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

Abstract

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.

Keywords:

• X-ray CT
• compaction
• aggregate characterisation
• image processing
• artificial neural networks
• filtered watershed transform

Acknowledgements

The research team would like to acknowledge the LCPC research team who provided the samples and the FHWA for scanning the images. Discussions with and review of the article by Prof. H. Bahia, the chair of the RILEM TG2 group, are greatly appreciated.