Modelling asphalt pavement analyzer rut depth using different statistical techniques

References

• Aho, B., Vavrik, W., & Carpenter, S. (2001). Effect of flat and elongated coarse aggregate on field compaction of hot-mix asphalt. Transportation Research Record, 1761, 26–31, Transportation Research Board, National Research Council, Washington, DC. doi: 10.3141/1761-04
   Google Scholar
• Al-Khateeb, G. G., Khedaywi, T. S., Turki, I., Obaidat, A. S., & Najib, A. M. (2013). Laboratory study for comparing rutting performance of limestone and basalt superpave asphalt mixtures. Journal of Materials in Civil Engineering, 25(1), 21–29. doi: 10.1061/(ASCE)MT.1943-5533.0000519
   Web of Science ®Google Scholar
• Al-Mosawe, H., Thom, N., Airey, G. D., & Al-Bayati, A. (2015). Effect of aggregate gradation on the stiffness of asphalt mixtures. International Journal on Pavement Engineering & Asphalt Technology, 16(2), 39–49. doi: 10.1515/ijpeat-2015-0008
   Google Scholar
• American Association of State Highway and Transportation Officials. (2006). AASHTO TP-63. Determining rutting susceptibility of hot mix asphalt (HMA) using the asphalt pavement analyzer (APA).
   Google Scholar
• Amirkhanian, S. N., Douglas, K., & James, B. (1991). Effects of Los Angeles abrasion test values on the strength of laboratory prepared Marshall specimens. Transportation Research Record, 1301, 77–86, Transportation Research Board, National Research Council, Washington, DC.
   Google Scholar
• Anochie-Boateng, J., & Maina, J. (2012). Permanent deformation testing for a new South African mechanistic pavement design method. Construction and Building Materials, 26, 541–546. doi: 10.1016/j.conbuildmat.2011.06.055
   Web of Science ®Google Scholar
• Archilla, A. R. (2006). Use of superpave gyratory compaction data for rutting prediction. Journal of Transportation Engineering, 132(9), 734–741. doi: 10.1061/(ASCE)0733-947X(2006)132:9(734)
   Google Scholar
• Archilla, A. R., & Madanat, S. (2000). Development of a pavement rutting model from experimental data. Journal of Transportation Engineering, 126(4), 291–299. doi: 10.1061/(ASCE)0733-947X(2000)126:4(291)
   Web of Science ®Google Scholar
• Bessa, I. S., Branco, V. T. F. C., Soares, J. B., & Neto, J. A. N. (2015). Aggregate shape properties and their influence on the behavior of hot-mix asphalt. J. Mater. Civ. Eng, 27(7), 04014212. doi: 10.1061/(ASCE)MT.1943-5533.0001181
   Web of Science ®Google Scholar
• Buchanan, M. S. (2000). Evaluation of the effect of flat and elongated particles on the performance of hot mix asphalt mixtures. Report No. 2000-03, National Center for Asphalt Technology, Auburn University, Alabama.
   Google Scholar
• Cao, W., Liu, S., Li, Y., & Xue, Z. (2016). Effect of aggregate gradation on volumetric parameters and the high temperature performance of asphalt mixtures. Proceedings of 4th Geo-China international conference, 25–27 July, Shandong, China.
   Google Scholar
• Chen, J. S., Wong, S. Y., & Lin, K. Y. (2005). Quantification of movements of flat and elongated particles in hot mix asphalt subject to wheel load test. Materials and Structures, 38, 395–402. doi: 10.1617/14151
   Web of Science ®Google Scholar
• Cheung, L. W., & Dawson, A. (2003). Effects of particle and mix characteristics on performance of some granular materials. Transportation Research Record, 1787, 90–98. Transportation Research Board, National Research Council, Washington D.C. doi: 10.3141/1787-10
   Google Scholar
• Choubane, B., Page, G. C., & Musselman, J. A., (2000). Suitability of asphalt pavement analyzer for predicting pavement rutting. Transportation Research Record, 1723, 107–115. Transportation Research Board, National Research Council, Washington D.C. doi: 10.3141/1723-14
   Google Scholar
• Darabadi, B. K., & Taherkhani, H. (2015). An investigation on the effects of flaky particles on the properties of asphaltic mixtures. Canadian Journal of Civil Engineering, 42(11), 26–31. doi: 10.1139/cjce-2014-0458
   Web of Science ®Google Scholar
• Doyle, J. D., & Howard, I. L. (2013). Rutting and moisture damage resistance of high reclaimed asphalt pavement warm mixed asphalt: Loaded wheel tracking vs. conventional methods. Road Materials and Pavement Design, 14(sup2), 148–172. doi: 10.1080/14680629.2013.812841
   Web of Science ®Google Scholar
• Feyissa, B. A. (2009). Analysis and modeling of rutting for long life asphalt concrete pavement (PhD Thesis). Technische Universität Darmstadt, Germany.
   Google Scholar
• Ghabchi, R., Singh, D., & Zaman, M. (2015). Laboratory evaluation of stiffness, low-temperature cracking, rutting, moisture damage, and fatigue performance of WMA mixes. Road Materials and Pavement Design, 16(2), 334–357. doi: 10.1080/14680629.2014.1000943
   Web of Science ®Google Scholar
• Ghanizadeh, A. R., & Rahrovan, M. (2016). Application of artificial neural network to predict resilient modulus of stabilized base subjected to wet-dry cycles. Computations and Materials in Civil Engineering, 1(1), 37–47.
   Google Scholar
• Golalipour, A., Jamshedi, E., Niazi, Y., Afsharikia, Z., & Khadem, M. (2012). Effect of aggregate gradation on rutting of asphalt pavements. Procedia-Social and Behavioral Sciences, 53, 440–449. doi: 10.1016/j.sbspro.2012.09.895
   Google Scholar
• Han, J., & Shiwakoti, H. (2016). Wheel tracking methods to evaluate moisture sensitivity of hot-mix asphalt mixtures. Frontiers of Structural and Civil Engineering, 10(1), 30–43. doi: 10.1007/s11709-016-0318-1
   Web of Science ®Google Scholar
• Hussan, S., Kamal, M. A., Hafeez, I., Farooq, D., Ahmad, N., & Khanzada, S. (2017). Statistical evaluation of factors affecting the laboratory rutting susceptibility of asphalt mixtures. International Journal of Pavement Engineering, 49(2), 1–15. doi: 10.1080/10298436.2017.1299527
   Google Scholar
• Jeong, K., Hwang, S., Lee, S., & Kim, K. W. (2011). Investigation of rutting potential of open graded friction course (OGFC) mixes using asphalt pavement analyzer. KSCE Journal of Civil Engineering, 15(7), 1259–1262. doi: 10.1007/s12205-011-1060-9
   Web of Science ®Google Scholar
• Ji, X., Zheng, N., Niu, S., Meng, S., & Xu, Q. (2016). Development of a rutting prediction model for asphalt pavements with the use of an accelerated loading facility. Road Materials and Pavement Design, 17(1), 15–31. doi: 10.1080/14680629.2015.1055337
   Web of Science ®Google Scholar
• Khedr, S. A., & Breakah, T. M., (2011). Rutting parameters for asphalt concrete for different aggregate structures. International Journal of Pavement Engineering, 12(1), 13–23. doi: 10.1080/10298430903578960
   Web of Science ®Google Scholar
• Khosla, N. P., & Sadasivam, S. (2002). Evaluation of the effects of mixture properties and compaction methods on the predicted performance of superpave mixtures. Report # FHWA/NC/2002-030 prepared by Department of Civil Engineering, North Carolina State University, USA.
   Google Scholar
• Kim, Y. K., Park, H. M., Aragao, F. T. S., & Lutif, J. E. F. (2009). Effects of aggregate structure on hot-mix asphalt rutting performance in low traffic volume local pavements. Construction and Building Materials, 23, 2177–2182. doi: 10.1016/j.conbuildmat.2008.12.007
   Web of Science ®Google Scholar
• Liu, H., Hao, P., & Xu, J. (2017). Effects of nominal maximum aggregate size on the performance of stone matrix asphalt. Applied Sciences, 7(2), 126. doi: 10.3390/app7020126
   Google Scholar
• Mahmud, M. Z. H., Yaacob, H., PutraJaya, R., & AbdulHassan, N. (2014). Laboratory investigation on the effects of flaky aggregates on dynamic creep and resilient modulus of asphalt mixtures. Jurnal Teknologi, 70(4), 107–110.
   Google Scholar
• Malladi, H., Ayyala, D., Tayebali, A., & Khosla, N. (2015). Laboratory evaluation of warm-mix asphalt mixtures for moisture and rutting susceptibility. Journal of Materials in Civil Engineering, 27(5), 04014162. doi: 10.1061/(ASCE)MT.1943-5533.0001121
   Web of Science ®Google Scholar
• Martin, A., & Park, D. (2003). Use of the asphalt pavement analyzer and repeated simple shear test at constant height to augment superpave volumetric mix design. Journal of Transportation Engineering, 129(5), 522–530. doi: 10.1061/(ASCE)0733-947X(2003)129:5(522)
   Web of Science ®Google Scholar
• Mirzahosseini, M., Najjar, Y. N., Alavi, A. H., & Gandomi, A. H. (2013). ANN-Based prediction model for rutting propensity of asphalt mixtures. Presented in 92nd annual meeting transportation research board, Washington DC.
   Google Scholar
• PutraJaya, R., AbdulHassan, N., Mahmud, M. Z. H., Aziz, M. A., Hamzah, M. O., & Chewan, C. N. (2014). Effect of aggregate shape on the properties of asphaltic concrete AC14. Jurnal Teknologi, 71(3), 69–73.
   Google Scholar
• Qasim, Z. I., Abbas, A. S., & Qasim, Z. I. (2017). Effect of filler content on properties of asphaltic mixtures for marshall and superpave gyratory compactor. Al-Nahrain Journal for Engineering Sciences, 20(1), 183–193.
   Google Scholar
• Qiao, Y., Flintsch, G., Dawson, A., & Parry, T. (2013). Examining effects of climatic factors on flexible pavement performance and service life. Transportation Research Record, 2349, 100–107, Transportation Research Board, National Research Council, Washington, DC. doi: 10.3141/2349-12
   Google Scholar
• Ramli, I., Yaacob, H., Abdul Hassan, N., Ismail, C. R., & Hainin, M. R. (2013). Fine aggregate angularity effects on rutting resistance of asphalt mixture. Jurnal Teknologi, 65(3), 105–109. doi: 10.11113/jt.v65.2154
   Google Scholar
• Remisova, E. (2015). Study of mineral filler effect on asphalt mixtures properties. Proceedings of 6th international conference ‘bituminous mixtures and pavements’, 10–12 June, Thessaloniki, Greece.
   Google Scholar
• Rushing, J. F., Little, D. N., & Garg, N. (2012). Using the asphalt pavement analyzer to assess rutting susceptibility of HMA designed for high tire pressure aircraft. Presented in 91st annual meeting transportation research board, Washington DC.
   Google Scholar
• Seo, Y., El-Haggan, O., King, M., Lee, J., & Kim, Y. R. (2007). Air void models for the dynamic modulus, fatigue cracking, and rutting of asphalt concrete. Journal of Materials in Civil Engineering, 19(10), 874–883. doi: 10.1061/(ASCE)0899-1561(2007)19:10(874)
   Web of Science ®Google Scholar
• Shafabakhsh, G. H., Ani, O. J., & Talebsafa, M. (2015). Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates. Construction and Building Materials, 85, 136–143. doi: 10.1016/j.conbuildmat.2015.03.060
   Web of Science ®Google Scholar
• Shafabakhsh, G. H., Sadeghnejad, M., & Sajed, Y. (2014). Case study of rutting performance of HMA modified with waste rubber powder. Case Studies in Construction Materials, 1, 69–76. doi: 10.1016/j.cscm.2014.04.005
   Google Scholar
• Souliman, M. I., Piratheepan, M., Hajj, E. Y., Sebaaly, P. E., & Sequeira, W. (2015). Impact of lime on the mechanical and mechanistic performance of hot mixed asphalt mixtures. Road Materials and Pavement Design, 16(2), 421–444. doi: 10.1080/14680629.2015.1017520
   Web of Science ®Google Scholar
• Suh, Y. C., & Choand, N. M. (2014). Development of a rutting performance model for asphalt concrete pavement based on test road and accelerated pavement test data. KSCE Journal of Civil Engineering, 18(1), 165–171. doi: 10.1007/s12205-014-0394-5.
   Web of Science ®Google Scholar
• Suo, Z., & Wong, W. G. (2009). Nonlinear properties analysis on rutting behaviour of bituminous materials with different air void contents. Construction and Building Materials, 23, 3492–3498. doi: 10.1016/j.conbuildmat.2009.07.004
   Web of Science ®Google Scholar
• Tapkin, S., Cevik, A., & Usar, U. (2009). Accumulated strain prediction of polypropylene modified Marshall specimens in repeated creep test using artificial neural networks. Expert Systems with Applications, 36, 11186–11197. doi: 10.1016/j.eswa.2009.02.089
   Web of Science ®Google Scholar
• Tarefder, R. A., Zaman, M., & Hobson, K. (2003). A laboratory and statistical evaluation of factors affecting rutting. International Journal of Pavement Engineering, 4(1), 59–68. doi: 10.1080/10298430310001593263
   Google Scholar
• Topal, A., & Sengoz, B. (2005). Determination of fine aggregate angularity in relation with the resistance to rutting of hot-mix asphalt. Construction and Building Materials, 19, 155–163. doi: 10.1016/j.conbuildmat.2004.05.004
   Web of Science ®Google Scholar
• Walubita, L. F., Faruk, A. N. M., Zhang, J., Hu, X., & Lee, S. I. (2016). The Hamburg rutting test – effects of HMA sample sitting time and test temperature variation. Construction and Building Materials, 108, 22–28. doi: 10.1016/j.conbuildmat.2016.01.031
   Web of Science ®Google Scholar
• Wang, H., Zhang, Q., & Tan, J. (2009). Investigation of layer contributions to asphalt pavement rutting. Journal of Materials in Civil Engineering, 21, 181–185. Special Issue: China: Innovative Use of Materials for Highway Construction. doi: 10.1061/(ASCE)0899-1561(2009)21:4(181)
   Web of Science ®Google Scholar
• Williams, S. G. (2003). The effects of HMA mixture characteristics on rutting susceptibility. 82nd annual meeting of the transportation research board, 12–16 January, Washington, DC, USA.
   Google Scholar
• Xiao, F., & Amirkhanian, S. N. (2015). Moisture susceptibility and rut resistance of RAP asphalt mixtures with high percentage of natural sand. Journal of Materials in Civil Engineering, 27(7), 04014217. doi: 10.1061/(ASCE)MT.1943-5533.0001186
   Web of Science ®Google Scholar
• Xie, Z., & Shen, J. (2016). Performance properties of rubberized stone matrix asphalt mixtures produced through different processes. Construction and Building Materials, 104, 230–234. doi: 10.1016/j.conbuildmat.2015.12.063
   Web of Science ®Google Scholar
• Yilmaz, I., & Kaynar, O. (2011). Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Systems with Applications, 38, 5958–5966. doi: 10.1016/j.eswa.2010.11.027
   Web of Science ®Google Scholar
• Ziari, H., Babagoli, R., & Akbari, A. (2015). Investigation of fatigue and rutting performance of hot mix asphalt mixtures prepared by bentonite-modified bitumen. Road Materials and Pavement Design, 16(1), 101–118. doi: 10.1080/14680629.2014.982156
   Web of Science ®Google Scholar