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SEMINAR ON
by
SUDARSHAN BAROLE
15CE64R07
DEPARTMENT OF CIVIL ENGINEERING
INDIAN INSTITUTE OF TECHNOLOGY
KHARAGPUR
SIZE DISTRIBUTION OF COARSE- GRAINED
SOIL BY SEDIMAGING
1. INTRODUCTION
1.1 PARTICLE SIZE DISTRIBUTION
Most fundamental property of coarse grained soil
Traditional method for particle size distribution is sieve
analysis.
Fig 1.1- Apparatus for Performing Sieve analysis test on soil.(a) Set of Sieve (b) mechanical sieve shaker
2.SEDIMAGING
A new method recently develop to determine the particle size distribution of coarse grain soil by using imaging technique.
practically implemented by Hyon-shok Ohm and Roman D. Hryciw
it gives the particle size distribution for particles between 0.075mm and 2.0 mm
This test also help to determine the percentage finer than .075mm
2.1 INTRODUCTION
In 2004 shin and Hryciw developed an image processing
technique based on wavelet transformation.
But due to absence of high resolution camera and laboratory
testing system at that time, the above concept was only a
theoretical concept
Ohm and hryciw then tracked the development of camera
technology until 20-40 megapixel camera became available in
early 2010.
2.2 EARLY ATTEMPS TOWARD SEDIMAGING
2.3 SEDIMAGING HARDWARE
Sedimentation column
Sediment accumulator
Pre segregationtube
Sedimaging Hardware
2.4 TEST PROCEDURE
Sedimentation with time
Pixel-
It is the smallest controllable element of a picture represented on the screen.
Pixels per diameter(PPD)-
It is the most important and useful in sedimaging. It is define as average number of pixel per particle diameter.
2.5 THEORETICAL CONCEPT
Grey scale -The intensity of a pixel is expressed within a given
range between a minimum and a maximum, inclusive. This
range is represented in an abstract way as a range from 0 (total
absence, black) and 1 (total presence, white), with any
fractional values in between.
WAVELET TRANSFORMATION
In sedimaging wavelet transformation is used for image processing and to determine it particle size distribution.
The sedimaging program performs all the wavelet operations in the background with no user input require.
Wavelet transformation decompose a image of 2ⁱ x 2ⁱ pixel size image into i decomposition level and calculate energy correspond to each level.
Energy is the measure of the magnitude of the difference between average gray scale of adjacent regions in a image.
Seven levels of downscaling beginning with a 128 x 128 image at PPD=13.2
As the concentration of
energy shifts to higher
energy level.
Wavelet index – The
centroid of area beneath a
normalize energy line with
respect to vertical axis
They gave the relation
between PPD and CA based
on experiments on pre-
seived soil
IMPORTANT POINT BASED ON ABOVE CONCEPTS
They produced the calibration curve as shown.
The relationship between CA and PPD is given as
Where A= 5.1 for saturated soil behind a glass window(A=5.9 for dry soil)
Once you determine CA for a portion, you can easily determine the PPD by using equation.
The actual grain size can be computed as
Where D- diameter of soil particle (mm) and M = camera magnification(pixels/mm)
Fig showing particle sorted by size
This image is analyzed incrementally with height to produce the particle size distribution
colorful squares represent area under analysis(128x128 pixels).
It gives about 5500 values of D from a 4520 x 1280 pixel image
Random values of D(only 20% of total value obtained
After sorting all the data points by particle size, final particle size distribution
Software for analyzing image was coded using MATLAB R2013a
comparison between sieve and sedimaging results of different soil
COMPARISON
Comparison of particle size distribution curve
5.ADVANTAGES OF SEDIMAGING
It is a quick test and takes 5-10 minutes.
Installation, operation and maintenance is easy.
It gives about 5000 points but in case of sieve analysis we get
only 10-11 data points for plotting the curve
In sedimaging energy consumption is low.
It help in improving work environment.
6.LIMITATIONSAs this test uses calibration curve which is developed using
sieving results and so this test just mimics sieving and yield comparable results.
It shows variation in results due to internal particle texture.
It does not redefine the particle.
7.CONCLUSION As we discussed earlier, sedimaging has many advantages over
sieving like reducing test time, low energy consumption, less
equipment maintenance and improvement in work
environment,
But before completely adopting this method, a little more
research on sedimaging to overcome the limitation is required.
Future research and advances in camera will make it possible
to develop sedimaging for fine soil like silt
Finally there is a need to put attention toward development of
sedimaging.
8. REFERENCES Ohm. H.S.(2013) “image based soil particle size and shape
characterization”
Shin, S., and Hryciw, R.D.(2004). “wavelet analysis of soil
mass images for particle size determination.” J. comput. Civ.
Eng. 10.1061/(ASCE) 0887-3801(2004)18:1(19),19-27
Ohm, H S., and Hryciw, R.D.(2013b). “the translucent segregation table test for sand and gravel particle size distribution.” J. ASTM Geotech Test.,36(4),592-605
THANK YOU