| Title: | Determining the grain size distribution of granular soils using image analysis |
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| Authors: | ID Dipova, Nihat (Author)
ID Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru (Copyright holder) |
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| Files: |  Acta_Geotechnica_Slovenica_2017_Dipova_Determining_the_grain_size_distribution_of_granular_soils_using_image_analysis.pdf (1,27 MB)
MD5: 2FEF44BB9FA5C564EAA75440D1E8E3CA
PID: 20.500.12556/dkum/0e414a97-4d30-48ec-b507-47e025e55abb
 http://fgserver3.fg.um.si/journal-ags/2017-1/article-3.asp
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
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| Abstract: | Image-processing technology includes storing the images of objects in a computer and processing them with the computer for a specified purpose. Image analysis is the numerical expression of the images of objects by means of mimicking the functioning of the human visual system and the generation of numerical data for calculations that will be made later. Digital image analysis provides the capability for rapid measurement, which can be made in near-real time, for numerous engineering parameters of materials. Recently, image analysis has been used in geotechnical engineering practices. Grain size distribution and grain shape are the most fundamental properties used to interpret the origin and behaviour of soils. Mechanical sieving has some limitations, e.g., it does not measure the axial dimension of a particle, particle shape is not taken into consideration, and especially for elongated and flat particles a sieve analysis will not yield a reliable measure. In this study the grain size distribution of sands has been determined following image-analysis techniques, using simple apparatus, non-professional cameras and open-code software. The sample is put on a transparent plate that is illuminated with a white backlight. The digital images were acquired with a CCD DSLR camera. The segmentation of the particles is achieved by image thresholding, binary coding and particle labeling. The geometrical measurements of each particle are obtained using an automated pixel-counting technique. Local contacts or limited overlaps were overcome using a watershed split. The same sample was tested by traditional sieve analysis. An image-analysis-based grain size distribution has been compared with a sieve-analysis distribution. The results show that the grain size distribution of the image-based analysis and the sieve analysis are in good agreement. |
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| Keywords: | image analysis, image processing, grain size, sand |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2017 |
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| Number of pages: | str. 29-37 |
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| Numbering: | Letn. 14, št. 1 |
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| PID: | 20.500.12556/DKUM-70883  |
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| ISSN: | 1854-0171 |
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| ISSN on article: | 1854-0171 |
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| COBISS.SI-ID: | 302975232 |
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| NUK URN: | URN:SI:UM:DK:FMVIM7NZ |
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| Copyright: | Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo Univerze v Mariboru |
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| Publication date in DKUM: | 18.06.2018 |
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| Views: | 1533 |
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| Downloads: | 160 |
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| Metadata: |    |
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| Categories: | Misc.
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