effect of particle size and shape on the grain-size ... · in sieve analysis, the particle size is...

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING

Volume 1, No 4, 2011

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4399

968

Effect of particle size and shape on the grain-size distribution using

Image analysis Seracettin Arasan

1, Suat Akbulut

1, A.Samet Hasiloglu

2

1 – Department of Civil Engineering, Engineering Faculty,

Ataturk University, Erzurum, Turkey

2 – Department of Computer Engineering, Engineering Faculty,

Ataturk University, Erzurum, Turkey

[email protected]

doi:10.6088/ijcser.00202010083

ABSTRACT

Grain-size distribution that is one of the base properties of soils and soil classification

systems gives idea about engineering properties of soils. Determination of grain-size

distribution derived from mechanical method (sieving) is time consuming and difficult.

Hence, the image analysis methods for determination of grain-size distribution have been

also investigated by several researchers. In that research, the grain-size distributions for

ten soil samples with different sizes and shapes are determined with image analysis

methods. The effects of particle shape (i.e., elongated, flat, spherical) and volume

computation techniques (cube, cylinder, ellipsoid, and modified ellipsoid) on grain-size

distribution were also researched. The results of the research indicated that the shape of

particles significantly affected grain-size distribution. Additionally, modified ellipsoid

method is determined as the best volume computation method.

Keywords: Grain-size distribution, image analysis, soil, particle shape

1. Introduction

The grain-size distributions (GSD) are one of the basic and most important properties of

soil. It is primarily used for soil classification and provided a first-order estimate of other

soil engineering properties such as permeability, shear strength, and compressibility. In

practice, the GSD of the full size spectrum of soil grains is determined by integrating data

obtained from two inherently dissimilar tests, mechanical sieving for the coarse grained

soil fraction and hydrometer tests for the fine fraction. In sieve analysis, the particle size

is characterized by a single linear dimension representing the minimum square sieve

aperture that which the particle just passed through (Ghalib and Hryciw, 1999). The

results of sieving are dependent upon the shape of the particles (Mora et al., 1998; Kwan

et al., 1999; Mora and Kwan, 2000). Particles passing through a sieve can actually have

one dimension that is larger than the size of the sieve apertures. From Fig. 1(a), it can be

seen that an elongated particle that has its length greater than the aperture size can pass

through the sieve without any difficulties. Therefore, the sieve aperture size is a measure

of the lateral dimensions of the particles only. A relatively flat particle can pass through

the sieve aperture, which is square in shape, diagonally as shown in Fig. 1(b). As a result,

the breadth of a particle passing through a sieve can also be greater than the sieve size,





969

although it has to be smaller than the diagonal length of the sieve aperture (Mora et al.,

1998).

(a) (b)

Figure 1: (a) An elongated particle passing through a sieve aperture. (b) Plan view of a

flat particle passing through a sieve aperture. (Kwan et al., 1999)

The determination of grain-size distribution with mechanical method (sieving) is difficult

and it takes long time. Hence, image analysis has been used by several researchers to

determine the size distribution, particle shape and surface texture of aggregates, both in

two and three dimensions (Mora et al., 1998; Kwan et al., 1999; Mora and Kwan, 2000;

Maerz and Lusher, 2001; Rao et al., 2001; Taylor, 2002; Maerz, 2004; Fernlund,

2005a,b,c; Tutumluer et al., 2005; Lira and Pina, 2007). Although soil particles are 3-D in

nature, most of the image analysis techniques treat particles as two-dimensional objects

because only the two-dimensional projection of the particles is captured and measured.

However, for obtaining 3D images, many researchers used laser scanners (Lanaro and

Tolppanen, 2002; Tolppanen et al., 2002; Lee et al., 2007) and flatbed color scanner (Lira

and Pina, 2007), some are also used autofocus microscope (Chandan et al., 2004; Masad

et al., 2005) and optical interferometer (Alshibli and Alsaleh, 2004), and also the others

used two different perspective images (Kuo et al., 1996; Rao and Tutumluer, 2000;

Maerz, 2004; Fernlund, 2005a). These methods analyzed only one particle at a time,

however, and significantly increased the complexity compared to two-dimensional

analysis.

When plotting grain-size curves by sieve analysis, the particle size is presented with

respect to the percent cumulative mass of particles. However, image analysis could not

measure particle mass so results could be only presented in percent of particles (Fernlund,

1998; Andriani and Walsh, 2002; Fernlund, 2005a, b, c) or percent of area (Mora et al.,

1998; Mora and Kwan, 2000; Lira and Pina, 2007). Several researchers have also claimed

that more accurate volume and mass determinations are necessary for accurate plotting of

grain-size curves (Taylor, 2002; Maerz, 2004; Tutumluer et al., 2005; Fernlund et al.,





970

2007). A great number of studies dealing with volume computations of particles are

based on stereology, to convert data from areas (Barbery, 1991; King, 1984; Lin et al.,

1987; Vallebuona et al., 2003). These methods are applied for obtaining parameter

estimates (mean, variance and skewness) in three dimensions from two-dimensional data.

Bozdag and Karpuz (1997) also estimated the 3D size distribution of soil particles from

2D measures such as length and area by using the method proposed by King (1984) and

the moment method. The other studies are focused on geometrical computations (Mertens

and Elsen, 2006; Lira and Pina, 2007; Fernlund et al., 2007).

The aims of this paper are to investigate the effect of particle size and shape (i.e.,

elongated, flat, spherical) on the GSD, to compare the image analysis methods (top view,

front view, and volume) and to determine the more accurate method for volume

computations. For this purpose, ten soil samples with different shapes and sizes are used.

The results from the research were compared with those of the mechanical analysis.

2. Materials and Methods

2.1. Soil Samples

In this research, four different size fractions (i.e., 2mm-4,75mm, 4,75mm-8mm, 2mm-

8mm, and 8mm-16 mm) of a river soil were used to determine grain-size distribution.

Additionally, 4,75mm-8mm and 8mm-16mm size fractions were used for investigation of

particle shapes effect on GSD. For this purpose, particles were selected by hand as flat,

elongated, spherical, and mixed as mentioned below section. The classification of soils is

also mentioned in Table 1 according to the Unified Soil Classification System-USCS.

Table 1: The size fractions and classes of the soils used in this study

Size

Fraction

Soil

Classification

Particle

Shape

S1 2 mm-4,75 mm SP Mixed

S2 2 mm-8 mm SP Mixed

S3 4,75 mm-8 mm GP Mixed

S4 8 mm-16 mm GP Mixed

S5 4,75 mm-8 mm GP Elongated

S6 4,75 mm-8 mm GP Flat

S7 4,75 mm-8 mm GP Spherical

S8 8 mm-16 mm GP Elongated

S9 8 mm-16 mm GP Flat

S10 8 mm-16 mm GP Spherical

2.2. Particle Shapes





971

Quantification of form (particle shape) requires the measurement of the length (L),

breadth (I) and thickness (S) of a particle. A number of different identifications have been

used historically. Most previous authors have firstly identified the dimension L, defined

as the maximum caliper dimension, and have then measured I and S dimensions

orthogonal to L (Blott and Pye, 2008). Zingg (1935) indicated that the particles classified

four base classes as mentioned in Figure 2(a) with using above measurement system.

Also, Sneed and Folk (1958) considered Zingg’s diagram to be inadequate in that it

contained only four form classes which divided the field of form variation very unequally.

They suggested that only three end-members limit the system of dimensional variation: a

prolate spheroid with one long axis and two short ones (L > I = S), an oblate spheroid

with two long axes and one short one (L = I > S), and a sphere with all axes equal (L = I

= S). A number of different notations have been used historically to refer to these three

classes, including, rod, disk and cube (Krumbein, 1941), columnar, flat and spherical

(Blott and Pye, 2008). Sneed and Folk (1958)’s three classes are also used in this study

with different notation as elongated, flat, and spherical, respectively (Figure 2b).

(a) (b)

Figure 2: (a)Four different particle shapes (Zingg, 1935) (b) Three different particle

shapes (Sneed and Folk, 1958)

2.3. Imaging System

A high quality image of the particles is needed before any image processing can be

performed. In this study, the equipment for taking pictures of particles is set up by

mounting a camera on a photographic stand as shown in Figure 3(a), adjusting the height

of the camera to obtain a sufficiently large measurement area on the sample tray, and

adjusting the light sources so that there is no shading of any object placed on the sample

tray. A white cotton cloth is laid on the sample tray before the particles are put in for

obtaining better contrast between the particle and the background pixels. A Nikon D80

Camera and Micro 60 mm objective manufactured by Nikon were used for taking photos.





972

The tests were performed in a dark room. Four fluorescent light sources were positioned

on the base plane to make the borders of the particles more visible for digital

measurements. The flashlight of the camera was not also utilized during image

acquisition. After the initial projected image (top view image) of the particles is captured

and measured, the photographic stand with camera is rotated 90 degrees so that the

particles are now perpendicular to their original orientation and another projection of the

particles (front view image) is captured and measured (Figure 3b).

(a) (b)

Figure 3: Schematic of the image analysis systems (a) for top view (b) for front view.

Figure 4 shows 3-D view of a regular-shaped solid, a rectangular box. Clearly, both the

top and front views of the solid are identical rectangles. Using a 2-D image analysis setup

consisting of only top camera and capturing an image of each solid would not be

effective in distinguishing the 3-D shapes of the solids (Rao and Tutumluer, 2000).

Figure 4: Three-dimensional views of two regular-shaped solid: rectangular box.

Top View

Front View

S

L

I





973

In this research, the particles were placed to the sample tray in their stable position and

two images from top view (longest portion of particle) and front view (thickest portion of

particle) were obtained by imaging system for capturing the each length of particle (i.e.,

small, intermediate, and long) as showed in Figure 5. Images of the top (Figure 5a) and

front views (Figure 5b) were used for the maximum and minimum projected areas of the

particles, respectively. The position of particles was not changed when the two images

were captured.

(a) (b)

Figure 5: (a)Top views of particles, (b) Front views of particles

2.4. Image Processing

Digital-image processing consists of converting camera pictures into digital form and

applying various mathematical procedures to extract relevant information from the

picture. In other words, digital image processing and analysis is concerned with the

transformation and analysis of picture by a computer. The process starts with the capture

of a digital image (usually referred to as imaging) followed by its storage, transmission,

processing, and analysis of the image by computer to extract information or features of

interest (Masad and Sivakumar, 2004).

This section describes the image analysis method used in this study. The output of

camera was a 3872-2592 pixel, 32-bit digital image of RGB color. The particles had to be

identified prior to analysis. ImageJ was used as the image analysis program. Threshold

gray intensity therefore had to be chosen. Thresholding determines the outline of the

aggregate particle in a captured image. Clearly, the particle should have a sharp contrast

against the background to accurately delineate the actual boundaries. A threshold value of

pixel gray level is typically specified, and the actual image is converted to a binary image.

This binary image has only black or white (gray level 0 or 255) pixels to clearly identify

the particle against its background. The gray intensity measured on a given point was

compared to the threshold value. Then, the initial gray image was converted into a binary

image in which the aggregate particles that have lower gray intensity than the threshold

value were set to black while the background was set to white. Applying a global

threshold value for all the image worked well only if the objects of interest (aggregate

particles) had uniform interior gray level and rested upon a background of different, but

uniform, gray level. This was made possible in this study by placing particles on white

background. The original image (32-bit digital image of RGB) (a), 8-bit 256 gray scale





974

image (b), 1-bit binary image (c) and output of ImageJ image analysis program (d) are

shown in Figure 6.

Figure 6: Image processing steps

2.5. Grain-Size Distribution with Mechanical and Image Analysis

Mechanical (sieve) analysis depends on measuring mass of particles; whereas image

analysis depends on measuring the number of particles or a single morphological

parameter (i.e., area, length, breadth, equivalent area diameter (dA), mean Feret's

diameter (dF)) of particles. Hence, it is not obvious that the results of these two methods

are comparable (Fernlund et al., 2007). Because image analysis produces area gradation

and uses a particle size definition different from the square sieve size used in mechanical

sieving, direct comparison is not possible, unless the gradation basis and particle size

definitions of the two methods are aligned. For this purpose, a simple method of

converting the area gradation to mass gradation was proposed in literature (Mora et al.,

1998). Moreover, a size correction factor is used to convert the particle sizes measured by

image analysis to equivalent square sieve sizes so that comparison between the image and

mechanical analysis results can be made.

In this research, grain-size distribution of soil samples determined by both mechanical

(sieve) and image analysis methods. In order for the sample to be representative,

approximately 1 kg of each soil sample was taken for mechanical analysis. For image

analysis, the number of particles that can be placed within the measurement area without





975

touching or overlapping each other ranges from 15 to about 100, depending on the size

and shape of the particles as shown in Figure 6. Because the soil samples contain more

than 500 particles, all of the particles could not be placed onto the sample tray at same

time. Therefore, the soil samples have to be divided into several sub-samples whose

images are taken successively so that the image of all particles can be captured.

The image processing steps explained above section were applied to all captured images

and the output of image processing program (ImageJ) was obtained. The output of the

ImageJ is area, perimeter, long dimension (length or Feret’s diameter) values in unit of

millimeter, and quantity of particles. These data are transferred to Excel, Microsoft

software. Similar to Lira and Pina (2007), to construct grain-size distribution curves of

the particles, the area gradation was used as described at equation (1). To make

comparison of mechanical and image analysis results, the area gradation points were

transformed to sieve size by extracting the square root of area. Due to the square sieve

openings, this simple method was used. After then, the results are sorted independently

and cumulative curves of the axial relationships plotted. In order to compare results of

top and front views, the images of both views were separately analyzed and plotted.

100*

...100sin 321

AreaTotal

AreaAreaAreaAreaXAreagPasPercent X (1)

2.6. Volume Computation of Particles

Image analysis does not measure mass of particles. However, several researchers have

claimed that more accurate volume and mass determinations are necessary for accurate

construction of grain-size curves (Taylor, 2002; Maerz, 2004; Tutumluer et al., 2005).

Three-dimensional representation of particles is considered very important to be able to

accurately determine the volume (Fernlund et al., 2007). In this sense, a study was

undertaken on gravel soils (S8, S9, and S10). Elongated (S8), flat (S9), and spherical

(S10) particles in gravel soil (8 mm-16 mm) were selected by hand and used for

determination of their volumes. 200 particles were selected for each particle shape. The

particles were also placed three by three within the sample tray for capturing the top and

front images as seen in Figure 5. These images were processed in ImageJ then output data

of ImageJ was transferred to Excel. The output data also contain length (L), breadth (I),

thickness (S), top area and front area of each particle.

Five different methods for calculating volume and hence mass have been tested: Volume

1 is the product of the axial dimensions (cube: V=L*S*I); Volume 2 is the product of the

area of top view and the mean thickness (cylinder1: V= Atop*S); Volume 3 is the product

of the front view area and the longest dimension (cylinder2: V= Afront*L); Volume 4 is

the product of the ellipsoid (V=4/3*Π*((L/2)*(I/2)*(S/2)); Volume 5 is the product of the

modified ellipsoid (V=4/3* Atop*(S/2)). The mass of analyzed particles was determined

with specific gravity. The mass was also calculated by multiplying the specific gravity of





976

soil (Gs=2,58) and volume. The grain-size distribution curve was also plotted and

compared with the other results.

3. Results and Discussion

In the following sections, the results of image analysis and mechanical analysis of

particles are presented. The findings of the experimental tests are compared with those of

other studies in the literature and discussed in detail.

3.1. Comparison of mechanical (MA) and image analysis (IA) on GSD

Grain-size distribution of S1 (2mm-4,45mm), S2 (2mm-8mm), S3 (4,75mm-8mm), and

S4 (8mm-16mm) soil samples were determined by using mechanical and image analysis.

The GSD curves are given in Figure 7. It is seen that the GSD curves obtained by IA and

MA do not quite agree with each other, with the curve obtained by MA being consistently

lower than that by IA. The difference between the results of IA and the results of MA

could be attributed that the particle size definitions used in the two analyses are different.

0

20

40

60

80

100

1 10 100Particle Size (mm)

Perc

en

t P

assin

g (

%)

S1-MAS1-IAS2-MAS2-IAS3-MAS3-IAS4-MAS4-IA

Figure 7: GSD curves of four soil samples with different sizes obtained by mechanical

and image analysis (The results of S1, S2, and S3 are also mentioned in Arasan and

Akbulut (2008))

In this study, grain-size distributions (GDS) of soils were estimated by the area

distribution of particles method used by King (1984) and Vallebuona et al. (2003) and

mechanical analysis is also performed by using the mass of particles. Hence, the

difference is obtained from analysis as indicated by Mora et al (1998) and Kwan et al.

(1999). Mora et al. (1998) presented a conversion factor, between 0.81 and 0.89 times the





977

length of the intermediated axis and obtained exactly same curves from IA and MA by

using these conversion factors.

On the other hand, some particle properties of soil samples were obtained from the

curves. Table 2 shows these results such as: D10, D30, D50, D60, Cu, Cc, and soil

classification according to the Unified Soil Classification System-USCS. It can be also

seen from Table 2 that D10, D30, D50, and D60 values of IA greater than that MA.

However, the classifications do not change. The soil classification is more important than

GSD curve in soil mechanics. Thus, it could be said that the difference between two

analyses results are insignificant and the size of particles do not significantly affect the

GSD.

Table 2: Some particle properties of soils used in mechanical and image analysis (The

results of S1, S2, and S3 are also mentioned in Arasan and Akbulut (2008))

S1 S2 S3 S4

MA IA MA IA MA IA MA IA

D10 2,3 2,8 2,5 2,9 5,3 6,3 8,8 11,5

D30 2,8 3,3 3,25 3,7 5,9 7,5 9,8 12,9

D50 3,4 4 3,8 4,5 6,3 8,2 11,5 14,1

D60 3,6 4,7 4 5,5 6,6 8,7 12,0 14,5

Cu 1,57 1,68 1,6 1,89 1,25 1,38 1,36 1,26

Cc 0,95 0,83 1,06 0,86 1,00 1,03 0,91 1,00

Soil Classification SP SP SP SP GP GP GP GP

3.2. Influence of volume calculations on GSD

Flat, elongated, and spherical particles in gravel soil (8 mm-16 mm) were selected by

hand as mentioned above and analyzed for determination of their volumes. Five different

methods for calculating volume and hence mass have been tested. The comparison of real

total weight with image analysis results is given in Figure 8. Particle shape did not

significantly affect the weight calculation. However, there are great differences between

the volume calculation methods. It is clearly seen that all of the estimated weights by

volume calculation are higher than real weight and also the modified ellipsoid method

give the best results. The total weights of the S4 (mixed), S8 (elongated), S9 (flat), and

S10 (spherical) were measured manually to be 176gr, 205gr, 126gr, and 143gr,

respectively, while the calculated value by the image analysis approach using a Gs of 2,58

gr/cm3 were 177gr, 212gr, 132gr, and 144gr, respectively for modified ellipsoid method.

These differences in total weights of about 1%, 3%, 5%, and 1% are insignificant.





978

0

100

200

300

400

500

Flat Elongated Spherical Mixed

Particle Shape

To

tal

Weig

ht

(gr)

Real WeightModif ied EllipsoidEllipsoidCylinder 1Cylinder 2Cube

Figure 8: The comparison of volume computation type on the total weight of particles

Similarly, Rao et al. (2001) and Tutumluer et al. (2005) proposed a method based on

assuming that the particle is an ellipsoid of revolution for volume calculation. Fernlund et

al. (2007) used a method based on assuming that the particles are ellipsoidal, then the

volume is two-thirds the product of the largest projected area times the thickness.

Furthermore, Rao and Tutumluer (2000) showed that the weights of the aggregates

estimated from the image analysis system agree quite well with the physically measured

values and the errors are clearly biased towards the positive side. They also advocated

that this is expected; as the volumes are slightly overestimated because the cameras

cannot capture information about hollow portions cannot seem by the cameras. The

results of the study described herein demonstrate that the proposed image analysis

method is a promising method for 3D analysis of soil particles.

3.3. Comparison of area (from top and front views) and volume methods

To study the affect of camera positions on the estimated distributions, the results of the

profile area approach applied to two types of analysis (top and front view) were

compared and the results were shown in Figure 9. By comparing the estimated size

distribution when the camera was positioned in the two different positions, it was found

that the top view analysis had the worst results (Figure 9). The reason may due to

direction of particle passing which had the most influence in the image analysis as

indicated previous researchers (Mora et al., 1998; Fernlund, 1998; Kwan et al., 1999).

Fernlund (1998) reported that the least cross-sectional area is most important and usually

the longest dimension of a particle has little effect on sieve results. In this study, the

maximal and least areas of a particle are obtained from top view and front view, as

illustrated in Figure 4 and 5, respectively. In this sense, it could be said that front views





979

give more accurate results than top views in image analysis used for grain-size

distribution.

On the other hand, it is clearly seen from Figure 9 difference between the GSD curves of

all methods is insignificant when soil classification results put in consideration. The GSD

curve of soils obtained from volume calculation is similar to the GSD curves of soils

obtained from 2D views (i.e., top and front view) and mechanical analysis. Similarly, Al-

Thyabat et al. (2007) investigated the size distribution of particles moving on a conveyer

belt. They also indicated that profile views give worst results but top and free falling

views are closely to the mechanical views. Consequently, it could be said that using

particle area (2D views) is adequate for obtaining GSD, when contrast using particle

volume/mass (3D view).

0

20

40

60

80

100

1 10 100

Particle Size (mm)

Perc

en

t P

assin

g (

%)

S4-MA

S4-IA(Area-Front View)

S4-IA(Area-Top View)

S4-IA(Volume/Mass-M.Ellipsoid)

Figure 9: The comparison of different views’ image analysis results on GSD curves

3.4. Influence of particle shape on GSD

Particle shape (elongated, flat, and spherical) is an important issue on soil mechanics. For

this reason, effect of particle shape on the GSD was investigated. Size fractions of 4.75

mm-8 mm (S5, S6, S7) and 8 mm-16mm (S8, S9, S10) in soil were selected by hand as

flat, elongated, and spherical and performed the image analysis for grain-size distribution.

Figure 10 shows the grain-size distributions of the samples have 4.75mm-8mm size

fraction. Figure 11 and 12 also show the GSD of samples have 8mm-16mm size fraction

from top and front views, respectively. It is clearly seen that the particle sizes do not

affect the results. The curves from IA in spherical and elongated particles are the nearest

and the farthest to the curve of the mixed particles from MA, respectively (Figure 10, 11).





980

Similar to Figure 7, the GSD curves obtained from IA and MA are not compatible with

each others.

0

20

40

60

80

100

1 10 100

Particle Size (mm)

Perc

en

t P

assin

g (

%)

v

MA-Mixed ParticlesIA-Mixed ParticlesIA-Elongated ParticlesIA-Flat ParticlesIA-Spherical Particles

Figure 10: GSD curves of gravel soil (4.75mm-8mm) with different shapes (Arasan and

Akbulut, 2008)

0

20

40

60

80

100

1.0 10.0 100.0

Particle Size (mm)

Perc

en

t P

assin

g (

%)

IA-Flat Particles

IA-Elongated Particles

IA-Spherical Particles

IA-Mixed Particles

MA-Mixed Particles

Figure 11: GSD curves of gravel soil (8 mm-16mm) with different shapes by using top

views of particles





981

0

20

40

60

80

100

1.0 10.0 100.0

Particle Size (mm)

Perc

en

t P

assin

g (

%)

IA- Flat Particles

IA-Elongated Particles

IA-Spherical Particles

IA-Mixed Particles

MA-Mixed Particles

Figure 12: GSD curves of gravel soil (8 mm-16mm) with different shapes by using front

views of particles

On the other hand, the curve obtained by MA consistently gave the higher particle size

than that of IA for front views. The GSD curve of spherical and mixed particles obtained

by IA are the nearest curve from the curve of mixed particles obtained by MA. Also, the

GSD curve of flat particles obtained by IA is the farthest curve from the curve of mixed

particles obtained by MA. The difference between the results of top and front views

analysis is explained by the passing mechanism of particles through sieve aperture. As

indicated previously, a particle passes through a sieve by longest dimension. In this sense,

it could be said that the particle form is more effects than particle size to the sieve results.

Similarly, Fernlund (1998) reported that the particle form influences the sieve results.

Taylor (2002) also points out that sieving does not separate particles according to

volume. There is a variation in the volume of particles retained on a sieve due to the

varying shapes of the particles.

It is previously mentioned that the results of sieving are dependent upon the shape of the

particles (Mora et al., 1998; Kwan et al., 1999; Mora and Kwan, 2000; Arasan and

Akbulut, 2008). Similarly, the results of this study showed that the shape of the particles

affected the GSD obtained from image analysis. In this sense, it could be said that the

particle shape is the most important factor on the GSD of soils.

4. Conclusions

The following results are concluded based on the results and on the discussion presented

in this research:





982

1. The classification of soils according to the USCS obtained from image and

mechanical (sieve) analysis do not change and the difference between results of

two analyses is insignificant

2. The size of particles does not significantly affect the grain-size distribution.

3. Particle shape does not significantly affect the weight calculation. However, there

are great differences between the volume calculation methods. All of the

estimated weights by volume calculation are higher than real weight and also the

modified ellipsoid method gives the best results.

4. The front views give more accurate results than top views in image analysis used

for grain-size distribution. The difference between the results of top and front

views analysis is explained by the passing mechanism of particles through sieve

aperture.

5. For determination of grain-size distribution, 2D view image analysis is adequate

rather than using volume/mass calculation.

6. The shape of particle significantly affects the grain-size distribution.

In this sense, the image analysis is considered to be a significant technological

advancement towards grain-size distribution of soils with images obtained from digital

cameras from taken photos. It should also be pointed out that further studies on the

determination of index properties of soils by using image analysis are needed to make

more reasonable judgments for utilization of image analysis in geotechnical engineering.

References

1. Alshibli, K.A., Alsaleh, M.I., 2004. Characterizing surface roughness and shape

of sands using digital microscopy. Journal of Computing in Civil Engineering 18

(1), pp 36-45.

2. Al-Thyabat, S., Miles, N.J., Koh, T.S., 2007. Estimation of the size distribution of

particles moving on a conveyor belt. Minerals Engineering 20, pp 72-83.

3. Andriani, G.F., Walsh, N., 2002. Physical properties and textural parameters of

calcarenitic rocks: qualitative and quantitative evaluations. Engineering Geology,

67, pp 5–15.

4. Arasan, S., Akbulut, S., 2008. Determination of grain-size distribution of soils

with image analysis. 12. National Soil Mechanic and Foundation Engineering

Congress, Konya, Turkey. pp. 323-332 (in Turkish with an English summary).

5. Barbery, G., 1991. Mineral Liberation. GB, Canada.

6. Blott, S.J., Pye, K., 2008. Particle shape: a review and new methods of

characterization and classification. Sedimentology 55, pp 31-63.





983

7. Bozdag, T., Karpuz, C., 1997. Development of a particle size distribution analysis

system by digital image processing. International Journal of Mining, Reclamation

and Environment 11, pp 59-67.

8. Chandan, C., Sivakumar, K., Masad, E., Fletcher, T., 2004. Application of image

techniques to geometry analysis of aggregate particles. Journal of Computing in

Civil engineering ASCE 18 (1), pp 75-82.

9. Fernlund, J.M.R., 1998. The effect of particle form on sieve analysis: a test by

image analysis. Engineering Geology 50, pp 111–124.

10. Fernlund, J.M.R., 2005a. Image analysis method for determining 3-D shape of

coarse aggregate. Cement and Concrete Research 35, pp 1629-1637.

11. Fernlund, J.M.R., 2005b. 3-D image analysis size and shape method applied to the

evaluation of the Los Angles test. Engineering Geology 77, pp 57-67.

12. Fernlund, J.M.R., 2005c. Image-analysis method for determining 3-D size-

distribution of coarse aggregates. Bulletin of Engineering Geology and the

Environment 64(2), pp 159-166.

13. Fernlund, J.M.R., Zimmerman, R.W., Kragic, D., 2007. Influence of volume/mass

on grain-size curves and conversion of image-analysis size to sieve size.

Engineering Geology 90, pp 124–137.

14. Ghalib, A.M. ve Hryciw, R.D., 1999. Soil particle size distribution by mosaic

imaging and watershed analysis. Journal of Computing in Civil Engineering 13(2),

pp 80-87.

15. King, R.P., 1984. Measurement of particle size distribution by image analyzer.

Powder Technology 39 (2), pp 279–289.

16. Krumbein, W.C.,1941. Measurement and geological significance of shape and

roundness of sedimentary particles. Journal of Sedimentary Petrology 11, pp 64–

72.

17. Kuo, C.Y., Frost, J.D., Lai, J.S., Wang, L.B., 1996. Three-Dimensional Image

Analysis of Aggregate Particles from Orthogonal Projections. Transportation

Research Record 1526, National Research Council, Washington D.C. pp. pp 98-

103.

18. Kwan, A.K.H., Mora, C.F., Chan, H.C., 1999. Particle shape analysis of coarse

aggregate using digital image processing. Cement and Concrete Research 29, pp

1403–1410.





984

19. Lanaro, F., Tolppanen, P., 2002. 3D characterization of coarse aggregates.

Engineering Geology 65, pp 17-30.

20. Lee, J.R.J., Smith, M.L., Smith, L.N., 2007. A new approach to the three-

dimensional quantification of angularity using image analysis of the size and form

of coarse aggregates. Engineering Geology 91, pp 254:264.

21. Lin, C.L., Miller, J.D., Herbst, J.A., 1987. Solutions to the transformation

equations for volumetric grade distribution from linear and/or areal grade

distributions. Powder Technology 50, pp 55–63.

22. Lira, C., Pina, P., 2007. Sedimentological analysis of sands. IbPRIA 2007. Part II.

LNCS 4478, pp. 388-395.

23. Maerz, N.H., 2004. Technical and Computational aspects of the measurement of

aggregate shape by digital image analysis. Journal of Computing in Civil

engineering ASCE 18 (1), pp 10-18.

24. Maerz, N.H., Lusher, M., 2001. Measurement of flat and elongation of coarse

aggregate using digital image processing. Paper No. 01- 0177, Transportation

Research Board 80th Annual Meeting, 14 pp.

25. Masad, E., Saadeh, S., Rousan, T.A., Garboczi, E., Little, D., 2005. Computations

of particle surface characteristics using optical and X-ray CT images.

Computational Materials Science 34, pp 406-424.

26. Masad, E., Sivakumar, K., 2004. Advanced in the characterization and modeling

of civil engineering materials using imaging techniques. Journal of Computing in

Civil Engineering 18(1), pp 1-1.

27. Mertens, G., Elsen, J., 2006. Use of computer assisted image analysis for the

determination of the grain-size distribution of sands used in mortars. Cement and

Concrete Research 36, pp 1453-1459.

28. Mora C.F., Kwan A.K.H., 2000. Sphericity, Shape Factor, and Convexity

Measurement of Coarse Aggregate for Concrete Using Digital Image Processing.

Cement and Concrete Research 30 (3), pp 351-358.

29. Mora, C.F., Kwan, A.K.H., Chan, H.C., 1998. Particle Size Distribution Analysis

Of Coarse Aggregate Using Digital Image Processing. Cement and Concrete

Research. 28(6), pp 921–932.

30. Rao, C., Tutumluer, E., 2000. Determination of volume of aggregates: New

image-analysis approach. Transportation Research Record 1721, Transportation

Research Board, National Research Council, Washington D.C., pp 73–80.





985

31. Rao, C., Tutumluer, E., Stefanski, J.A., 2001. Coarse aggregate shape and size

properties using a new image analyzer. ASTM Journal of Testing and Evaluation

29, pp 79–89.

32. Sneed, E.D. and Folk, R.L., 1958. Pebbles in the Lower Colorado River, Texas: a

study in particle morphogenesis. Journal of Geology 66, pp 114–150.

33. Taylor, M.A., 2002. Quantitative measures for shape and size of particles. Powder

Technology 124, pp 94–100.

34. Tolppanen, P., Stephansson, O., Stenlid, L., 2002. 3-D degradation analysis of

railroad ballast. Bulletin of Engineering Geology and the Environment 61, pp 35-

42.

35. Tutumluer, E., Pan, T., Carpenter, S.H., 2005. Investigation of aggregate shape

effects on hot mix performance using an image analysis approach. Civil

Engineering Studies. Transportation Engineering Series 137 University Illinois,

Urbana-Champaign, 122 pp.

36. Vallebuona, G., Arbro, K., Casali, A., 2003. A procedure to estimate particle

distributions from area measurements. Minerals Engineering 16, pp 323–329.

37. Zingg, T., 1935. Bietrahe zur Schotteranalyse. Schweiz Mineral Petrography 15,

pp 139-140.

effect of particle size and shape on the grain-size ... · in sieve analysis, the particle size is...

Documents