Raveling on asphalt surfaces is a loss of fine and coarse aggregates from the asphalt matrix. The severity of raveling can be an indicator of the state of pavements, as excessive raveling not only reduces the ride quality but eventually leads to pothole formation or cracking. Hence, raveling must be detected and quantified. In this study and for the first time, raveling was quantified from a combination of two- and three-dimensional images. First, a texture descriptor method called Laws’ texture energy measure was used in conjunction with Gabor filters and other morphological operations to distinguish road areas. Then, digital signal processing techniques were used to detect and to quantify raveling. Hundreds of images captured by an automated pavement surveying system were used to test and to show the promise of the proposed algorithm.

Surface cracks can be the bellwether of the failure of a road. Hence, crack detection is indispensable for the condition monitoring and quality control of road surfaces. Pavement images have high levels of intensity variation and texture content; hence, the crack detection is generally difficult. Moreover, shallow cracks are very low contrast, making their detection difficult. Therefore, studies on pavement crack detection are active even after years of research. The fuzzy Hough transform is employed, for the first time, to detect cracks from pavement images. A careful consideration is given to the fact that cracks consist of near straight segments embedded in a surface of considerable texture. In this regard, the fuzzy part of the algorithm tackles the segments that are not perfectly straight. Moreover, tiled detection helps reduce the contribution of texture and noise pixels to the accumulator array. The proposed algorithm is compared against a state-of-the-art algorithm for a number of crack datasets, demonstrating its strengths. Precision and recall values of more than 75% are obtained, on different image sets of varying textures and other effects, captured by industrial pavement imagers. The paper also recommends numerical values for parameters used in the proposed method.

Although ground-penetrating radar (GPR) technology has existed for many decades, it has only been in the last 15 to 20 years that it has undergone great development and is now a commonly used non-destructive technique to assess layer thicknesses and material condition of trunk road pavement structures. Intrusive investigations provide vital additional information, but are often costly and time-consuming, and have the limitation that only data at discrete points are obtained. The nature of urban sites means that ground conditions are highly variable, and urban pavements are often subject to much maintenance and reconstruction. This can result in roads containing several pavement types or layers of materials of differing age and condition, often overlying discrete buried objects, services or structures. Other site-specific factors can also affect the quality of data obtained. However, it is possible to tailor a GPR survey to optimise data by adjusting the investigation methodology. Using an example of a recent urban pavement investigation, this paper shows how the use of detailed and extensive GPR data collection can be used to target concurrent invasive investigations to optimise the analysis of variable urban pavement structures and hence focus maintenance treatments and methodologies.

The use of ground-penetrating radar (GPR) for pavement investigation has developed rapidly over the past 20 years. The technique involves recording the passage of electromagnetic pulses transmitted into the pavement structure. GPR has enhanced and improved the range and certainty of information that can be obtained from pavement investigations. Analysis of data can provide information on layer depths, material condition, moisture, voiding, reinforcement, and location of other features. The dielectric constant is a material property that affects the speed and reflection amplitude of electromagnetic GPR pulses. Accurate determination or estimation of the dielectric constant is required for accurate analysis of pavement material information from GPR data. Typical pavement materials will have a bulk dielectric constant used in analysis that is the result of both the material constituents (binder, aggregate, etc.) and condition (moisture content, amount of voiding, etc.). This paper aims to provide a review and assessment of in situ dielectric constants of bituminous pavement materials determined from analysis of GPR data. The results of a large number of in situ pavement investigations, on a range of bituminous materials of varying condition, are reported. Dielectric constants from analysis of GPR investigations are determined and compared with existing data, and the effect of material condition and properties are discussed and assessed. The paper concludes that improved assessment of the in situ dielectric constant can be conducted and provide enhanced information from radar data analysis if consideration of material condition is made when selecting the values used in the analysis.

The development of the falling weight deflectometer (FWD) in the late 1970s made it possible to determine quickly the in situ modulus and critical stresses/strains in pavement structures, which are generally considered the most important input for the ‘mechanistic’ part of the mechanistic–empirical pavement design method. In 2015, the newly designed FastFWD was released and provided the opportunity to speed up the testing procedure and overall productivity significantly. The increased rate of loading prompted the current study into the possibility of performing in situ accelerated pavement testing to predict pavement deterioration, and to fill the gap between the heavy vehicle simulator and small-scale laboratory test methods. Numerous experimental sequences and test sites have been initiated since the start of the research; in the last of these, 1·6 million load applications were applied and the dynamic modulus master curve was back-calculated and used to filter out the viscoelastic response of the asphalt layer caused by temperature changes within the material from the repeated loading. Based on the findings of this research, an incremental-recursive fatigue model has been used to predict accurately the reduction in asphalt modulus as a function of any combination of loads and temperatures for a known material.