the asphalt mix design needed to be set into place, and therefore alter the desired pavement performance over the expected life span of the structure. In recent studies, some methods refer to predicting pavement performance based on the asphalt mixtures volumetric properties.
In this research, an effort was undertaken to gather and collect most recent asphalt mixtures’ design data and compare it to historical data such as those available in the Long-Term Pavement Performance (LTPP), maintained by the Federal Highway Administration (FHWA). The new asphalt mixture design data was collected from 25 states within the United States and separated according to the four suggested climatic regions. The previously designed asphalt mixture designs in the 1960’s present in the LTPP Database implemented for the test sections were compared with the recently designed pavement mixtures gathered, and pavement performance was assessed using predictive models.
Three predictive models were studied in this research. The models were related to three major asphalt pavement distresses: Rutting, Fatigue Cracking and Thermal Cracking. Once the performance of the asphalt mixtures was assessed, four ranking criteria were developed to support the assessment of the mix designs quality at hand; namely, Low, Satisfactory, Good or Excellent. The evaluation results were reasonable and deemed acceptable. Out of the 48 asphalt mixtures design evaluated, the majority were between Satisfactory and Good.
The evaluation methodology and criteria developed are helpful tools in determining the quality of asphalt mixtures produced by the different agencies. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure.
In this research, an effort was undertaken to gather and collect most recent asphalt mixtures’ design data and compare it to historical data such as those available in the Long-Term Pavement Performance (LTPP), maintained by the Federal Highway Administration (FHWA). The new asphalt mixture design data was collected from 25 states within the United States and separated according to the four suggested climatic regions. The previously designed asphalt mixture designs in the 1960’s present in the LTPP Database implemented for the test sections were compared with the recently designed pavement mixtures gathered, and pavement performance was assessed using predictive models.
Three predictive models were studied in this research. The models were related to three major asphalt pavement distresses: Rutting, Fatigue Cracking and Thermal Cracking. Once the performance of the asphalt mixtures was assessed, four ranking criteria were developed to support the assessment of the mix designs quality at hand; namely, Low, Satisfactory, Good or Excellent. The evaluation results were reasonable and deemed acceptable. Out of the 48 asphalt mixtures design evaluated, the majority were between Satisfactory and Good.
The evaluation methodology and criteria developed are helpful tools in determining the quality of asphalt mixtures produced by the different agencies. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2020
Agent
- Author (aut): Karam, Jolina Joseph
- Thesis advisor (ths): Kaloush, Kamil
- Thesis advisor (ths): Mamlouk, Michael
- Committee member: Ozer, Hasan
- Publisher (pbl): Arizona State University