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Articles

Neural network-based prediction of sideway force coefficient for asphalt pavement using high-resolution 3D texture data

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Pages 3157-3166 | Received 02 Nov 2020, Accepted 29 Jan 2021, Published online: 11 Feb 2021

References

  • Ayenu-Prah, A.Y. and Attoh-Okine, N.O., 2009. Comparative study of Hilbert–Huang transform, Fourier transform and wavelet transform in pavement profile analysis. Vehicle System Dynamics, 47 (4), 437–456.
  • BS 7941-1. 2006. Methods for measuring the skid resistance of pavement surfaces: part 1: sideway-force coefficient routine investigation machine. London, UK: British Standards Institution.
  • Davies, R.B., Cenek, P.D., and Henderson, R.J., 2005. The effect of skid resistance and texture on crash risk. In: International Surface Friction Conference. Christchurch, New Zealand.
  • Dong, N., Prozzi, J.A., and Ni, F., 2019. Reconstruction of 3D pavement texture on handling dropouts and spikes using multiple data processing methods. Sensors, 19 (2), 278.
  • Flintsch, G.W., et al., 2003. Pavement surface macrotexture measurement and applications. Transportation Research Record: Journal of the Transportation Research Board, 1860 (1), 168–177.
  • Gonzalez, R. and Woods, R. 2008. Digital image processing. 3rd ed. London, UK: Pearson Education.
  • 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.
  • Hsiao, P.Y., et al., 2006. A parameterizable digital-approximated 2D Gaussian smoothing filter for edge detection in noisy image. 2006 IEEE International Symposium on Circuits and Systems, Island of Kos. doi:https://doi.org/10.1109/ISCAS.2006.1693303.
  • ISO 13473-2, 2002. Characterization of pavement texture by use of surface profiles–part 2: terminology and basic requirements related to pavement texture profile analysis. Geneva, Switzerland: ISO.
  • ISO 25178-2, 2012. Geometrical product specifications–surface texture: areal–part 2: terms, definitions and surface texture parameters. Geneva, Switzerland: ISO.
  • Izeppi, E.L., Flintsch, G., and McCarthy, R., 2017. Evaluation of methods for pavement surface friction, testing on non-tangent roadways and segments. FHWA/NC/2017-02. Blacksburg, VA: Virginia Tech Transportation Institute.
  • JTG D50, 2017. Specifications for design of highway asphalt pavement. Beijing, China: Ministry of Transport of the People's Republic of China.
  • JTG E60-2008, 2008. Field test methods of subgrade and pavement for highway engineering. Beijing, China: Ministry of Transport of the People's Republic of China.
  • Kane, M., Rado, Z., and Timmons, A., 2015. Exploring the texture–friction relationship: from texture empirical decomposition to pavement friction. International Journal of Pavement Engineering, 16 (10), 919–928.
  • Kogbara, R.B., et al., 2016. A state-of-the-art review of parameters influencing measurement and modeling of skid resistance of asphalt pavements. Construction and Building Materials, 114, 602–617.
  • 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.
  • Kouchaki, S., et al., 2018. Field investigation of relationship between pavement surface texture and friction. Transportation Research Record: Journal of the Transportation Research Board, 2672 (40), 395–407.
  • Kumar, B.S., 2013. Image denoising based on Gaussian/bilateral filter and its method noise thresholding. Signal, Image and Video Processing, 7 (6), 1159–1172.
  • Leach, R., ed., 2013. Characterisation of areal surface texture. Berlin: Springer-Verlag. doi:https://doi.org/10.1007/978-3-642-36458-7.
  • Lee, B.J., and Lee, H.D., 2004. Position-invariant neural network for digital pavement crack analysis. Computer-Aided Civil and Infrastructure Engineering, 19 (2), 105–118.
  • Li, Q., et al., 2017. Novel macro-and microtexture indicators for pavement friction by using high-resolution three-dimensional surface data. Transportation Research Record: Journal of the Transportation Research Board, 2641 (1), 164–176.
  • Najafi, S., Flintsch, G.W., and Medina, A., 2017. Linking roadway crashes and tire–pavement friction: a case study. International Journal of Pavement Engineering, 18 (2), 119–127.
  • Pan, Y.Y. and Chen, W., 2006. Analysis of meteorological conditions for traffic accident. Meteorological Science and Technology, 34 (6), 778–782.
  • Priddy, K.L. and Keller, P.E., 2005. Artificial neural networks: an introduction (Vol. 68). Bellingham, WA: SPIE Press.
  • Sanders, P.D., Brittain, S., and Premathilaka, A., 2012. Performance review of skid resistance measurement devices. PPR737. Berks, UK: Transportation Research Laboratory.
  • Sapna, S., Tamilarasi, A., and Kumar, M.P., 2012. Backpropagation learning algorithm based on Levenberg-Marquardt algorithm. Computer Science and Information Technology (CS and IT), 2, 393–398.
  • Srivastav, M.K., 2017. Study of correlation theory with different views and methods among variables in Mathematics. International Journal of Mathematics and Statistics Invention (IJMSI), 5 (2), 21–23.
  • Ueckermann, A., et al., 2015. A contribution to non-contact skid resistance measurement. International Journal of Pavement Engineering, 16 (7), 646–659.
  • Wen, B. and Cao, D.W., 2006. Statistical analysis of traffic accident and skid-resistance of expressway pavement. Journal of Highway and Transportation Research and Development, 23 (8), 72–75.
  • Yang, G., et al., 2019. Random forest–based pavement surface friction prediction using high-resolution 3D image data. Journal of Testing and Evaluation, 49 (2), 12. doi:https://doi.org/10.1520/JTE20180937.
  • Yang, G., Wang, K.C., and Li, J.Q., 2020. Multiresolution analysis of three-dimensional (3D) surface texture for asphalt pavement friction estimation. International Journal of Pavement Engineering, 1–10. doi:https://doi.org/10.1080/10298436.2020.1726350.
  • Yu, X. and Salari, E., 2011. Pavement pothole detection and severity measurement using laser imaging. In: 2011 IEEE International Conference on Electro/Information Technology. Mankato, MN, 1–5. doi:https://doi.org/10.1109/EIT.2011.5978573.
  • Zahouani, H., Vargiolu, R., and Do, M.T., 2000. Characterization of microtexture related to wet road/tire friction. SURF 2000: Fourth International Symposium on Pavement Surface Characteristics on Roads and Airfields. Nantes, France.
  • Zelelew, H., Khasawneh, M., and Abbas, A., 2014. Wavelet-based characterisation of asphalt pavement surface macro-texture. Road Materials and Pavement Design, 15 (3), 622–641.
  • Zuniga-Garcia, N. and Prozzi, J.A., 2016. Contribution of micro- and macro-texture for predicting friction on pavement surfaces. CHPP Report-UTA# 3-2016. Okemos, MI, USA: Michigan State University.
  • Zuniga-Garcia, N. and Prozzi, J.A., 2019. High-definition field texture measurements for predicting pavement friction. Transportation Research Record: Journal of the Transportation Research Board, 2673 (1), 246–260.

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