Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

  • Lin Li College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, China
  • Kelvin C.P. Wang School of Civil and Environmental Engineering, Oklahoma State University, USA
  • Qiang Li School of Civil and Environmental Engineering, Oklahoma State University, USA
  • Wenting Luo College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, China
  • Jiangang Guo College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, China

Abstract

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements.

Keywords

pavement texture indicators, Mean Texture Depth (MTD), Mean Profile Depth (MPD), Power Spectral Density (PSD), pass filter, 3D Data
Published in Onlinefirst
04-09-2017
How to Cite
LI, Lin et al. Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data. Periodica Polytechnica Transportation Engineering, [S.l.], 2017. ISSN 1587-3811. Available at: <https://pp.bme.hu/tr/article/view/9587>. Date accessed: 23 nov. 2017. doi: https://doi.org/10.3311/PPtr.9587.
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Articles