A soft computing approach to predict and evaluate asphalt mixture aging characteristics using asphaltene as a performance indicator


Prediction of long-term asphalt mixture aging using fundamental characteristics of asphalt binders and mixtures is a complex task. Asphaltene has been reported as one of the major chemical components of asphalt cement binder. Several research studies have established asphaltene content as the fundamental characteristic ingredient present in the asphalt binders required to understand aging-related performance. Dynamic complex modulus (|E*|) is recognized as the paramount performance response parameter for asphalt mixtures, routinely used in pavement design and evaluation exercises. Hence, there is a definitive need to develop mixture aging predictive models using asphaltene content as the fundamental parameter with its effect on the resulting |E*| performance of asphalt mixtures. The objective of this research study was to develop asphalt mixture aging predictive models with asphaltene content as a fundamental performance parameter, using soft computing techniques. Asphalt binders and corresponding asphalt mixtures were subjected to short- and long-term aging conditions. Asphaltene contents and rheological properties were measured for different asphalt binders. Volumetric properties and |E*| were conducted for corresponding asphalt mixtures. Artificial Neural Network (ANN) method was employed to develop a rational model for evaluating asphalt mixture aging behavior considering asphaltene content values from asphalt binders. A total of seven different dense-graded asphalt mixtures with virgin, polymer-, and rubber-modified binders with two different asphalt contents were produced for experimentation purposes. The results showed that the predictive model developed using the ANN approach provided a robust relationship with asphaltene aging indices, a fundamental asphalt property used to quantify asphalt mixture properties at various aging conditions.

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  1. 1.

    Bell CA (1989) Aging of Asphalt-Aggregate systems, SR-OSU-A-003A-89-2, Summary Report, Strategic highway research program, National Research Council, Washington, DC, USA

  2. 2.

    Airey GD (2003) State of the art report on ageing test methods for bituminous pavement materials. Int J Pavement Eng 4(3):165–176

    Article  Google Scholar 

  3. 3.

    Petersen D, Link R, Abbas A, Choi B, Masad E, Papagiannakis T (2002) The influence of laboratory aging method on the rheological properties of asphalt binders. J Test Eval 30(2):171

    Article  Google Scholar 

  4. 4.

    Branthaver JF, Petersen JC, Robertson RE, Duvall JJ, Kim SS, Harnsberger PM, Mill T, Ensley EK, Barbour FA, Scharbron JF (1993) Binder characterization and evaluation. Volume 2: Chemistry. no. SHRP-A-368

  5. 5.

    Anderson DA, Christensen DW, Bahia HU, Dongre R, Sharma MG, Antle CE, Button J (1994) Binder characterization and evaluation. Volume 3: physical characterization, Strategic highway research program, National Research Council, Report No. SHRP-A-369

  6. 6.

    Corbett LW (1969) Composition of asphalt based on generic fractionation, using solvent deasphaltening, elution-adsorption chromatography, and densimetric characterization. Anal Chem 41(4):576–579

    Article  Google Scholar 

  7. 7.

    Sirin O, Paul DK, Kassem E (2018) State of the art study on aging of asphalt mixtures and use of antioxidant additives. Adv Civil Eng 2018:18. https://doi.org/10.1155/2018/3428961

    Article  Google Scholar 

  8. 8.

    Morian N, Hajj E, Glover C, Sebaaly P (2011) Oxidative aging of asphalt binders in hot-mix asphalt mixtures. Transp Res Rec J Transp Res Board 2207(2207):107–116

    Article  Google Scholar 

  9. 9.

    Yin F, Arámbula-Mercado E, Epps Martin A, Newcomb D, Tran N (2017) Long-term ageing of asphalt mixtures. Road Mater Pavement Des 18:2–27

    Article  Google Scholar 

  10. 10.

    Siddiqui MN, Ali MF (1999) Studies on the aging behavior of the Arabain asphalts. Fuel 78:1005–1015

    Article  Google Scholar 

  11. 11.

    Michalica P, Kazatchkov IB, Stastna J, Zanzotto L (2008) Relationship between chemical and rheological properties of two asphalts of different origins. Fuel 87(15–16):3247–3253

    Article  Google Scholar 

  12. 12.

    Kumbargeri YS, Biligiri KP (2016) A novel approach to understanding asphalt binder aging behavior using asphaltene proportion as a performance indicator. J Test Eval Am Soc Test Mater Int 44(1S):1–11. https://doi.org/10.1520/JTE20140490

    Article  Google Scholar 

  13. 13.

    Kumbargeri YS, Biligiri KP (2016) Understanding aging behaviour of conventional asphalt binders used in India. Transp Res Proc 17:282–290

    Article  Google Scholar 

  14. 14.

    Lee S-J, Hu J, Kim H, Amirkhanian SN, Jeong K-D (2011) Aging analysis of rubberized asphalt binders and mixes using gel permeation chromatography. Constr Build Mater 25(3):1485–1490

    Article  Google Scholar 

  15. 15.

    Hofko B, Alavi MZ, Grothe H, Jones D, Harvey J (2017) Repeatability and sensitivity of FTIR ATR spectral analysis methods for bituminous binders. Mater Struct 50(3):187

    Article  Google Scholar 

  16. 16.

    Hofko B et al (2018) FTIR spectral analysis of bituminous binders: reproducibility and impact of ageing temperature. Mater Struct 51(2):45

    Article  Google Scholar 

  17. 17.

    Shen J, Amirkhanian SN, Lee S-J (2007) HP-GPC characterization of rejuvenated aged CRM binders. J Mater Civ Eng 19(6):515–522

    Article  Google Scholar 

  18. 18.

    Wang M, Liu L (2017) Investigation of microscale aging behavior of asphalt binders using atomic force microscopy. Constr Build Mater 135:411–419

    Article  Google Scholar 

  19. 19.

    Chen A, Liu G, Zhao Y, Li J, Pan Y, Zhou J (2018) Research on the aging and rejuvenation mechanisms of asphalt using atomic force microscopy. Constr Build Mater 167:177–184

    Article  Google Scholar 

  20. 20.

    Hofko B et al (2016) Impact of maltene and asphaltene fraction on mechanical behavior and microstructure of bitumen. Mater Struct Constr 49(3):829–841

    Article  Google Scholar 

  21. 21.

    Baek C, Underwood B, Kim Y (2012) Effects of oxidative aging on asphalt mixture properties. Transp Res Rec J Transp Res Board 2296:77–85

    Article  Google Scholar 

  22. 22.

    Kumbargeri YS, Biligiri KP (2016) Rational performance indicators to evaluate asphalt aging characteristics. J Mater Civ Eng 28(12):1–9

    Article  Google Scholar 

  23. 23.

    Tavassoti Kheiry P, Boz I, Solaimanian M, Barzegari S (2018) Effect of age hardening on modules of warm mix asphalt mixtures. In: Canadian society for civil engineering, 2018

  24. 24.

    Boz I, Chen X, Solaimanian M (2017) Assessment of laboratory oven-aging of asphalt concrete mixtures via impact resonance test. In: International conference on advances in construction materials and systems

  25. 25.

    Witczak M (2008) Specification criteria for simple performance tests for rutting, volume I: dynamic modulus (E*), volume II: flow number and flow time: NCHRP, Report 580

  26. 26.

    Seitllari A, Lanotte MA, Kutay ME (2019) Calibration of the MEPDG rutting model: issues and consequences on rutting prediction. In: Transportation research board 98th annual meeting, 2019, p 6

  27. 27.

    Seitllari A, Lanotte MA Kutay ME (2019) Comparison of uniaxial tension-compression fatigue test results with SCB test performance indicators developed for performance-based mix design procedure

  28. 28.

    Boz I, Tavassoti-Kheiry P, Solaimanian M (2017) The advantages of using impact resonance test in dynamic modulus master curve construction through the abbreviated test protocol. Mater Struct 50:176. https://doi.org/10.1617/s11527-017-1045-3

    Article  Google Scholar 

  29. 29.

    Kim YR et al (2017) Long-term aging of asphalt mixtures for performance testing and prediction. NCHRP rep. 871, 2017

  30. 30.

    Kumbargeri YS, Biligiri KP (2016) A novel approach to understanding asphalt binder aging behavior using asphaltene proportion as a performance indicator. J Test Eval Am Soc Test Mater Int 44(1S):1–11

    Google Scholar 

  31. 31.

    Naser MZ, Seitllari A (2019) Concrete under fire: an assessment through intelligent pattern recognition. Eng Comput. https://doi.org/10.1007/s00366-019-00805-1

    Article  Google Scholar 

  32. 32.

    Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398(3–4):292–302

    Article  Google Scholar 

  33. 33.

    Mansouri I, Kisi O (2015) Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches. Compos B Eng 70:247–255

    Article  Google Scholar 

  34. 34.

    Guclu A, Ceylan H (2007) Condition assessment of composite pavement systems using neural-network-based rapid backcalculation algorithms. In: TRB 86th annual meeting compendium of papers CD-ROM, 2007

  35. 35.

    Ceylan H, Gopalakrishnan K, Guclu A (2007) Advanced approaches to characterizing nonlinear pavement system responses. Transp Res Rec J Transp Res Board 2005(1):86–94

    Article  Google Scholar 

  36. 36.

    Andrew L, Kim YR, Ranjithan SR (2008) Backcalculation of dynamic modulus from resilient modulus of asphalt concrete with an artificial neural network. Transp Res Rec J Transp Res Board 2057(1):107–113

    Article  Google Scholar 

  37. 37.

    Xiao F, Amirkhanian S, Juang CH (2009) Prediction of fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement using artificial neural networks. J Mater Civ Eng 21(6):253–261

    Article  Google Scholar 

  38. 38.

    Lytton RL, Tsai FL, Lee SI, Luo R, Hu S, Zhou F (2010) Models for predicting reflection cracking of hot-mix asphalt overlays. NCHRP Report 669, Transportation research board, Transportation Institute of Texas A&M University, College Station, TX

  39. 39.

    Choi J, Adams TM, Bahia HU (2004) Pavement roughness modeling using back-propagation neural networks. Comput Civ Infrastruct Eng 19(4):295–303

    Article  Google Scholar 

  40. 40.

    Seitllari A, Kutay ME (2018) Soft computing tools to predict progression of percent embedment of aggregates in chip seals. Transp Res Rec J Transp Res Board 2672(12):32–39

    Article  Google Scholar 

  41. 41.

    Kisi Ö, Çobaner M (2009) Modeling river stage-discharge relationships using different neural network computing techniques. CLEAN Soil Air Water 37(2):160–169

    Article  Google Scholar 

  42. 42.

    Seitllari A, Naser MZ (2019) Leveraging artificial intelligence to assess explosive spalling in fire-exposed RC columns. Comput Concr 24(3):271–282. https://doi.org/10.12989/CAC.2019.24.3.271

    Article  Google Scholar 

  43. 43.

    Seitllari A (2014) Traffic flow simulation by neuro-fuzzy approach. In: Proceedings of second international conference on traffic and transport engineering (ICTTE), 2014, pp 97–102

  44. 44.

    Boussabaine AH (1996) The use of artificial neural networks in construction management: a review. Constr Manag Econ 14(5):427–436

    Article  Google Scholar 

  45. 45.

    Mashhadban H, Kutanaei SS, Sayarinejad MA (2016) Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 119:277–287

    Article  Google Scholar 

  46. 46.

    Yu Y, Li W, Li J, Nguyen TN (2018) A novel optimised self-learning method for compressive strength prediction of high performance concrete. Constr Build Mater 184:229–247

    Article  Google Scholar 

  47. 47.

    Alavi AH, Hasni H, Zaabar I, Lajnef N (2017) A new approach for modeling of flow number of asphalt mixtures. Arch Civ Mech Eng 17(2):326–335

    Article  Google Scholar 

  48. 48.

    Sollazzo G, Fwa TF, Bosurgi G (2017) An ANN model to correlate roughness and structural performance in asphalt pavements. Constr Build Mater 134:684–693

    Article  Google Scholar 

  49. 49.

    Ozturk HI, Kutay ME (2014) An artificial neural network model for virtual Superpave asphalt mixture design. Int J Pavement Eng 15(2):151–162

    Article  Google Scholar 

  50. 50.

    Sebaaly H, Varma S, Maina JW (2018) Optimizing asphalt mix design process using artificial neural network and genetic algorithm. Constr Build Mater 168:660–670

    Article  Google Scholar 

  51. 51.

    Zhang H, Fu X, Jiang H, Liu X, Lv L (2015) The relationships between asphalt ageing in lab and field based on the neural network. Road Mater Pavement Des 16(2):493–504

    Article  Google Scholar 

  52. 52.

    Haykin S (1994) Neural networks: a comprehensive foundation, 1st edn. Prentice Hall, Hamilton

    Google Scholar 

  53. 53.

    Bro R, Smilde A (2014) Principal component analysis. Anal Methods 6(9):2812

    Article  Google Scholar 

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Seitllari, A., Kumbargeri, Y.S., Biligiri, K.P. et al. A soft computing approach to predict and evaluate asphalt mixture aging characteristics using asphaltene as a performance indicator. Mater Struct 52, 100 (2019). https://doi.org/10.1617/s11527-019-1402-5

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  • Asphalt aging
  • Dynamic modulus
  • Artificial neural networks
  • Asphaltene
  • Predictive model
  • Validation