Edith Arambula

Three-dimensional image processing methods to identify and characterise aggregates in compacted asphalt mixtures

X-ray computed tomography (CT) is a novel tool to quantify the aggregate characteristics in asphalt pavements. This tool can potentially be used in QA, acceptance, design and forensic applications in pavement engineering. However, there have been challenges associated with the processing of the 3D X-ray CT images, including: (1) segmentation of aggregates that are in close proximity and (2) processing noisy or poor contrast images. This paper describes image processing methods to overcome these challenges and describes methods for computation of size, location, contact points and orientation of the aggregates in HMA. Validations of the algorithms as well as example computations of contact points and orientation have been presented. A significant increase in the number of contact points with increasing compaction level and preferred orientation perpendicular to the direction of compaction in the gyratory compactor were some of the findings presented in this paper.

Tension-Compression Fatigue Test Evaluation Using Fracture Mechanics and Field Data

ABSTRACT Fatigue cracking is one of the main distresses that affect asphalt pavements. In order to evaluate the cracking potential of several asphalt mixtures equivalent to the ones used in the sections of an accelerated loading pavement testing facility (PTF), uniaxial stress-controlled and strain-controlled direct tension-compression laboratory tests were performed. The laboratory-acquired data was analyzed using a crack growth equation with two separate techniques for estimating the dissipated pseudostrain energy. The results were compared to the observed PTF performance after subjecting the field sections to repeated load using a super-single truck tire accelerated loading device. The rankings of the mixtures based on their observed field performance and on the results of the crack growth equation were different, yielding parallel results for the strain-controlled laboratory test and opposite trends for the stress-controlled loading mode. The cause of the discrepancies could be explained by the onset and evolution of damage in each test protocol.

Use of artificial neural networks to detect aggregates in poor-quality X-ray CT images of asphalt concrete

Different laboratory compactors and protocols are employed to simulate field compaction using a reduced representative asphalt mixture specimen. Studies show that the mixture density, air voids, and mechanical properties vary within the results of each compaction method and between different compaction protocols, which may yield to estimates that mislead the design and performance prediction of the asphalt pavement. X-ray Computed Tomography (X-ray CT), a non-destructive technique for generating three-dimensional (3D) imaging of the internal structure of opaque materials, has commonly been used to quantify the air void distribution of asphalt mixtures. However, aggregate location, orientation and aggregate-to-aggregate contact points have rarely been successfully quantified using the same technique mainly because of image noise and poor contrast between the coarser aggregates and the other phases of the asphalt mixture (i.e. the air voids and the blend of the finer fraction aggregates and the asphalt binder). To overcome these shortcomings, an advanced tool has been developed utilizing 3D X-Ray CT image processing and artificial neural networks (ANN) to perform image segmentation and identify the coarser aggregates even in poor contrast X-ray CT images. This paper presents the details of the ANN tool and its application in determining the approximate size and location of the coarse aggregates in asphalt specimens.