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

July 08, 2021

Alexis Jair Enríquez-León (1), Thiago Delgado de Souza (1), Francisco Thiago Sacramento Aragão (1), Delson Braz (2), André Maués Brabo Pereira (3), Liebert Parreiras Nogueira (4)
International Journal of Pavement Engineering, July 2021. DOI: 10.1080/10298436.2021.1931197


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


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.

How Our Software Was Used

Dragonfly was used to perform 3D rendering and Watershed segmentation.

Author Affiliation

(1) Civil Engineering Program – COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
(2) Nuclear Engineering Program – COPPE, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
(3) Institute of Computing, Fluminense Federal University, Rio de Janeiro, Brazil.
(4) Department of Biomaterials, University of Oslo, Oslo, Norway.