Determination of the air void content of asphalt concrete mixtures using artificial intelligence techniques to segment micro-CT images

ABSTRACT

X-ray micro-computed tomography (micro-CT) is an advanced technique able to provide a comprehensive examination of the volumetric characteristics of asphalt mixtures. A key step for the air void (AV) quantification using micro-CT images is the segmentation, which is a stage of the digital image processing. The most common segmentation technique, the manual threshold (TH) selection, depends significantly on the operator skills, image homogeneity, and material complexity. These factors that can limit the reproducibility of the TH procedure. Machine learning and deep learning recently appeared as promising alternatives to solve this challenge. In this paper, images of an asphalt concrete (AC) specimen were acquired in a modern high-resolution micro-CT scanner to determine its AV content using four different segmentation tools, i.e. TH, watershed, machine learning, and deep learning. All methods presented similar results for the total AV content. The advantages and limitations of using each technique were discussed in terms of computational effort, user-friendliness, and accuracy of the results. Machine learning and deep learning were identified as powerful tools for AC segmentation, being accurate and easy to adjust, however taking longer data processing times.

KEYWORDS:

• X-ray micro-computed tomography
• air void
• digital image processing
• threshold
• machine learning
• deep learning

Acknowledgements

The authors wish to express their gratitude to the Engineering School of Fluminense Federal University for kindly allowing the use of its micro-CT scanner. This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Finance Code 001, by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and by the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de Financiamento 001, by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), and by the Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ). The authors wish to express their gratitude to the Engineering School of Fluminense Federal University for kindly allowing the use of its micro-CT scanner.