Design of Computer Program for Soil Classification

CHAPTER ONE INTRODUCTION

I

n the field of civil engineering, nearly all projects are built on to, or

into, the ground. Whether the project is a structure, a roadway, a tunnel, or a bridge, the nature of the soil at that location is of great importance to the civil engineer. Geotechnical engineers are not the only professionals interested in the ground: soil physicists, agricultural engineers, fanners and gardeners all take an interest in the types of soil with which they are working. These workers, however, concern themselves mostly with the organic top soils found at the soil surface. In contrast, the geotechnical engineer is mainly interested in the engineering soils found beneath the topsoil. It is the engineering properties and behavior of these soils which are their concern. Different soils with similar properties may be classified into groups and sub-groups according to their engineering behavior. Classification systems provide a common language to concisely express the general characteristics of soils, which are infinitely varied, without detailed descriptions. Most of the soil classification systems that have been developed for engineering purposes are based on simple index properties such as particlesize distribution and plasticity. Although several classification systems are now in use, none is totally definitive of any soil for all possible applications because of the wide diversity of soil properties.

Soils in nature rarely exist separately as gravel, sand, silt, clay or organic matter, but are usually found as mixtures with varying proportions of these components. Grouping of soils on the basis of certain definite principles would help the engineer to rate the performance of a given soil either as a sub-base material for roads and airfield pavements, foundations of  structures, etc. Following some types of different soils in nature shown in figures 1-1 to 1-?

Figure 1-1: associated surface environment and profile of a brunisol soil.

Figure 1-2: associated surface environment and profile of a chernozem soil.

Soils in nature rarely exist separately as gravel, sand, silt, clay or organic matter, but are usually found as mixtures with varying proportions of these components. Grouping of soils on the basis of certain definite principles would help the engineer to rate the performance of a given soil either as a sub-base material for roads and airfield pavements, foundations of  structures, etc. Following some types of different soils in nature shown in figures 1-1 to 1-?

Figure 1-1: associated surface environment and profile of a brunisol soil.

Figure 1-2: associated surface environment and profile of a chernozem soil.

Figure 1-3: associated surface environment and profile of a Cryosol soil.

Figure 1-4: associated surface environment and profile of a gleysol soil.

Figure 1-5: associated surface environment and profile of a luvisol soil.

Figure 1-6: associated surface environment and profile of an organic soil.

Figure 1-7: associated surface environment and profile of a podzol soil.

Figure 1-8: associated surface environment and profile of a regosol soil.

Figure 1-9: associated surface environment and profile of a solonetzic soil.

1.1

Project Objectives

The objective of this project is to design a computer program to classify and maintain a database for Iraqi soils.

CHAPTER TWO LITERATURE REVIEW AND THEORY

S

oil can be described as gravel, sand, silt and clay according to grain

size. Most of the natural soils consist of a mixture of organic material in the partly or fully decomposed state. The proportions of the constituents in a mixture vary considerably and there is no generally recognized definition concerning the percentage of, for instance, clay particles that a soil must have to be classified as clay, etc. When a soil consists of the various constituents in different proportions, the mixture is then given the name of the constituents that appear to have significant influence on its behavior, and then other constituents are indicated by adjectives. Thus a sandy clay has most of the properties of a clay but contains a significant amount of sand. The individual constituents of a soil mixture can be separated and identified as gravel, sand, silt and clay on the basis of mechanical analysis. The clay mineral that is present in a clay soil is sometimes a matter of engineering importance. According to the mineral present, the clay soil can be classified as kaolinite, montmorillonite or illite. The minerals present in a clay can be identified by either X-ray diffraction or differential thermal analysis. Buildings, bridges, dams etc. are built on natural soils (undisturbed soils), whereas earthen dams for reservoirs, embankments for roads and railway lines, foundation bases for pavements of roads and airports are made out of  remolded soils. Sites for structures on natural soils for embankments, etc, will have to be chosen first on the basis of preliminary examinations of the

soil that can be carried out in the field. An engineer should therefore be conversant with the field tests that would identify the various constituents of  a soil mixture. The behavior of a soil mass under load depends upon many factors such as the properties of the various constituents present in the mass, the density, the degree of saturation, the environmental conditions etc. If soils are grouped on the basis of certain definite principles and rated according to their performance, the properties of a given soil can be understood to a certain extent, on the basis of some simple tests. The objectives of the following sections of this chapter are to discuss the following: 1. Field identification of soils. 2. Classification of soils. 2.1

FIELD IDENTIFICATION OF SOILS

The methods of field identification of soils can conveniently be discussed under the headings of coarse-grained and fine-grained soil materials.

2.1.1 Coarse-Grained Soil Materials

The coarse-grained soil materials are mineral fragments that may be identified primarily on the basis of grain size. The different constituents of  coarse-grained materials are sand and gravel. The size of sand varies from 0.075 mm to 4.75 mm and that of gravel from 4.75 mm to 80 mm. Sand can further be classified as coarse, medium and fine. The engineer should have an idea of the relative sizes of the grains in order to identify the various fractions. The description of sand and gravel should include an estimate of  the quantity of material in the different size ranges as well as a statement of 

the shape and mineralogical composition of the grains. The mineral grains can be rounded, subrounded, angular or subangular. The presence of mica or a weak material such as shale affects the durability or compressibility of the deposit. A small magnifying glass can be used to identify the small fragments of shale or mica. The properties of a coarse grained material mass depend also on the uniformity of the sizes of the grains. A well-graded sand is more stable for a foundation base as compared to a uniform or poorly graded material.

2.1.2 Fine-Grained Soil Materials Inorganic Soils: The constituent parts of fine-grained materials are the silt

and clay fractions. Since both these materials are microscopic in size, physical properties other than grain size must be used as criteria for field identification. The classification tests used in the field for preliminary identification are: 1. Dry strength test. 2. Shaking test. 3. Plasticity test. 4. Dispersion test. Dry strength: The strength of a soil in a dry state is an indication of its

cohesion and hence of its nature. It can be estimated by crushing a 3 mm size dried fragment between thumb and forefinger. A clay fragment can be broken only with great effort, whereas a silt fragment crushes easily. Shaking test: The shaking test is also called as dilatancy test. It helps to

distinguish silt from clay since silt is more permeable than clay. In this test a part of soil mixed with water to a very soft consistency is placed in the palm

of the hand. The surface of the soil is smoothed out with a knife and the soil pat is shaken by tapping the back of the hand. If the soil is silt, water will rise quickly to the surface and give it a shiny glistening appearance. If the pat is deformed either by squeezing or by stretching, the water will flow back into the soil and leave the surface with a dull appearance. Since clay soils contain much smaller voids than silts and are much less permeable, the appearance of the surface of the pat does not change during the shaking test. An estimate of the relative proportions of silt and clay in an unknown soil mixture can be made by noting whether the reaction is rapid, slow or nonexistent. Plasticity test: If a sample of moist soil can be manipulated between the

palms of the hands and fingers and rolled into a long thread of about 3 mm diameter, the soil then contains a significant amount of clay. Silt cannot be rolled into a thread of 3 mm diameter without severe cracking. Dispersion test: This test is useful for making a rough estimate of sand, silt

and clay present in a material. The procedure consists in dispersing a small quantity of the soil in water taken in a glass cylinder and allowing the particles to settle. The coarser particles settle first followed by finer ones. Ordinarily sand particles settle within 30 seconds if the depth of water is about 10 cm. Silt particles settle in about 1/2 to 240 minutes, whereas particles of clay size remain in suspension for at least several hours and sometimes several days.

Organic soils

Surface soils and many underlying formations may contain significant amounts of solid matter derived from organisms. While shell fragments and similar solid matter are found at some locations, organic material in soil is

usually derived from plant or root growth and consists of almost completely disintegrated matter, such as muck or more fibrous material, such as peat. The soils with organic matter are weaker and more compressible than soils having the same mineral composition but lacking in organic matter. The presence of an appreciable quantity of organic material can usually be recognized by the dark-grey to black color and the odor of decaying vegetation which it lends to the soil. Organic silt: It is a fine grained more or less plastic soil containing mineral

particles of silt size and finely divided particles of organic matter. Shells and visible fragments of partly decayed vegetative matter may also be present. Organic clay: It is a clay soil which owes some of its significant physical

properties to the presence of finely divided organic matter. Highly organic soil deposits such as peat or muck may be distinguished by a dark-brown to black color, and by the presence of fibrous particles of vegetable matter in varying states of decay. The organic odor is a distinguishing characteristic of  the soil. The organic odor can sometimes be distinguished by a slight amount of heat.

2.2

Textural Classification

In a general sense, texture of soil refers to its surface appearance. Soil texture is influenced by the size of the individual particles present in it. Table 2.1 divided soils into gravel, sand, silt, and clay categories on the basis of particle size. In most cases, natural soils are mixtures of particles from several size groups. In the textural classification system, the soils are named after their principal components, such as sandy clay, silty clay, and so forth.

Table 2.1 Soil Fractions as per U.S. Department of Agriculture

A number of textural classification systems were developed in the past by different organizations to serve their needs, and several of those are in use today. Figure 2.1 shows the textural classification systems developed by the U.S. Department of Agriculture (USDA). This classification method is based on the particle-size limits as described under the USDA system in Table 2.1; that is: • Sand size: 2.0 to 0.05 mm in diameter • Silt size: 0.05 to 0.002 mm in diameter • Clay size: smaller than 0.002 mm in diameter The use of this chart can best be demonstrated by an example. If the particlesize distribution of soil  A shows 30% sand, 40% silt, and 30% clay-size particles, its textural classification can be determined by proceeding in the manner indicated by the arrows in Figure 2.1. This soil falls into the zone of  clay loam. Note that this chart is based on only the fraction of soil that passes through the No. 10 sieve. Hence, if the particle-size distribution of a soil is such that a certain percentage of the soil particles is larger than 2 mm in diameter, a correction will be necessary. For example, if soil  B has a particle size distribution of 20% gravel, 10% sand, 30% silt, and 40% clay, the modified textural compositions are:

Figure 2.1 U.S. Department of Agriculture textural classification

On the basis of the preceding modified percentages, the USDA textural classification is clay. However, because of the large percentage of gravel, it may be called gravelly clay. Several other textural classification systems are also used, but they are no longer useful for civil engineering purposes.

2.3

Classification by Engineering Behavior Although the textural classification of soil is relatively simple, it is

based entirely on the particle-size distribution. The amount and type of clay minerals present in fine-grained soils dictate to a great extent their physical properties. Hence, the soils engineer must consider  plasticity, which results from the presence of clay minerals, to interpret soil characteristics properly. Because textural classification systems do not take plasticity into account and are not totally indicative of many important soil properties, they are inadequate for most engineering purposes. Currently, two more elaborate classification systems are commonly used by soils engineers. Both systems take into consideration the particle-size distribution and Atterberg limits. They are the American Association of State Highway and Transportation Officials

(AASHTO)

classification

system

and

the

Unified

Soil

Classification System. The AASHTO classification system is used mostly by state and county highway departments. Geotechnical engineers generally prefer the Unified system.

2.3.1 AASHTO Classification System The AASHTO system of soil classification was developed in 1929 as the Public Road Administration classification system. It has undergone several revisions, with the present version proposed by the Committee on Classification of Materials for Subgrades and Granular Type Roads of the Highway Research Board in 1945 (ASTM designation D-3282; AASHTO method M145).

The AASHTO classification in present use is given in Table 2.2. According to this system, soil is classified into seven major groups: A-1 through A-7. Soils classified under groups A-1, A-2, and A-3 are granular materials of  which 35% or less of the particles pass through the No. 200 sieve. Soils of  which more than 35% pass through the No. 200 sieve are classified under groups A-4, A-5, A-6, and A-7. These soils are mostly silt and clay-type materials. This classification system is based on the following criteria: 1. Grain size a. Gravel: fraction passing the 75-mm (3-in.) sieve and retained on the No. 10 (2-mm) U.S. sieve. b. Sand: fraction passing the No. 10 (2-mm) U.S. sieve and retained on the No. 200 (0.075-mm) U.S. sieve. c. Silt and clay: fraction passing the No. 200 U.S. sieve. 2. Plasticity: The term silty is applied when the fine fractions of the soil have a plasticity index of 10 or less. The term clayey is applied when the fine fractions have a plasticity index of 11 or more. 3. If cobbles and boulders (size larger than 75 mm) are encountered, they are excluded from the portion of the soil sample from which classification is made. However, the percentage of such material is recorded. To classify a soil according to Table 2.2, one must apply the test data from left to right. By process of elimination, the first group from the left into which the test data fit is the correct classification. Figure 2.2 shows a plot of the range of  the liquid limit and the plasticity index for soils that fall into groups A-2, A4, A-5, A-6, and A-7.

To evaluate the quality of a soil as a highway subgrade material, one must also incorporate a number called the group index (GI ) with the groups and subgroups of the soil. This index is written in parentheses after the group or subgroup designation. The group index is given by the equation

Table 5.1 Classification of Highway Subgrade Materials

Figure 2.2 Range of liquid limit and plasticity index for soils in groups A-2,

A-4, A-5, A-6, and A-7

2.3.2 Unified soil classification system

The original form of this system was proposed by Casagrande in 1942 for use in the airfield construction works undertaken by the Army Corps of  Engineers during World War II. In cooperation with the U.S. Bureau of  Reclamation, this system was revised in 1952. At present, it is used widely by engineers (ASTM Test Designation D-2487). The Unified classification system is presented in Table 2.. This system classifies soils into two broad categories: 1. Coarse-grained soils that are gravelly and sandy in nature with less than 50% passing through the No. 200 sieve. The group symbols start with a prefix of G or S. G stands for gravel or gravelly soil, and S for sand or sandy soil. 2. Fine-grained soils are with 50% or more passing through the No. 200 sieve. The group symbols start with prefixes of M, which stands for inorganic silt, C for inorganic clay, or O for organic silts and clays. The symbol Pt is used for peat, muck, and other highly organic soils.

Other symbols used for the classification are: • W—well graded • P—poorly graded

• L—low plasticity (liquid limit less than 50) • H—high plasticity (liquid limit more than 50)

Figure2-3 Plasticity Chart

For proper classification according to this system, some or all of the following information must be known: 1. Percent of gravel—that is, the fraction passing the 76.2-mm sieve and retained on the No. 4 sieve (4.75-mm opening) 2. Percent of sand—that is, the fraction passing the No. 4 sieve (4.75-mm opening) and retained on the No. 200 sieve (0.075-mm opening) 3. Percent of silt and clay—that is, the fraction finer than the No. 200 sieve (0.075-mm opening)

Figure 2-4

4. Uniformity coefficient ( Cu) and the coefficient of gradation ( Cc) 5. Liquid limit and plasticity index of the portion of soil passing the No. 40 sieve. The group symbols for coarse-grained gravelly soils are GW, GP, GM, GC, GC-GM, GW-GM, GW-GC, GP-GM, and GP-GC. Similarly, the group symbols for fine-grained soils are CL, ML, OL, CH, MH, OH, CL-ML, and Pt. More recently, ASTM designation D-2487 created an elaborate system to assign group names to soils. These names are summarized in Figures 5.4, 5.5, and 5.6. In using these figures, one needs to remember that, in a given soil, • Fine fraction _ percent passing No. 200 sieve • Coarse fraction _ percent retained on No. 200 sieve • Gravel fraction _ percent retained on No. 4 sieve • Sand fraction _ (percent retained on No. 200 sieve) _ (percent retained on No. 4 sieve)

Figure 2-5

Figure 2-6

Figure 2-7

Figure 2-8

CHAPTER THREE CASE STUDY

T

hree different soil samples were taken for analysis and classification

using the unified soil classification system. Soil A, B, and C where taken as samples of different types. Experiments were conducted on these soils to determine their class. Soil A has a silty nature, soil B has a clayey nature, and soil C has sandy nature. Many tests were conducted in addition to the required tests to determine the proper classification of these soils, these tests were: 1. Specific gravity. 2. Water content. 3. Sieve analysis. 4. Liquid limit. 5. Plastic limit. The results are shown below.

3.1 Specific Gravity

Weight of every soil sample = 5 gm. Soil A U

Wt. sample (water + soil) = 96.97 gm

Wt. sample (water) = 93.82 gm

Gs =

5

(

5 − 93.82 − 96.97

)

= 2.703

2.7 ≤ G s ≤ 2.703 R

R

silty soil

Soil B U

Wt. sample (water + soil) = 88.71 gm Wt. sample (water) = 85.55 gm Gs

5 =

(

)

5 − 85.55 − 88.71

=

2.72

2.7 ≤ G s ≤ 2.74 R

R

Soil C U

Wt. sample (water + soil) = 93.96 gm Wt. sample (water) = 90.83 gm Gs =

5

(

5 − 90.83 − 93.96

2.65 ≤ G s ≤ 2.69 R

R

Sandy soil

)

= 2.67

3.2

Water content %

Soil A U

W wet =61 gm R

R

W dry =49 gm R

R

W% = (61– 49)/49 *100% = 24.49% Soil B U

w wet =36 R

R

w dry=27 R

R

w% =(36–27)/27 *100% = 33.33%

Soil C U

Dry 3.3

Liquid limit tests

Soil A:No. of blows

Wt. wet

Wt. dry

W%

24

16.85

13.01

29.52

16

18.85

14.24

32.37

27

16.25

12.73

27.65

L.L =48

Soil B:No. of blows

Wt. wet 16.6

11.06

50.09

29

10.55

7.23

45.92

16

11.28

7.34

53.68

Soil C No liquid limit

U

W%

21

L.L =28.5

3.4

Wt. dry

Plastic Limit tests

Soil A

Wt. container= 10.72 gm Wt. (cont.+water+soil)= 17.41 gm Wt. (cont.+dry soil)= 16.15 gm Wt. (wet soil)= 17.41-10.72= 6.69 gm Wt. dry soil= 16.15-10.72= 5.43 gm W%=[(6.69-5.43)/5.43]*100%

PL=23.2%

Soil B U

Wt. container=11.52 gm Wt. (cont.+soil+water)=20.71 gm Wt. (cont.+dry soil)=18.76 gm Wt. wet soil=20.71-11.52=9.19 gm Wt. dry soil=18.76-11.52=7.24 gm W%=[(9.91-7.24)/7.24]*100% PL=26.43%

Soil C U

Non Plastic

3.5

Plasticity index

Soil A U

PI = LL – PL = 48.0 – 23.2 = 24.8 Soil B U

PI = LL – PL

= 28.5 – 26.43 = 2.07

Soil C U

Non Plastic

3.6

Sieve analysis tests

Soil A U

Wt. of the total sample = 200 gm Sieve no.

Opening dia.

mm

% wt.

%

retained

retained

cumulative

% passing

4

4.75

0

0

0

100

8

2.38

0

0

0

100

25

0.71

0

0

0

100

50

0.3

0

0

0

100

100

0.15

17.5

8.61

8.61

91.39

200

0.075

4.5

2.25

10.86

89.14

178

89.13

100

0

Pan

∑ of retained soil= 200.0 gm

Soil B U

Wt.

Wt. of the total sample = 200 gm All passed sieve 200

Soil C U

Wt. of the total sample = 200 gm Sieve no.

Opening

Wt.

%

dia.

retained

retained

cumulative

4

4.75

16

7.9

7.9

92.1

8

2.38

25

12.3

20.2

79.8

25

0.71

70

35.16

55.36

44.64

50

0.3

26

13.05

68.41

31.59

100

0.15

56

28.03

96.44

3.56

200

0.075

5

2.51

98.95

1.05

2

1.005

99.955

0.045

Pan

wt. %

% passing

∑ retained soil= 200.0 gm 100

80

60

40

20

0 10

1

0 1

0 01

Form graph D 10 = 0.178 R

R

D 30 = 0.34 R

R

D 60 = 1.2 R

R

C u =

 D60  D10

=

1.2 0.178

2

C c =

 D30  D60 ×  D10

3.7

=

= 6.74

0.34

2

1.2 × 0.178

Soil classification according to USCS

Soil A U

F200 = 89.14 > 50 It's Silty clay L.L = 48 < 50 It's ML or CL or CL-ML PI = 24.8 It's CL

Soil B U

= 0.541

F200 = 100.00 > 50 L.L = 28 < 50 PI = 2.07 ML

Soil C U

F200 = 1.05 < 50 It's Gravel or Sand  R4  R200

=

100 − 92.1 100 − 1.05

It's sandy soil F200 = 1.05 < 5 C u = 6.74 ≥ 6 R

R

C c = 0.541 ≤ 1 R

R

It's SP

= 0.08 ≤ 0.5

CHAPTER FOUR COMPUTER PROGRAM

A

computer program using MATLAB 2009 program was designed

especially for soil classification using the unified soil classification system. The computer program has the following user interface as shown in fig. 4.1.

Figure 4-1 user interface

Through the user interface, the necessary data are obtained and the sieve data are loaded from an excel sheet file containing sieve number, opening diameter, and weight of retained soil. A sample of an empty excel sheet is shown in table 4-1.

Table 4-1 Excel sheet content Sieve no.

Dia. mm

Wt. retained gm

4

4.75

5

4

6

3.35

7

2.8

8

2.36

10

2

12

1.7

14

1.4

16

1.18

18

1

20

0.85

25

0.71

30

0.6

35

0.5

40

0.425

50

0.355

60

0.25

70

0.212

80

0.18

100

0.15

120

0.125

140

0.106

170

0.09

200

0.075

270

0.053

The results data obtained from a sieve analysis test are entered to an empty excel sheet, the unwanted empty rows are omitted and the file is saved with a unique name. The weight of the soil retained on the pan is entered in the textbox in the user interface in addition to the liquid limit (L.L.), plasticity index (P.I.).

The (Get Data) button then is clicked and the required excel sheet file is selected. The program uses these data to draw the original particle size distribution, and also find the best fit curve to obtain D 60 , D 30 , and D 10 from the best fit R

R

R

R

R

R

curve. The computer program uses two best fit functions: 1. Exponential function  y

=

 BX 

 A × e

+

 Dx

Ce

 A +  Bx 2. Power function

1 − Cx

+

Dx

2

Many functions were tested to see the most appropriate function to give the best fit curve using trial and error method of sample sieve test results. These two functions were found to give the best results for fitting the particle distribution curves for the sieve analysis. The computer program chooses the best fit function according the minimum residual between the two functions using Minimum Square of residuals regression. Then the computer program draws the two best fit curves in addition to the original curve. The usual calculations are made to classify the soil as mentioned in chapter two. D 60 , D30 , and D 10 are obtained and accordingly C u , C c are calculated and R

R

R

R

R

R

R

R

R

R

showed in the user interface. A table in the user interface also shows the results of the calculations needed to obtain the percentage passing each sieve number. There is a filed in the user interface also shows the residual of the two functions and the best one to be used for this particular problem.

The last two fields in the user interface shows the classification of the soil and the description of the soil. The computer program was tested in solving an example of classification problem as shown below. Classification problem after (Das. 2006)

Sieve analysis problem is shown after (Das, 2006) was also analyzed using the new program, the results of the computer program is show in figure 4.2.

Figure 4-2

Figure 4-3 user interface showing the results of classification problem after (Das, 2006)

This is a good verification for the computer program that shows the way it works. The computer program now is used to classify the three soil samples mentioned in chapter three.

1.

Soil sample A U

The sieve analysis results (as shown in chapter three) are entered in an excel sheet as shown in table 4-2

Table 4-2 sieve analysis results for soil A Sieve no.

Dia. mm

Wt. retained gm

4

4.75

0

8

2.36

0.00

25

0.71

0.00

50

0.355

0.00

100

0.15

17.50

200

0.075

4.50

Pan

-

178

Figure 4-4 user interface results for soil A classification

Since the soil is fine, no particle size distribution curve is drawn. Other calculations are conducted and the soil was found to be CL with the soil description as (The Soil is Fine, Clayey Soil, Low Plasticity ) is shown in the user interface, as shown in figure 4-4.

2.

Soil sample B U

Soil B has a particle size all passing no. 200 sieve. Which means that it is all clay with L.L. = 28.5, and P.I. = 2.07.

Figure 4-5 user interface results for soil B classification

The soil classification is found by the computer program to be ML as classified earlier in chapter three. The results are shown in figure 4-5 with soil description as (The Soil is Fine, Silty Soil, Low Plasticity ).

3.

Soil sample C  U

Soil C sieve analysis results are entered in an excel sheet with data as shown in table 4-3 below. Table 4-2 sieve analysis results for soil C Sieve no.

Dia. mm

Wt. retained gm

4

4.75

16

8

2.36

25.00

25

0.71

70.00

50

0.355

26.00

100

0.15

56.00

200

0.075

5

Soil C is non plastic which means that liquid limit and plastic limit indices could not be obtained. This is obvious since the soil is coarse and sandy in its nature. Figure 4-6 shows the user interface for Soil C with the plot of the original particle distribution curve in addition to the best fit functions. As could be seen, that the power function is the most suitable function for this problem and has the less residual value, so it was used automatically by the computer program to calculate the values of D 60 , D 30 , D 10 , C c, and C u as R

shown below. D 60 = 1.1164 R

R

D 30 = 0.39532 R

R

D 10 = 0.14797 R

R

C u = 7.5443 R

R

C c = 0.94605 R

R

R

R

R

R

R

R

R

R

R

Figure 4-6 user interface results for soil C classification

Comparing these values to the values obtained from the manual classification method, it was found an acceptable agreement between the two sets of values D 60 = 1.2 R

R

D 30 = 0.34 R

R

D 10 = 0.178 R

R

C u = 6.74 R

R

C c = 0.541 R

R

The soil classification was obtained from the computer program as SP and the soil description was ( The Soil is Coarse, Sand, Poorly Graded ).

CHAPTER FIVE RESULTS AND DISCUSSION

R

esults obtained from the application of the computer program

reveals that soil classification can be easily made using a computer program. The three types of soils chosen in this project were classified by using the appropriate laboratory tests, then the same data were used as input data for the computer program and the results were identical for the two classifications, the manual classification and the classification by the computer program. The two curve fit functions gave a good smooth curve for the particle size distribution when the most appropriate one is chosen.

CHAPTER SIX CONCLUSIONS AND RECOMMENDATIONS

I

t is concluded that:

1- The computer program gave very good results on soil classification. 2- Using curve fit functions gave a smooth curve for the particle size distribution which facilitates the estimation of D 60 , D30 , D 10 , C c, and R

R

R

R

R

R

R

R

Cu. R

R

3- The computer program could be used to classify various types of soils.

Recommendations for further studies are: 1- Other types of classification systems could be programmed to obtain more general program. 2- Languages other than MATLAB could be used in the computer programming. 3- Other curve fit functions could be used to enhance the particle distribution curve.