Using Mixture Design Data and Existing Prediction Models to Evaluate the Potential Performance of Asphalt Pavements
Publication: Journal of Materials in Civil Engineering
Volume 34, Issue 7
Abstract
The objective of this study was to develop a simplified process and tool to assess the potential performance of Hot Mix Asphalt (HMA) mixtures in terms of rutting, fatigue, and thermal cracking, using only asphalt mix design information and volumetric data. Recent HMA mixture design data was collected from 25 states within the US and then compared to equivalent historical data available in the Long-Term Pavement Performance (LTPP) database for highways. The data was separated according to the four climatic regions within the LTPP and performance was assessed using existing predictive models. Three predictive models were used in the evaluation and comparison process. Then, four ranking criteria were developed to support the evaluation of the mix designs quality: Low, Satisfactory, Good, or Excellent. The evaluation results were reasonable based on the predictive models’ findings and the performance found in the LTPP. Out of the 48 HMA mixture designs studied, the majority were ranked either Satisfactory or Good. The overall results showed that state agencies have changed their mix designs over the years for the better, especially when considering fatigue cracking and rutting resistance. The methodology and criteria developed in this study are intended to be used as support tools in determining the quality of asphalt mixtures accepted by state agencies or local governments. They provide a quick insight on the needed improvement/modification against the potential development of distress during the lifespan of the pavement structure.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some data used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data):
•
The asphalt pavement mixture data collected from the state agencies;
•
Other data used in this study are available in a repository or online in accordance with funder data retention policies;
•
Long Term Pavement Performance: InfoPave Database: https://infopave.fhwa.dot.gov/Data/DataSelection;
•
Rutting Model (Rodezno et al. 2010): Rodezno, M. C., Kaloush, K. E., & Corrigan, M. R., 2010. Development of a Flow Number Predictive Model. Transportation Research Record: Journal of the Transportation Research Board, 2181 (1), 79–87. doi: 10.3141/2181-09;
•
Thermal Cracking Models (Zborowski 2007): Zborowski, Aleksander, 2007. Development of a Modified Superpave Thermal Cracking Model for Asphalt Concrete Mixtures Based on the Fracture Energy Approach, Arizona State University; and
•
Fatigue Cracking (Salim 2019): Salim, R., 2019. Asphalt Binder Parameters and their Relationship to the Linear Viscoelastic and Failure Properties of Asphalt Mixtures. Arizona State University.
Acknowledgments
The authors would like to thank all of the Materials Engineers from the 25 states included in this study for providing typical and most recent asphalt mixture designs used by their departments. Special thanks to FORTA Corporation for their encouragement and partially funding the study.
References
Bari, J., and M. W. Witczak. 2007. “New predictive models for viscosity and complex shear modulus of asphalt binders: For use with mechanistic-empirical pavement design guide.” Transp. Res. Rec. 2001 (1): 9–19. https://doi.org/10.3141/2001-02.
Brown, S. F., and C. A. Bell. 1979. “The prediction of permanent deformation in asphalt pavements.” In Vol. 48 of Proc., 77th Association of Asphalt Paving Technologists, 438–476. Lino Lakes, MN: Association of Asphalt Paving Technologists.
El-Basyouny, M., and M. G. Jeong. 2010. Probabilistic performance-related specifications methodology based on mechanistic-empirical pavement design guide.” Transp. Res. Rec. 2151 (1): 93–102. https://doi.org/10.3141/2151-12.
Federal Register. 2017. National performance management measures; assessing pavement condition for the national highway performance program and bridge condition for the national highway performance program, 82(11), 23 CFR, Part 490. Washington, DC: USDOT, Federal Highway Administration.
FHWA (Federal Highway Administration) 2015. Evaluation of LTPP climatic data for use in Mechanistic-Empirical Pavement Design Guide calibration and other pavement analysis. McLean, VA: FHWA.
Heukelom, W. 1966. “Observations on the rheology and fracture of bitumens and asphalt mixes.” In Proc., Association Asphalt Paving Technology. Washington, DC: Transportation Research Board. https://trid.trb.org/view/100915.
Kaloush, K. E., 2001. “Simple performance test for permanent deformation of asphalt mixtures.” Ph.D. dissertation, Dept. of Civil and Environmental Engineering, Arizona State Univ.
Kvasnak, A., C. J. Robinette, and R. C. Williams. 2007. Statistical development of a flow number predictive equation for the mechanistic-empirical pavement design guide. Washington, DC: Transportation Research Board.
Lee, H., Y. R. Kim, and S. Lee. 2003. “Prediction of asphalt mix fatigue life with viscoelastic material properties.” Transp. Res. Rec. 1832 (1): 139–147. https://doi.org/10.3141/1832-17.
Lee, H.-J., J. S. Daniel, and Y. R. Kim. 2000. “Continuum damage mechanics-based fatigue model of asphalt concrete.” J. Mater. Civ. Eng. 12 (2): 105–112. https://doi.org/10.1061/(ASCE)0899-1561(2000)12:2(105).
Little, D. N., and K. A. M. Y. A. R. Mahboub. 1985. “Engineering properties of first generation plasticized sulfur binders and low temperature fracture evaluation of plasticized sulfur paving mixtures.” Transp. Res. Rec. 1034: 103–111.
Rodezno, M. C., and K. Kaloush. 2013. “Development and validation of a rutting model for asphalt mixtures based on the flow number test.” In Vol. 82 of Proc., Asphalt Paving Technology: Association of Asphalt Paving Technologists—Proc. Technical Sessions, 557–578. Lino Lakes, MN: Association of Asphalt Paving Technologist.
Rodezno, M. C., K. E. Kaloush, and M. R. Corrigan. 2010. “Development of a flow number predictive model.” Transp. Res. Rec. 2181 (1): 79–87. https://doi.org/10.3141/2181-09.
Roque, R., D. Hiltunen, W. Buttlar, and T. Farwana. 1995. “Development of the SHRP superpave mixture specification test method to control thermal cracking performance of pavements.” In Proc., Engineering Properties of Asphalt Mixtures and the Relationship to their Performance, 55–73. West Conshohocken, PA: ASTM. https://doi.org/10.1520/STP15562S.
Salim, R. 2019. Asphalt binder parameters and their relationship to the linear viscoelastic and failure properties of asphalt mixtures. Ann Arbor, MI: ProQuest Dissertations Publishing.
Schapery, R. A. 1990. “A theory of mechanical behavior of elastic media with growing damage and other changes in structure.” J. Mech. Phys. Solids 38 (2): 215–253. https://doi.org/10.1016/0022-5096(90)90035-3.
Vanelstraete, A., D. Leonard, and J. Veys. 2000. Structural design of roads with steel reinforcing nettings.” In Proc., 4th Int. RILEM Conf.–Reflective Cracking in Pavements, 56–67. Paris: RILEM.
Von Quintus, H. L. 1994. Vol. 699 of Performance prediction models in the superpave mix design system. Washington, DC: National Research Council.
Witczak, M. W. 2007. Specification criteria for simple performance tests for rutting. Washington, DC: Transport Research Board, National Research Council.
Witczak, M. W., and K. E. Kaloush. 1998. Performance evaluation of CitgoFlex asphalt modified mixture using advanced material characterization tests. College Park, MD: Dept. of Civil Engineering, Univ. of Maryland.
Zborowski, A. 2007. Development of a modified superpave thermal cracking model for asphalt concrete mixtures based on the fracture energy approach. Ann Arbor, MI: ProQuest Dissertations Publishing.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 34 • Issue 7 • July 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Mar 19, 2021
Accepted: Nov 8, 2021
Published online: Apr 28, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 28, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.