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A sieve analysis (or gradation test) is a practice or procedure used in geology, civil engineering, [1] and chemical engineering [2] to assess the particle size distribution (also called gradation) of a granular material by allowing the material to pass through a series of sieves of progressively smaller mesh size and weighing the amount of material that is stopped by each sieve as a fraction ...
Although such information contains long lists of sieve sizes, in practice sieves are normally used in series in which each member sieve is selected to pass particles approximately 1/ √ 2 smaller in diameter or 1/2 smaller in cross-sectional area than the previous sieve. For example the series 80mm, 63, 40, 31.5, 20, 16, 14, 10, 8, 6.3, 4, 2.8 ...
During the test, material from the test specimen that can pass through a No. 4 sieve is mixed with solutions of calcium chloride, formaldehyde and glycerin in a cylinder. The content is then left for sedimentation. After about 20 minutes, the level of clay suspension and the sand level is read on the cylinder scale.
Soil gradation is determined by analyzing the results of a sieve analysis or a hydrometer analysis. [4] [5] In a sieve analysis, a coarse-grained soil sample is shaken through a series of woven-wire square-mesh sieves. Each sieve has successively smaller openings so particles larger than the size of each sieve are retained on the sieve.
Wentworth grain size chart from United States Geological Survey Open-File Report 2006-1195: Note size typos; 33.1mm is 38.1 & .545mm is .594 Beach cobbles at Nash Point, South Wales Grain size (or particle size ) is the diameter of individual grains of sediment , or the lithified particles in clastic rocks .
The complete test requires 500 drum revolutions at a speed of 30-33 revolutions per minute. [6] Crushed sample is then separated from fine dust on a sieve, washed, dried and weighed. [6] The test reports loss of mass to abrasion and impact, expressed as a percentage of initial sample mass. [7]
Sieve estimators have been used extensively for estimating density functions in high-dimensional spaces such as in Positron emission tomography (PET). The first exploitation of Sieves in PET for solving the maximum-likelihood image reconstruction problem was by Donald Snyder and Michael Miller, [1] where they stabilized the time-of-flight PET problem originally solved by Shepp and Vardi. [2]
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