[(TRI), Transportation Research Institute. 2010. “Experimental Study of Gradation Type and Voids Effect on Bleeding and Rutting in Asphalt Concrete in Iran, Tehran, Iran.” Transportation Research Institute.]Search in Google Scholar
[Al-Khateeb, G.G., Al-Suleiman Obaidat, T.I., Khedaywi, T.S. and Elayan, M.S., 2018. Studying rutting performance of Superpave asphalt mixtures using unconfined dynamic creep and simple performance tests. Road Materials and Pavement Design, 19(2), pp.315-333.10.1080/14680629.2016.1261722]Search in Google Scholar
[Anderson, R.M., 2002. Relationship of Superpave gyratory compaction properties to HMA rutting behavior (Vol. 478). Transportation Research Board.]Search in Google Scholar
[Anderson, R.M., Christensen, D.W. and Bonaquist, R., 2003. Estimating the rutting potential of asphalt mixtures using Superpave gyratory compaction properties and indirect tensile strength (with discussion). Journal of the Association of Asphalt Paving Technologists, 72.]Search in Google Scholar
[Barman, M., Imran, S.A., Nazari, M., Commuri, S. and Zaman, M., 2018. Use of Intelligent Compaction in Detecting and Remediating Under-Compacted Spots During Compaction of Asphalt Layers. In Civil Infrastructures Confronting Severe Weathers and Climate Changes Conference, Springer, Cham, pp. 131-141.10.1007/978-3-319-95792-0_11]Search in Google Scholar
[Code, Iran Highway Asphalt Paving. 2011. Vice Presidency for Strategic Planning and Supervision. The Ministry of Roads and Urban Development, Asphalt Institute of Iran.]Search in Google Scholar
[Du, Y., Chen, J., Han, Z. and Liu, W., 2018. A review on solutions for improving rutting resistance of asphalt pavement and test methods. Construction and Building Materials, 168, pp.893-905.10.1016/j.conbuildmat.2018.02.151]Search in Google Scholar
[Gong, H., Sun, Y., Hu, W. and Huang, B., 2019. Neural networks for fatigue cracking prediction using outputs from pavement mechanistic-empirical design. International Journal of Pavement Engineering, pp.1-11.10.1080/10298436.2019.1580367]Search in Google Scholar
[Gu, F., Luo, X., Zhang, Y., Chen, Y., Luo, R. and Lytton, R.L., 2018. Prediction of geogrid-reinforced flexible pavement performance using artificial neural network approach. Road Materials and Pavement Design, 19(5), pp.1147-1163.10.1080/14680629.2017.1302357]Search in Google Scholar
[Inzerillo, L., Di Mino, G., Bressi, S., Di Paola, F. and Noto, S., 1995. Image Based Modeling Technique for Pavement Distress Surveys: a Specific Application to Rutting. International Journal of Engineering and Technology, 16(5), pp.1-9.]Search in Google Scholar
[Kaya, O., Garg, N., Ceylan, H. and Kim, S., 2018. Development of Artificial Neural Networks Based Predictive Models for Dynamic Modulus of Airfield Pavement Asphalt Mixtures. In International Conference on Transportation and Development 2018: Airfield and Highway Pavements, Reston, VA: American Society of Civil Engineers, pp. 1-7.10.1061/9780784481554.001]Search in Google Scholar
[Liao, G., Wang, S. and Shi, Q., 2018. Enhancing anti-rutting performance of asphalt pavement by dispersing shear stresses within asphalt layers. Road Materials and Pavement Design, 19(2), pp.453-469.10.1080/14680629.2016.1253494]Search in Google Scholar
[Mamlouk, M., Vinayakamurthy, M., Underwood, B.S. and Kaloush, K.E., 2018. Effects of the International Roughness Index and Rut Depth on Crash Rates. Transportation Research Record, 2672(40), pp.418-429.10.1177/0361198118781137]Search in Google Scholar
[Mansourian, A., Ghanizadeh, A.R. and Golchin, B., 2019. Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement using Feed-Forward and Generalized Regression Neural Networks. Journal of Rehabilitation in Civil Engineering, 7(1), pp.21-41.]Search in Google Scholar
[McGarvey, K., Panko, M., Hurt, C., Mehta, Y. and Sukumaran, B., 2010. Use of the Superpave Gyratory Compactor as a Predictor of Field Performance of Unbound Material. In 2010 FAA Worldwide Airport Technology Transfer ConferenceFederal Aviation AdministrationAmerican Association of Airport Executives.]Search in Google Scholar
[Parsons, T.A., Kazmee, H. and Garg, N., 2017. Sensitivity Analysis of Rut Depth to Longitudinal Measurement Location in Accelerated Pavement Testing with a Heavy Vehicle Simulator-Airfield. In Airfield and Highway Pavements 2017 (pp. 115-126).10.1061/9780784480953.011]Search in Google Scholar
[Radhakrishnan, V., Dudipala, R.R., Maity, A. and Sudhakar Reddy, K., 2019. Evaluation of rutting potential of asphalts using resilient modulus test parameters. Road Materials and Pavement Design, 20(1), pp.20-35.10.1080/14680629.2017.1374994]Search in Google Scholar
[Shafabakhsh, G.A., Divandari, H. and Sajadi, S.R., 2018. Evaluation of Optimum Content of Rice Husk Ash to Improve the Hot Asphalt Concrete Performance. International Journal of Engineering and Technology, Vol. 7, No. 4.20, pp. 338-346, DOI: 10.14419/ijet.v7i4.20.26131.10.14419/ijet.v7i4.20.26131]Search in Google Scholar
[Wang, D., Falchetto, A.C., Goeke, M., Wang, W., Li, T. and Wistuba, M.P., 2017. Influence of computation algorithm on the accuracy of rut depth measurement. Journal of traffic and transportation engineering (English edition), 4(2), pp.156-164.10.1016/j.jtte.2017.03.001]Search in Google Scholar
[Yi, W., Wang, Y.H. and Zhou, R., 2011. Study on Rutting of Asphalt Pavement. In Advanced Materials Research, Trans Tech Publications.Vol. 163, pp. 1096-1099.10.4028/www.scientific.net/AMR.163-167.1096]Search in Google Scholar
[Zaniewski, J.P. and Srinivasan, G,. 2004. Evaluation of indirect tensile strength to identify asphalt concrete rutting potential. Asphalt Technology Program, Department of Civil and Environmental Engineering, West Virginia University, Performed in Cooperation with the US Department of Transportation-Federal Highway Administration.]Search in Google Scholar
[Zhang, W., Shen, S., Wu, S., Chen, X., Xue, J. and Mohammad, L.N., 2019. Effects of In-Place Volumetric Properties on Field Rutting and Cracking Performance of Asphalt Pavement. Journal of Materials in Civil Engineering, 31(8), p.04019150.10.1061/(ASCE)MT.1943-5533.0002767]Search in Google Scholar
[Ziari, H. and Divandari, H., 2013. Presenting asphalt mixtures flow number prediction model using gyratory curves. International Journal of Civil Engineering, 11 (2), pp. 125-133.]Search in Google Scholar
[Ziari, H., Divandari, H., Behbahan, H. and Ameri, M., 2012. Developing a Forecasting Model for Asphalt Rutting Potential Using Gyratory Compactor Parameters. Life Science Journal, 9 (4), pp. 4140-4149.]Search in Google Scholar
[Ziari, H., Divandari, H., Hajiloo, M. and Amini, A., 2019. Investigating the effect of amorphous carbon powder on the moisture sensitivity, fatigue performance and rutting resistance of rubberized asphalt concrete mixtures. Construction and Building Materials, Vol. 217, pp. 62-72, DOI: 10.1016/j.conbuildmat.2019.05.039.10.1016/j.conbuildmat.2019.05.039]Open DOISearch in Google Scholar