Modeling of Resilient Modulus of Asphalt Concrete Containing Reclaimed Asphalt Pavement Using Feed-Forward and Generalized Regression Neural Networks

Document Type : Regular Paper


1 Bitumen and Asphalt Department, Road, Building and Urban development research center, Tehran, Iran

2 Department of Civil Engineering, Sirjan University of Technology, Sirjan, Iran

3 Department of Civil Engineering, Ahar Branch, Islamic Azad University, Ahar, Iran


Reclaimed asphalt pavement (RAP) is one of the waste materials that highway agencies promote to use in new construction or rehabilitation of highways pavement. Since the use of RAP can affect the resilient modulus and other structural properties of flexible pavement layers, this paper aims to employ two different artificial neural network (ANN) models for modeling and evaluating the effects of different percentages of RAP on resilient modulus of hot-mix asphalt (HMA). In this research, 216 resilient modulus tests were conducted for establishing the experimental dataset. Input variables for predicting resilient modulus were temperature, penetration grade of asphalt binder, loading frequency, change of asphalt binder content compared to optimum asphalt binder content and percentage of RAP. Results of modeling using feed-forward neural network (FFNN) and generalized regression neural network (GRNN) model were compared with the measured resilient modulus using two statistical indicators. Results showed that for FFNN model, the coefficient of determination between observed and predicted values of resilient modulus for training and testing sets were 0.993 and 0.981, respectively. These two values were 0.999 and 0.967 in case of GRNN. So, according to comparison of R2 for testing set, the accuracy of FFNN method was superior to GRNN method. Tests results and artificial neural network analysis showed that the temperature was the most effective parameter on the resilient modulus of HMA containing RAP materials. In addition by increasing RAP content, the resilient modulus of HMA increased.


Main Subjects

[1] West, R.C., Rada, G.R., Willis, J.R., Marasteanu, M.O. (2013), NCHRP report 752, Improved Mix Design, Evaluation and Materials Management Practices for Hot Mix Asphalt with High Reclaimed Asphalt Pavement Content, TRB, National Research Council, Washington, DC, USA.
[2] Colbert, B., You, Z. (2012). The Determination of Mechanical Performance of Laboratory Produced Hot Mix Asphalt Mixtures Using Controlled RAP and Virgin Aggregate Size Fractions, Construction and Building Materials, 26:655-662. (DOI: 10.1016/j.conbuildmat.2011.06.068)
[3] Sondag, M.S., Chadbourn, B.A., Drescher, A. (2002). Investigation of Recycled Asphalt Pavement (RAP) Mixtures, Minnesota Department of Transportation, Report No: MN/RC - 2002-15.
[4] Zaumanis, M., Mallick, R.B. (2015). Review of Very High-Content Reclaimed Asphalt Use in Plant-Produced Pavements: State of the Art, International Journal of Pavement Engineering, 16: 39-55. (DOI: 10.1080/10298436.2014.893331)
[5] Tarefder, R.A., White, L., Zaman, M. (2005). Neural Network Model for Asphalt Concrete Permeability, Journal of Materials in Civil Engineering, 17:19-27. (DOI: 10.1061/(ASCE)0899-1561(2005)17:1(19))
[6] Ozgan, E. (2011). Artificial Neural Network Based Modeling of the Marshall Stability of Asphalt Concrete, Expert Systems with Applications, 38:6025-6030. (DOI: 10.1016/j.eswa.2010.11.018)
[7] Xiao, F., Amirkhanian, S.N. (2009). Artificial Neural Network Approach to Estimating Stiffness Behavior of Rubberized Asphalt Concrete Containing Reclaimed Asphalt Pavement, Journal of Transportation Engineering, 135:580-589. (DOI: 10.1061/(ASCE)TE.1943-5436.0000014)
[8] Zeghal, M. (2008). Modeling the Creep Compliance of Asphalt Concrete Using the Artificial Neural Network Technique, GeoCongress: Characterization, Monitoring and Modeling of GeoSystems, 910-916. (DOI: 10.1061/40972(311)114)
[9] Ozsahin, T.S., Oruc, S. (2008). Neural Network Model for Resilient Modulus of Emulsified Asphalt Mixtures, Construction and Building Materials, 22:1436-1445. (DOI:10.1016/j.conbuildmat.2007.01.031)
[10] Xiao, F., Amirkhanian, S.N. (2008). Effects of Binders on Resilient Modulus of Rubberized Mixtures Containing RAP Using Artificial Neural Network Approach, Journal of Testing and Evaluation, 37:129-138. (DOI: 10.1520/JTE101834)
[11] Vadood, M., Johari, M.S., and Rahai, A. (2015). Developing a Hybrid Artificial Neural Network-Genetic Algorithm Model to Predict Resilient Modulus of Polypropylene/Polyester Fiber-Reinforced Asphalt Concrete, The Journal of the Textile Institute, 106:1239-1250. (DOI: 10.1080/00405000.2014.985882)
[12] Kezhen, Y., Yin, H., Liao, H., Huang, L. (2011) Prediction of Resilient Modulus of Asphalt Pavement Material Using Support Vector Machine, Road pavement and material characterization, modeling, and maintenance, 16-23. (DOI: 10.1061/47624(403)3)
[13] Huang, Y.H. (2004), Pavement Design and Analysis. Pearson/Prentice Hall.
[14] ASTM D4123, Standard Test Method for Indirect Tension Test for Resilient Modulus of Bituminous Mixtures. (1995) West Conshohocken, PA: ASTM International, USA.
[15] Witzcak, M.W., Kaloush, K., Pellinen, T., El-Basyouny, M, Von Quintus, H. (2002). Simple Performance Test for Superpave Mix Design (Vol. 465), TRB, National Research Council, Washington, DC, USA.
[16] Hornik, K. (1991). Approximation Capabilities of Multilayer Feed-forward Networks, Neural Networks, 4:251–257. (DOI: 10.1016/0893-6080(91)90009-T)
[17] Hagan, M.T., Demuth, H.B., Beale M.H. (1996), Neural Network Design, PWS Pub. Co., Boston, USA.
[18] Haykin, S.S. (2001), Neural Networks: a Comprehensive Foundation, Tsinghua University Press.
[19] Werbos, P. (1974), Beyond regression: New Tools for Prediction and Analysis in the Behavioral Sciences.
[20] Rumelhart, D.E., Hinton G.E., Williams, R.J. (1988). Learning Representations by back-Propagating Errors, Cognitive Modeling 5:1.
[21] Freeman, J.A., Skapura, D.M., (1992), Neural Networks: Algorithms, Applications and Programming Techniques, Addison-Wesley Publishing Company.
[22] Specht, D.F., (1991). A General Regression Neural Network, IEEE Transactions on Neural Networks, 2:568-576. (DOI: 10.1109/72.97934)
[23] Li, H.Z., Guo, S., Li, C.J., Sun, J.Q. (2013). A Hybrid Annual Power Load Forecasting Model Based on Generalized Regression Neural Network with Fruit Fly Optimization Algorithm, Knowledge-Based Systems, 37:378-387. (DOI: 10.1016/j.knosys.2012.08.015)
[24] Leung, M.T., Chen, A.S., Daouk, H. (2002). Forecasting Exchange Rates Using General Regression Neural Networks, Computers & Operations Research, (DOI: 27:1093–1110. 10.1016/S0305-0548(99)00144-6)