Mujib Rahman

Absorption of Bitumen into Crumb Rubber Using the Basket Drainage Method

This paper describes an evaluation of the absorption of different sources and grades of bitumen into particles of crumb rubber using a basket drainage method. The effect of the rubber–bitumen interaction has been investigated in terms of the absorption of the light fractions of the bitumen into the rubber and the chemical composition and rheological properties of the residual binder. Eight bitumens from two crude oil sources and four penetration grades ranging from 200 to 35 pen have been mixed with 2–8 mm sized granulated crumb rubber at three rubber to binder ratios of 1:4, 1:6 and 1:8 by mass. The increased mass of the crumb rubber was used to determine the loss of volatiles and light fractions absorbed from the different bitumens. The residual binders were then subjected to asphaltene content tests, high temperature viscosity and dynamic mechanical analysis using a dynamic shear rheometer to determine the chemical composition and rheological properties of the binders following their interaction with crumb rubber. The results show that the rate of adsorption is directly related to the penetration grade (viscosity) of the binders as well as to the chemical composition of the bitumen (crude source) but that the total amount of absorption is controlled by the nature of the crumb rubber. In terms of the rheological properties of the residual bitumen, all the binders showed an increase in viscosity, stiffness (complex modulus) as well as elastic response with these changes being consistent for both crude sources and all four penetration grades.

Pavement crack detection using the Gabor filter

Crack is a common form of pavement distress and it carries significant information on the condition of roads. The detection of cracks is essential to perform pavement maintenance and rehabilitation. Many of the highways agencies, in different countries, are still employing conventional, costly and very time consuming techniques which involve direct human intervention and assessment. Although automated recognition has been successfully performed for many pavement distresses, crack detection remains, to this date, a topic where reservations exist. A novel approach to automatically distinguish cracks in digital pavement images is proposed in this paper. The Gabor filter is proven to be a highly potential technique for multidirectional crack detection that was not done previously using the Gabor filter. Image analysis using the Gabor function is directly related to the mammalian visual perception, hence the choice of this method for crack detection. Results reported in this paper concentrate on pavement images with high levels of surface texture that makes crack detection difficult. An initial detection precision of up to 95% has been reported in this paper showing a good promise in the proposed method.

Metrology and visualization of potholes using the microsoft kinect sensor

Pavement distress and wear detection is of prime importance in transportation engineering. Due to degradation, potholes and different types of cracks are formed and they have to be detected and repaired in due course. Estimating the amount of filler material that is needed to fill a pothole is of great interest to prevent any shortage or excess, thereby wastage, of filler material that usually has to be transported from a different location. Metrological and visualization properties of a pothole play an important role in this regard. Using a low-cost Kinect sensor, the pavement depth images are collected from concrete and asphalt roads. Meshes are generated for better visualization of potholes. Area of pothole is analyzed with respect to depth. The approximate volume of pothole is calculated using trapezoidal rule on area-depth curves through pavement image analysis. In addition pothole area, length, and width are estimated. The paper also proposes a methodology to characterize potholes.

Moisture susceptibility of high and low compaction dry process crumb rubber-modified asphalt mixtures

The field performance of dry process crumb rubber–modified (CRM) asphalt mixtures has been reported to be inconsistent with stripping and premature cracking on the surfacing. One of the concerns is that, because achieving field compaction of CRM material is difficult due to the inherent resilient nature of the rubber particle, nonuniform field compaction may lead to a deficient bond between rubber and bitumen. To assess the influence of compaction, a series of CRM and control mixtures was produced and compacted at two levels: 4% (low, optimum laboratory compaction) and 8% (high, field experience) air void content. The long-term durability, in regard to moisture susceptibility of the mixtures, was assessed by conducting repeated moisture conditioning cycles. Mechanical properties (stiffness, fatigue, and resistance to permanent deformation) were determined in the Nottingham Asphalt Tester. Results indicated that compared with conventional mixtures, the CRM mixtures, regardless of compaction effort, are more susceptible to moisture with the degree of susceptibility primarily depending on the amount of rubber in the mixture, rather than the difference in compaction. This behavior is different from that of conventional mixtures in which, as expected, poorly compacted mixtures were found to be more susceptible to moisture than were well-compacted mixtures.

Crumb rubber and bitumen interaction as a function of crude source and bitumen viscosity

ABSTRACT This paper describes the interaction between crumb rubber and various penetration grade bitumens in terms of the absorption of the light fractions of the bitumen by the rubber and the chemical composition and rheological properties of the residual binder. Eight bitumens from two crude oil sources and four penetration grades ranging from 200 pen to 35 pen have been mixed with 2 mm to 8 mm sized crumb rubber at three rubber to binder ratios of 1:8, 1:6 and 1:4 by mass. The increased mass of the crumb rubber was used to determine the loss of volatiles and light fractions that have been absorbed from the different bitumens under constant temperature and equiviscous temperature conditions. The residual binders were then subjected to dynamic mechanical analysis using a dynamic shear rheometer to determine their rheological properties following the interaction with crumb rubber. The results show that the rate of adsorption as well as the total amount of absorption is directly related to the penetration grade (viscosity) of the binders, although the threshold (maximum) amount of absorption is also a function of the nature of the crumb rubber. In terms of the rheological properties of the residual bitumen, all the binders showed an increase in stiffness (complex modulus) as well as elastic response with these changes being consistent for both crude sources and all four penetration grades.

A Review of Three-Dimensional Imaging Technologies for Pavement Distress Detection and Measurements

With the ever-increasing emphasis on maintaining road assets to a high standard, the need for fast accurate inspection for road distresses is becoming extremely important. Surface distresses on roads are essentially three dimensional (3-D) in nature. Automated visual surveys are the best option available. However, the imaging conditions, in terms of lighting, etc., are very random. For example, the challenge of measuring the volume of the pothole requires a large field of view with a reasonable spatial resolution, whereas microtexture evaluation requires very accurate imaging. Within the two extremes, there is a range of situations that require 3-D imaging. Three-dimensional imaging consists of a number of techniques such as interferometry and depth from focus. Out of these, laser imagers are mainly used for road surface distress inspection. Many other techniques are relatively unknown among the transportation community, and industrial products are rare. The main impetus for this paper is derived from the rarity of 3-D industrial imagers that employ alternative techniques for use in transportation. In addition, the need for this work is also highlighted by a lack of literature that evaluates the relative merits/demerits of various imaging methods for different distress measurement situations in relation to pavements. This overview will create awareness of available 3-D imaging methods in order to help make a fast initial technology selection and deployment. The review is expected to be helpful for researchers, practicing engineers, and decision makers in transportation engineering.

Use of a Self-Organizing Map for Crack Detection in Highly Textured Pavement Images

AbstractA study on using an unsupervised learning technique, called a self-organizing map (SOM) or Kohonen map, for the detection of road cracks from pavement images is described in this paper. The main focus is on highly textured road images that make the crack detection very difficult. Road images are split into smaller rectangular cells, and a representative data set is generated for each cell by analyzing image texture and color properties. Texture and color properties are combined with a Kohonen map to distinguish crack areas from the background. Using this technique, cracks are detected to a precision of 77%. The algorithm also resulted in a recall of 73% despite the background having very strong visual texture. The technique applied here shows a great deal of promise despite the images being captured in an uncontrolled environment devoid of state-of-the-art image-acquisition setups. The results are also benchmarked against an advanced algorithm reported in a recent research paper. The benchmarking ...

Application of Texture Analysis and Kohonen Map for Region Segmentation of Pavement Images for Crack Detection

The first phase of a research study on detecting cracks in pavements is described. For reliable crack detection, various regions in a road image have to be segmented accurately. A procedure based on the texture and color properties of different regions of images is used in conjunction with the Kohonen map, also known as the self-organizing map. Accuracy of 89.7% was obtained with classification based on the Kohonen map of images taken with a regular digital camera and simple lighting setup. Furthermore, a complementary algorithm is described to remove spurious classifications caused by inaccuracies in the trained Kohonen map. With the help of this algorithm, an overall segmentation accuracy of 97.7% is reported. This research is expected to affect other problems in transportation engineering, such as road boundary detection and road marking inspection. The detection of cracks from the segmented regions will be addressed in the future.

Pavement Raveling Detection and Measurement from Synchronized Intensity and Range Images

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.

Fast segmentation of industrial quality pavement images using Laws texture energy measures and k-means clustering

Abstract. Thousands of pavement images are collected by road authorities daily for condition monitoring surveys. These images typically have intensity variations and texture nonuniformities that make their segmentation challenging. The automated segmentation of such pavement images is crucial for accurate, thorough, and expedited health monitoring of roads. In the pavement monitoring area, well-known texture descriptors, such as gray-level co-occurrence matrices and local binary patterns, are often used for surface segmentation and identification. These, despite being the established methods for texture discrimination, are inherently slow. This work evaluates Laws texture energy measures as a viable alternative for pavement images for the first time. k-means clustering is used to partition the feature space, limiting the human subjectivity in the process. Data classification, hence image segmentation, is performed by the k-nearest neighbor method. Laws texture energy masks are shown to perform well with resulting accuracy and precision values of more than 80%. The implementations of the algorithm, in both MATLAB® and OpenCV/C++, are extensively compared against the state of the art for execution speed, clearly showing the advantages of the proposed method. Furthermore, the OpenCV-based segmentation shows a 100% increase in processing speed when compared to the fastest algorithm available in literature.