Skip to main content
SpringerLink
Log in

Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The mix design of asphalt concrete is usually accomplished in the Iranian ministry of road and transportation according to the Marshall method. Marshall mix design parameters are a function of grading and properties of aggregates, amount and type of bitumen in asphalt mixtures. Therefore, in order to determine these parameters and the optimum bitumen content, many samples with different compounds and conditions must be manufactured and tested in the laboratory, a process that requires considerable time and cost. Accordingly, the necessity of using new and advanced methods for the design and quality control of asphalt mixtures is becoming more and more evident. Therefore, in this study, a genetic programming simulation method was employed to predict the Marshall mix design parameters of asphalt mixtures. Also, multiple linear regression models were adopted as the base model to evaluate the models presented by the genetic programming method. The models proposed here predict the Marshall mix design parameters based on parameters such as the index of aggregate particle shape and texture, the amount and viscosity of the bitumen. The results demonstrated that the proposed methods are more efficient than the costly laboratory method, and genetic programming models with minimal error (identified in this study with RMSE and MAE parameters) and correlation coefficients > 0.9 can predict relatively accurate Marshall mix design parameters.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Iran highway asphalt code, No. 234: Iran Ministry of Road and Transportation, Management and Planning Organization, Iran (2003).

  2. Hasani, A.; Heydari Panah, A.: Modeling percentage of fractured faces aggregates in Marshall asphalt resistance using artificial neural networks. J. Transp. Eng. 3, 173–182 (2013)

    Google Scholar 

  3. Ozgan, E.: Artificial neural network based modelling of the Marshall stability of asphalt concrete. Expert Syst. Appl. 38, 6025–6030 (2011)

    Article  Google Scholar 

  4. Khuntia, S.; Das, A.K.; Mohanty, M.; Panda, M.: Prediction of Marshall parameters of modified bituminous mixtures using artificial intelligence techniques. Int. J. Transp. Sci. Technol. 3, 211–227 (2014)

    Article  Google Scholar 

  5. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, London (1992)

    MATH  Google Scholar 

  6. Alavi, A.H.; Gandomi, A.H.; Sahab, M.G.; Gandomi, M.: Multi expression programming: a new approach to formulation of soil classification. Eng. Comput. 26, 111–118 (2010)

    Article  Google Scholar 

  7. Alavi, A.H.; Gandomi, A.H.: A robust data mining approach for formulation of geotechnical engineering systems. Eng. Comput. 28, 242–274 (2011)

    Article  Google Scholar 

  8. Rezania, M.; Javadi, A.A.: A new genetic programming model for predicting settlement of shallow foundations. Can. Geotech. J. 44, 1462–1473 (2007)

    Article  Google Scholar 

  9. Shahnazari, H.; Dehnavi, Y.; Alavi, A.H.: Numerical modeling of stress–strain behavior of sand under cyclic loading. Eng. Geol. 116, 53–72 (2010)

    Article  Google Scholar 

  10. Johari, A.; Nejad, A.H.: Prediction of soil–water characteristic curve using gene expression programming. Iran. J. Sci. Technol. 39, 143–151 (2015)

    Google Scholar 

  11. Cabalar, A.F.; Cevik, A.; Guzelbey, I.H.: Constitutive modeling of Leighton Buzzard Sands using genetic programming. Neural Comput. Appl. 19, 657–665 (2010)

    Article  Google Scholar 

  12. Cevik, A.; Cabalar, A.F.: A genetic-programming-based formulation for the strength enhancement of fiber-reinforced-polymer-confined concrete cylinders. J. Appl. Polym. Sci. 110, 3087–3095 (2008)

    Article  Google Scholar 

  13. Mollahasani, A.; Alavi, A.H.; Gandomi, A.H.: Empirical modeling of plate load test moduli of soil via gene expression programming. Comput. Geotech. 38, 281–286 (2011)

    Article  Google Scholar 

  14. Terzi, S.: Modeling the deflection basin of flexible highway pavements by gene expression programming. J. Appl. Sci. 5, 309–314 (2005)

    Article  Google Scholar 

  15. Saltan, M.; Terzi, S.: Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness. Indian J. Eng. Mater. Sci. (IJEMS) 12, 42–50 (2005)

    Google Scholar 

  16. Gandomi, A.H.; Alavi, A.H.; Mirzahosseini, M.R.; Moghadas Nejad, F.: Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. J. Mater. Civ. Eng. 23, 248–263 (2011)

    Article  Google Scholar 

  17. Alavi, A.H.; Ameri, M.; Gandomi, A.H.; Mirzahosseini, M.R.: Formulation of flow number of asphalt mixes using a hybrid computational method. Constr. Build. Mater. 25, 1338–1355 (2011)

    Article  Google Scholar 

  18. Azarhoosh, A.; Zojaji, Z.; Moghadas Nejad, F.: Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate. Road Mater. Pavement Des. 21, 850–866 (2020)

    Article  Google Scholar 

  19. Azarhoosh, A.; Moghadas Nejad, F.; Khodaii, A.: The influence of cohesion and adhesion parameters on the fatigue life of hot mix asphalt. J. Adhes. 93, 1048–1067 (2017)

    Article  Google Scholar 

  20. Azarhoosh, A.; Falahi Abandansari, H.; Hamedi, G.H.: Surface-free energy and fatigue performance of hot-mix asphalt modified with nano lime. J. Mater. Civ. Eng. 31, 04019192 (2019)

    Article  Google Scholar 

  21. ASTM D6927–15: Standard Test Method for Marshall Stability and Flow of Asphalt Mixtures. ASTM, West Conshohocken (2015)

    Google Scholar 

  22. ASTM D 3398-00: Standard Test Method for Index of Aggregate Particle Shape and Texture. ASTM, West Conshohocken (2000)

    Google Scholar 

  23. Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, London (1996)

    MATH  Google Scholar 

  24. Caroff, G.: Investigation of rutting of asphalt surface layers: influence of binder and axle loading configuration. Transp. Res. Rec. 1436, 28–37 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Bojnord, Bojnord, North Khorasan, Iran

    Alireza Azarhoosh & Salman Pouresmaeil

Authors
  1. Alireza Azarhoosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salman Pouresmaeil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Azarhoosh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azarhoosh, A., Pouresmaeil, S. Prediction of Marshall Mix Design Parameters in Flexible Pavements Using Genetic Programming. Arab J Sci Eng 45, 8427–8441 (2020). https://doi.org/10.1007/s13369-020-04776-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-04776-0

Keywords

Search

Navigation