Representation of Flow Number Results of Hot-Mix Asphalt Using Genetic-Based Model
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 2
Abstract
Rutting, one of the main failures in flexible pavements, is the result of permanent deformation aggregation in pavement layers under traffic loading. Rutting decreases the life of the pavement, and, by influencing control properties of vehicles, creates serious dangers for road users. Therefore, it is very important to predict the rutting potential of different types of asphalt mixtures (before construction and operation) based on the characteristics of the mixture ingredients (bitumen and aggregate), environmental conditions, and traffic loads. This study used genetic programming to represent flow number results of different asphalt mixtures. The models presented predict the flow number (as an index of rutting potential) based on parameters such as the index of aggregate particle shape and texture (particle index), bitumen rutting parameter (), and the stress level. Experimental data were collected from studies conducted on materials (gradation, dynamic shear rheometer, and particle index) and dynamic creep tests of asphalt samples at different levels of stress and temperature. The genetic programming models were compared with a multiple linear regression model. The results demonstrated that the genetic programming model predicted the flow number of asphalt mixtures with better accuracy rather than the regression model. The results of statistical studies revealed that three parameters, particle index, rutting potential, and stress level, influence the flow number, and the stress level is the most significant parameter.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
References
AASHTO. 2013. Standard method of test for effect of heat and air on a moving film of asphalt (rolling thin-film oven test). AASHTO T 240. Washington, DC: AASHTO.
AASHTO. 2015. Standard method of test for determining the rheological properties of asphalt binder using a dynamic shear rheometer (DSR). AASHTO T 315. Washington, DC: AASHTO.
Alavi, A. H., M. Ameri, A. H. Gandomi, and M. R. Mirzahosseini. 2011. “Formulation of flow number of asphalt mixes using a hybrid computational method.” Constr. Build. Mater. 25 (3): 1338–1355. https://doi.org/10.1016/j.conbuildmat.2010.09.010.
Alavi, A. H., and A. H. Gandomi. 2011. “A robust data mining approach for formulation of geotechnical engineering systems.” Eng. Comput. 28 (3): 242–274. https://doi.org/10.1108/02644401111118132.
Alavi, A. H., A. H. Gandomi, M. G. Sahab, and M. Gandomi. 2010. “Multi expression programming: A new approach to formulation of soil classification.” Eng. Comput. 26 (2): 111–118. https://doi.org/10.1007/s00366-009-0140-7.
Alavi, A. H., H. Hasni, I. Zaabar, and N. Lajnef. 2017. “A new approach for modeling of flow number of asphalt mixtures.” Arch. Civ. Mech. Eng. 17 (2): 326–335. https://doi.org/10.1016/j.acme.2016.06.004.
ASTM. 1976. Standard test method for softening point of bitumen (ring and ball apparatus). ASTM D36-76. West Conshohocken, PA: ASTM.
ASTM. 1978a. Standard test method for flash and fire points by cleveland open cup tester. ASTM D92-78. West Conshohocken, PA: ASTM.
ASTM. 1978b. Standard test method for penetration of bituminous materials. ASTM D5-73. West Conshohocken, PA: ASTM.
ASTM. 2000a. Standard test method for compressive strength of asphalt mixtures. ASTM D1074. West Conshohocken, PA: ASTM.
ASTM. 2000b. Standard test method for index of aggregate particle shape and texture. ASTM D3398. West Conshohocken, PA: ASTM.
ASTM. 2005. Standard test method for soundness of aggregates by use of sodium sulfate or magnesium sulfate. ASTM C88. West Conshohocken, PA: ASTM.
ASTM. 2006a. Standard test method for determining the percentage of fractured particles in coarse aggregate. ASTM D5821. West Conshohocken, PA: ASTM.
ASTM. 2006b. Standard test method for resistance to degradation of small-size coarse aggregate by abrasion and impact in the Los Angeles machine. ASTM C131. West Conshohocken, PA: ASTM.
ASTM. 2007. Standard test method for ductility of bituminous materials. ASTM D113-79. West Conshohocken, PA: ASTM.
ASTM. 2010a. Standard test method for flat particles, elongated particles, or flat and elongated particles in coarse aggregate. ASTM D4791. West Conshohocken, PA: ASTM.
ASTM. 2010b. Standard test methods for specific gravity of soil solids by water pycnometer. ASTM D854. West Conshohocken, PA: ASTM.
ASTM. 2012a. Standard test method for density, relative density (specific gravity), and absorption of coarse aggregate. ASTM C127. West Conshohocken, PA: ASTM.
ASTM. 2012b. Standard test method for density, relative density (specific gravity), and absorption of fine aggregate. ASTM C128. West Conshohocken, PA: ASTM.
ASTM. 2015. Standard test method for Marshall stability and flow of asphalt mixtures. ASTM D6927. West Conshohocken, PA: ASTM.
Azarhoosh, A., F. Moghadas Nejad, and A. Khodaii. 2017. “The influence of cohesion and adhesion parameters on the fatigue life of hot mix asphalt.” J. Adhes. 93 (13): 1048–1067. https://doi.org/10.1080/00218464.2016.1201656.
Azarhoosh, A., Z. Zojaji, and F. Moghadas Nejad. 2020. “Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate.” Road Mater. Pavement Des. 21 (3): 850–866. https://doi.org/10.1080/14680629.2018.1513372.
Azarhoosh, A. R., G. H. Hamedi, and H. F. Abandansari. 2018. “Providing laboratory rutting models for modified asphalt mixes with different waste materials.” Period. Polytech. Civ. Eng. 62 (2): 308–317. https://doi.org/10.3311/PPci.10684.
Bahuguna, S. 2004. “Permanent deformation and rate effects in asphalt concrete: Constitutive modeling and numerical implementation.” Ph.D. dissertation, Dept. of Civil Engineering, Case Western Reserve Univ.
Cabalar, A. F., A. Cevik, and I. H. Guzelbey. 2010. “Constitutive modeling of leighton buzzard sands using genetic programming.” Neural Comput. Appl. 19 (5): 657–665. https://doi.org/10.1007/s00521-009-0317-4.
Caroff, G. 1994. “Investigation of rutting of asphalt surface layers: Influence of binder and axle loading configuration.” Transp. Res. Rec. 1436 (1): 28–37.
CEN (European Committee for Standardization). 2016. Bituminous mixtures—Test methods: Cyclic compression test. EN 12697-25. London: British Standards Institution.
Corotis, R. B. 1988. Probability and statistics in civil engineering. Edited by G. N. Smith, 244. New York: Nichols.
Gandomi, A. H., A. H. Alavi, M. R. Mirzahosseini, and F. M. Nejad. 2011. “Nonlinear genetic-based models for prediction of flow number of asphalt mixtures.” J. Mater. Civ. Eng. 23 (3): 248–263. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000154.
Harvey, J., and C. L. Monismith. 1993. “Effects of laboratory asphalt concrete specimen preparation variables on fatigue and permanent deformation test results using strategic highway research program A-003A proposed testing equipment.” Transp. Res. Rec. 1417: 38–48.
IHAC (Iran Highway Asphalt Code). 2003. Iran highway asphalt code. Tehran, Iran: Iran Ministry of Road and Transportation, Management and Planning Organization.
Johari, A., and A. H. Nejad. 2015. “Prediction of soil-water characteristic curve using gene expression programming.” Iran. J. Sci. Technol. Trans. B: Eng. 39 (C1): 143–165.
Kaloush, K. E., M. Witczak, R. Roque, S. Brown, J. D’Angelo, M. Marasteanu, and E. Masad. 2002. “Tertiary flow characteristics of asphalt mixtures.” In Proc., Asphalt Paving Technology: Association of Asphalt Paving Technologists—Proc. of the Technical Sessions. Lino Lakes, MN: Association of Asphalt Paving Technologists.
Kasabov, N. K. 1996. Foundations of neural networks, fuzzy systems, and knowledge engineering. New York: Marcel Alencar.
Kenis, W. J. 1977. “Predictive design procedures: A design method for flexible pavements using the VESYS structural subsystem.” In Vol. 1 of Proc., 4th Int. Conf. on Structural Design of Asphalt Pavements. Washington, DC: Transportation Research Board.
Kim, Y. R. 2008. Modeling of asphalt concrete. Washington, DC: Transportation Research Board.
Koza, J. R., and J. R. Koza. 1992. Genetic programming: On the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
Mazari, M., and Y. Niazi. 2015. “Modeling the effect of filler materials on performance of hot mix asphalt using genetic programming.” In Proc., Airfield and Highway Pavements 2015, 107–119. Reston, VA: ASCE.
Mirabdolazimi, S., and G. Shafabakhsh. 2017. “Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique.” Constr. Build. Mater. 148 (Sep): 666–674. https://doi.org/10.1016/j.conbuildmat.2017.05.088.
Moghadas Nejad, F., E. Geraee, and A. R. Azarhoosh. 2018. “The effect of nano calcium carbonate on the dynamic behaviour of asphalt concrete mixture.” Eur. J. Environ. Civ. Eng. 24 (8): 1219–1228. https://doi.org/10.1080/19648189.2018.1456486.
Monismith, C., J. Epps, and F. Finn. 1985. “Improved asphalt mix design.” In Proc., Association of Asphalt Paving Technologists Technical Sessions, 347–406. Washington, DC: Transportation Research Board.
Pardhan, M. 1995. “Permanent deformation characteristics of asphalt-aggregate mixture using varied material and modeling procedure with Marshall method.” Ph.D. thesis, Dept. of Civil Engineering, Montana Univ.
Pirabarooban, S., M. Zaman, and R. A. Tarefder. 2003. “Evaluation of rutting potential in asphalt mixes using finite element modeling.” In Proc., Transportation Factor 2003—Annual Conf. and Exhibition of the Transportation Association of Canada. Ottawa: Transportation Association of Canada.
Rezania, M., and A. A. Javadi. 2007. “A new genetic programming model for predicting settlement of shallow foundations.” Can. Geotech. J. 44 (12): 1462–1473. https://doi.org/10.1139/T07-063.
Saltan, M., and S. Terzi. 2005. “Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness.” Indian J. Eng. Mater. Sci. 12 (1): 42–50.
Shahnazari, H., Y. Dehnavi, and A. H. Alavi. 2010. “Numerical modeling of stress–strain behavior of sand under cyclic loading.” Eng. Geol. 116 (1–2): 53–72. https://doi.org/10.1016/j.enggeo.2010.07.007.
Sousa, J. B., J. Craus, and C. L. Monismith. 1991. Summary report on permanent deformation in asphalt concrete. Washington, DC: Transportation Research Board.
Tapkin, S., A. Çevik, Ü. Uşar, and A. Kurtoğlu. 2015. “Modelling Marshall design test results of polypropylene modified asphalt by genetic programming techniques.” Period. Polytech. Civ. Eng. 59 (3): 249–265. https://doi.org/10.3311/PPci.7624.
Terzi, S. 2005. “Modeling the deflection basin of flexible highway pavements by gene expression programming.” J. Appl. Sci. 5 (2): 309–314. https://doi.org/10.3923/jas.2005.309.314.
Timm, D. H., and D. E. Newcomb. 2003. “Calibration of flexible pavement performance equations for Minnesota Road Research Project.” Transp. Res. Rec. 1853 (1): 134–142. https://doi.org/10.3141/1853-15.
Topal, A., and B. Sengoz. 2005. “Determination of fine aggregate angularity in relation with the resistance to rutting of hot-mix asphalt.” Constr. Build. Mater. 19 (2): 155–163. https://doi.org/10.1016/j.conbuildmat.2004.05.004.
Wilson, J. D., and L. D. Klotz. 1998. Automated aggregate shape analysis and rutting/stripping performance. Little Rock, AR: Mack-Blackwell Transportation Center, Univ. of Arkansas at Little Rock.
Witczak, M. W. 2002. Simple performance test for superpave mix design. Washington, DC: Transportation Research Board.
Zhao, W., F. Xiao, S. N. Amirkhanian, and B. J. Putman. 2012. “Characterization of rutting performance of warm additive modified asphalt mixtures.” Constr. Build. Mater. 31 (Jun): 265–272. https://doi.org/10.1016/j.conbuildmat.2011.12.101.
Zhou, F., T. Scullion, and L. Sun. 2004. “Verification and modeling of three-stage permanent deformation behavior of asphalt mixes.” J. Transp. Eng. 130 (4): 486–494. https://doi.org/10.1061/(ASCE)0733-947X(2004)130:4(486).
Ziari, H., H. Divandari, M. Hajiloo, and A. Amini. 2019. “Investigating the effect of amorphous carbon powder on the moisture sensitivity, fatigue performance and rutting resistance of rubberized asphalt concrete mixtures.” Constr. Build. Mater. 217 (Aug): 62–72. https://doi.org/10.1016/j.conbuildmat.2019.05.039.
Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 33 • Issue 2 • February 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Apr 14, 2020
Accepted: Jul 6, 2020
Published online: Nov 18, 2020
Published in print: Feb 1, 2021
Discussion open until: Apr 18, 2021
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.