Performance Evaluation of Dynamic Modulus Predictive Models for Asphalt Mixtures

Document Type : Regular Paper


Department of Civil Engineering, Urmia University, Urmia, Iran


Dynamic modulus characterizes the viscoelastic behavior of asphalt materials and is the most important input parameter for design and rehabilitation of flexible pavements using Mechanistic–Empirical Pavement Design Guide (MEPDG). Laboratory determination of dynamic modulus is very expensive and time consuming. To overcome this challenge, several predictive models were developed to determine dynamic modulus of asphalt mixtures instead of laboratory testing. Present study utilizes a large database of 1320 dynamic modulus test results developed at the University of Maryland to evaluate the performance and accuracy of different dynamic modulus predictive models. For this purpose, six conventional dynamic modulus predictive models including Witczak, Modified Witczak, Hirsch, Al-Khateeb, Global and Simplified Global models were considered and dynamic moduli of asphalt mixtures were determined. These moduli were then compared with those determined from laboratory test results. Performance evaluation of the models showed high prediction accuracy and low prediction bias with good correlation between predicted moduli and measured values for Witczak and Global models.


Main Subjects

[1]        ARA. (2004). “Guide for mechanistic-empirical design of new and rehabilitated pavement structures” (NCHRP 1-37A, National Cooperative Highway Research Program). Transportation Research Board, National Research Council, Washington, D.C.
[2]        Andrei, D., Witczak, M. W. and Mirza, M. W. (1999). “Development of a revised predictive model for the dynamic (complex) modulus of asphalt mixtures” (Inter Team Technical Rep. prepared for the NCHRP 1-37A Project). Department of Civil Engineering, University of Maryland, College Park, MD.
[3]        Bari, J. and Witczak, M. W. (2006). “Development of a new revised version of the Witczak E* predictive model for hot mix asphalt mixtures”, Journal of Association of Asphalt Paving Technology, 75, 381-417.
[4]        Christensen, D. W., Pellinen, T. and Bonaquist, R. F. (2003). “Hirsch model for estimating the modulus of asphalt concrete”, Journal of the Association of Asphalt Paving Technologists, Vol. 72, pp. 97-121.
[5]        Al-Khateeb, G., Shenoy, A., Gibson, N. and Harman, T. (2006). “A new simplistic model for dynamic modulus predictions of asphalt paving mixtures”, Journal of the Association of Asphalt Paving Technologists, Vol. 75, pp. 1254-1293.
[6]        Sakhaeifar, M. S., Kim, Y. R. and Kabir, P. (2015). “New predictive models for the dynamic modulus of hot mix asphalt”, Construction and Building Materials, No. 76, pp. 221-231. doi:
[7]        Solatifar, N. (2018). “Analysis of Conventional Dynamic Modulus Predictive Models of Asphalt Mixtures”, Amirkabir Journal of Civil Engineering. doi:
[8]        Ceylan, H., Gopalakrishnan, K. and Kim. S. (2008). “Advanced approaches to hot-mix asphalt dynamic modulus prediction”, Canadian Journal of Civil Engineering, Vol. 35, No. 7, pp. 699-707. doi:
[9]        Ceylan, H., Schwartz, C. W., Kim. S. and Gopalakrishnan, K. (2009). “Accuracy of predictive models for dynamic modulus of hot-mix asphalt”, Journal of Materials in Civil Engineering, Vol. 21, No. 6, pp. 286–293. doi:
[10]      Sakhaeifar, M. S., Underwood, B. S., Kim, Y. R., Puccinelli, J. and Jackson, N. (2010). “Development of artificial neural network predictive models for populating dynamic moduli of long-term pavement performance sections”, Transportation Research Record: Journal of Transportation Research Board, No. 2181, pp. 88–97. doi:
[11]      Loulizi, A., Flintsch, G. W. and McGhee, K. (2007). “Determination of the in-place hot-mix asphalt layer modulus for rehabilitation projects by a mechanistic-empirical procedure”, Transportation Research Record: Journal of the Transportation Research Board, 2037, 53-62. doi:
[12]      Kavussi, A., Solatifar, N. and Abbasghorbani, M. (2016). “Mechanistic-empirical analysis of asphalt dynamic modulus for rehabilitation projects in Iran”, Journal of Rehabilitation in Civil Engineering, Vol. 4, No. 1, pp. 18-29. doi:
[13]      Solatifar, N., Kavussi, A., Abbasghorbani, M. and Katicha, S. W. (2019). “Development of dynamic modulus master curves of in-service asphalt layers using MEPDG models”, Road Materials and Pavement Design, 20(1), 225-243. doi:
[14]      Biswas, K. G. and Pellinen, T. K. (2007). “Practical methodology of determining the in situ dynamic (complex) moduli for engineering analysis”, Journal of Materials in Civil Engineering, 19(6), 508-514. doi:
[15]      Seo, J., Kim, Y., Cho, J. and Jeong, S. (2013). “Estimation of in situ dynamic modulus by using MEPDG dynamic modulus and FWD data at different temperatures”, International Journal of Pavement Engineering, Vol. 14, No. 4, pp. 343–353. doi:
[16]      Georgouli, K., Pomoni, M., Cliatt, B. and Loizos, A. (2015). “A simplified approach for the estimation of HMA dynamic modulus for in service pavements”, 6th International Conference ‘Bituminous Mixtures and Pavements’, Thessaloniki, Greece: 10-12 Jun.
[17]      Solatifar, N., Kavussi, A., Abbasghorbani, M. and Sivilevičius, H. (2017). “Application of FWD data in developing dynamic modulus master curves of in-service asphalt layers”. Journal of Civil Engineering and Management, 23(5), 661-671. doi:
[18]      ASTM. (2009). “Standard viscosity-temperature chart for asphalts” (D2493/D2493M-09). West Conshohocken, PA. doi:
[19]      El-Badawy, S., Bayomy, F. and Awed, A. (2012). “Performance of MEPDG dynamic modulus predictive models for asphalt concrete mixtures: local calibration for Idaho”, Journal of Materials in Civil Engineering, 24(11), 1412-1421. doi:
[20]      Witczak, M. W., El-Basyouny, M., & El-Badawy, S. (2007). “Incorporation of the new (2005) E* predictive model in the MEPDG”, (NCHRP 1-40D Inter-Team Technical Rep.). Tempe: Arizona State University.
[21]      Kim, Y. R., Underwood, B. S., Sakhaeifar, M. S., Jackson, N. and Puccinelli, J. (2011). “LTPP computed parameter: dynamic modulus”, Final Report for Project: DTFH61-02-D-00139, Federal Highway Administration, Washington, D.C.
[22]      AASHTO. (2012). “Standard method of test for determining the rheological properties of asphalt binder using a dynamic shear rheometer (DSR)”, AASHTO Designation: T 315-12.
[23]      Pellinen, T. K. (2001). “Investigation of the use of dynamic modulus as an indicator of hot-mix asphalt performance”, Thesis (PhD). Arizona State University.