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| A numerical method for analyzing the permeability of heterogeneous geomaterials based on digital image processing |
| Long Yan, Qing-xiang Meng, Wei-ya Xu, Huan-ling Wang, Qiang Zhang, Jiu-chang Zhang, Ru-bin Wang |
| Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, Hohai University, Nanjing 210098, China; Department of Civil & Environmental Engineering, University of Waterloo, Waterloo N2L 3G1, Canada; Key Laboratory of Coastal Disaster and Defense of Ministry of Education, Hohai University, Nanjing 210098, China; Department of Civil Engineering, University of Toronto, Toronto M5S 1A4, Canada |
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Abstract In this study, we propose a digital image processing technology for estimating the macro permeability property of heterogeneous geomaterials. The technology is based on a connected-component labeling algorithm and provides a novel and effective method for geometry vectorization and microstructure identification. A color photo of a soil and rock mixture (SRM) is taken as an example. Information about the distribution of aggregate and a vectorgraph, which can be used in numerical analysis, are obtained automatically. A numerical permeability test is carried out to estimate the macro permeability coefficient of the heterogeneous medium. The effects on macro permeability of three parameters, scale dependency, material heterogeneity and the rock fraction, are discussed. The results indicate that the SRM has a scale dependent property and the representative element volume (REV) length is about six times the maximum major axis of the aggregate. The heterogeneity parameter has a major effect on macro permeability characteristics within a certain range. There is a weak tendency for the macro permeability to decrease as the rock fraction increases. Although the rock fraction is not the only factor, it does have an influence on the macro permeability. We conclude that the novel method developed in this study has good prospects for widespread application in macro parameter estimation and related research fields.
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Received: 15 December 2015
Published: 24 January 2017
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