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.

Fracture mechanics of idealised bituminous mixes

© 2014 Taylor & Francis. The durability of asphalt pavements is strongly impaired by cracks, caused primarily by traffic loads and environmental effects. In this work, fracture behaviour of idealised asphalt mixes is investigated. Experiments on idealised asphalt mixes under pure-tension mode (mode I cracking) were performed and fracture parameters were evaluated. In these three-point bend fracture tests, the test variables were temperature and load rate. The test data were stored in an asphalt materials database and special-purpose tools were implemented to analyse and handle the laboratory data automatically. Fracture mechanism maps were constructed, showing the conditions associated with ductile, brittle and ductile-brittle transition regimes of behaviour. The mechanism maps show the failure response of the material in terms of the stress intensity factor, strain energy release rate and J-integral as a function of the temperature-compensated crack mouth opening strain rate. Fracture behaviour of asphalt mix specimens was simulated by cohesive zone model in conjunction with a novel material constitutive model for asphalt mixes. The finite element model agrees well with the experimental results and provides insights into fracture response of the notched asphalt mix beam specimens.

Ranking binder creep performance using the arrb elastometer

This paper discusses a laboratory study used to characterize bituminous binders based on their dynamic creep resistance. Laboratory testing using four different loading regimes on asphalt mixes with six different bituminous binders was undertaken. Creep cycles to 2% accumulated strain were used to define the creep resistance of the asphalt mixes with the various binders. Underlying viscosities of the bitumens were derived using the Australian Road Research Board (ARRB) Elastometer. Marshall stability was measured on the specimens that were prepared using gyratory compaction. Regression plots were prepared that link creep resistance, underlying viscosity, and Marshall stability. It was found that the ARRB Elastometer is able to measure underlying viscosity, which is a reasonable predictor of dynamic creep resistance. Marshall stability was also shown to be a good indicator of dynamic creep resistance. Therefore, simpler tests such as Marshall stability and Elastometer can be used to rank bituminous materials for asphalt mix design purposes in the laboratory. © 2010 ASCE.

Re-examination of the coefficient of determination (r2) using road materials engineering case studies

The analysis of scientific data is integral to materials engineering and science. The correlation between measured variables is often quantified by estimating the coefficient of determination or the r2 value. This is the recognised procedure for determining linear relationships. The authors review the derivation of the r2 value and derive an associated quantity, termed the relative deviation (RD), which is the ratio of the root mean square of the deviations about the fitted line to the root mean square of the deviations about the y bar line expressed as a percentage. The relative deviation has an advantage over the coefficient of determination in that it has greater numerical sensitivity to changes in the spread of data about the fitted line, especially when the scatter is small. In addition, the relative deviation is able to define, in percentage terms, the reduction in scatter when different independent variables are correlated with a common dependent variable. Four case studies in the materials field (aggregate crushing value, Atterberg limits, permeability and creep of asphalt) from work carried out at the Queensland Main Roads Department are presented to show the use of the new parameter RD.

Identification of Materials from Construction Site Images Using Content Based Image Retrieval Techniques

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. Examples include surface cracks detection, assessment of fire-damaged mortar, fatigue evaluation of asphalt mixes, aggregate shape measurements, velocimentry, vehicles detection, pore size distribution in geotextiles, damage detection and others. This capability is a product of the technological breakthroughs in the area of Image and Video Processing that has allowed for the development of a large number of digital imaging applications in all industries ranging from the well established medical diagnostic tools (magnetic resonance imaging, spectroscopy and nuclear medical imaging) to image searching mechanisms (image matching, content based image retrieval). Content based image retrieval techniques can also assist in the automated recognition of materials in construction site images and thus enable the development of reliable methods for image classification and retrieval. The amount of original imaging information produced yearly in the construction industry during the last decade has experienced a tremendous growth. Digital cameras and image databases are gradually replacing traditional photography while owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks. However, construction companies tend to store images without following any standardized indexing protocols, thus making the manual searching and retrieval a tedious and time-consuming effort. Alternatively, material and object identification techniques can be used for the development of automated, content based, construction site image retrieval methodology. These methods can utilize automatic material or object based indexing to remove the user from the time-consuming and tedious manual classification process. In this paper, a novel material identification methodology is presented. This method utilizes content based image retrieval concepts to match known material samples with material clusters within the image content. The results demonstrate the suitability of this methodology for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.