Field Aging Characterization of Asphalt Pavement Based on the Artificial Neural Networks and Gray Relational Analysis
Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 7
Abstract
Accurate characterization of field aging of asphalt pavement is critical to precisely assessing its in-service performance. However, most of the traditional test/predictive methods either cannot fully capture the field aging characteristics or involve costly testing/computational efforts to ensure satisfactory prediction accuracy. To alleviate these problems, this study developed a new field aging predictive model based on artificial neural networks (ANNs) and gray relational analysis (GRA), which takes the field-aged viscosity of asphalt binder as the target predictive property. A series of influencing factors that may affect the field-aged viscosity were systematically investigated, among which the eight most significant ones were screened out for the ANN modeling through the GRA. A total of 479 data extracted from long-term pavement performance (LTPP) database were used for the training, validation, and testing of the ANN model. The calculation results showed that the predictive model developed using the ANN approach provided a high prediction accuracy with value greater than 0.90. Furthermore, the falling-weight deflectometer (FWD) data collected from the database were utilized to evaluate the predictive performance of the well-trained ANN model. Consistent results were obtained between the viscosity values predicted from the ANN model and those back-calculated from the FWD data, indicating that the newly developed field aging model has the capability to accurately characterize the field aging evolution of asphalt pavement.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central University under Grant No. 2020kfyXJJS123.
References
AASHTO. 2012. Standard practice for accelerated aging of asphalt binder using a pressurized aging vessel (PAV). AASHTO R 28. Washington, DC: AASHTO.
Ahmed, T. M., P. L. Green, and H. A. Khalid. 2017. “Predicting fatigue performance of hot mix asphalt using artificial neural networks.” Road Mater. Pavement Des. 18 (2): 141–154. https://doi.org/10.1080/14680629.2017.1306928.
Alavi, A. H., A. H. Gandomi, A. Mollahassani, A. A. Heshmati, and A. Rashed. 2010. “Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks.” J. Plant Nutr. Soil Sci. 173 (3): 368–379. https://doi.org/10.1002/jpln.200800233.
Amoosoltani, E., A. Ameli, F. Jabari, and S. Asadi. 2021. “Employing a hybrid GA-ANN method for simulating fracture toughness of RCC mixture containing waste materials.” Constr. Build. Mater. 272 (3): 121928. https://doi.org/10.1016/j.conbuildmat.2020.121928.
ASTM. 2019. Standard practice for accelerated aging of asphalt binder using a pressurized aging vessel (PAV). ASTM D6521. West Conshohocken, PA: ASTM.
Camargo, I. G. D., T. Ben Dhia, A. Loulizi, B. Hofko, and J. Mirwald. 2021. “Anti-aging additives: Proposed evaluation process based on literature review.” Road Mater. Pavement Des. 22 (Apr): 134–153. https://doi.org/10.1080/14680629.2021.1906738.
Cao, Y., Y. Zandi, A. S. Agdas, Q. F. Wang, X. M. Qian, L. J. Fu, K. Wakil, A. Selmi, A. Issakhov, and A. Roco-Videla. 2021. “A review study of application of artificial intelligence in construction management and composite beams.” Steel Compos. Struct. 39 (6): 685–700. https://doi.org/10.12989/scs.2021.39.6.685.
Ceylan, H., K. Gopalakrishnan, and R. L. Lytton. 2011. “Neural networks modeling of stress growth in asphalt overlays due to load and thermal effects during reflection cracking.” J. Mater. Civ. Eng. 23 (3): 221–229. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000153.
Chen, Z. H., H. L. Zhang, H. H. Duan, and C. J. Shi. 2021. “Improvement of thermal and optical responses of short-term aged thermochromic asphalt binder by warm-mix asphalt technology.” J. Cleaner Prod. 279 (Apr): 123675. https://doi.org/10.1016/j.jclepro.2020.123675.
Cui, P. D., Y. Xiao, M. J. Fang, Z. W. Chen, M. W. Yi, and M. L. Li. 2018. “Residual fatigue properties of asphalt pavement after long-term field service.” Materials 11 (6): 892. https://doi.org/10.3390/ma11060892.
Das, P. K., N. Kringos, and B. Birgisson. 2015. “Numerical study on the effect of mixture morphology on long-term asphalt mixture ageing.” Int. J. Pavement Eng. 16 (8): 710–720. https://doi.org/10.1080/10298436.2014.943222.
de Souza, W. M., A. A. Ribeiro, and C. A. U. da Silva. 2020. “Use of ANN and visual-manual classification for prediction of soil properties for paving purposes.” Int. J. Pavement Eng. 23 (5): 1482–1490. https://doi.org/10.1080/10298436.2020.1807546.
Ghorbani, B., A. A. Rulrajah, G. Narsilio, and S. Horpibulsuk. 2020. “Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes.” Transp. Geotech. 24 (Sep): 100365. https://doi.org/10.1016/j.trgeo.2020.100365.
Gu, F., X. Luo, Y. Q. Zhang, and R. L. Lytton. 2015. “Using overlay test to evaluate fracture properties of field aged asphalt concrete.” Constr. Build. Mater. 101 (Dec): 1059–1068. https://doi.org/10.1016/j.conbuildmat.2015.10.159.
Han, F., W. B. Yu, X. F. Zhang, F. Yu, X. Yi, and D. Hu. 2017. “Parameter sensitivity analyses of influence on thermal regime of embankment in permafrost regions along the Qinghai-Tibet engineering corridor.” Cold Reg. Sci. Technol. 166 (Sep): 102817. https://doi.org/10.1016/j.coldregions.2019.102817.
Han, R. B., X. Jin, and C. J. Glover. 2011. “Modeling pavement temperature for use in binder oxidation models and pavement performance prediction.” J. Mater. Civ. Eng. 23 (4): 351–359. https://doi.org/10.1061/(ASCE)MT.1943-5533.0000169.
Kim, Y. R., C. Castorena, M. Elwardany, F. Y. Rad, S. Underwood, A. Gundla, P. Gudipudi, M. J. Farrar, and R. R. Glaser. 2017. National academies of sciences, engineering, and medicine, long-term aging of asphalt mixtures for performance testing and prediction. Washington, DC: National Academies Press.
Kim, Y. R., C. Castorena, N. F. Saleh, E. Braswell, and M. Elwardany. 2021. “National academies of sciences, engineering, and medicine. Long-term aging of asphalt mixtures for performance testing and prediction: Phase III results.” In Pre-publication draft of NCHRP Research Report 973. Washington, DC: Transportation Research Board.
Li, X. J., Y. Y. Li, J. Li, Y. X. Shen, C. H. Zhang, and J. C. Wei. 2020. “Effect of laboratory long-term oven aging on the stiffness of asphalt mixtures.” Constr. Build. Mater. 258 (11): 120252. https://doi.org/10.1016/j.conbuildmat.2020.120252.
Ling, M., X. Luo, S. Hu, F. Gu, and R. L. Lytton. 2017. “Numerical modeling and artificial neural network for predicting j-integral of top-down cracking in asphalt pavement.” Transp. Res. Rec. 2631 (1): 83–95. https://doi.org/10.3141/2631-10.
Lira, B., D. Jelagin, and B. Birgisson. 2013. “Gradation-based framework for asphalt mixture.” Mater. Struct. 46 (8): 1401–1414. https://doi.org/10.1617/s11527-012-9982-3.
Liu, K., P. X. Xu, F. Wang, C. Jin, Q. T. Liu, H. P. Pang, and H. Z. Xie. 2021a. “The accumulated stress damage and residual life prediction of unreinforced concrete pavement with electric heating pipes.” Constr. Build. Mater. 278 (12): 122258. https://doi.org/10.1016/j.conbuildmat.2021.122258.
Liu, K., P. X. Xu, F. Wang, L. Y. You, X. C. Zhang, and C. L. Fu. 2021b. “Assessment of automatic induction self-healing treatment applied to steel deck asphalt pavement.” Autom. Constr. 133 (Jan): 104011. https://doi.org/10.1016/j.autcon.2021.104011.
Luo, X., F. Gu, M. Ling, and R. L. Lytton. 2018. “Review of mechanistic empirical modeling of top-down cracking in asphalt pavements.” Constr. Build. Mater. 191 (4): 1053–1070. https://doi.org/10.1016/j.conbuildmat.2018.10.005.
Luo, X., F. Gu, and R. L. Lytton. 2015. “Prediction of field aging gradient in asphalt pavements.” Transp. Res. Rec. 2507 (1): 19–28. https://doi.org/10.3141/2507-03.
Luo, X., F. Gu, and R. L. Lytton. 2017. “Kinetics-based aging prediction of asphalt mixtures using field deflection data.” Int. J. Pavement Eng. 20 (3): 287–297. https://doi.org/10.1080/10298436.2017.1293262.
Mathworks. 2017. Matlab and statistics toolbox release 2017a. Natick, MA: Matlab.
Mirza, M. W. 1993. “Development of a global aging system for short and long term aging of asphalt cements.” Ph.D. thesis, Dept. of Civil Engineering, Univ. of Maryland, College Park, Ann Arbor.
Naderpour, H., A. H. Rafiean, and P. Fakharian. 2018. “Compressive strength prediction of environmentally friendly concrete using artificial neural networks.” J. Build Eng. 16 (Jan): 213–219. https://doi.org/10.1016/j.jobe.2018.01.007.
NCHRP (National Cooperative Highway Research Program). 2004. Guide for mechanistic—Empirical design of new and rehabilitated pavement structures. Champaign, IL: NCHRP.
Oskooei, P. R., A. Mohammadinia, A. Arulrajah, and S. Horpibulsuk. 2020. “Application of artificial neural network models for predicting the resilient modulus of recycled aggregates.” Int. J. Pavement Eng. 23 (4): 1121–1133. https://doi.org/10.1080/10298436.2020.1791863.
Ozturk, H. I., and M. E. 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.
Radhakrishnan, V., G. S. Chowdari, K. S. Reddy, and R. Chattaraj. 2018. “A predictive model for estimating the viscosity of short-term-aged bitumen.” Road Mater. Pavement Des. 19 (3): 605–623. https://doi.org/10.1080/14680629.2016.1261730.
Rasool, R., H. R. Yao, A. Hassan, S. F. Wang, and H. Y. Zhang. 2018. “In-field aging process of high content SBS modified asphalt in porous pavement.” Polym. Degrad. Stab. 155 (3): 220–229. https://doi.org/10.1016/j.polymdegradstab.2018.07.023.
Ren, S., X. Y. Liu, P. Lin, S. Erkens, and Y. Xiao. 2021. “Chemo-physical characterization and molecular dynamics simulation of long-term aging behaviors of bitumen.” Constr. Build. Mater. 302 (21): 124437. https://doi.org/10.1016/j.conbuildmat.2021.124437.
Seitllari, A., T. F. Fwa, and G. Bosurgi. 2019. “A soft computing approach to predict and evaluate asphalt mixture aging characteristics using asphaltene as a performance indicator.” Mater. Struct. 52 (5): 100. https://doi.org/10.1617/s11527-019-1402-5.
Shafabakhsh, G. H., O. J. 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 (5): 136–143. https://doi.org/10.1016/j.conbuildmat.2015.03.060.
Singh, D., M. Zaman, and S. Commuri. 2011. “Evaluation of predictive models for estimating dynamic modulus of hot-mix asphalt in Oklahoma.” Transp. Res. Rec. 2210 (1): 57–72. https://doi.org/10.3141/2210-07.
Smith, B. T., B. R. Wazuhair, C. Daranga, G. L. Baumgardner, and I. L. Howard. 2019. “Comparing laboratory pressure aging vessel conditioning to field aging of asphalt binder within compacted mixtures.” J. Mater. Civ. Eng. 31 (11): 04019271. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002926.
Sollazzo, G., T. F. Fwa, and G. Bosurgi. 2017. “An ANN model to correlate roughness and structural performance in asphalt pavements.” Constr. Build. Mater. 134 (Dec): 684–693. https://doi.org/10.1016/j.conbuildmat.2016.12.186.
Sol-Sanchez, M., F. Moreno-Navarro, G. Garcia-Trave, and M. C. Rubio-Gamez. 2015. “Laboratory study of the long-term climatic deterioration of asphalt mixtures.” Constr. Build. Mater. 88 (5): 32–40. https://doi.org/10.1016/j.conbuildmat.2015.03.090.
Sun, G. Q., D. Q. Sun, A. Guarin, J. M. Ma, F. Chen, and E. Ghafooriroozbahany. 2019. “Low temperature self-healing character of asphalt mixtures under different fatigue damage degrees.” Constr. Build. Mater. 223 (7): 870–882. https://doi.org/10.1016/j.conbuildmat.2019.07.040.
Tarbox, S., and J. S. Daniel. 2012. “Effects of long-term oven aging on reclaimed asphalt pavement mixtures.” Transp. Res. Rec. 2294 (1): 1–15. https://doi.org/10.3141/2294-01.
Tian, Y., H. Li, H. J. Zhang, B. Yang, X. Zuo, and H. B. Wang. 2021. “Comparative investigation on three laboratory testing methods for short-term aging of asphalt binder.” Constr. Build. Mater. 266 (1): 121204. https://doi.org/10.1016/j.conbuildmat.2020.121204.
Wu, Z. H., S. Hu, and F. J. Zhou. 2014. “Prediction of stress intensity factors in pavement cracking with neural networks based on semi-analytical FEA.” Expert Syst. Appl. 41 (4): 1021–1030. https://doi.org/10.1016/j.eswa.2013.07.063.
Xiao, F. P., B. J. Putman, and S. N. 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.
Xing, H. Y., Y. S. Liang, G. Liu, Y. Zhang, X. Liu, and W. Fu. 2020. “Organically treating montmorillonite with dual surfactants to modify bitumen.” Constr. Build. Mater. 264 (13): 120705. https://doi.org/10.1016/j.conbuildmat.2020.120705.
Yideti, T. F., B. Birgisson, D. Jelagin, and A. Guarin. 2013. “Packing theory-based framework to evaluate permanent deformation of unbound granular materials.” Int. J. Pavement Eng. 14 (3): 309–320. https://doi.org/10.1080/10298436.2012.736620.
Yousif, R. A., R. Muniandy, S. Hassim, and F. Jakarni. 2018. “Effect of steam aging on performance-related properties of hot asphalt mixture.” J. Mater. Civ. Eng. 30 (3): 04017307. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002166.
Zavrtanik, N., J. Prosen, M. Tusar, and G. Turk. 2016. “The use of artificial neural networks for modeling air void content in aggregate mixture.” Autom. Constr. 63 (Mar): 155–161. https://doi.org/10.1016/j.autcon.2015.12.009.
Zhang, D. R., B. Birgisson, and X. Luo. 2020a. “A new dynamic modulus predictive model for asphalt mixtures based on the law of mixtures.” Constr. Build. Mater. 255 (6): 119348. https://doi.org/10.1016/j.conbuildmat.2020.119348.
Zhang, D. R., B. Birgisson, X. Luo, and I. Onifade. 2019a. “A new short-term aging model for asphalt binders based on rheological activation energy.” Mater. Struct. 52 (4): 68. https://doi.org/10.1617/s11527-019-1364-7.
Zhang, D. R., B. Birgisson, X. Luo, and I. Onifade. 2020b. “A new long–term aging model for asphalt pavements using morphology–kinetics based approach.” Constr. Build. Mater. 229 (9): 117032. https://doi.org/10.1016/j.conbuildmat.2019.117032.
Zhang, H. T., Z. Q. Liu, and X. C. Meng. 2019b. “Noise reduction characteristics of asphalt pavement based on indoor simulation tests.” Constr. Build. Mater. 215 (4): 285–297. https://doi.org/10.1016/j.conbuildmat.2019.04.220.
Zhang, R. H., J. E. Sias, and E. V. Dave. 2021. “Development of a rheology-based mixture aging model for asphalt material cracking performance evaluation.” Mater. Struct. 54 (4): 150. https://doi.org/10.1617/s11527-021-01743-5.
Zhang, W. G., A. Bahadori, S. H. Shen, S. H. Wu, and B. Muhunthan. 2018. “Comparison of laboratory and field asphalt aging for polymer-modified and warm-mix asphalt binders.” J. Mater. Civ. Eng. 30 (7): 04018150. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002354.
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Journal of Materials in Civil Engineering
Volume 35 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
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Received: Jun 3, 2022
Accepted: Oct 14, 2022
Published online: Apr 25, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 25, 2023
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