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In this paper, an image-based technique for the assessment of a 3D model of pavement texture is presented.
A dozen of methods have been introduced for determining the pavement texture and each of them has advantages and disadvantages.
We developed a line-based skid mark segmentation and measurement system to solve issues related to randomness from pavement texture and measurement subjectivity at car accident scenes.
Micro-texture of aggregates is a critical component of pavement texture as well as a major contributing factor in roadway safety, especially in low speed conditions.
The stress distribution increased with an increase of pavement texture depth or tire load or a decrease of the tire inflation pressure.
Compared to the general pavement texture depth (sand patch method), the surface fractal dimension adequately described the surface roughness including macro-texture and micro-texture, and it was directly affected by the proportion of coarse aggregate.
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The latest developments on pavement surface texture measurement and characterization are highlighted.
Such models should account for tire and pavement surface texture characteristics, and the influence of environmental factors.
Proprietary software were employed for creation and analysis of 3D models from the images to determine pavement surface texture parameters.
Safety, skid resistance and noise of roads highly depend on the characteristics of pavement surface texture, for both porous and dense-graded wearing courses.
According to the results, the PSIAD based on the camera with charge coupled device (CCD) and shading black-box can avoid the information error of original pavement surface texture image, which is caused by the change of light conditions.
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