Predicting the Permeability of Pervious Concretes from Planar Images

Published

May 1, 2009

Author(s)

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

Abstract

This paper discusses the reconstruction of three-dimensional material structures of pervious concretes using two-dimensional digital images obtained from actual specimens, and computational permeability predictions using these reconstructed three-dimensional material structures. The computer programs developed at the National Institutes of Standards and Technology (NIST) are used to obtain three-dimensional structures based on two-point correlation functions of real two-dimensional images, and to evaluate the permeability of six different pervious concrete mixtures including three single sized aggregates mixtures and three blended aggregate mixtures. The reconstructed microstructures are found to have similar porosities as that of the actual specimens. Experimental permeability values are obtained using a falling head permeameter, and the predicted permeability values are compared with the experimental values. The predicted values of permeability are found to match the experimental values closely for specimens made with smaller sized aggregates, rather than those for specimens made with larger aggregates. This is because of the fact that the pores in specimens made with smaller sized aggregates are distributed more uniformly, thus satisfying the assumptions of homogeneity and isotropy in the two-dimensional and three-dimensional material structures.

Proceedings Title

NRMCA 2009 Concrete Technology Forum: Focus on Performance Prediction

Conference Dates

May 13-15, 2009

Conference Location

Cinncinnati, OH, US

Pub Type

Conferences

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Keywords

Building technology, image processing, microstructure, permeability, porosity, reconstruction.

Citation

Sumanasooriya, M. , Bentz, D. and Neithalath, N. (2009), Predicting the Permeability of Pervious Concretes from Planar Images, NRMCA 2009 Concrete Technology Forum: Focus on Performance Prediction, Cinncinnati, OH, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=902014 (Accessed October 17, 2025)