A prediction model of the friction coefficient of asphalt pavement considering traffic volume and road surface characteristics

References

• Acharjee, A., et al., 2016. Integration of multi-omics data for prediction of phenotypic traits using random forest. BMC Bioinformatics, 17 (Suppl 5), 180. https://doi.org/10.1186/s12859-016-1043-4.
   PubMedGoogle Scholar
• Cao, P., 2009. Study on effects of texture and contaminants to skid resistance of asphant pavements. Wuhan: Wuhan University of Technology.
   Google Scholar
• Chen, X., 2015. From texture to skid resistance: A multi-scale modeling approach. Journal of Testing and Evaluation, 43 (2), 20130291. https://doi.org/10.1520/JTE20130291.
   Web of Science ®Google Scholar
• Chen, D., Roohi Sefidmazgi, N., and Bahia, H., 2015. Exploring the feasibility of evaluating asphalt pavement surface macro-texture using image-based texture analysis method. Road Materials and Pavement Design, 16 (2), 405–420. https://doi.org/10.1080/14680629.2015.1016547.
   Web of Science ®Google Scholar
• Chou, C., et al., 2017. Using a constructive pavement texture index for skid resistance screening. International Journal of Pavement Research and Technology, 10 (4), 360–368. https://doi.org/10.1016/j.ijprt.2017.05.002.
   Google Scholar
• Dong, Z., 2011. Study on accelerated polishing test and the regularity of skid resistance degradation of asphalt pavement material. Xi'an: Chang' an University.
   Google Scholar
• Doycheva, K., Koch, C., and König, M., 2016. Implementing textural features on GPUs for improved real-time pavement distress detection. Journal of Real-Time Image Processing, 16 (5), 1383–1394. https://doi.org/10.1007/s11554-016-0648-1.
   Google Scholar
• Edgar de Leon Izeppi, G.W.F., and McGhee, K., 2010. “Field Performance of High Friction Surfaces.” Virginia Tech Transportation Institute Center for Sustainable Transportation Infrastructure 3500 Transportation Research Plaza Blacksburg, VA 24061-0105 Virginia Transportation Research Council.
   Google Scholar
• Ejsmont, J.A., et al., 2016. Road texture influence on tyre rolling resistance. Road Materials and Pavement Design, 18 (1), 181–198. https://doi.org/10.1080/14680629.2016.1160835.
   Web of Science ®Google Scholar
• Fwa, T.F., 2017. Skid resistance determination for pavement management and wet-weather road safety. International Journal of Transportation Science and Technology, 6 (3), 217–227. https://doi.org/10.1016/j.ijtst.2017.08.001.
   Google Scholar
• Hartikainen, L., Petry, F., and Westermann, S., 2014. Frequency-wise correlation of the power spectral density of asphalt surface roughness and tire wet friction. Wear, 317 (1-2), 111–119. https://doi.org/10.1016/j.wear.2014.05.017.
   Web of Science ®Google Scholar
• Hofko, B., et al., 2017. A laboratory procedure for predicting skid and polishing resistance of road surfaces. International Journal of Pavement Engineering, 20 (4), 439–447. https://doi.org/10.1080/10298436.2017.1309191.
   Web of Science ®Google Scholar
• Hu, L., et al., 2016. Effect of three-dimensional macrotexture characteristics on dynamic frictional coefficient of asphalt pavement surface. Construction and Building Materials, 126, 720–729. https://doi.org/10.1016/j.conbuildmat.2016.09.088.
   Web of Science ®Google Scholar
• Huang, Y., and Shao, L., 2001. Laboratory test study on antiskid property of asphalt pavement. Journal of Highway and Transportation Research and Development, 19 (3), 5.
   Google Scholar
• Huang, X., and Zheng, B., 2019. The status quo and prospect of anti-skid performance of asphalt pavement. China Journal of Highway and Transport, 32 (4), 32–49.
   Google Scholar
• Hui, W., et al., 2022. Effect of loading rate on failure characteristics of asphalt mixtures using acoustic emission technique. Construction and Building Materials, 364 (2023), 129835.
   Google Scholar
• Iuele, T., et al., 2016. The influence of aggregate lithological nature on pavement texture polishing: a comparative investigation on a test site in southern Italy. Advances in Civil Engineering Materials, 5 (1). https://doi.org/10.1520/ACEM20160036.
   Google Scholar
• Jiang, J., et al., 2018. Characterization and identification of asphalt mixtures based on convolutional neural network methods using X-ray scanning images. Construction and Building Materials, 174, 72–80. https://doi.org/10.1016/j.conbuildmat.2018.04.083.
   Web of Science ®Google Scholar
• Kamani, M., and Ajalloeian, R., 2022. Investigation of the changes in aggregate morphology during different aggregate abrasion/degradation tests using image analysis. Construction and Building Materials, 314, 125614. https://doi.org/10.1016/j.conbuildmat.2021.125614.
   Web of Science ®Google Scholar
• Kargah-Ostadi, N., and Howard, A., 2019. Monitoring pavement surface macrotexture and friction. Transportation Research Record: Journal of the Transportation Research Board, 2525 (1), 111–117. https://doi.org/10.3141/2525-12.
   Google Scholar
• Kogbara, R.B., et al., 2018. Relating surface texture parameters from close range photogrammetry to grip-tester pavement friction measurements. Construction and Building Materials, 166, 227–240. https://doi.org/10.1016/j.conbuildmat.2018.01.102.
   Web of Science ®Google Scholar
• Kouchaki, S., et al., 2017. Evaluation of aggregates surface micro-texture using spectral analysis. Construction and Building Materials, 156, 944–955. https://doi.org/10.1016/j.conbuildmat.2017.08.174.
   Web of Science ®Google Scholar
• Leandri, P., and Losa, M., 2015. Peak friction prediction model based on surface texture characteristics. Transportation Research Record: Journal of the Transportation Research Board, 2525 (1), 91–99. https://doi.org/10.3141/2525-10.
   Google Scholar
• Li, Q.J., et al., 2018. Pavement skid resistance as a function of pavement surface and aggregate texture properties. International Journal of Pavement Engineering, 21 (10), 1159–1169.
   Web of Science ®Google Scholar
• Li, L., Wang, K.C.P., and Li, Q.J., 2016. Geometric texture indicators for safety on AC pavements with 1mm 3D laser texture data. International Journal of Pavement Research and Technology, 9 (1), 49–62. https://doi.org/10.1016/j.ijprt.2016.01.004.
   Google Scholar
• Liang, J., et al., 2020. A novel pavement mean texture depth evaluation strategy based on three-dimensional pavement data filtered by a new filtering approach. Measurement, 166, 108265. https://doi.org/10.1016/j.measurement.2020.108265.
   Web of Science ®Google Scholar
• Liu, Y., 2019. Research on skid resistance mechanism of asphalt pavement for steel bridge deck based on surface mesoscopic texture. Nanjing: Southeast University.
   Google Scholar
• Liu, Q., and Shalaby, A., 2015. Relating concrete pavement noise and friction to three-dimensional texture parameters. International Journal of Pavement Engineering, 18 (5), 450–458. https://doi.org/10.1080/10298436.2015.1095897.
   Web of Science ®Google Scholar
• Miao, Y., Cao, D., and Liu, Q., 2011. Relationship between macro-structure of asphalt pavement surface and anti-sliding performance. Journal of Beijing University of Technology, 37 (4), 347–353.
   Google Scholar
• Miller, T., et al., 2012. Characterization of asphalt pavement surface texture. Transportation Research Record: Journal of the Transportation Research Board, 2295 (1), 19–26. https://doi.org/10.3141/2295-03.
   Google Scholar
• Omar, L.G., El, H.A.O., and Ismail, K., 2017. Investigating predictability of pavement friction on rural roads in Ontario, Canada. In: 2017 annual meeting of transportation research board, Washington DC, USA. Available from: https://trid.trb.org/view/1438045#:~:text=Investigating%20Predictability%20of%20Pavement%20Friction%20on%20Rural%20Roads,maintain%20vehicle%20control%20during%20braking%20and%20cornering%20maneuvers.
   Google Scholar
• Peng, Y., et al., 2020. Evaluation of pavement anti-skid performance based on regional three-dimensional texture features. Journal of Southeast University (Natural Science Edition), 50 (4), 667–676.
   Google Scholar
• Plati, C., and Pomoni, M., 2019. Impact of traffic volume on pavement macrotexture and skid resistance long-term performance. Transportation Research Record: Journal of the Transportation Research Board, 2673 (2), 314–322. https://doi.org/10.1177/0361198118821343.
   Web of Science ®Google Scholar
• Sha, A., Tong, Z., and Gao, J., 2018. Road surface disease identification and measurement based on convolutional neural network. Chinese Journal of Highways, 31 (01), 1–10.
   Google Scholar
• Shi, L., 2020. Research on the prediction model of friction coefficient of asphalt pavement-based on the tire-road contacting characteristics. Chongqing: Chongqing Jiaotong University.
   Google Scholar
• Sun, Y., 2010. Research on the fractal nature of the surface micro-structure of coarse aggregate and the relationship with the anti-slide performance of asphalt pavement. Guangzhou: South China University of Technology.
   Google Scholar
• Tan, Y., Xiao, S., and Xiong, X., 2021. Review on detection and prediction methods for pavament skid resistance. Journal of Traffic and Transportation Engineering, 21 (4), 32–47.
   Google Scholar
• Tong, Z., et al., 2018. Convolutional neural network for asphalt pavement surface texture analysis. Computer-Aided Civil and Infrastructure Engineering, 33 (12), 1056–1072. https://doi.org/10.1111/mice.12406.
   Web of Science ®Google Scholar
• Tong, S., 2020. The simulation study on the dynamic friction behaviors of tire-asphalt pavement: considering the characteristics of pavement texture. Chongqing: Chongqing Jiaotong University.
   Google Scholar
• Vaiana, R., et al., 2019. Microtexture performance of EAF slags used as aggregate in asphalt mixes: A comparative study with surface properties of natural stones. Applied Sciences, 9 (15). https://doi.org/10.3390/app9153197.
   Google Scholar
• Wang, Y., 2017. Study on the relationship between sliding resistance of asphalt pavament and ITS surface rough characteristics. Nanjing: Southeast University.
   Google Scholar
• Wu, Z., and Abadie, C., 2018. Laboratory and field evaluation of asphalt pavement surface friction resistance. Frontiers of Structural and Civil Engineering, 12 (3), 372–381. https://doi.org/10.1007/s11709-017-0463-1.
   Web of Science ®Google Scholar
• Yang, G., et al., 2018. Convolutional neural network–based friction model using pavement texture data. Journal of Computing in Civil Engineering, 32, 6.
   Web of Science ®Google Scholar
• Ye, X., 2018. A new multivariate regression model for pavement friction prediction based on different pavement texture types. Fuzhou: Fujian Agriculture and Forestry University.
   Google Scholar
• Yu, M., et al., 2020a. Dynamic friction coefficient between tire and compacted asphalt mixtures using tire-pavement dynamic friction analyzer. Construction and Building Materials, 258.
   Web of Science ®Google Scholar
• Yu, M., et al., 2020b. Measurement and modeling of skid resistance of asphalt pavement: A review. Construction and Building Materials, 260. https://doi.org/10.1016/j.conbuildmat.2020.119878.
   PubMed Web of Science ®Google Scholar
• Yu, M., and Tong, S., 2020. A review of tire-asphalt pavement friction measurement and skid resistance model study. Journal of Highway and Transportation Research And Development, 37 (10), 12–24.
   Google Scholar
• Zhan, Y., et al., 2020. Relationship between macro-structure of asphalt pavement surface and anti-sliding performance. Journal of Zhejiang University (Engineering Science), 4 (55), 684–694.
   Google Scholar
• Zhang, X., and Sun, Y., 2011. Study on laser measurement method and fractal properties of surface micro-texture of coarse aggregate. Journal of Highway and Transportation Science and Technology, 28 (1), 19–24.
   Google Scholar
• Zheng, D., et al., 2018. Prediction and sensitivity analysis of long-term skid resistance of epoxy asphalt mixture based on GA-BP neural network. Construction and Building Materials, 158, 614–623. https://doi.org/10.1016/j.conbuildmat.2017.10.056.
   Web of Science ®Google Scholar
• Zhou, J., 2019. Study on skid resistance of asphalt pavements based on different temperatures and polishing conditions. Xi'an: Chang' an University.
   Google Scholar