Using a Neural Network Model to Assess the Effect of Antistripping Agents on the Performance of Moisture-Conditioned Asphalt
Publication: Journal of Materials in Civil Engineering
Volume 29, Issue 4
Abstract
Moisture damage in asphalt is one of the prime concerns for flexible pavements degradation worldwide. Many of the pavement distresses are the direct and indirect outcomes of the moisture intrusion in asphalt pavement. This study focuses on developing a neural network (NN) to determine the effect of types and percentages of chemical antistripping agents (ASAs) on the adhesion forces of polymer-modified dry and wet asphalt binder samples. Atomic force microscopy (AFM) test is conducted to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and ASAs using four different functionalized and industrial tips. A NN adhesion force prediction model is developed on the basis of AFM laboratory data with varying percentages of ASAs. Except for adhesion loss measured by the tip, all results show improvement in adhesion loss attributed to the addition of ASAs. Among all the chemical ASAs, Morlife shows the best performance in the presence of 3% styrene-butadiene and 5% styrene-butadiene-styrene in reducing moisture effect (average 18% reduction for all sample types) on adhesion and cohesion forces. In all cases, an increase in percentage of additives of greater than 1% does not aid in resistance to the damage caused by moisture in polymer-modified asphalt.
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Acknowledgments
The authors would like to thank University of New Mexico (UNM) and University of Bahrain (UOB) for supporting this study.
References
Ahsan, S. (2013). “Assessment of antistripping agents on adhesion of damaged asphalt by neural network.” M.S. thesis, Univ. of New Mexico, Albuquerque, NM.
Anderson, D. A., Dukatz, E. L., and Petersen, J. C. (1982). “The effect of antistrip additives on the properties of asphalt cement.” Asphalt Paving Technol., 51(1), 298–317.
Arifuzzaman, M. (2010). “Nano-scale evaluation of moisture damage in asphalt.” Ph.D. dissertation, Univ. of New Mexico, Albuquerque, NM.
Castano, N., Ferré, P., Fossas, F., and Puñet, A. (2004). “A real heat stable bitumen antistripping agent.” Proc., 8th Conf. on Asphalt Pavements for Southern Africa (CAPSA’04), Document Transformation Technologies, Sun City, South Africa.
Curtis, C. W. (1990). “A literature review of liquid antistripping and tests for measuring stripping.”, Strategic Highway Research Program, Washington, DC.
DiVito, J. A., and Morris, G. R. (1982). “Silane pretreatment of mineral aggregate to prevent stripping in flexible pavements.” Transp. Res. Rec., 843, 104–111.
Gandhi, S., Copeland, K., Putman, B., and Amirkhanian, S. (2007). “Laboratory evaluation of long term effectiveness of liquid antistripping agents.” TRB Annual Meeting, Washington, DC.
Gandhi, T., Xiao, F., and Amirkhanian, S. N. (2009). “Estimating indirect tensile strength of mixtures containing anti-stripping agents using an artificial neural network approach.” Int. J. Pavement Res. Technol., 2(1), 1–12.
Garson, G. (1991). “Interpreting neural network connection weights.” Artif. Intell. Expert, 6(4), 47–51.
Gopalakrishnan, K. (2010). “Effect of training algorithms on neural networks aided pavement diagnosis.” Int. J. Eng. Sci. Tech., 2(2), 83–92.
Hicks, R. G. (1991). “NCHRP synthesis of highway practice 175: Moisture damage in asphalt concrete.” Transportation Research Board, National Research Council, Washington, DC.
Huang, Y. (1993). Pavement analysis and design, 2nd Ed., Prentice Hall, Upper Saddle River, NJ.
Kanitpong, K., and Bahia, H. (2005). “Relating adhesion and cohesion of asphalts to the effect of moisture on laboratory performance of asphalt mixtures.” Transp. Res. Rec., 1901, 33–43.
Kennedy, T. W., Roberts, F. L., and Lee, K. W. (1983). “Evaluation of moisture effects on asphalt concrete mixtures.” Transp. Res. Rec., 911, 134–143.
Lefteri, H. T., and Robert, E. U. (1997). Fuzzy and neural approaches in engineering, Wiley, New York.
Martin, A. E., Rand, D., Weitzel, D., Sebaaly, P., Lane, L., Bressette, T., and Maupin, G. (2003). “Field experience.” National Seminar on Moisture Sensitivity of Asphalt Pavements, Transportation Research Board (TRB), Washington, DC, 229–258.
MATLAB [Computer software]. Mathworks, Natick, MA.
Tarefder, R., and Ahsan, S. (2014). “Neural network modelling of asphalt adhesion determined by AFM.” J. Microsc., 254(1), 31–41.
Tarefder, R., Ahsan, S., and Ahmed, M. (2015). “Neural network-based thickness determination model to improve backcalculation of layer moduli without coring.” Int. J. Geomech., .
Tarefder, R., White, L., and Zaman, M. (2005). “A neural network model for asphalt concrete permeability.” J. Mater. Civ. Eng., 19–27.
Vijayasarathy, M., Rajendran, K., and Kumaran, S. (2013). “Fd-calc: Atomic force microscopy intermolecular force calculator.” Quantum Matter, 2(3), 238–240.
Xiao, F., and Amirkhanian, S. (2009). “Artificial neural network approach to estimating stiffness behavior of rubberized asphalt concrete containing reclaimed asphalt pavement.” J. Transp. Eng., 580–589.
Xiao, F., Amirkhanian, S., and Juang, C. (2009). “Prediction of fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement using artificial neural networks.” J. Mater. Civ. Eng., 253–261.
Yen, T., and Chilingarian, G. (2000). Asphaltenes and asphalts, developments in petroleum science, Elsevier, New York.
Zaman, M., Solanki, P., Ebrahimi, A., and White, L. (2010). “Neural network modeling of resilient modulus using routine subgrade soil properties.” Int. J. Geomech., 1–12.
Information & Authors
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Published In
Journal of Materials in Civil Engineering
Volume 29 • Issue 4 • April 2017
Copyright
©2016 American Society of Civil Engineers.
History
Received: Nov 17, 2015
Accepted: Aug 9, 2016
Published online: Oct 26, 2016
Discussion open until: Mar 26, 2017
Published in print: Apr 1, 2017
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