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Comparative Study of 2D Petrographic and 3D X-ray Tomography Investigations of Air Voids in Asphalt.

Moritz MiddendorfCristin UmbachStefan BöhmJia LiuBernhard Middendorf
Published in: Materials (Basel, Switzerland) (2023)
Knowledge of the exact composition of building materials (aggregate, binder, air voids, etc.) is essential for the further development of more resistant and sustainable building materials. In numerous scientific studies, the material behavior of asphalt is tested using mechanical methods. Here, the overall material behavior is determined (bitumen, air voids, aggregate). With the advent of imaging techniques, it is becoming possible to determine the individual constituents separately and perform a more detailed analysis of their location, shape and composition. Three-dimensional and two-dimensional methods are available for this purpose. For this study, two different types of asphalt (porous asphalt and asphalt concrete) were analyzed using 3D X-ray computed tomography and asphalt petrology as 2D methods; the results of both investigations are compared. The objective of this study is to determine whether the 2D method provides suitable results for the microstructural analysis of asphalt samples and how the results differ from those studied by the 3D method. The comparison shows that both methods can be used to analyze voids in asphalt samples. The 2D method provides valuable insight into the distribution of voids in a sample. In addition to the distribution of voids within a 2D section, the 2D method can also be used to make some structural statements about the location and structure of the voids in the 2D plane. The X-ray computed tomography method allows more complex analyses of the pore structure because of the third direction (3D). In addition, the 3D method provides more data, so that the pore structure can be described even more precisely, and the pore size (length, width, height) can be mapped and analyzed with a high degree of accuracy.
Keyphrases
  • computed tomography
  • high resolution
  • healthcare
  • magnetic resonance imaging
  • body mass index
  • machine learning
  • magnetic resonance
  • big data
  • contrast enhanced
  • white matter
  • artificial intelligence