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2101-04

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Three-Point Imaging Test for AASHTO
Soil Classification
Roman D. Hryciw and Yongsub Jung
methods for the determination of grain size from images of contacting
particle assemblies (as opposed to individual noncontacting particles)
are necessary.
The present paper discusses a simple procedure that uses grayscale
images of contacting particles to rapidly obtain the volume fraction
of soils in the four grain size ranges needed to fully classify AASHTO
soils in the A-1 and A-3 groups—that is, soils with predominantly
coarse-grained materials. However, soil classification in all other
groups requires knowledge of the percentage passing the No. 200
sieve and separation of the minus No. 40 fraction for Atterberg limits testing. The proposed imaging method facilitates both tasks. The
key to the method’s success is rapid segregation of the soil specimen
by grain size before image collection.
A rapid, digital image–based test has been developed for the classification of highway subgrade materials according to the AASHTO system.
The test involves segregating particles in a sedimentation column, then
determining the elevations at which 2.0-, 0.425-, and 0.075-mm grains
(No. 10, No. 40, and No. 200 sieves, respectively) are found. If it is assumed
that porosity variations throughout the column are insignificant, as established in previous studies, then the distances between these elevations
translate to the percentages that pass through the various sieves. The
method uses a previously developed calibration between an image wavelet
decomposition index and the pixels per particle diameter. At a camera
magnification of 32.5 pixels/mm, the entire grain size range from 2.0 to
0.075 mm is resolved. Thus, only a single fixed magnification lens is
needed for the system. The accuracy of the new test is demonstrated by
comparing the digital image–based results and conventional sieving for
three soil specimens: well-graded sand, uniformly fine sand, and sandy
silt. The advantages of the three-point imaging test for AASHTO soil
classification over conventional sieving and hydrometer tests include
reduced testing time, low noise, possibly lower equipment costs, and the
elimination of airborne particles in the lab.
WAVELET DECOMPOSITION
Following Haralick et al. (4) and other early attempts (5–8) to use classic textural indices for soils, Shin and Hryciw (9) showed that the mathematical wavelet decomposition of grayscale images of relatively
uniform soils yields a more robust index of grain size. Details of the
wavelet decomposition are available elsewhere (e.g., 10, 11), as are the
procedures adapted for developing the soil grain size index (7, 9).
In short, the simple Harr wavelet transformation is performed on
a 2-in. × 2-in. pixel image, where n is an integer (12). The original
image is decomposed n times into n images of geometrically increasing pixel size; the number of pixels representing the image is therefore quartered with each decomposition. For each decomposition
level, an “energy” is then computed that reflects the magnitude of
the differences between average grayscale values of adjacent pixels
at that level (9). A pixelized region with few grayscale differences at
a particular decomposition level is considered to have a low energy.
The highest energy is associated with the decomposition level at which
the image pixel size approaches soil grain size. The energies computed at each decomposition level (eight levels for a 256- × 256-pixel
image) are normalized by the total energy of the original image, as
described by Shin and Hryciw (9) or Jung et al. (13).
Normalized energy distributions are charted versus decomposition
level in Figure 1 for various soil specimens whose uniform grain size
is defined by the number of pixels per particle diameter (PPD). The
PPD measure was first suggested by Ghalib et al. as a way to normalize image indices by the camera magnification (5). Figure 1 shows
that as PPD increases, the energy curves shift to higher decomposition
levels.
Shin and Hryciw sought a single-index parameter that would relate
a normalized energy curve to PPD (9). The first moment with respect
to the ordinate in Figure 1 was ideal. It also can be thought of as the
horizontal coordinate of the center of area (CA) under a normalized
energy plot. The CA–PPD correlation will be valuable to AASHTO
Both the AASHTO system for the classification of highway subgrade
materials (AASHTO M145; ASTM D3282) and the Unified Soil Classification System (USCS; ASTM D2487) require sieving to determine
the grain size of a coarse-grained soil fraction. Whereas the USCS
often requires developing a complete distribution curve of grain size
to determine the coefficients of uniformity (D60/D10) and gradation
(D302/D60D10), the AASHTO system requires only knowledge of the
percentages passing the No. 10, No. 40, and No. 200 sieves.
Soil sieving is a noisy, dusty, and energy-intensive task; the equipment is expensive, and parts often need to be replaced—particularly
the finer-meshed sieves. Image-based methods for determining grain
size have been proposed. Hryciw et al. summarize the development
of image-processing techniques used for this purpose (1). If the soil
particles can be separated (noncontacting)—say, on a glass back-lit
plate—then the grain size distribution can be assessed deterministically from images. For soils with a wide range of grain sizes, this
task requires capturing images at several magnifications with statistical data reduction (2) or by mosaic imaging (3). However, no simple method for particle separation has been developed, and doing so
manually—grain by grain—is an unappealing task. For this reason,
Department of Civil and Environmental Engineering, University of Michigan, 2340
GG Brown, 2355 Hayward Street, Ann Arbor, MI 48109-2125. Corresponding
author: R. D. Hryciw, romanh@umich.edu.
Transportation Research Record: Journal of the Transportation Research Board,
No. 2101, Transportation Research Board of the National Academies, Washington,
D.C., 2009, pp. 27–33.
DOI: 10.3141/2101-04
27
28
Transportation Research Record 2101
Normalized Energy (%)
40
PPD
0.9
1.8
3.1
4.4
6.4
12.0
20.9
32.3
35
30
25
20
15
Support tower
Soil release chute
Sedimentation
column
10
Camera tower
5
0
0
1
2
3
4
5
Decomposition Level
6
7
8
Camera
FIGURE 1 Normalized energy versus decomposition level for
images of various PPD (1).
Monitor
soil classification because it spans almost three orders of magnitude
of PPD (Figure 2). The PPD values used to develop Figure 2 were
based on soil grain sizes obtained by conventional sieve analysis.
PARTICLE SEGREGATION
Unfortunately, the CA–PPD concept and calibration shown in Figure 2 has no application to soils with mixed grain sizes. The authors’
attempts to extend the CA parameter to mixed soils have been
unsuccessful to date, and future successes are uncertain. Given that
most coarse-grained soil specimens obtained by traditional sampling
techniques contain a range of grain sizes, the particles would have
to be segregated by size to use Figure 2. Sieving would achieve segregation but also would make image-processing techniques (and this
paper) pointless. Therefore, a sedimentation column 5 cm × 5 cm ×
122 cm was constructed to rapidly separate the grains by size (Figure 3). The column was extended to 244 cm to improve particle segregation. After sedimentation, which takes only a few minutes for soils
without clay-sized particles, continuous images are collected along
the length of the column. Because of the segregation, each image
contains only soil grains of a relatively uniform size. Note that images
are not collected during sedimentation.
6.0
No. 16-20 (1.19-0.85 mm)
No. 20-40 (0.85-0.425 mm)
No. 170-200 (0.088-0.075 mm)
No. 200-270 (0.075-0.053 mm)
No. 270-400 (0.053-0.038 mm)
No. 40-50 (0.36 mm): multi-color
No. 40-60 (0.33 mm): uniform color
5.5
Wavelet Index, CA
5.0
4.5
3.0
2.5
2.0
10
Pixels per Diameter, PPD
FIGURE 2
Initially, it was feared that, because of self-weight consolidation
and variable settlement velocities in accordance with Stokes’ law,
the porosity would decrease with depth in the sedimented column.
This situation would have necessitated various height-measurement
corrections to produce a valid grain size distribution. Because porosity variations in the sedimented soil column fortunately were found
to be insignificant in other experiments, each height increment in
the soil column corresponds to a proportional percentage of soil
solids (14).
Percentages for AASHTO Soil Classification
3.5
1
Sedimentation column and camera system (14).
SOIL CLASSIFICATION
4.0
0.1
FIGURE 3
Wavelet index (CA) versus PPD (1).
100
Image processing by wavelet decomposition lends itself particularly
well to the AASHTO soil classification system because all of the
images can be taken at a single fixed magnification of 32.5 pixels
per image millimeter. At this resolution, the No. 10 sieve opening
(2.00 mm) corresponds to PPD10 = 65.0, the No. 40 sieve opening
(0.425 mm) corresponds to PPD40 = 13.8, and the No. 200 sieve
opening (0.075 mm) corresponds to PPD200 = 2.4. From Figure 2,
the corresponding CA values for PPD10, PPD40, and PPD200 are
CA10 = 5.1, CA40 = 3.9, and CA200 = 2.9, respectively. These values
are summarized in Table 1 for quick reference.
Hryciw and Jung
29
the four soil sizes are easily computed. The results are in good agreement with those from sieving (Figure 5a). Under the AASHTO system, the soil would be classified as A-1-b; ASTM D3282 would
describe it as “material consisting predominantly of coarse sand, either
with or without a well-graded binder.”
A second test was performed on a poorly graded sand (Figure 6).
Again, the sieve- and image-based results show good agreement; the
observed percentages of the various soil sizes are within approximately
2% of each other. Under the AASHTO system, the soil is classified as
A-3. It typically is a fine beach sand or a fine desert-blown sand either
without silty or clay fines or with a small amount of nonplastic silt.
The soils used in Examples 1 and 2 were not used to develop the
CA–PPD calibration in Figure 2. As such, the good agreements
between sieve- and image-based grain size distributions reaffirm the
CA–PPD calibration curve.
TABLE 1 Pixels per Particle Diameter and Center of Area
for Critical Sieve Openings
Sieve #
10
40
200
Sieve Opening
(mm)
Pixels per
Diameter (PPD)
Wavelet
Index (CA)
2.000
0.425
0.075
65.0
13.8
2.4
5.1
3.9
2.9
NOTE: Image resolution = 32.5 pixel/mm.
Three-Point Imaging Test for AASHTO
Soil Classification
Unlike the USCS, which may require extending a grain size distribution curve above the No. 10 sieve and below the No. 200 sieve
to compute the coefficients of uniformity and gradation, the
AASHTO system requires only the percentages of soil in the four
zones shown in Figure 4. Therefore, the only task for image processing is to locate the height in the sedimented soil column at
which CA10 (5.1), CA40 (3.9), and CA200 (2.9) are found. The procedure can appropriately be called the three-point imaging (TPI)
test for AASHTO soil classification.
Examples of Soil Classification
A well-graded sand with approximately 16% fines and 18% coarse
sand and gravel was used to demonstrate the TPI test procedure. The
sieve-based grain size distribution is shown in Figure 5a, and the
distribution from the sedimented soil column is shown in Figure 5b.
The soil column height can be expressed in image pixels or actual
distance. Although the authors use image pixels as the unit of measure, units are easily converted to millimeters by dividing the pixels
by the fixed image magnification (32.5 pixels/mm). CA10 was found
at a height of 810 pixels (24.9 mm), CA40 at 2,080 pixels (64.0 mm),
and CA200 at 3,490 pixels (107.4 mm) (Figure 5b). With an overall
sedimented soil column height of 4,096 pixels, the percentages of
Sedimented
Soil Column
CA200
One-Point Imaging Test for Fine-Grained Soils
The key value for the classification of soils in AASHTO Groups A-2,
A-4, A-5, A-6, and A-7 is the percentage passing the No. 200 sieve.
If 35% or less of the material passes the No. 200 sieve, then the soil
is in the broad A-2 grouping, which can be further refined into
subgroups A-2-4, A-2-5, A-2-6, or A-2-7, depending on Atterberg
limits. If 36% or more of the material passes the No. 200 sieve, then
the soil is A-4, A-5, A-6, or A-7, depending on the Atterberg limits.
As such, to make the “35% determination” by imaging, only the
depth in the sedimentation column at which CA = 2.9 must be determined. If this depth is more than 35% of the distance from the surface, then the soil is a silt or clay. Depending on the Atterberg limits,
the soil would be A-4, A-5, A-6, or A-7.
If more than 10% of a soil passes the No. 200 sieve, then
AASHTO soil classification calls for Atterberg limits tests to be performed on the soil fraction passing the No. 40 sieve. It requires splitting the original soil sample to perform both sedimentation imaging
and Atterberg tests. Alternatively, soil may be recovered from the
sedimented column to the depth of CA40 (3.9). As a practical matter, the authors recommend removing a bit more soil and washing it
over a No. 40 sieve to collect soil for the Atterberg tests.
AASHTO Soil
Classification
% Fines
(silt and clay)
CA
Coarse Sand
and Gravel
CA10
% Fine Sand
Medium Sand
CA40
CA40
Fine Sand
% Medium Sand
CA200
Fines
CA10
% Coarse Sand
and Gravel
PPD200
FIGURE 4
Soil regions by type, PPD, and CA for fixed magnification.
PPD40
PPD10
PPD
30
Transportation Research Record 2101
Sieve # 10
100
# 40
# 200
18.1%
Coarse Sand
& Gravel
90
% Finer by Weight
80
81.9%
70
31.9%
Medium Sand
60
50
50.0%
40
34.2%
Fine Sand
30
20
10
15.8%
Fines (Silt & Clay)
15.8%
0
10
1
0.1
0.01
Grain Size (mm)
(a)
Sieve # 200 # 40
# 10
4000
14.8%
Fines
(Silt & Clay)
3500
(3490)
34.4%
Fine Sand
Soil Column Height (pixel)
3000
2500
2000
(2080)
31.0%
Medium Sand
(810)
19.8%
Coarse Sand
& Gravel
1500
1000
500
0
2
3
4
CA
5
6
(b)
FIGURE 5 Well-graded sand: (a) sieve-based grain size distribution and (b) CA versus
soil column height.
Test results on a sandy silt are shown in Figure 7. The height corresponding to CA200 (2.9) is 1,408 pixels. With a measured total
specimen height of 4,040 pixels, the fines content is computed as
65.1%, in good agreement with the 63.6% observed by sieving. The
soil is A-4, A-5, A-6, or A-7 depending on the Atterberg limits.
Finally, CA at the highest elevation (finest grain size) is uncharacteristically high in Figures 5b and 7b. This apparent departure
from the expected trend is caused by laboratory fluorescent light
illumination from above and will be fixed by using a more uniform
light source.
DISCUSSION OF RESULTS
The following discussion addresses two topics that explain and justify the use of a single camera magnification and a recommended
PPD range of 2.4 to 65 for use in the TPI test.
Hryciw and Jung
31
Sieve # 10
100
# 40
# 200
2.1%
Coarse Sand &
Gravel
97.9%
90
% Finer by Weight
80
39.6%
Medium Sand
70
60
58.3%
50
40
56.2%
Fine Sand
30
20
10
2.1%
Fines (Silt & Clay)
2.1%
0
10
1
0.1
0.01
Grain Size (mm)
(a)
Sieve # 200
4000
# 10
# 40
(4066)
3500
54.5%
Fine Sand
Soil Column Height (pixel)
3000
2500
2000
(1850)
1500
42.3%
Medium Sand
1000
3.2%
Coarse Sand
& Gravel
500
(130)
0
2
3
4
CA
5
6
(b)
FIGURE 6 Poorly graded sand: (a) sieve-based grain size distribution and (b) CA
versus soil column height.
Single Versus Multiple Magnification
TPI is ideal for classifying soil according to the AASHTO system
(Figure 5b). The soil specimen used in the example was “engineered” to have a log-linear distribution of grain sizes by weight
(Figure 5a); Figure 5b shows this log-linear distribution between the
No. 10 and No. 200 sieves. Above the No. 10 and below the No. 200
sieve openings, the results depart somewhat from linearity. This
result is to be expected because at low PPD values (less than about
1.0) and at high PPD values (greater than about 70), the data used to
develop the calibration curve in Figure 2 show greater scatter. The
problem arises at low PPD values because individual pixels contain
information about more than one particle; thus, the grayscale information blurs. Meanwhile, at high PPD values, the wavelet indices
may be more affected by the internal textures of particles than by distinctions in grayscales between particles. Also at high PPD values,
32
Transportation Research Record 2101
Sieve # 10
100
# 40
# 200
100%
90
36.4%
Fine Sand
% Finer by Weight
80
70
60
63.6%
50
63.6%
Fines (Silt)
40
30
20
10
0
10
1
0.1
0.01
Grain Size (mm)
(a)
# 40
Sieve # 200
4000
3500
Soil Column Height (pixel)
3000
65.1%
Fines (Silt)
2500
2000
1500
(1408)
1000
34.9%
Fine Sand
500
(0)
0
2.5
3.0
3.5
4.0
CA
(b)
FIGURE 7 Sandy silt: (a) sieve-based grain size distribution and (b) CA versus soil
column height.
the number of particles per image becomes too small to be statistically accurate. The latter phenomenon is the cause of the premature
peak in the CA plot at a height of about 650 pixels (Figure 5b); the
two large particles responsible for this erratic high point are evident
in the image.
Both problems cited above can be overcome by using variable
magnification so that all of the PPD values fall into the “sweet
range” of about 2 to 60 on the calibration curve. While doable,
this method introduces unnecessary complexity to the hardware
requirements, data acquisition, and data reduction: a zooming
lens would be needed; focal length and focusing would have to be
adjusted during data acquisition; and in data reduction, Table 1
would require new values for each magnification. Fortuitously,
none of these modifications are necessary because the actual size
Hryciw and Jung
distributions of grains larger than the No. 10 sieve opening and
smaller than the No. 200 sieve opening are not needed for AASHTO
classification.
33
ACKNOWLEDGMENT
Partial funding for this study was provided by a grant from the National
Science Foundation Geomechanics and Geotechnical Systems
Program.
Optimal PPD Range
Close inspection of Figure 2 reveals that the absolute best CA–PPD
correlations are in the PPD range of about 0.8 to about 30. This raises
the question of why 2.4 ≤ PPD ≤ 65 is recommended.
To achieve a PPD of 30 for a grain size of 2.0 mm (No. 10 sieve)
and a corresponding PPD of 1.1 for a grain size of 0.075 (No. 200
sieve), a fixed image magnification of 15 pixels/mm would be
required. In turn, a 17- × 17-mm field of view is required to procure
the requisite 256- × 256-pixel image for eight-level wavelet decomposition. (In contrast, the recommended image magnification of
32.5 pixels/mm requires a field of view of only 8 mm × 8 mm.) A PPD
range of 1.1 to 30 would necessitate more than double the dimensions
of the field of view (more than quadruple the area) and would reduce
the data points shown in Figures 5b, 6b, and 7b by more than 50%,
resulting in an unacceptable loss in grain size resolution. Moreover,
each individual image would contain a large grain size distribution.
The upshot would be less-precise CA value locations corresponding
to the No. 10, No. 40, and No. 200 sieve openings and correspondingly
poorer soil classification.
To rectify this situation, modifications to the test procedure would
have to be considered, including the use of larger soil specimens or
a smaller column cross-section. Unfortunately, such modifications
create other problems, including poorer particle segregation during
sedimentation. Fortunately, the excellent results and agreement
shown between the TPI test using 2.4 ≤ PPD ≤ 65 and sieve analysis
suggest that such test modifications are unnecessary.
CONCLUSIONS
A TPI test was proposed to rapidly classify soils according to the
AASHTO system. A soil specimen is sedimented through a long vertical column to segregate grains by size. Continuous grayscale images
of the sedimented soil are taken at a fixed resolution of 32.5 pixels/mm.
At this resolution, the three critical grain sizes that separate coarse
sands and gravels, medium sands, and fine sands (i.e., 2.00, 0.425, and
0.075 mm, respectively) correspond to PPD values of 65.0, 13.8, and
2.4, respectively. Previous studies developed a calibration between
PPD and CA. The three critical CA values corresponding to the three
sieves of interest are 5.1, 3.9, and 2.9, respectively. AASHTO soil classification requires experimental determination of the heights in the sedimented soil column that correspond to these three CA values. Results
of tests on a well-graded sand with some silt and gravel, a poorly
graded medium to fine sand, and a sandy silt demonstrated good agreement between the newly proposed TPI test and conventional sieve
analysis.
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The Exploration and Classification of Earth Materials Committee sponsored
publication of this paper.
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