Characterisation and simulation of asphalt road surfaces

Accurate simulation of rolling-tyre vibrations, and the associated noise, requires knowledge of road-surface topology. Full scans of the surface types in common use are, however, not widely available, and are likely to remain so. Ways of producing simulated surfaces from incomplete starting information are thus needed. In this paper, a simulation methodology based solely on line measurements is developed, and validated against a full two-dimensional height map of a real asphalt surface. First the tribological characteristics-asperity height, curvature and nearest-neighbour distributions-of the real surface are analysed. It is then shown that a standard simulation technique, which matches the (isotropic) spectrum and the probability distribution of the height measurements, is unable to reproduce these characteristics satisfactorily. A modification, whereby the inherent granularity of the surface is enforced at the initialisation stage, is introduced, and found to produce simulations whose tribological characteristics are in excellent agreement with the measurements. This method will thus make high-fidelity tyre-vibration calculations feasible for researchers with access to line-scan data only. In addition, the approach to surface tribological characterisation set out here provides a template for efficient cataloguing of road textures, as long as the resulting information can subsequently be used to produce sample realisations. A third simulation algorithm, which successfully addresses this requirement, is therefore also presented. © 2011 Elsevier B.V.

A simple prediction model for asphalt surface texture incorporating mix gradation and air voids

This paper presents the results of a preliminary study that seeks to show how asphalt grading and air voids are related to the texture depth of asphalt. The fiftieth percentile particle size (D50) is shown to be a good predictor of texture depth measurements from a collected database of field and laboratory studies. The D50 is used to normalise collected texture data and this 'relative texture' is shown to correlate with air voids. Regression analyses confirm that air voids should be included along with a measure of gradation in the interpretation of asphalt surface texture.The derived formulae are used to develop correlation charts.

Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.