Planar image-based reconstruction of pervious concrete pore structure and permeability prediction

Milani S. Sumanasooriya, Dale P. Bentz, Narayanan Neithalath

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Transport properties of porous materials such as pervious concretes are inherently dependent on a variety of pore structure features. Empirical equations are typically used to relate the pore structure of a porous material to its permeability. In this study, a computational procedure is employed to predict the permeability of 12 different pervious concrete mixtures from three-dimensional (3D) material structures reconstructed from starting planar images of the original material. Two-point correlation (TPC) functions of the two-dimensional (2D) images from real pervious concrete specimens are employed along with the measured volumetric porosities in the reconstruction process. The pore structure features of the parent material and the reconstructed images are found to be similar. The permeabilities predicted using Darcy's law applied to the reconstructed structures and the experimentally measured permeabilities of pervious concretes are found to be in reasonably good agreement. The 3D reconstruction process provides a relatively inexpensive method (instead of methods such as X-ray tomography) to explore the nature of the pore space in pervious concretes and predict permeability, thus facilitating its use in understanding the changes in pore structure as a result of changes in mixture proportions.

Original languageEnglish (US)
Pages (from-to)413-421
Number of pages9
JournalACI Materials Journal
Issue number4
StatePublished - Jul 1 2010
Externally publishedYes


  • Permeability
  • Pervious concrete
  • Porosity
  • Three-dimensional reconstruction

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Materials Science(all)


Dive into the research topics of 'Planar image-based reconstruction of pervious concrete pore structure and permeability prediction'. Together they form a unique fingerprint.

Cite this