Application of Artificial Neural Networks for Hot Mix Asphalt Dynamic Modulus (E*) Prediction

  • Sherif El-BadawyEmail author
  • Ragaa Abd El-Hakim
Conference paper
Part of the Sustainable Civil Infrastructures book series (SUCI)


The hot mix asphalt (HMA) dynamic modulus (E*) is a fundamental mechanistic property that defines the strain response of asphalt concrete mixtures as a function of loading rate and temperature. It is one of the HMA primary material inputs for the Pavement ME Design. The laboratory testing of dynamic modulus requires expensive advanced testing equipment that is not readily available in the majority of laboratories in the Middle Eastern countries, yet some of these countries are looking for implementing new pavement design methods such as Pavement ME Design. Thus, many research studies have been dedicated to develop predictive models for E*. This paper aims to apply artificial neural networks (ANNs) for E* predictions based on the inputs of the models most widely used today, namely: Witczak NCHRP 1-37A, Witczak NCHRP 1-40D and Hirsch E* predictive models. It also aims at investigating the effect of the different hierarchical binder input levels of the Pavement ME Design on the E* prediction accuracy. A total of 25 mixes from the Kingdom of Saudi Arabia (KSA), and 25 mixes from Idaho state were combined together in one database containing 3720 E* measurements. The database also contains the volumetric properties and aggregate gradations for all mixes as well as the binder complex shear modulus (Gb*), phase angle (δ), and Brookfield viscosity.

The results of this study show that using the same input variables of the three models, the ANNs models generally yielded more accurate E* predictions. Finally, there is a strong evidence of the influence of binder input level on the dynamic modulus E* prediction accuracy of both regression and ANNs.


  1. Ali, Y., Irfan, M., Ahmed, S., Khanzada, S., Mahmood, T.: Investigation of factors affecting dynamic modulus and phase angle of various asphalt concrete mixtures. Mater. Struct. 2016(49), 857–868 (2015). doi: 10.1617/s11527-015-0544-3 Google Scholar
  2. Andrei, D., Witczak, M.W., Mirza, M.W.: Development of revised predictive model for the dynamic (complex) modulus of asphalt mixtures. In: Development of the 2002 Guide for the Design of New and Rehabilitated Pavement Structures, NCHRP 1-37A, College Park, MD, Interim team Technical Report (1999)Google Scholar
  3. ARA, Inc., ERES Consultants Division: Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures. NCHRP 1-37A Final Report, Transportation Research Board, National Research Council, Washington, DC (2004)Google Scholar
  4. Awed, A.M., El-Badawy, S.M., Bayomy, F.M., Santi, M.: Influence of the MEPDG binder characterization input level on the predicted dynamic modulus for Idaho asphalt concrete mixtures. In: 90th Annual Meeting of the Transportation Research Board. Washington, DC (2011)Google Scholar
  5. Bari, J., Witczak, M.W.: Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. J. Assoc. Asphalt Paving Technol. 75, 381–417 (2006)Google Scholar
  6. Bari, J., Witczak, M.W.: New predictive models for the viscosity and complex shear modulus of asphalt binders for use with the mechanistic-empirical pavement design guide. J. Transp. Res. Rec. 2001, 9–19 (2007)CrossRefGoogle Scholar
  7. Bayomy, F., El-Badawy, S., Awed, A.: Implementation of the MEPDG for flexible pavements in Idaho. ITD Project RP 194, NIATT Project KLK557, National Institute for Advanced Transportation Technology, University of Idaho, Moscow, Idaho, December (2011)Google Scholar
  8. Ceylan, H., Gopalakrishnan, K., Kim, S.: Looking to the future: the next-generation hot mixasphalt dynamic modulus prediction models. Int. J. Pavement Eng. 10(5), 341–352 (2009). doi: 10.1080/10298430802342690 CrossRefGoogle Scholar
  9. Ceylan, H., Gopalakrishnan, K., Kim, S.: Advanced approaches to hot-mix asphalt dynamic modulus prediction. Can. J. Civ. Eng. 35, 699–707 (2008). doi: 10.1139/L08-016 CrossRefGoogle Scholar
  10. Ceylan, H., Schwartz, C.W., Kim, S., Gopalakrishnan, K.: Accuracy of predictive models for dynamic modulus of hot-mix asphalt. J. Mater. Civ. Eng. 21(6). ©ASCE. ISSN: 0899-1561/2009/6-286–293/$25.00. doi: 10.1061/_ASCE_0899-1561_2009_21:6_286_ (2009)
  11. Christensen Jr., D.W., Pellinen, T.K., Bonaquist, R.F.: Hirsch model for estimating the modulus of asphalt concrete. In: Asphalt paving technology, pp. 97–121. Journal of the Association of Asphalt Paving Technologists, Lexington, KY (2003)Google Scholar
  12. El-Badawy S.M., Bayomy F.M., Awed, A.M.: Performance of MEPDG dynamic modulus predictive models for asphalt concrete mixtures: local calibration for Idaho. J. Mater. Civ. Eng. 24(11). © SCE, ISSN 0899-1561/2012/11-1412-1421/$25.00. doi: 10.1061/(ASCE)MT.1943-5533.0000518 (2012)
  13. El-Badawy, S., Khattab, A., Al Hazmi, A.: Using artificial neural networks (ANNs) for hot mix asphalt E* predictions. Geo-China 2016, 83–91 (2016). doi: 10.1061/9780784480076.010 Google Scholar
  14. Far, M.S.S., Underwood, B., Ranjithan, S., Kim, R.Y.: Transportation Research Record: Journal of the Transportation Research Board, No. 2127, Transportation Research Board of the National Academies, Washington, D.C., pp. 173–186 (2009). doi: 10.3141/2127-20
  15. Georgouli, K., Loizos, A., Plati, C.: Calibration of dynamic modulus predictive model. Constr. Build. Mater. J. (2015). doi: 10.1016/j.conbuildmat.2015.10.163Elsevier Google Scholar
  16. Georgouli, K., Loizos, A., Plati, C.: Assessment of dynamic modulus prediction models in fatigue cracking estimation. Mater. Struct. J. 2016(49), 5007–5019 (2016). doi: 10.1617/s11527-016-0840-6 CrossRefGoogle Scholar
  17. Harran, G., Shalaby, A.: Improving the prediction of the dynamic modulus of fine-graded asphalt concrete mixtures at high temperatures Canadian. J. Civ. Eng. (2009). doi: 10.1139/L08-123 Google Scholar
  18. Jamrah A., Kutay, M.E., Ozturk, H.I.: Characterization of asphalt materials common to Michigan in support of the implementation of the mechanistic-empirical pavement design guide. In: 93rd Transportation Research Board Annual Meeting, 12–16 Jan 2014. Washington, DC (2014)Google Scholar
  19. Khatab, A.: Dynamic Modulus Predictive Models for Superpave Asphalt Concrete Mixtures. M.Sc. Thesis, Public Works Engineering. Mansoura University, Egypt (2015)Google Scholar
  20. Khattab, A.M., El-Badawy, S.M., Al Hazmi, A., Elmwafi, M.: Comparing Witczak NCHRP 1-40D with Hirsh E* predictive models for Kingdom of Saudi Arabia asphalt mixtures. In: 3rd Middle East Society of Asphalt Technologists (MESAT) Conference American University in Dubai, UAE, 6–8 Apr 2015 (2014)Google Scholar
  21. Khattab, A.M., El-Badawy, S.M., Al, Hazmi A., Elmwafi, M.: Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-pavement ME design in the Kingdom of Saudi Arabia. Constr. Build. Mater. J. (2014). doi: 10.1016/j.conbuildmat.2014.04.0660950-0618/_2014 Google Scholar
  22. Pellinen, T.K.: Investigation of the Use of Dynamic Modulus as an Indicator of Hot-Mix Asphalt Performance. Ph.D. Dissertation. Tempe, Arizona: Arizona State University (2001)Google Scholar
  23. Rahmani, E., Darabi, M., Abu Al-Rub, R., Kassem, E., Masad, E., Little, D.: Effect of confinement pressure on the nonlinear-viscoelastic response of asphalt concrete at high temperatures. Constr. Build. Mater. V(47), PP779–PP788 (2013)CrossRefGoogle Scholar
  24. Witczak, M., El-Basyouny, M., El-Badawy, S.: Incorporation of the new (2005) E* predictive model in the MEPDG. In: NCHRP1-40D Final Report (2007)Google Scholar
  25. Witczak, M.W., El-Basyouny, M., El-Badawy, S.: Incorporation of the New 2005 E* predictive model in the MEPDG. In: NCHRP 1-40D Final Report, Arizona State University, Tempe, AZ (2007)Google Scholar
  26. Yousefdoost, S., Binh, V., Rickards, I., Armstrong, P., Sullivan, B.: Evaluation of dynamic modulus predictive models for typical Australian asphalt mixes. In: 15th AAPA International Flexible Pavements Conference, 22-25 September, Royal International Conference Centre, Brisbane (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Public Works Engineering Department, Faculty of EngineeringMansoura UniversityMansouraEgypt
  2. 2.Public Works Engineering Department, Faculty of EngineeringTanta UniversityTantaEgypt

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