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Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size
KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Engineering Geology and Geophysics.
KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0003-2965-2953
2007 (English)In: Engineering Geology, ISSN 0013-7952, E-ISSN 1872-6917, Vol. 90, no 04-mar, 124-137 p.Article in journal (Refereed) Published
Abstract [en]

Image analysis of aggregates does not measure the same size as sieve analysis. The size of aggregates, determined by sieve analysis, is presented with respect to the percent cumulative mass, whereas image analysis does not measure mass. Results are often presented in percent particles or percent area. Several researchers have claimed that more accurate volume and mass determinations are necessary for accurate construction of grain-size curves. In the present work, several methods for reconstructing volume and thus mass of aggregates from image analysis (IA) have been tested to see how they influence the grain-size distribution curves. The actual mass of the individual particles was found to have little or no influence on the grain-size distribution curve, which is normalized and thus insensitive to mass. Accurate conversion of image-analysis size to sieve size is dependent upon how particles pass through sieves. Most existing methods base their conversion of image-analysis size to sieve size on the intermediate axis, multiplied by some factor. The present work shows that there is no direct correlation between the intermediate axes and sieve size. A universal conversion of image-analysis size to sieve size has been developed, using the minimum-bounding square around the minimum projected area. This measure yields very good correlation with sieve-analysis results.

Place, publisher, year, edition, pages
2007. Vol. 90, no 04-mar, 124-137 p.
Keyword [en]
grain-size distribution, image analysis, sieve analysis, aggregates, computer vision, coarse aggregate, shape
URN: urn:nbn:se:kth:diva-16558DOI: 10.1016/j.enggeo.2006.12.007ISI: 000245754000002ScopusID: 2-s2.0-33847697252OAI: diva2:334600
QC 20100525Available from: 2010-08-05 Created: 2010-08-05Bibliographically approved

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Robison Fernlund, JoanneKragic, Danica
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Engineering Geology and GeophysicsCentre for Autonomous Systems, CAS
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