Advanced ANN Prediction of Moisture Damage in CNT Modified Asphalt Binder

Document Type : Regular Article


Assistant Professor, Department of Civil Engineering, University of Bahrain, Bahrain


Moisture penetration causes many direct and indirect distresses in flexible asphalt pavement. Due to damage in asphalt concrete and binder by moisture are the prime concern of failure for flexible pavement worldwide. The causes and prediction are investigated in this study. The asphalt binder was modified with carbon nanotubes (CNT) with very small percentages. The modified binder was simulated with moisture damage with AASHTO T-283 methods. In this study, polymer and carbon nanotubes (CNT) have been added to liquid asphalt binder to examine whether the resulting modified binder has improved moisture damage resistance. Using laboratory tested data, an artificial intelligence modeling technique has been utilized to determine the moisture damage behavior of the modified binder. Multi-Layer Perceptron (MLP) provides the best prediction for wet and dry samples AFM readings with R2 values respectively 0.6407 and 0.8371.


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[1]     DiVito JA, Morris GR. Silane pretreatment of mineral aggregate to prevent stripping in flexible pavements, Transportation Research Board; 1982, p. 104–11.
[2]     Martin AE, Rand D, Weitzel D, Sebaaly P, Lane L, Bressette T, et al. Topic 7: Field experience. Natl Semin Moisture Sensit Asph Pavements, Transp Res Board (TRB), Washington, DC, 2003, p. 229–58.
[3]     Sebaaly PE. Comparison of lime and liquid additives on the moisture damage of hot mix asphalt mixtures. National Lime Association; 2007.
[4]     Kutay ME, Aydilek AH, Masad E, Harman T. Computational and experimental evaluation of hydraulic conductivity anisotropy in hot-mix asphalt. Int J Pavement Eng 2007;8:29–43. doi:10.1080/10298430600819147.
[5]     Gandhi T, Xiao F-P, Amirkhanian SN. Estimating indirect tensile strength of mixtures containing anti-stripping agents using an artificial neural network approach. Int J Pavement Res Technol 2009;2:1–12. doi:10.6135/
[6]     Tarefder RA, Ahsan S, Ahmed MU. Neural Network–Based Thickness Determination Model to Improve Backcalculation of Layer Moduli without Coring. Int J Geomech 2015;15:04014058. doi:10.1061/(ASCE)GM.1943-5622.0000407.
[7]     Xiao F, Amirkhanian SN. Artificial Neural Network Approach to Estimating Stiffness Behavior of Rubberized Asphalt Concrete Containing Reclaimed Asphalt Pavement. J Transp Eng 2009;135:580–9. doi:10.1061/(ASCE)TE.1943-5436.0000014.
[8]     Flood I, Kartam N. Neural Networks in Civil Engineering. I: Principles and Understanding. J Comput Civ Eng 1994;8:131–48. doi:10.1061/(ASCE)0887-3801(1994)8:2(131).
[9]     Mohammadhassani M, Nezamabadi-Pour H, Jumaat M, Jameel M, Hakim SJS, Zargar M. Application of the ANFIS model in deflection prediction of concrete deep beam. Struct Eng Mech 2013;45:323–36. doi:10.12989/sem.2013.45.3.323.
[10]    Arifuzzaman M, Hassan MR. Moisture Damage Prediction of Polymer Modified Asphalt Binder Using Support Vector Regression. J Comput Theor Nanosci 2014;11:2221–7. doi:10.1166/jctn.2014.3630.
[11]    Al-Adham K, Arifuzzaman M. Moisture damage evaluation in carbon nanotubes reinforced asphalts. Sustain Eco-efficiency, Conserv Transp Infrastruct Asset Manag, CRC Press; 2014, p. 103–9. doi:10.1201/b16730-17.
[12]    Amirkhanian AN, Xiao F-P, Amirkhanian SN. Characterization of unaged asphalt binder modified with carbon nano particles. Int J Pavement Res Technol 2011;4:281–6. doi:10.6135/
[13]    Hassan MR. Modeling of Moisture Damage in Carbon Nano Tube Modified Asphalt Using Hybrid of Artificial Neural Network and Other Computational Intelligence Approaches. J Comput Theor Nanosci 2015;12:4927–34. doi:10.1166/jctn.2015.4461.
[14]    Becker Y, Mendez MP, Rodriguez Y. Polymer modified asphalt. Vis Tecnol 2001;9:39–50.
[15]    Ball P. Roll up for the revolution. Nature 2001;414:142–4. doi:10.1038/35102721.
[16]    Arepalli S, Nikolaev P, Holmes W, Files BS. Production and measurements of individual single-wall nanotubes and small ropes of carbon. Appl Phys Lett 2001;78:1610–2. doi:10.1063/1.1352659.
[17]    Mann S. Nanotechnology and Construction. Nanoforum Report European Nanotechnology Gateway. 2008.
[18]    Tarefder RA, Zaman A. Carbon Nanotube Modified Asphalt Binders for Sustainable Roadways, 2017, p. 623–33. doi:10.1007/978-3-319-41682-3_52.
[19]    Tsoukalas LH, Uhrig RE. Fuzzy and neural approaches in engineering. John Wiley & Sons, Inc.; 1997.
[20]    Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665–85. doi:10.1109/21.256541.
[21]    Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273–97. doi:10.1007/BF00994018.
[22]    Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 2000;22:717–27. doi:10.1016/S0731-7085(99)00272-1.