Prediction of Skid Resistance of Steel Slag Asphalt Mixture Based on Grey Residual GM(1,1)-Markov Model
Publication: Journal of Materials in Civil Engineering
Volume 36, Issue 1
Abstract
To predict the long-term skid resistance of steel slag asphalt mixtures, accelerated wear tests were conducted using an indoor accelerated loading device on the steel slag asphalt mixtures with different aggregate types, different steel slag blends, and different temperatures. The skid resistance decay law applied to the steel slag asphalt mixture under different influencing factors was investigated. Based on the skid resistance evaluation index measured during testing, a grey residual grey model (GM)(1,1)-Markov model was established to predict skid resistance. The results showed that the incorporation of steel slag significantly improves skid resistance while helping to reduce skid attenuation loss. Skid resistance increased with the increase in steel slag incorporation. With 100% steel slag incorporation, it was optimal. In addition, the test temperature did not change the decay law of the skid resistance index. With changes in temperature, skid resistance showed a decreasing trend. The prediction accuracy of the grey residual GM(1,1)-Markov model was significantly better than that of the grey GM(1,1) model and so can be used for skid resistance prediction. The results of the study can help to determine the attenuation characteristics of skid resistance of steel slag asphalt mixtures, and provide a simple and reliable method for predicting the skid resistance of these mixtures and other pavement aggregates. At the same time, it is certain guidelines for the establishment of the prediction model of asphalt mixture skid resistance under small sample conditions.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
The authors would like to thank the Xinjiang Huli Jiayuan Environmental Protection Technology Co., Ltd. for its financial support.
References
Akbari, A., and R. Babagoli. 2021. “Laboratory evaluation of the effect of temperature on skid resistance of different asphalt mixtures.” Mater. Res. Innovations 25 (2): 83–89. https://doi.org/10.1080/14328917.2020.1741145.
Anupam, K., S. K. Srirangam, A. Scarpas, and C. Kasbergen. 2013. “Influence of temperature on tire-pavement friction: Analyses.” Transp. Res. Rec. 2369 (1): 114–124. https://doi.org/10.3141/2369-13.
Asi, I. M. 2007. “Evaluating skid resistance of different asphalt concrete mixes.” Build. Environ. 42 (1): 325–329. https://doi.org/10.1016/j.buildenv.2005.08.020.
Bessa, I. S., V. T. F. Castelo Branco, and J. B. Soares. 2014. “Evaluation of polishing and degradation resistance of natural aggregates and steel slag using the aggregate image measurement system.” Road Mater. Pavement Des. 15 (2): 385–405. https://doi.org/10.1080/14680629.2014.883323.
Chen, B., X. Zhang, J. Yu, and Y. Wang. 2017. “Impact of contact stress distribution on skid resistance of asphalt pavements.” Constr. Build. Mater. 133 (Feb): 330–339. https://doi.org/10.1016/j.conbuildmat.2016.12.078.
Cui, P., S. Wu, Y. Xiao, C. Yang, and F. Wang. 2020. “Enhancement mechanism of skid resistance in preventive maintenance of asphalt pavement by steel slag based on micro-surfacing.” Constr. Build. Mater. 239 (Apr): 117870. https://doi.org/10.1016/j.conbuildmat.2019.117870.
Duan, H., and Y. Liu. 2021. “Research on a grey prediction model based on energy prices and its applications.” Comput. Ind. Eng. 162 (Dec): 107729. https://doi.org/10.1016/j.cie.2021.107729.
Gao, J., A. Sha, Z. Wang, Z. Tong, and Z. Liu. 2017. “Utilization of steel slag as aggregate in asphalt mixtures for microwave deicing.” J. Cleaner Prod. 152 (May): 429–442. https://doi.org/10.1016/j.jclepro.2017.03.113.
Hu, L., S. Wang, and Q. Zhang. 2021. “Evaluation of long-term skid resistance of asphalt mixture with multi-content basic oxygen furnace (BOF) steel slag using the circular vehicle simulator (CVS).” In Vol. 248 of Proc., E3S Web of Conf., 01044. Les Ulis, France: Executive Development Programs (EDP) Sciences.
Huang, X. M., and B. S. Zheng. 2019. “Research status and progress for skid resistance performance of asphalt pavement.” Chin. J. Highway Transp. 32 (4): 32–49. https://doi.org/10.19721/j.cnki.1001-7372.2019.04.003.
Jia, Z. Q., Z. F. Zhou, H. J. Zhang, B. Li, and Y. X. Zhang. 2020. “Forecast of coal consumption in Gansu Province based on Grey-Markov chain model.” Energy 199 (May): 117444. https://doi.org/10.1016/j.energy.2020.117444.
Kane, M., I. Artamendi, and T. Scarpas. 2013. “Long-term skid resistance of asphalt surfacings: Correlation between Wehner–Schulze friction values and the mineralogical composition of the aggregates.” Wear 303 (1–2): 235–243. https://doi.org/10.1016/j.wear.2013.03.022.
Kehagia, F. 2009. “Skid resistance performance of asphalt wearing courses with electric arc furnace slag aggregates.” Waste Manage. Res. 27 (3): 288–294. https://doi.org/10.1177/0734242X08092025.
Kogbara, R. B., E. A. Masad, E. Kassem, A. T. Scarpas, and K. Anupam. 2016. “A state-of-the-art review of parameters influencing measurement and modeling of skid resistance of asphalt pavements.” Constr. Build. Mater. 114 (Jul): 602–617. https://doi.org/10.1016/j.conbuildmat.2016.04.002.
Li, J., J. Yu, S. Wu, and J. Xie. 2022. “The mechanical resistance of asphalt mixture with steel slag to deformation and skid degradation based on laboratory accelerated heavy loading test.” Materials 15 (3): 911. https://doi.org/10.3390/ma15030911.
Li, S., R. Xiong, J. Zhai, K. Zhang, W. Jiang, F. Yang, X. Yang, and H. Zhao. 2020. “Research progress on skid resistance of basic oxygen furnace (BOF) slag asphalt mixtures.” Materials 13 (9): 2169. https://doi.org/10.3390/ma13092169.
Ministry of Communications Highway Science Research Institute. 2004. Technical Specifications for Highway Asphalt Pavement Recycling. [In Chinese.] JTG F40-2004. Beijing: Communications Press.
Ministry of Communications Highway Science Research Institute. 2011. Standard test methods of bitumen and bituminous mixtures for highway engineering. [In Chinese.] JTG E20-2011. Beijing: Communications Press.
Najafi, S., G. W. Flintsch, and A. Medina. 2017. “Linking roadway crashes and tire-pavement friction: A case study.” Int. J. Pavement Eng. 18 (2): 119–127. https://doi.org/10.1080/10298436.2015.1039005.
Qian, Z. D., Y. Liu, C. B. Liu, and D. Zheng. 2016. “Design and skid resistance evaluation of skeleton-dense epoxy asphalt mixture for steel bridge deck pavement.” Constr. Build. Mater. 114 (Jul): 851–863. https://doi.org/10.1016/j.conbuildmat.2016.03.210.
Ruan, M., X. Chen, and H. Zhou. 2019. “Centrality prediction based on K-order Markov chain in mobile social networks.” Peer-to-Peer Networking Appl. 12 (6): 1662–1672. https://doi.org/10.1007/s12083-019-00746-y.
Shen, A. Q., X. Chen, Y. H. Guo, and P. Li. 2019a. “Road performance evaluate on effects of steel slag asphalt mixture based on grey target decision.” Bull. Chin. Ceram. Soc. 38 (Mar): 1245–1252. https://doi.org/10.16552/j.cnki.issn1001-1625.2019.04.051.
Shen, A. Q., B. Liu, Y. C. Guo, P. Yu, and M. Y. Yu. 2019b. “Skid resistance attenuation of steel slag asphalt mixtures on tunnel pavement.” J. Build. Mater. 22 (2): 284–291.
Tataranni, P., and C. Sangiorgi. 2019. “Synthetic aggregates for the production of innovative low impact porous layers for urban pavements.” Infrastructures 4 (3): 48. https://doi.org/10.3390/infrastructures4030048.
Tien, T. L. 2012. “A research on the grey prediction model GM (1, n).” Appl. Math. Comput. 218 (9): 4903–4916. https://doi.org/10.1016/j.amc.2011.10.055.
Wang, D., Z. Zhang, J. Kollmann, and M. Oeser. 2020. “Development of aggregate micro-texture during polishing and correlation with skid resistance.” Int. J. Pavement Eng. 21 (5): 629–641. https://doi.org/10.1080/10298436.2018.1502436.
Wang, D. Y., G. Wang, Z. Li, S. S. Shao, and Y. Y. Sun. 2017. “Evaluation of anti-sliding durability of asphalt mixture based on pressure film technology.” Chin. J. Highway Transp. 30 (9): 1–9. https://doi.org/10.19721/j.cnki.1001-7372.2017.09.001.
Wang, Z., X. Wu, H. Wang, and T. Wu. 2021. “Prediction and analysis of domestic water consumption based on optimized grey and Markov model.” Water Supply 21 (7): 3887–3899. https://doi.org/10.2166/ws.2021.146.
Xue, X., X. Zheng, B. Guan, J. Liu, D. Ding, R. Xiong, H. Zhao, and F. Wei. 2022. “Long-term skid resistance of high-friction surface treatment of pavement using high-alumina refractory waste.” Constr. Build. Mater. 351 (Oct): 128961. https://doi.org/10.1016/j.conbuildmat.2022.128961.
Yanchao, C., L. Xiqiao, and Z. Shuangping. 2019. “A proportion prediction model of terminal energy structure of IPS based on hidden Markov chain.” Procedia CIRP 83 (Jan): 456–460. https://doi.org/10.1016/j.procir.2019.04.136.
Yu, H., G. Deng, Z. Zhang, M. Zhu, M. Gong, and M. Oeser. 2021. “Workability of rubberized asphalt from a perspective of particle effect.” Transp. Res. Part D Transp. Environ. 91 (Feb): 102712. https://doi.org/10.1016/j.trd.2021.102712.
Yu, H., Z. He, G. Qian, X. Gong, and X. Qu. 2020. “Research on the anti-icing properties of silicone modified polyurea coatings (SMPC) for asphalt pavement.” Constr. Build. Mater. 242 (Mar): 117793. https://doi.org/10.1016/j.conbuildmat.2019.117793.
Zhang, D. B., X. Li, Y. Zhang, and H. Zhang. 2019. “Prediction method of asphalt pavement performance and corrosion based on grey system theory.” Int. J. Corros. 2019 (Jan): 2534794. https://doi.org/10.1155/2019/2534794.
Zhou, X. L., W. K. Liu, W. X. Xiao, M. P. Ran, and X. M. Huang. 2017. “Influence of asphalt mixture volume indexes on asphalt pavement skid resistance performance.” J. Traffic Transp. Eng. 17 (6): 1–9.
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© 2023 American Society of Civil Engineers.
History
Received: Jan 10, 2023
Accepted: May 17, 2023
Published online: Oct 27, 2023
Published in print: Jan 1, 2024
Discussion open until: Mar 27, 2024
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