Viscosity Prediction of Rubberized Asphalt–Rejuvenated Recycled Asphalt Pavement Binders Using Artificial Neural Network Approach
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 5
Abstract
The objective of this study was to develop artificial neural networks to predict the viscosity of rubberized asphalt rejuvenated reclaimed asphalt pavement binders. Eight variables were selected as input factors, namely, viscosity measuring temperature, rubber blending time, reclaimed asphalt pavement blending time, original binder blending time, rubber content, reclaimed asphalt pavement content, blending temperature for aged binder, and asphalt type. Two viscosity analysis models, backpropagation artificial neural networks and genetic algorithm modified artificial neural networks, were developed in this study. It was found that both artificial neural network models were effective in predicting the viscosity of rubberized asphalt rejuvenated reclaimed asphalt pavement binders. Through sensitivity analysis, blending temperature for aged binder, viscosity measuring temperature, original binder blending time, and reclaimed asphalt pavement blending time were found to be important variables that contributed to the binder viscosity. On the contrary, the asphalt type and rubber blending time were found to be less important. As a result, the viscosity of rubberized asphalt rejuvenated reclaimed asphalt pavement binders changed significantly with the blending temperature, blending time of the aged binder, and blending time of the original binder. Both backpropagation artificial neural networks and genetic algorithm modified artificial neural networks viscosity models were validated using data collected from prior studies, and the results were barely acceptable.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including the data from Figs. 3–8 as well as the data in Tables 4 and 11.
Acknowledgments
The authors gratefully acknowledge the financial supports by the National Natural Science Foundation of China under Grant No. 51861145402.
References
Abdelrahman, M., and S. Carpenter. 1999. “Mechanism of interaction of asphalt cement with crumb rubber modifier.” Transp. Res. Rec. 1661 (1): 106–113. https://doi.org/10.3141/1661-15.
Airey, G., M. Rahman, and A. Collop. 2003. “Absorption of bitumen into crumb rubber using the basket drainage method.” Int. J. Pavement Eng. 4 (2): 105–119. https://doi.org/10.1080/1029843032000158879.
Ambrožic, T., and G. Turk. 2003. “Prediction of subsidence due to underground mining by artificial neural networks.” Comput. Geosci. 29 (5): 627–637. https://doi.org/10.1016/S0098-3004(03)00044-X.
Androjic, I., and Z. Dolacek-Alduk. 2018. “Artificial neural network model for forecasting energy consumption in hot mix asphalt (HMA) production.” Constr. Build. Mater. 170 (May): 424–432. https://doi.org/10.1016/j.conbuildmat.2018.03.086.
ASTM. 2015a. Standard test method for determining the rheological properties of asphalt binder using a dynamic shear rheometer. ASTM D7175-15. West Conshohocken, PA: ASTM.
ASTM. 2015b. Standard test method for viscosity determination of asphalt at elevated temperatures using a rotational viscometer. ASTM D4402. West Conshohocken, PA: ASTM.
ASTM. 2016. Standard test method for determining the flexural creep stiffness of asphlat binder using the blending beam rheometer (BBR). ASTM D6648-08(2016). West Conshohocken, PA: ASTM.
ASTM. 2017a. Standard practice for recovery of asphalt from solution using the rotary evaporator. ASTM D5404. West Conshohocken, PA: ASTM.
ASTM. 2017b. Standard test methods for quantitative extraction of asphlat binder from asphalt mixtures. ASTM D2172/D2172M-17el. West Conshohocken, PA: ASTM.
ASTM. 2020a. Standard test method for penetration of bituminous materials. ASTM D5/D5M. West Conshohocken, PA: ASTM.
ASTM. 2020b. Standard test method for softening point of bitumen (ring-and-ball apparatus). ASTM D36/D36M-14. West Conshohocken, PA: ASTM.
Bahia, H., and R. Davies. 1994. “Effects of crumb rubber type and content on performance related properties of asphalt binders.” In Proc., Infrastructure: New Materials and Methods of Repair, 449–466. Reston, VA: ASCE.
Baldo, N., E. Manthos, and M. Pasetto. 2018. “Analysis of the mechanical behaviour of asphalt concretes using artificial neural networks.” Adv. Civ. Eng. 2018: 17. https://doi.org/10.1155/2018/1650945.
Barugahare, J., A. Amirkhanian, F. Xiao, and S. Amirkhanian. 2020. “ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models.” Int. J. Pavement Eng. 1–11. https://doi.org/10.1080/10298436.2020.1799209.
Bressi, S., M. Cavalli, M. Partl, G. Tebaldi, G. Dumont, and L. Poulikakos. 2015. “Particle clustering phenomena in hot asphalt mixtures with high content of reclaimed asphalt pavements.” Constr. Build. Mater. 100 (Dec): 207–217. https://doi.org/10.1016/j.conbuildmat.2015.09.052.
Ceylan, H., K. Gopalakrishnan, and S. Kim. 2009. “Looking to the future: The next-generation hot mix asphalt dynamic modulus prediction models.” Int. J. Pavement Eng. 10 (5): 341–352. https://doi.org/10.1080/10298430802342690.
Chen, Z., J. Pei, T. Wang, and S. Amirkhanian. 2019a. “High temperature rheological characteristics of activated crumb rubber modified asphalts.” Constr. Build. Mater. 194 (Jan): 122–131. https://doi.org/10.1016/j.conbuildmat.2018.10.223.
Chen, Z., T. Wang, J. Pei, S. Amirkhanian, F. Xiao, Q. Ye, and Z. Fan. 2019b. “Low temperature and fatigue characteristics of treated crumb rubber modified asphalt after a long term aging procedure.” J. Cleaner Prod. 234 (Oct): 1262–1274. https://doi.org/10.1016/j.jclepro.2019.06.147.
Girimath, S., D. Singh, E. Manthos, and W. Mampearachchi. 2018. “Effects of reclaimed asphalt binder on rheological properties and cohesion energy of crumb rubber modified binder.” Innovative Infrastruct. Solutions 3 (1): 57. https://doi.org/10.1007/s41062-018-0164-1.
Grima, A., and R. Babuska. 1999. “Fuzzy model for the prediction of unconfined compressive strength of rock samples.” Int. J. Rock Mech. Min. Sci. 36 (3): 339–349. https://doi.org/10.1016/S0148-9062(99)00007-8.
Hu, J., and Z. Qian. 2018. “The prediction of adhesive failure between aggregates and asphalt mastic based on aggregate features.” Constr. Build. Mater. 183 (Sep): 22–31. https://doi.org/10.1016/j.conbuildmat.2018.06.145.
Huang, B., L. Mohammad, P. Graves, and C. Abadie. 2002. “Louisiana experience with crumb rubber-modified hot-mix asphalt pavement.” Transp. Res. Rec. 1789 (1): 1–13. https://doi.org/10.3141/1789-01.
Izaks, R., V. Haritonovs, I. Klasa, and M. Zaumanis. 2015. “Hot mix asphalt with high RAP content.” Procedia Eng. 114: 676–684. https://doi.org/10.1016/j.proeng.2015.08.009.
Kim, Y., J. Bae, W. Hong, H. Park, K. Moon, and S. Shin. 2001. “Neural network based prediction of ground surface settlements due to tunnelling.” Comput. Geotech. 28 (6): 517–547. https://doi.org/10.1016/S0266-352X(01)00011-8.
Li, P., X. Jiang, Z. Ding, J. Zhao, and M. Shen. 2018. “Analysis of viscosity and composition properties for crumb rubber modified asphalt.” Constr. Build. Mater. 169 (Apr): 638–647. https://doi.org/10.1016/j.conbuildmat.2018.02.174.
Liu, J., K. Yan, J. Liu, and X. Zhao. 2018. “Using artificial neural networks to predict the dynamic modulus of asphalt mixtures containing recycled asphalt shingles.” J. Mater. Civ. Eng. 30 (4): 04018051. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002242.
Luo, Z., F. Xiao, S. Hu, and Y. Yang. 2013. “Probabilistic analysis on fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement.” Constr. Build. Mater. 41 (Apr): 401–410. https://doi.org/10.1016/j.conbuildmat.2012.12.013.
Ma, T., H. Wang, X. Huang, Z. Wang, and F. Xiao. 2015. “Laboratory performance characteristics of high modulus asphalt mixture with high-content RAP.” Constr. Build. Mater. 101 (Part 1): 975–982. https://doi.org/10.1016/j.conbuildmat.2015.10.160.
Ozgan, E. 2011. “Artificial neural network based modelling of the Marshall stability of asphalt concrete.” Expert Syst. Appl. 38 (5): 6025–6030. https://doi.org/10.1016/j.eswa.2010.11.018.
Ozturk, H., and M. Kutay. 2014. “An artificial neural network model for virtual Superpave asphalt mixture design.” Int. J. Pavement Eng. 15 (2): 151–162. https://doi.org/10.1080/10298436.2013.808341.
Palit, K., S. Reddy, and B. Pandey. 2004. “Laboratory evaluation of crumb rubber modified asphalt mixes.” J. Mater. Civ. Eng. 16 (1): 45–53. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:1(45).
Peralta, J., L. Hilliou, H. Silva, V. Machado, and C. Williams. 2014. “Rheological changes in the bitumen caused by heating and interaction with rubber during asphalt–rubber production.” Rheol. Acta 53 (2): 143–157. https://doi.org/10.1007/s00397-013-0748-9.
Plati, C., P. Georgiou, and V. Papavasiliou. 2016. “Simulating pavement structural condition using artificial neural networks.” Struct. Infrastruct. Eng. 12 (9): 1127–1136. https://doi.org/10.1080/15732479.2015.1086384.
Rahman, A., and R. Tarefder. 2018. “Dynamic modulus predictive model based on artificial neural network for Superpave asphalt mixtures of New Mexico.” In Proc., ASME Int. Mechanical Engineering Congress and Exposition. New York: ASME.
Saberi, F., M. Fakhri, and A. Azami. 2017. “Evaluation of warm mix asphalt mixtures containing reclaimed asphalt pavement and crumb rubber.” J. Cleaner Prod. 165 (Nov): 1125–1132. https://doi.org/10.1016/j.jclepro.2017.07.079.
Sebaaly, H., S. Varma, and J. Maina. 2018. “Optimizing asphalt mix design process using artificial neural network and genetic algorithm.” Constr. Build. Mater. 168 (Apr): 660–670. https://doi.org/10.1016/j.conbuildmat.2018.02.118.
Shafabakhsh, G., O. Ani, and M. Talebsafa. 2015. “Artificial neural network modeling (ANN) for predicting rutting performance of nano-modified hot-mix asphalt mixtures containing steel slag aggregates.” Constr. Build. Mater. 85 (Jun): 136–143. https://doi.org/10.1016/j.conbuildmat.2015.03.060.
Shangguan, P., I. Al-Qadi, and S. Lahouar. 2014. “Pattern recognition algorithms for density estimation of asphalt pavement during compaction: A simulation study.” J. Appl. Geophys. 107 (Aug): 8–15. https://doi.org/10.1016/j.jappgeo.2014.05.001.
Singh, D., D. Sawant, and F. Xiao. 2017. “High and intermediate temperature performance evaluation of crumb rubber modified binders with RAP.” Transp. Geotech. 10 (Mar): 13–21. https://doi.org/10.1016/j.trgeo.2016.10.003.
Singh, D., M. Zaman, and S. Commuri. 2013. “Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties.” J. Mater. Civ. Eng. 25 (1): 54–62. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000548.
Sollazzo, G., T. Fwa, and G. Bosurgi. 2017. “An ANN model to correlate roughness and structural performance in asphalt pavements.” Constr. Build. Mater. 134 (Mar): 684–693. https://doi.org/10.1016/j.conbuildmat.2016.12.186.
Venudharan, V., and K. Biligiri. 2017. “Heuristic principles to predict the effect of crumb rubber gradation on asphalt binder rutting performance.” J. Mater. Civ. Eng. 29 (8): 04017050. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001880.
Wang, T., F. Xiao, X. Zhu, B. Huang, J. Wang, and S. Amirkhanian. 2018. “Energy consumption and environmental impact of rubberized asphalt pavement.” J. Cleaner Prod. 180 (Apr): 139–158. https://doi.org/10.1016/j.jclepro.2018.01.086.
Xiao, F., and S. Amirkhanian. 2009. “Artificial neural network approach to estimating stiffness behavior of rubberized asphalt concrete containing reclaimed asphalt pavement.” J. Transp. Eng. 135 (8): 580–589. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000014.
Xiao, F., S. Amirkhanian, and C. H. Juang. 2007. “Rutting resistance of rubberized asphalt concrete pavements containing reclaimed asphalt pavement mixtures.” J. Mater. Civ. Eng. 19 (6): 475–483. https://doi.org/10.1061/(ASCE)0899-1561(2007)19:6(475).
Xiao, F., S. Amirkhanian, and C. H. Juang. 2009. “Prediction of fatigue life of rubberized asphalt concrete mixtures containing reclaimed asphalt pavement using artificial neural networks.” J. Mater. Civ. Eng. 21 (6): 253–261. https://doi.org/10.1061/(ASCE)0899-1561(2009)21:6(253).
Xiao, F., B. Putman, and S. Amirkhanian. 2011. “Viscosity prediction of CRM binders using artificial neural network approach.” Int. J. Pavement Eng. 12 (5): 485–495. https://doi.org/10.1080/10298430903578903.
Xiao, F., B. Putman, and S. Amirkhanian. 2014. “Viscosity prediction of CRM binders using artificial neural network approach.” Int. J. Pavement Eng. 12 (5): 485–495. https://doi.org/10.1080/10298430903578903.
Xiao, F., N. Su, S. Yao, S. Amirkhanian, and J. Wang. 2019. “Performance grades, environmental and economic investigations of reclaimed asphalt pavement materials.” J. Cleaner Prod. 211 (Feb): 1299–1312. https://doi.org/10.1016/j.jclepro.2018.11.126.
Xu, T., and X. Huang. 2012. “Investigation into causes of in-place rutting in asphalt pavement.” Constr. Build. Mater. 28 (1): 525–530. https://doi.org/10.1016/j.conbuildmat.2011.09.007.
Yang, Y., and Q. Zhang. 1997. “A hierarchical analysis for rock engineering using artificial neural networks.” Rock Mech. Rock Eng. 30 (4): 207–222. https://doi.org/10.1007/BF01045717.
Yu, X., M. Zaumanis, S. Dos Santos, and L. Poulikakos. 2014. “Rheological, microscopic, and chemical characterization of the rejuvenating effect on asphalt binders.” Fuel 135 (11): 162–171. https://doi.org/10.1016/j.fuel.2014.06.038.
Ziari, H., A. Amini, A. Goli, and D. Mirzaeiyan. 2018. “Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks.” Constr. Build. Mater. 160 (Jan): 415–426. https://doi.org/10.1016/j.conbuildmat.2017.11.071.
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Published In
Journal of Materials in Civil Engineering
Volume 33 • Issue 5 • May 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 17, 2020
Accepted: Sep 17, 2020
Published online: Feb 25, 2021
Published in print: May 1, 2021
Discussion open until: Jul 25, 2021
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