Impact of Asphalt Concrete Properties on the Illinois Flexibility Index Cracking and Hamburg Wheel Tracking Test Rutting Potential
Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 4
Abstract
Superpave balanced mix design (BMD) approaches have been adopted using the Illinois Flexibility Index test (I-FIT) for cracking and Hamburg wheel tracking test (HWTT) for rutting. The objective of this study was to evaluate the impact of the asphalt concrete (AC) properties on I-FIT’s flexibility index and HWTT’s rut depth. The study was intended to determine the most important parameters the influence the prediction of flexibility index and rut depth. An extensive database of I-FIT and HWTT results was collected from the Illinois Department of Transportation. A total of 18,594 I-FIT data sets were collected from 2061 mix designs. For HWTT, 8,263 data sets were collected from 3,782 mix designs. Data exploration analysis was conducted to evaluate the impact of the AC properties on the I-FIT and the HWTT results. Finally, feature ranking analysis was performed to determine the properties that significantly influence the flexibility index and rut depth. The result indicates that most of the AC properties identified in the database had an impact on flexibility index and rut depth. To rank the influential parameters, a random forest regression model was developed to execute recursive feature elimination analysis. The parameters , recycled content, air voids, and asphalt binder replacement were the most impactful on flexibility index and rut depth results.
Practical Applications
In this study, a large database of Illinois Flexibility Index and Hamburg wheel tracking data was collected. Then an evaluation of the impact of each AC property on the flexibility index and rut depth was conducted. Finally, a feature ranking analysis was conducted to identify the properties that impacted the flexibility index and rut depth more significantly. Contractors and AC designers may be challenged to adjust an AC mix design to control potential AC cracking or rutting. The results of this research identified the critical properties to predict flexibility index and rut depth. The outcome would allow the AC mix designer to achieve the required thresholds for cracking and rutting potential effectively by controlling the mix design parameters.
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Data Availability Statement
The data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions as indicated in the Acknowledgments.
Acknowledgments
The research was conducted in cooperation with the Illinois Department of Transportation. The authors appreciate the help of IDOT materials technicians and engineers in all District and Central Bureau of Materials laboratories for providing the data used to build the database. The data are owned by the Illinois Department of Transportation and include the database of I-FIT and HWTT test results with AC properties and code to develop the feature ranking analysis. For more information on how the data can be requested from IDOT, contact the corresponding author. The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Illinois Center for Transportation or the Illinois Department of Transportation.
References
AASHTO. 2011. Standard method of test for Hamburg wheel-track testing of compacted hot mix asphalt (AC). AASHTO T 324. Washington, DC: AASHTO.
AASHTO. 2021. Standard method of test for determining the fracture potential of asphalt mixtures using the Illinois Flexibility Index Test (I-FIT). AASHTO T 393. Washington, DC: AASHTO.
Abdi, H., and L. J. Williams. 2010. “Principal component analysis.” Wiley Interdiscip. Rev. Comput. Stat. 2 (4): 433–459. https://doi.org/10.1002/wics.101.
Al-Qadi, I. L., H. Ozer, J. Lambros, A. El Khatib, P. Singhvi, T. Khan, J. Rivera-Perez, and B. Doll. 2015. Testing protocols to ensure the performance of high asphalt binder replacement mixes using RAP and RAS. Rantoul, IL: Illinois Center for Transportation.
Al-Qadi, I. L., H. Ozer, Z. Zhu, P. Singhvi, U. Mohamed Ali, M. Sawalha, and T. G. Zehr. 2019. Development of long-term aging protocol for implementation of the Illinois flexibility index test (I-FIT). Rantoul, IL: Illinois Center for Transportation.
Al-Qadi, I. L., J. Rivera-Perez, H. Ozer, W. Sayeh, J. J. G. Mainieri, H. Meidani, J. Huang, and A. J. Hand. 2020. Data trends and variability in quality control for performance and pay for performance specifications: Statistical analysis. Rantoul, IL: Illinois Center for Transportation.
Anderson, D. A., H. U. Bahia, and R. Dongre. 1992. Rheological properties of mineral filler-asphalt mastics and its importance to pavement performance. West Conshohocken, PA: ASTM.
Benesty, J., J. Chen, Y. Huang, and I. Cohen. 2009. “Pearson correlation coefficient.” In Noise reduction in speech processing, 1–4. Berlin: Springer.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324.
Brown, E. R., J. E. Haddock, R. B. Mallick, and T. A. Lynn. 1997. “Development of a mixture design procedure for stone matrix asphalt (SMA).” J. Assoc. Asphalt Paving Technol. 66: 1–25.
Brown, E. R., P. S. Kandhal, F. L. Roberts, Y. R. Kim, D. Y. Lee, and T. W. Kennedy. 2009. Hot mix asphalt materials, mixture design, and construction. Greenbelt, MD: NAPA Research and Education Foundation.
Brown, E. R., P. S. Kandhal, and J. Zhang. 2001. Performance testing for hot mix asphalt. Auburn, AL: Auburn Univ.
Chakravarty, H., and S. Sinha. 2021. “Application of machine learning to determine the influence of particle size distribution on stability of bituminous mixes.” In Proc., Green and Intelligent Technologies for Sustainable and Smart Asphalt Pavements: Proc., 5th Int. Symp. on Frontiers of Road and Airport Engineering, 12–14 July, 2021, Delft, Netherlands (IFRAE), 168. Boca Raton, FL: CRC Press.
Chandrashekar, G., and F. Sahin. 2014. “A survey on feature selection methods.” Comput. Electr. Eng. 40 (1): 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024.
Chehab, G. R., and Y. S. Hamdar. 2020. “Framework for hybrid performance-based quality assurance for flexible airfield pavements.” J. Transp. Eng. Part B. Pavements 146 (2): 04020025. https://doi.org/10.1061/JPEODX.0000178.
Chehab, G. R., Y. S. Hamdar, and A. J. Haddad. 2019. “Investigating high-temperature PG grade adjustment recommendations for airfield pavements.” Transp. Res. Rec. 2673 (5): 365–373. https://doi.org/10.1177/0361198119838259.
Cominsky, R. J., B. M. Killingsworth, R. M. Anderson, D. A. Anderson, and W. Crockford. 1997. Field procedures and equipment to implement SHRP asphalt specifications. Washington, DC: National Cooperative Highway Research Program.
Dahhan, A. Z., and J. D. Miller. 2018. “Stone matrix asphalt (SMA) case study, Thornton, Illinois: Analysis of 20-year stone matrix asphalt material on Williams Street.” In Proc., 1st Int. Conf. on Stone Matrix Asphalt (SMA). Greenbelt, MD: National Asphalt Pavement Association.
Gong, H., Y. Sun, Y. Dong, B. Han, P. Polaczyk, W. Hu, and B. Huang. 2020. “Improved estimation of dynamic modulus for hot mix asphalt using deep learning.” Constr. Build. Mater. 263 (Dec): 119912. https://doi.org/10.1016/j.conbuildmat.2020.119912.
Granitto, P. M., C. Furlanello, F. Biasioli, and F. Gasperi. 2006. “Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products.” Chemom. Intell. Lab. Syst. 83 (2): 83–90. https://doi.org/10.1016/j.chemolab.2006.01.007.
Habbouche, J., E. Y. Hajj, P. E. Sebaaly, and M. Piratheepan. 2020. “A critical review of high polymer-modified asphalt binders and mixtures.” Int. J. Pavement Eng. 21 (6): 686–702. https://doi.org/10.1080/10298436.2018.1503273.
Hafeez, I., M. A. Kamal, and M. W. Mirza. 2015. “An experimental study to select aggregate gradation for stone mastic asphalt.” J. Chin. Inst. Eng. 38 (1): 1–8. https://doi.org/10.1080/02533839.2014.953242.
IDOT (Illinois DOT). 2010. “Internal memorandum: Special provision for friction aggregate.” Accessed January 10, 2022. https://www.idot.illinois.gov/Assets/uploads/files/Doing-Business/Standards/District-Specific-Standards/5/Special-Provisions/80265.doc.
IDOT (Illinois DOT). 2016. “Standard specifications for road and bridge construction.” Accessed January 20, 2022. https://idot.illinois.gov/Assets/uploads/files/Doing-Business/Manuals-Guides-&-Handbooks/Highways/Construction/Standard-Specifications/Standard%20Specifications%20for%20Road%20and%20Bridge%20Construction%202016.pdf.
IDOT (Illinois DOT). 2021. “Manual of test procedures for materials.” Accessed January 20, 2022. https://www.idot.illinois.gov/Assets/uploads/files/Doing-Business/Manuals-Guides-&-Handbooks/Highways/Materials/Manual%20of%20Test%20Procedures%20for%20Materials%202021.pdf.
Khosla, N. P., and D. Ayyala. 2013. “A performance-based evaluation of Superpave design gyrations for high traffic surface mixes.” Procedia-Soc. Behav. Sci. 104 (Dec): 109–118. https://doi.org/10.1016/j.sbspro.2013.11.103.
Kim, S. S., J. J. Yang, and R. A. Etheridge. 2020. “Effects of mix design variables on flexibility index of asphalt concrete mixtures.” Int. J. Pavement Eng. 21 (10): 1275–1280. https://doi.org/10.1080/10298436.2018.1538514.
Liu, H., P. Hao, and J. Xu. 2017. “Effects of nominal maximum aggregate size on the performance of stone matrix asphalt.” Appl. Sci. 7 (2): 126. https://doi.org/10.3390/app7020126.
Lu, Y., I. Cohen, X. S. Zhou, and Q. Tian. 2007. “Feature selection using principal feature analysis.” In Proc., 15th ACM Int. Conf. on Multimedia, 301–304. San Antonio, TX: Univ. of Texas at San Antonio.
Michaut, J. P. 1995. “Ruflex or stone mastic asphalt.” Asphalt Rev. 4 (2): 7–10.
Ozer, H., I. L. Al-Qadi, P. Singhvi, T. Khan, J. Rivera-Perez, and A. El-Khatib. 2016. “Fracture characterization of asphalt mixtures with high recycled content using Illinois semicircular bending test method and flexibility index.” Transp. Res. Rec. 2575 (1): 130–137. https://doi.org/10.3141/2575-14.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in Python.” J. Mach. Learn. Res.12 (Nov): 2825–2830. https://doi.org/10.5555/1953048.2078195.
Płoński, P. 2020. “Random forest feature importance computed in 3 ways with Python.” MLJAR. Accessed January 20, 2022. https://mljar.com/blog/feature-importance-in-random-forest/#:%7E:text=Random%20Forest%20Built%2Din%20Feature%20Importance&text=It%20is%20a%20set%20of,sets%20with%20similars%20responses%20within.
Rivera-Pérez, J., H. Ozer, and I. L. Al-Qadi. 2018. “Impact of specimen configuration and characteristics on Illinois flexibility index.” Transp. Res. Rec. 2672 (28): 383–393. https://doi.org/10.5555/1953048.2078195.
Rivera-Pérez, J., H. Ozer, J. Lambros, and I. L. Al-Qadi. 2021. “Illinois flexibility index test: Effect of specimen geometry and test configuration on the asphalt concrete damage zone.” J. Transp. Eng. Part B. Pavements 147 (1): 04020085. https://doi.org/10.1061/JPEODX.0000243.
SETScholars Team. 2021. “Machine learning for beginners in python: How to use ANOVA F-value for feature selection.” SETScholars. Accessed May 20, 2022. https://setscholars.net/machine-learning-for-beginners-in-python-how-to-use-anova-f-value-for-feature-selection/.
Singh, D., M. Zaman, and S. Commuri. 2013. “Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties.” J. Mater. Civ. Eng. 25 (1): 54–62. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000548.
Sreedhar, S., and E. Coleri. 2018. “Effects of binder content, density, gradation, and polymer modification on cracking and rutting resistance of asphalt mixtures used in Oregon.” J. Mater. Civ. Eng. 30 (11): 04018298. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002506.
Taskesen, E. 2022. “Algorithm—PCA PCA documentation.” PCA PCA Documentation. Accessed May 21, 2022. https://erdogant.github.io/pca/pages/html/Algorithm.html#loadings.
Vavrik, W. R., W. J. Pine, G. Huber, S. H. Carpenter, and R. Bailey. 2001. “The bailey method of gradation evaluation: The influence of aggregate gradation and packing characteristics on voids in the mineral aggregate (with discussion).” J. Assoc. Asphalt Paving Technol. 70: 132–175.
Wang, H., T. M. Khoshgoftaar, and K. Gao. 2010. “A comparative study of filter-based feature ranking techniques.” In Proc., 2010 IEEE Int. Conf. on Information Reuse & Integration, 43–48. New York: IEEE.
Watson, D., and D. Jared. 1996. Summary of Georgia’s experience with stone matrix asphalt mixes. Washington, DC: Transportation Research Board.
West, R., C. Rodezno, F. Leiva, and F. Yin. 2018. Development of a framework for balanced mix design. Washington, DC: Transportation Research Board.
Williams, R. C., and B. D. Prowell. 1999. “Comparison of laboratory wheel-tracking test results with Wes Track performance.” Transp. Res. Rec. 1681 (1): 121–128. https://doi.org/10.3141/1681-15.
Wu, S., I. L. Al-Qadi, D. L. Lippert, H. Ozer, A. F. E. Luque, and F. R. Safi. 2017a. “Early-age performance characterization of hot-mix asphalt overlay with varying amounts of asphalt binder replacement.” Constr. Build. Mater. 153 (Oct): 294–306. https://doi.org/10.1016/j.conbuildmat.2017.07.114.
Wu, S., H. Wen, S. Chaney, K. Littleton, and S. Muench. 2017b. “Evaluation of long-term performance of stone matrix asphalt in Washington state.” J. Perform. Constr. Facil. 31 (1): 04016074. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000939.
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Published In
Journal of Transportation Engineering, Part B: Pavements
Volume 149 • Issue 4 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 3, 2022
Accepted: Jul 13, 2023
Published online: Oct 4, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 4, 2024
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