The impact of data noise on permanent deformation behaviour of asphalt concrete mixtures

References

• AASHTO, 1993. American association of state highway transportation officials (AASHTO) guide for design of pavement structures. AASHTO.
   Google Scholar
• AASHTO, TP 79, 2009. Standard method of test for determining the dynamic modulus and flow number for Hot Mix asphalt (HMA) using the asphalt mixture performance tester (AMPT). American Association of State Highway and Transportation Officials, Washington, DC.
   Google Scholar
• Alavi, A. H., et al., 2011. Formulation of flow number of asphalt mixes using a hybrid computational method. Construction and Building Materials, 25, 1338–1355. doi: 10.1016/j.conbuildmat.2010.09.010
   Web of Science ®Google Scholar
• Ali, Y., et al., 2017b. Empirical correlation of permanent deformation tests for evaluating the rutting response of conventional asphaltic concrete mixtures. Journal of Materials in Civil Engineering, 29, 04017059. doi: 10.1061/(ASCE)MT.1943-5533.0001888
   Web of Science ®Google Scholar
• Ali, Y., et al., 2017c. Permanent deformation prediction of asphalt concrete mixtures – A synthesis to explore a rational approach. Construction and Building Materials, 153, 588–597. doi: 10.1016/j.conbuildmat.2017.07.105
   Web of Science ®Google Scholar
• Ali, Y., et al., 2018a. Revisiting the relationship of dynamic and resilient modulus test for asphaltic concrete mixtures. Construction and Building Materials, 170, 698–707. doi: 10.1016/j.conbuildmat.2018.03.098
   Web of Science ®Google Scholar
• Ali, Y., Irfan, M., and Ahmed, S., 2017a. Can asphaltic concrete permanent deformation test methods be used as surrogate to each other? In: 96th transportation research board annual Meeting. Washington, DC: Transportation Research Board.
   Google Scholar
• Ali, Y., Zheng, Z., and Haque, M. M., 2018b. Connectivity’s impact on mandatory lane-changing behaviour: evidences from a driving simulator study. Transportation Research Part C: Emerging Technologies, 93, 292–309. doi: 10.1016/j.trc.2018.06.008
   Web of Science ®Google Scholar
• Apeagyei, A. K., 2011. Rutting as a function of dynamic modulus and gradation. Journal of Materials in Civil Engineering, 23, 1302–1310. doi: 10.1061/(ASCE)MT.1943-5533.0000309
   Web of Science ®Google Scholar
• Apeagyei, A. K., 2014. Flow number predictive models from volumetric and binder properties. Construction and Building Materials, 64, 240–245. doi: 10.1016/j.conbuildmat.2014.04.069
   Web of Science ®Google Scholar
• Archilla, A. R., Diaz, L. G., and Carpenter, S. H., 2007. Proposed method to determine the flow number from laboratory axial repeated loading tests in bituminous mixtures. In: Transportation research board 86th annual meeting.
   Google Scholar
• ASTM, 2010. Standard practice for preparation of bituminous specimens using marshall apparatus. ASTM D6926. ASTM International, West Conshohocken, PA.
   Google Scholar
• Biligiri, K., et al., 2007. Rational modeling of tertiary flow for asphalt mixtures. Transportation Research Record: Journal of the Transportation Research Board, 2001, 63–72. doi: 10.3141/2001-08
   Web of Science ®Google Scholar
• Bonaquist, R. F., Christensen, D. W., and Stump, W., 2003. Simple performance tester for Superpave mix design: first-article development and evaluation. Washington, DC: Transportation Research Board.
   Google Scholar
• Cao, W., and Kim, Y. R., 2016. The extended shift model as a mechanistic-empirical approach to simulating confined permanent deformation of asphalt concrete in compression. Construction and Building Materials, 115, 520–526. doi: 10.1016/j.conbuildmat.2016.04.079
   Web of Science ®Google Scholar
• Dongré, R., D’angelo, J., and Copeland, A, 2009. Refinement of flow number as determined by asphalt mixture performance tester: use in routine quality control–quality assurance practice. Transportation Research Record: Journal of the Transportation Research Board, 2127, 127–136. doi: 10.3141/2127-15
   Google Scholar
• Faheem, A., Bahia, H., and Ajideh, H., 2005. Estimating results of a proposed simple performance test for hot-mix asphalt from Superpave gyratory compactor results. Transportation Research Record: Journal of the Transportation Research Board, 1929, 104–113. doi: 10.1177/0361198105192900113
   Google Scholar
• Gandomi, A. H., et al., 2010. Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. Journal of Materials in Civil Engineering, 23, 248–263. doi: 10.1061/(ASCE)MT.1943-5533.0000154
   Web of Science ®Google Scholar
• Garba, R., 2002. Permanent deformation properties of asphalt concrete mixtures. Trondheim, Norway: Norwegian University of Science and Technology.
   Google Scholar
• Gogula, A., et al., 2003. Effect of PG binder grade and source on performance of superpave mixtures under Hamburg Wheel Tester. Proceedings of the 2003 Mid-Continent Transportation Research Symposium, Ames, Iowa.
   Google Scholar
• Goh, S., et al., 2011. Determination of flow number in asphalt mixtures from deformation rate during secondary state. Transportation Research Record: Journal of the Transportation Research Board, 2210, 106–112. doi: 10.3141/2210-12
   Google Scholar
• Huang, Y. H., 2004. Pavement analysis and design. London, United Kingdom: Prentice Hall.
   Google Scholar
• Irfan, M., et al., 2016. Characterization of various plant-produced asphalt concrete mixtures using dynamic modulus test. Advances in Materials Science and Engineering, 2016, 1–12. doi: 10.1155/2016/5618427
   Web of Science ®Google Scholar
• Irfan, M., et al., 2017a. Rutting evaluation of asphalt mixtures using static, dynamic, and repeated creep load tests. Arabian Journal for Science and Engineering, 43 (10), 5143–5155. doi: 10.1007/s13369-017-2982-4
   Web of Science ®Google Scholar
• Irfan, M., et al., 2017b. Performance evaluation of elvaloy as a fuel-resistant polymer in asphaltic concrete airfield pavements. Journal of Materials in Civil Engineering, 29, 04017163. doi: 10.1061/(ASCE)MT.1943-5533.0002018
   Web of Science ®Google Scholar
• Irfan, M., et al., 2018. Performance evaluation of crumb rubber-modified asphalt mixtures based on laboratory and field investigations. Arabian Journal for Science and Engineering, 43, 1795–1806. doi: 10.1007/s13369-017-2729-2
   Web of Science ®Google Scholar
• Junaid, M., et al., 2017. Effect of binder grade on performance parameters of asphaltic concrete paving mixtures. International Journal of Pavement Research and Technology, 11, 435–444. doi: 10.1016/j.ijprt.2017.11.006
   Google Scholar
• Kaloush, K. E., Witczak, M. W., and Sullivan, B. W., 2003. Simple performance test for permanent deformation evaluation of asphalt mixtures. In: Sixth international RILEM symposium on performance testing and evaluation of bituminous materials. RILEM Publications SARL, 498–505.
   Google Scholar
• Kamil, E., 2001. Simple performance test for permanent deformation Of asphalt mixtures. Dissertation for PhD, Arizona State University, USA.
   Google Scholar
• Kandhal, P. S. and Cooley, L. A., 2003. Accelerated laboratory rutting tests: evaluation of the asphalt pavement analyzer. Washington, DC: Transportation Research Board.
   Google Scholar
• Kim, D., 2015. Modulus and permanent deformation characterization of asphalt mixtures and pavements. Raleigh, North Carolina: North Carolina State University.
   Google Scholar
• Kvasnak, A., Robinette, C. J., and Williams, R. C., 2007. Statistical development of a flow number predictive equation for the mechanistic-empirical pavement design guide. In: Transportation research board 86th annual meeting.
   Google Scholar
• Miljković, M., and Radenberg, M, 2011. Rutting mechanisms and advanced laboratory testing of asphalt mixtures resistance against permanent deformation. Facta Universitatis-Series: Architecture and Civil Engineering, 9, 407–417.
   Google Scholar
• Mohammad, L., et al., 2006. Permanent deformation analysis of hot-mix asphalt mixtures with simple performance tests and 2002 mechanistic-empirical pavement design software. Transportation Research Record: Journal of the Transportation Research Board, 1970, 133–142.
   Google Scholar
• Norouzi, A., Kim, D., and Kim, Y. R., 2015. Numerical evaluation of pavement design parameters for the fatigue cracking and rutting performance of asphalt pavements. Materials and Structures, 49 (9), 3619–3634. doi: 10.1617/s11527-015-0744-x
   Web of Science ®Google Scholar
• Rodezno, M., Kaloush, K., and Corrigan, M., 2010. Development of a flow number predictive model. Transportation Research Record: Journal of the Transportation Research Board, 2181, 79–87. doi: 10.3141/2181-09
   Web of Science ®Google Scholar
• Tahir, H. B., et al., 2018. Predicting the permanent deformation behaviour of the plant produced asphalt concrete mixtures: a first order regression approach. Construction and Building Materials, 189, 629–639. doi: 10.1016/j.conbuildmat.2018.08.164
   Web of Science ®Google Scholar
• Walubita, L., et al., 2014. Laboratory hot-mix asphalt performance testing: asphalt mixture performance tester versus universal testing machine. Transportation Research Record: Journal of the Transportation Research Board, 2447, 61–73. doi: 10.3141/2447-07
   Google Scholar
• Witczak, M. W., 2005. Simple performance tests: summary of recommended methods and database. Washington, DC: Transportation Research Board.
   Google Scholar
• Zhang, J., et al., 2013. Comparison of flow number, dynamic modulus, and repeated load tests for evaluation of HMA permanent deformation. Construction and Building Materials, 44, 391–398. doi: 10.1016/j.conbuildmat.2013.03.013
   Web of Science ®Google Scholar
• Zhang, J., et al., 2016. Development and validation of nonlinear viscoelastic damage (NLVED) model for three-stage permanent deformation of asphalt concrete. Construction and Building Materials, 102 (Part 1), 384–392. doi: 10.1016/j.conbuildmat.2015.10.201
   Google Scholar
• Zhou, F., Scullion, T., and Sun, L., 2004. Verification and modeling of three-stage permanent deformation behavior of asphalt mixes. Journal of Transportation Engineering, 130, 486–494. doi: 10.1061/(ASCE)0733-947X(2004)130:4(486)
   Web of Science ®Google Scholar