Fatigue–healing performance evaluation of asphalt mixture using four-point bending test


The design of asphalt pavements is mostly based on fatigue performance. However, the fatigue–healing characteristics of asphalt pavements can repair pavement cracks and prolong fatigue life, which are not considered in the conservative traditional pavement designs. In this study, a four-point bending fatigue–healing–fatigue test and a single-factor comparative analysis method are used to investigate the fatigue–healing performance of asphalt mixtures. The effects of healing temperature, healing time, degree of damage, and loading strain on the fatigue–healing characteristics of unmodified and styrene–butadiene–styrene (SBS)-modified asphalt mixtures are investigated. The degree of influence of each factor is evaluated using the grey correlation method. Results show that the fatigue-life healing index of the asphalt mixtures is proportional to the healing time and inversely proportional to the degree of damage and the loading strain. The healing indices of unmodified and SBS-modified asphalt mixtures reached their maximum values at healing temperatures of 50 °C and 60 °C, respectively. All the factors have significant effects on the fatigue–healing performance; however, the healing temperature had the greatest effect.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7


  1. 1.

    Bazin P, Saunier J (1967) Deformability, fatigue and healing properties of asphalt mixes. In Intl Conf Struct Design Asphalt Pvmts, 438–451

  2. 2.

    Kim YR, Little DN, Lytton RL (2003) Fatigue and healing characterization of asphalt mixtures. J Mater Civ Eng 15(1):75–83. https://doi.org/10.1061/(ASCE)0899-1561(2003)15:1(75)

    Article  Google Scholar 

  3. 3.

    Seo Y, Kim YR (2008) Using acoustic emission to monitor fatigue damage and healing in asphalt concrete. KSCE J Civ Eng 12(4):237–243. https://doi.org/10.1007/s12205-008-0237-3

    MathSciNet  Article  Google Scholar 

  4. 4.

    Al-Rub RKA, Darabi MK, Little DN, Masad EA (2010) A micro-damage healing model that improves prediction of fatigue life in asphalt mixes. Int J Eng Sci 48(11):966–990. https://doi.org/10.1016/j.ijengsci.2010.09.016

    Article  Google Scholar 

  5. 5.

    García Á (2012) Self-healing of open cracks in asphalt mastic. Fuel 93:264–272. https://doi.org/10.1016/j.fuel.2011.09.009

    Article  Google Scholar 

  6. 6.

    Liu Q, Schlangen E, van de Ven M (2013) Characterization of the material from the induction healing porous asphalt concrete trial section. Mater Struct 46(5):831–839. https://doi.org/10.1617/s11527-012-9936-9

    Article  Google Scholar 

  7. 7.

    Yang X, You Z, Dai Q, Mills-Beale J (2014) Mechanical performance of asphalt mixtures modified by bio-oils derived from waste wood resources. Constr Build Mater 51:424–431. https://doi.org/10.1016/j.conbuildmat.2013.11.017

    Article  Google Scholar 

  8. 8.

    Sun D, Hu J, Zhu X (2015) Size optimization and self-healing evaluation of microcapsules in asphalt binder. Colloid Polym Sci 293(12):3505–3516. https://doi.org/10.1007/s00396-015-3721-6

    Article  Google Scholar 

  9. 9.

    Tang J, Liu Q, Wu S, Ye Q, Sun Y, Schlangen E (2016) Investigation of the optimal self-healing temperatures and healing time of asphalt binders. Constr Build Mater 113:1029–1033. https://doi.org/10.1016/j.conbuildmat.2016.03.145

    Article  Google Scholar 

  10. 10.

    Xu G, Wang H (2017) Molecular dynamics study of oxidative aging effect on asphalt binder properties. Fuel 188:1–10. https://doi.org/10.1016/j.fuel.2016.10.021

    Article  Google Scholar 

  11. 11.

    Dinh BH, Park DW, Phan TM (2018) Healing performance of granite and steel slag asphalt mixtures modified with steel wool fibers. KSCE J Civ Eng 22(6):2064–2072. https://doi.org/10.1007/s12205-018-1660-8

    Article  Google Scholar 

  12. 12.

    Sun D, Sun G, Zhu X, Ye F, Xu J (2018) Intrinsic temperature sensitive self-healing character of asphalt binders based on molecular dynamics simulations. Fuel 211:609–620. https://doi.org/10.1016/j.fuel.2017.09.089

    Article  Google Scholar 

  13. 13.

    Grossegger D, Garcia A (2019) Influence of the thermal expansion of bitumen on asphalt self-healing. Appl Thermal Eng 156:23–33. https://doi.org/10.1016/j.applthermaleng.2019.04.034

    Article  Google Scholar 

  14. 14.

    Zhu X, Ye F, Cai Y, Birgisson B, Lee K (2019) Self-healing properties of ferrite-filled open-graded friction course (OGFC) asphalt mixture after moisture damage. J Clean Prod 232:518–530. https://doi.org/10.1016/j.jclepro.2019.05.353

    Article  Google Scholar 

  15. 15.

    Shen S, Chiu HM, Huang H (2010) Characterization of fatigue and healing in asphalt binders. J Mater Civ Eng 22(9):846–852. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000080

    Article  Google Scholar 

  16. 16.

    Qiu J, Van de Ven M, Wu S, Molenaar A, Yu J (2014) Self-healing characteristics of bituminous mastics using a modified direct tension test. J Intell Mater Syst Struct 25(1):58–66. https://doi.org/10.1177/1045389X12467515

    Article  Google Scholar 

  17. 17.

    Kim B, Roque R (1970) (2006) Evaluation of healing property of asphalt mixtures. Transp Res Rec 1:84–91. https://doi.org/10.1177/0361198106197000108

    Article  Google Scholar 

  18. 18.

    Huang M, Wang X, Huang WD (2013) Analysis of influencing factors for self-healing of fatigue performance of asphalt rubber mixture. China J Highw Transp 26(4):16–22. (in chinese)

    Google Scholar 

  19. 19.

    Sun D, Li B, Ye F, Zhu X, Lu T, Tian Y (2018) Fatigue behavior of microcapsule-induced self-healing asphalt concrete. J Clean Prod 188:466–476. https://doi.org/10.1016/j.jclepro.2018.03.281

    Article  Google Scholar 

  20. 20.

    Rowe GM, Bouldin MG (2000). Improved techniques to evaluate the fatigue resistance of asphaltic mixtures. In: 2nd Eurasphalt & Eurobitume Congress Barcelona, vol 2000. https://www.researchgate.net/publication/304115473_Improved_techniques_to_evaluate_the_fatigue_resistance_of_asphaltic_mixtures?enrichId=rgreqd01e6bcfa1f4ba34483395178d300d3b-XXX&enrichSource=Y292ZXJQYWdlOzMwNDExNTQ3MztBUzozODA1NDg5ODQ5MTgwMTZAMTQ2Nzc0MTM1NjU2OA%3D%3D&el=1_x_2&_esc=publicationCoverPdf

  21. 21.

    Shan L, Tan Y, Kim YR (2013) Establishment of a universal healing evaluation index for asphalt binder. Constr Build Mater 48:74–79. https://doi.org/10.1016/j.conbuildmat.2013.06.039

    Article  Google Scholar 

  22. 22.

    Liu Q, Schlangen E, van de Ven M, Van Bochove G, Van Montfort J (2012) Evaluation of the induction healing effect of porous asphalt concrete through four point bending fatigue test. Constr Build Mater 29:403–409. https://doi.org/10.1016/j.conbuildmat.2011.10.058

    Article  Google Scholar 

  23. 23.

    Qiu J, Van de Ven M, Wu S, Yu J, Molenaar A (2009) Investigating the self healing capability of bituminous binders. Road Mater Pavement Des 10(sup1):81–94. https://doi.org/10.1080/14680629.2009.9690237

    Article  Google Scholar 

  24. 24.

    Bhasin A, Little DN, Bommavaram R, Vasconcelos K (2008) A framework to quantify the effect of healing in asphalt materials using material properties. Road Mater Pavement Des 9(sup1):219–242. https://doi.org/10.1080/14680629.2008.9690167

    Article  Google Scholar 

  25. 25.

    Zhang Z (2000) Identification of suitable crack growth law for asphalt mixtures using the Superpave indirect tensile test (IDT). (Doctoral dissertation, University of Florida)

  26. 26.

    Liu G, Yu J (2007) Gray correlation analysis and prediction models of living refuse generation in Shanghai city. Waste Manag 27(3):345–351. https://doi.org/10.1016/j.wasman.2006.03.010

    Article  Google Scholar 

  27. 27.

    Yufeng G, Jing WANG, Zidian H (2013) Gray correlation analysis on influencing factors of postgraduates’ innovative capacity. J Theor Appl Inf Technol 49(1):419–425

    Google Scholar 

  28. 28.

    Wang Q, Liu J, Zhu X, Liu J, Liu Z (2016) The experiment study of frost heave characteristics and gray correlation analysis of graded crushed rock. Cold Reg Sci Technol 126:44–50. https://doi.org/10.1016/j.coldregions.2016.03.003

    Article  Google Scholar 

  29. 29.

    Zhang J, Zhang A, Li J, Li F, Peng J (2019) Gray correlation analysis and prediction on permanent deformation of subgrade filled with construction and demolition materials. Materials 12(18):3035. https://doi.org/10.3390/ma12183035

    Article  Google Scholar 

Download references


This paper was supported by Maintenance Technology Project of Qilu Transportation Development Group Co., Ltd. in 2018, China (2018YHKY-01).

Author information



Corresponding author

Correspondence to Hao Xiang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiang, H., Zhang, W., Liu, P. et al. Fatigue–healing performance evaluation of asphalt mixture using four-point bending test. Mater Struct 53, 47 (2020). https://doi.org/10.1617/s11527-020-01482-z

Download citation


  • Asphalt mixture
  • Fatigue–healing
  • Four-point bending
  • Influence factor
  • Grey correlation