Application of Conventional Mathematical and Soft Computing Models for Determining the Effects of Extended Aging on Rutting Properties of Asphalt Mixtures

Document Type : Research Paper

Authors

1 PhD., Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium

2 Associate Professor, Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.

3 Assistant Professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

4 Professor, School of Civil Engineering, Universiti Sains Malaysia, Nibong, Tebal, Malsysis

Abstract

Pavement performance prediction is a mounting task due to the many varied influencing factors particularly aging which varies with time, weather, production, type of pavement and etc. This paper presents a conventional mathematical model named Superpave model, Artificial Neural Network (ANN), and Supporting Vector Machine (SVM) techniques to predict the effects of extended aging on asphalt mixture performance measured in terms of rutting properties determined from the dynamic creep test. The accuracy of each method was compared to select the most reliable technique that can be used to forecast the rutting behavior of asphalt mixtures subjected to different aging conditions. The results indicated that the Superpave model was only reliable at lower temperatures, while ANN and SVM techniques showed the capability of precise prediction under all conditions. The overall results showed that the ANN was the most promising technique that can be adopted to satisfactorily forecast the effects of aging on rutting properties of all mixtures. The developed model can be embraced by the pavement management sector for more precise estimation of the pavement life cycle subjected to different aging conditions which can be used to design efficient pavement maintenance and rehabilitation plans.

Keywords

References

Azari, H., and Mohseni, A. (2013) “Effect of short-term conditioning and long-term ageing on permanent deformation characteristics of asphalt mixtures”, Road Materials and Pavement Design, Vol. 14, No. 2, pp. 79-91.
Barksdale, R. D. (1972) “Laboratory evaluation of rutting in base course materials”, In Presented at the Third International Conference on the Structural Design of Asphalt Pavements, Grosvenor House, Park Lane, London, England, Sept. (Vol. 1, No. Proceeding) pp. 11-15, 1972.
Daniel, J. S., and Lachance, A. (2005) “Mechanistic and volumetric properties of asphalt mixtures with recycled asphalt pavement”, Transportation Research Record, Vol. 1929, No. 1, pp. 28-36.
Ghanizadeh, A. R. (2017) “Application of support vector machine regression for predicting critical responses of flexible pavements”, International Journal of Transportation Engineering, Vol. 4, No. 4, pp. 305-315.
Ghanizadeh, A. R., and Ahadi, M. R. (2015) “Application of artificial neural networks for analysis of flexible pavements under static loading of standard axle”, International Journal of Transportation Engineering, Vol. 3, No. 1, pp. 31-43.
Ghanizadeh, A. R., and Fakhri, M. (2014) “Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN”, The Scientific World Journal, Vol. 2014, pp. 1-16.
Gopalakrishnan, K., and Kim, S. (2010) “Support vector machines approach to HMA stiffness prediction”, Journal of Engineering Mechanics, Vol. 137, No. 2, pp. 138-146.
Hamzah, M. O. and Omranian, S. R. (2015) “Effects of ageing on pavement air voids during mixture transportation from plant to field”, Materials Research Innovations, Vol. 19, No. 5, pp. 592-595.
Hamzah, M. O., Omranian, S. R., and Yahaya, A. S. (2015) “Evaluation of the impact of extended aging duration on visco-elastic properties of asphalt binders”, Archives of Civil and Mechanical Engineering, Vol. 15, No. 4, pp. 1118-1128.
Haykin, S. (2001) “Neural networks: a comprehensive foundation”, 2nd edition, Prentice Hall, Upper Saddle River, New Jersey.
Izadi, A., Motamedi, M., Alimi, R., and Nafar, M. (2018) “Effect of aging conditions on the fatigue behavior of hot and warm mix asphalt”, Construction and Building Materials, Vol. 188, pp. 119-129.
Julaganti, A., Choudhary, R., and Kumar, A. (2019) “Permanent deformation characteristics of warm asphalt binders under reduced aging conditions”, KSCE Journal of Civil Engineering, Vol. 23. No. 1, pp. 160-172.
Kenis, W. J. (1978) “Predictive design procedures, VESYS users’ manual”, Rep. No. FHWA-RD-77-154, Federal Highway Administration, McLean, Va. 1977. US Government Printing Office.
Kırbaş, U., and Karaşahin, M. (2016) “Performance models for hot mix asphalt pavements in urban roads”, Construction and Building Materials, Vol. 116, pp. 281-288.
Lytton, R. L., Uzan, J., Fernando, E. G., Roque, R., Hiltunen, D., and Stoffels, S. M. (1993) “Development and validation of performance prediction models and specifications for asphalt binders and paving mixes (Vol. 357)” Washington, DC: Strategic Highway Research Program.
Majidzadeh, K., Aly, M., Bayomy, F., and El-Laithy, A. (1980) “Implementation of a pavement design system”, Vol. 1 and 2. Final Rep. No. EES 578, the Ohio State University. Engineering Experiment Station, Columbus, Ohio, USA.
Mehrara, A., and Khodaii, A. (2010) “Evaluation of asphalt mixtures’ moisture sensitivity by dynamic creep test”, Journal of Materials in Civil Engineering, Vol. 23, No. 2, pp. 212-219.
Moghaddam, T. B., Soltani, M., Shahraki, H. S., Shamshirband, S., Noor, N. B. M., and Karim, M. R. (2016) “The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures”, Measurement, Vol. 90, pp. 526-533.
Monismith, C. L., Ogawa, N., and Freeme, C. R. (1975) “Permanent deformation characterization of subgrade soils due to repeated loading”, Transportation Research Record. Vol. 537, pp. 1–17.
Nazemi, M., and Heidaripanah, A. (2016) “Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials”, Road Materials and Pavement Design, Vol. 17, No. 3, pp. 768-778.
Omranian, S. R., Hamzah, M. O., Yee, T. S., and Mohd Hasan, M. R. (2018a) “Effects of short-term ageing scenarios on asphalt mixtures’ fracture properties using imaging technique and response surface method”, International Journal of Pavement Engineering, pp. 1-19.
Omranian, S. R., Hamzah, M. O., Valentin, J., and Hasan, M. R. M. (2018b) “Determination of ptimal mix from the standpoint of short term aging based on asphalt mixture fracture properties using response surface method”, Construction and Building Materials, Vol. 179, pp. 35-48.
Public Works Department (PWD), (2008) “Standard specification for road works, Section 4: Flexible Pavement”, Malaysia.
Sakhaei Far, M. S., Underwood, B. S., Ranjithan, S. R., Kim, Y. R., and Jackson, N. (2009) “Application of artificial neural networks for estimating dynamic modulus of asphalt concrete”, Transportation Research Record, Vol. 2127, No. 1, pp. 173-186.
Smola, A. J., and Schölkopf, B. (2004) “A tutorial on support vector regression, statist”, Comput, Vol. 14, pp. 199-222.
Witczak, M.W., Kaloush, K., Pellinen, T., El-Basyouny, M., University, A. S., Tempe, A., Quintus, H.V., Fugro-Bre, I., and Austin, T. (2002) “Simple performance test for superpave mix design”, Transportation Research Board.
Yan, K., and You, L. (2014) “Investigation of complex modulus of asphalt mastic by artificial neural networks”, Indian Journal of Engineering and Materials Sciences. Vol. 21, No. 4, pp. 445-450.
Yin, F., Arámbula-Mercado, E., Epps Martin, A., Newcomb, D., and Tran, N. (2017) “Long-term ageing of asphalt mixtures”, Road Materials and Pavement Design, Vol. 18, No. 1, pp. 2-27.
Zhao, W., Xiao, F., Amirkhanian, S. N., and Putman, B. J. (2012) “Characterization of rutting performance of warm additive modified asphalt mixtures”, Construction and Building Materials, Vol. 31, pp. 265-272.
Zhou, F., Scullion, T., and Sun, L. (2004) “Verification and modeling of three-stage permanent deformation behavior of asphalt mixes”, Journal of Transportation Engineering, Vol. 130, No. 4, pp. 486-494.
International Journal of Transportation Engineering
Volume 8, Issue 3 - Serial Number 30
January 2021
Pages 247-260

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