Planar Image-Based Reconstruction of Pervious Concrete Pore Structure and Permeability Prediction

Published

July 1, 2010

Author(s)

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

Abstract

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 twelve different pervious concrete mixtures from three-dimensional material structures reconstructed from starting planar images of the original material. Two-point correlation functions of the two-dimensional 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 microstructures and the experimentally measured permeabilities of pervious concretes are found to be in reasonably good agreement. The three-dimensional reconstruction process provides a relatively inexpensive method (in lieu 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 during service or as a result of changes in mixture proportions.

Citation

American Concrete Institute (ACI) Materials Journal

Volume

107

Issue

4

Pub Type

Journals

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Keywords

Pervious concrete, Permeability, Three-dimensional reconstruction, Pore structure, Porosity

Citation

Sumanasooriya, M. , Bentz, D. and Neithalath, N. (2010), Planar Image-Based Reconstruction of Pervious Concrete Pore Structure and Permeability Prediction, American Concrete Institute (ACI) Materials Journal, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=903884 (Accessed April 16, 2024)