Abstract
Pavement friction and texture characteristics are important aspects of road safety. Despite extensive studies conducted in the past decades, knowledge gaps still remain in understanding the relationship between pavement macrotexture and surface skid resistance. This paper implements discrete wavelet transform to decompose pavement surface macrotexture profile data into multi-scale characteristics and investigate their suitability for pavement friction prediction. Pavement macrotexture and friction data were both collected within the wheel-path from six High Friction Surface Treatment sites in Oklahoma using a high-speed profiler and a Grip Tester. The collected macrotexture profiles are decomposed into multiple wavelengths, and the total and relative energy components are calculated as indicators to represent macrotexture characteristics at various wavelengths. Correlation analysis is performed to examine the contribution of the energy indicators on pavement friction. The macrotexture energy within wavelengths from 0.97 mm to 3.86 mm contributes positively to pavement friction while that within wavelengths from 15.44 mm to 61.77 mm shows negative impacts. Subsequently, pavement friction prediction model is developed using multivariate linear regressive analysis incorporating the macrotexture energy indicators. Comparisons between predicted and monitored friction data demonstrates the robustness of the proposed friction prediction model.
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Abbasnia, R. and Farsaei, A. (2013). “Corrosion detection of reinforced concrete beams with wavelet analysis.” International Journal of Civil Engineering, Transaction A: Civil Engineering, Vol. 11, No. 3, 160–169.
Ahammed, M. A. and Tighe, S. L. (2008). “Concrete pavement surface textures and multivariables frictional performance analysis: a north american case study.” Canadian Journal of Civil Engineering, Vol. 35, No. 7, 727–738.
Ahammed, M. A. and Tighe, S. L. (2012). “Asphalt pavements surface texture and skid resistance–exploring the reality.” Canadian Journal of Civil Engineering, Vol. 39, pp. 1–9.
Alhasan, A., White, D. J., and Brabanterb, K. D. (2016). “Continuous wavelet analysis of pavement profiles.” Automation in Construction, Vol. 63, pp. 134–143.
American Traffic Safety Services Association (ATSSA). (2013). “Safety Opportunities in High Friction Surfacing.” Available from http://www.dbiservices.com/sites/default/files/resources/ATSSA-HFST-LoRes. pdf. [accessed 14 Mar. 2016].
ASTM Standard E1845-09 (2009). “Standard practice for calculating pavement macrotexture mean profile depth.” ASTM International, West Conshohocken, PA, DOI: 10.1520/E1845-09.
Ergun, M., Iyinam, S., and Iyinam, A. F. (2005). “Prediction of road surface friction coefficient using only macro-and microtexture measurements.” Journal of Transportation Engineering, Vol. 131, No. 4, pp. 311–319.
Hall, J. W., Smith, K.L., Titus-Glover, L., Wambold, J. C., Yager, T. J., and Rado, Z. (2009). “NCHRP Web Document 108: Guide for pavement friction.” Transportation Research Board, National Research Council, Washington, D.C.
Hassan, R. (2015). “Two Applications of wavelet analysis for project level pavement management.” International Journal of Sustainable Development and Planning, Vol. 10, No. 2, pp. 217–228.
Henry, J. J. (2000). “NCHRP Synthesis 291: Evaluation of pavement friction characteristics.” Transportation Research Board, National Research Council, Washington, D.C.
Hester. D. and Gonzàlez. A. (2012). “A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle.” Mechanical Systems and Signal Processing, Vol. 28, pp. 145–166.
ISO (2002). “Characterization of Pavement Texture by Use of Surface Profiles–Part 2: Terminology and Basic Requirements Related to Pavement Texture Profile Analysis.” ISO 13473-2: 2002.
Izeppi E., Flintsch G., and McGhee K. (2010). “Field performance of high friction surfaces.” Publication FHWA/VTRC 10-CR6. FHWA, U.S. Department of Transportation. Available from http://www.virginiadot.org/vtrc/main/online_reports/pdf/10-cr6.pdf.[accessed 15 Mar. 2016].
Merritt, D. and Moravec, M. (2014). “An update on HFST for horizontal curves.” Proceeding of Pavement Evaluation 2014, Blacksburg, Virginia. Available from https://vtechworks.lib.vt.edu/bitstream/handle/10919/54619/Merritt_2.pdf?sequence=1&isAllowed=y[accessed 14 Mar. 2016].
Misiti, M., Oppenheim, G., and Poggi, J. (2000). “Wavelet toolbox for use with MATLAB: User’s Guide.” The MathWorks, Inc., Natick, MA.
Najafi, S., Flintsch, G. W., and Medina, Alejandra (2015). “Linking roadway crashes and Tire-Pavement Friction: A case study.” International Journal of Pavement Engineering, DOI: 10.1080/10298436.2015.1039005.
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, pp. 919–918, DOI: 10.1080/10298436.2014.972956.
Papagiannakis, T., Zelelew, H., and Muhunthan, B. (2007). “A wavelet interpretation of pavement-vehicle interaction.” International Journal of Pavement Engineering, Vol. 8, No. 3, pp. 245–252.
Wang, D., Oeser, M., and Steinauer, B. (2015). “A contribution to non-contact skid resistance measurement.” International Journal of Pavement Engineering, Vol. 16, No. 7, 646–659.
Wang, D., Oeser, M., and Steinauer, B. (2015). “Calculation of skid resistance from texture measurements.” Journal of Traffic and Transportation Engineering, Vol. 2, No. 1, pp. 3–16.
Rado, Z. and Kane, M. (2014). “An initial attempt to develop an empirical relation between texture and pavement friction using the HHT approach.” Wear, Vol. 309, pp. 233–236.
Rezaei, A. and Masad, E. (2013). “Experimental-based model for predicting the skid resistance of asphalt pavements.” International Journal of Pavement Engineering, Vol. 14, No. 1, pp. 24–35.
Wang Kelvin, C. P., Li, Q., and Gong, W. (2007). “Wavelet-based pavement distress image edge detection with à trous algorithm.” In Transportation Research Record: Journal of the Transportation Research Board, No. 2024, Transportation Research Board of the National Academies, Washington, D.C., pp. 73–81.
Wei, L., Fwa, T. F., and Zhe, Z (2005). “Wavelet Analysis and Interpretation of Road Roughness.” Journal of Transportation Engineering, Vol. 131, No. 2, pp. 120–130.
Zelelew, H. M., Papagiannakis, A. T., and de León Izeppi, E. D. (2013). “Pavement macro-texture analysis using wavelets.” International Journal of Pavement Engineering, 14:8, pp. 725–735, DOI: 10.1080/10298436.2012.705004.
Zelelew, H. M., Khasawneh, W., and Abbas, A. (2014). “Wavelet-based characterisation of asphalt pavement surface macro-texture.” Road Materials and Pavement Design, 15:3, pp. 622–641, DOI: 10.1080/14680629.2014.908137.
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Yang, G., Li, Q.J., Zhan, Y.J. et al. Wavelet based macrotexture analysis for pavement friction prediction. KSCE J Civ Eng 22, 117–124 (2018). https://doi.org/10.1007/s12205-017-1165-x
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DOI: https://doi.org/10.1007/s12205-017-1165-x