Modeling Marshall Test Results of Hot Mix Asphalt Using Nonlinear Genetic Programming Techniques

Document Type : Research Article

Author

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

Abstract

The Marshall test method is widely used for the design and control of hot mix asphalt (HMA). The Marshall and modified Marshall mix design methods are most widely used in Iran. Determining Marshall test results (Marshall stability, flow, and Marshall quotient (MQ)) are time-consuming. Therefore, using new and advanced methods to determine the results of Marshall testing is essential. In this study, the genetic programming method based on artificial intelligence was used for the prediction of Marshall test results. Input variables in the genetic programming models use the volumetric properties of standard Marshall specimens such as air voids, voids in mineral aggregate (VMA), and voids filled with asphalt (VFA). Also, multiple linear regression models were used as the base model to evaluate the models presented by the genetic programming method. The results indicated that the proposed methods are more efficient than the laboratory costly method and the performance of the genetic programming model is completely satisfactory in comparison to the base model and has been able to predict the results of Marshall testing based on the input parameters. The GP models have a higher coefficient of determination and fewer errors than MLR models. The presented models will also help further researchers willing to perform similar studies, without carrying out destructive tests.

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References

[1] F.L. Roberts, L.N. Mohammad, L. Wang, History of hot mix asphalt mixture design in the United States, Journal of Materials in Civil Engineering, 14(4) (2002) 279-293.
[2] IHAC, Iran highways asphalt code, in:  No. 234, Iran Ministry of Road and Transportation, Management and Planning Organization, Iran, 2003.
[3] S. Zoorob, L. Suparma, Laboratory design and investigation of the properties of continuously graded Asphaltic concrete containing recycled plastics aggregate replacement (Plastiphalt), Cement and Concrete Composites, 22(4) (2000) 233-242.
[4] A. Hasani, Heydari Panah, A., Modeling Percentage of Fractured faces Aggregates in Marshall Asphalt Resistance Using Artificial Neural Networks, Journal of transportation engineering, 3(3) (2013) 173-182.
[5] E. Ozgan, Artificial neural network based modelling of the Marshall Stability of asphalt concrete, Expert Systems with Applications, 38(5) (2011) 6025-6030.
[6] S. Khuntia, A.K. Das, M. Mohanty, M. Panda, Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques, International Journal of Transportation Science and Technology, 3(3) (2014) 211-227.
[7] J.R. Koza, Genetic programming: on the programming of computers by means of natural selection, MIT press, 1992.
[8] A. Azarhoosh, Z. Zojaji, F. Moghadas Nejad, Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate, Road Materials and Pavement Design,  (2018) 1-17.
[9] A. Azarhoosh, F. Moghadas Nejad, A. Khodaii, The influence of cohesion and adhesion parameters on the fatigue life of hot mix asphalt, The Journal of Adhesion, 93(13) (2017) 1048-1067.
[10] A.H. Alavi, A.H. Gandomi, M.G. Sahab, M. Gandomi, Multi expression programming: a new approach to formulation of soil classification, Engineering with Computers, 26(2) (2010) 111-118.
[11] A. Hossein Alavi, A. Hossein Gandomi, A robust data mining approach for formulation of geotechnical engineering systems, Engineering Computations, 28(3) (2011) 242-274.
[12] ASTM. D6927–15, Standard test method for Marshall stability and flow ofasphalt mixtures, in, West Conshohocken, PA, 2015.
[13] T.P. Ryan, Statistical methods for quality improvement, John Wiley & Sons, 2011.
[14] N.K. Kasabov, Foundations of neural networks, fuzzy systems, and knowledge engineering, Marcel Alencar, 1996.
[15] G. CAROFF, Investigation of rutting of asphalt surface layers: influence of binder and axle loading configuration, Asphalt Concrete Mix Materials,  (1994) 28.
AUT Journal of Civil Engineering
Volume 5, Issue 1
March 2021
Pages 171-182

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