correlation of index properties with the cbr values of soils with and without...

50

th

IGC

50th

INDIAN GEOTECHNICAL CONFERENCE

17th

– 19th

DECEMBER 2015, Pune, Maharashtra, India

Venue: College of Engineering (Estd. 1854), Pune, India

CORRELATION OF INDEX PROPERTIES WITH THE CBR VALUES OF SOILS

WITH AND WITHOUT ADMIXTURES

Dr.H.S.Prasanna 1, Professor, NIE, Mysuru, [email protected]

Sindhu V 2, Student, NIE, Mysuru, [email protected]

SatishUppin 3, Student, NIE, Mysuru, [email protected]

Ezhilan Ashok V 4, Student, NIE, Mysuru, [email protected] Mohan G.K 5, Student, NIE, Mysuru, [email protected]

ABSTRACT: In the present experimental study, six field soils having different clay mineralogical compositions

were selected. The physical properties of the soils were determined as per BIS specifications. The compaction

characteristics of the soils at different energy levels i.e. SP, MP, RSP, RMP were determined. Keeping the fly ash

content as 20% by weight of soil, the amount of RBI Grade 81 is varied from 2% to 6% by weight of the soil. The

CBR tests were conducted on compacted plain soils and soils having 20% fly ash and varied percentage of RBI

Grade 81(2, 4 and 6%) with soaked conditions. It was observed from the experimental results that, WL, WP, PI of

the plain soil could be better related with CBR values in comparison with soils with admixtures and without

admixtures. It was also found that the optimum value of CBR was obtained for the mix proportion of 4%RBI and

20% fly ash. It is also evident that CBR values are having better correlation with index properties and compaction

characteristics of the soil with 2% RBI and 20% fly ash.

Keywords: admixtures, CBR value, clay mineralogy, energy levels and index properties.

INTRODUCTION

The development of any country can be monitored

by the progress in infrastructural facilities, with

particular reference to transportation. Large scale

road constructions are being developed over the

length and breadth of the country due to adoption

of highly intensified activities in road construction

by Central and State Governments like PMGSY

(Pradhan Mantri Gram Sadak Yojana), Golden

Quadrilateral to name a few. Almost the entire road

network consists of flexible pavements. It is

particularly observed that, the quality and

durability of flexible pavements is greatly affected

by the type of sub-grade soil over which pavements

are to be constructed. The durability of pavement

predominantly depends upon the characteristics of

sub-grade which provides the base for such

pavement structure. For the design of flexible

pavements CBR value is one of the important

parameter. It is often observed that CBR values are

determined in the laboratory as well as in the field

(to a little extent) by obtaining the

undisturbed/remoulded soil samples. These

samples are obtained at suitable intervals as per the

guidelines laid by agencies like BIS, ASTM,

HMSO to name a few. It is observed that the

limited number of soil samples obtained as per the

guidelines laid by the agencies aforesaid may not

be the representative value for the entire length of

the road, due to the fact that the soil samples so

obtained may have variations in their engineering

properties due to variety of reasons. Hence, it is

always difficult for designers to obtain

representative CBR values for use in design of

flexible pavements, which can be avoided only by

mailto:[email protected]





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taking large number of soil samples at short

intervals along the alignment. This necessities the

increase in large number of CBR tests to be carried

out in the laboratory which enhances the direct and

indirect increase in time and cost of the associated

infrastructure projects. This would result in serious

delay in the progress of the project. Any such delay

would add to project cost. It is also observed that

quality control of large number of soil samples is

also a difficult task. Hence it becomes necessary to

predict the CBR values more accurately by indirect

methods i.e. by using the established relationships

provided between various index properties and

CBR values.

Modification of the soils to improve their

engineering properties to the optimum levels at the

site plays a major role in practical situations. Well-

compacted soils increase the performance of the

soil by improving its shear strength and resistance

to settlement behavior. The compaction

characteristics of any soil are optimum moisture

content (OMC) and maximum Dry density (MDD).

Thus, the behavior of the coarse-gained soils and

fine-grained soils are entirely different altogether.

In order to increase the load carrying capacity of

the soil, stabilization is brought about. Stabilization

can be through addition of admixtures like fly ash,

RHA, moorum, lime, cow dung, RBI grade 81 etc.

These admixtures when added with the soil in

certain proportions bring about the stabilization.

The stabilization of soil is measured about in terms

of CBR.

LITERATURE REVIEW

Attempts have been made by several researchers to

establish relationship between index properties of

soils with compaction characteristics of fine-

grained soils, Nagaraj [1], Gurtug and Sridharan

[2]. Attempts have also been made by several

research workers to establish relation between

CBR with index properties of soil and compaction

characteristics i.e. γdmaxand OMC Venkataraman, et

al (1995)(3), Kumar, et al (2000)(4), Karunaprema

and Edirisinghe (2002)[05],to name a few. Vinod

and Cletus [06] established relationship between

CBR and liquid limit, maximum dry density and

suggested following correlation based on an

experimental study on lateritic soil,

𝐶𝐵𝑅 = −0.889 (WLM) + 45.616 --------- (1)

where WLM is modified liquid limit which is

given by

𝑊𝐿𝑀 = 𝐿𝐿 (1 − 𝐶/100) ----------- (2)

Where LL is the liquid limit on passing 425

micron sieve in percent where C is fraction of soil

coarser than 425 micron (percent).

Patel and Desai [07] proposed correlation between

CBR and plasticity index (PI), maximum dry

density (MDD) and optimum moisture content

(OMC) of soils under soaked conditions.

𝐶𝐵𝑅 𝑠𝑜𝑎𝑘𝑒𝑑 = − 43.907 − 0.093 𝑃𝐼 −

18.78 𝑀𝐷𝐷 − 0.3081 𝑂𝑀𝐶 −

− − −(3)

Where MDD is in gm/cc

Roy et.al., [08] proposed a method to predict the

value of soaked CBR in terms of optimum

moisture content (OMC) and maximum dry

density(MDD) of a soil which is given by,

𝐿𝑜𝑔 𝐶𝐵𝑅 = 𝐿𝑜𝑔 (𝛾𝑑𝑚𝑎𝑥/𝛾𝑤) − 𝐿𝑜𝑔 (𝑂𝑀𝐶) −

− − (4)

Where γdmax and γw are in the same unit

Suksun et al [09], based on their extensive

experimental study on fine-grained soil, lateritic

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soils and crushed rocks established the relation

between maximum CBR values with maximum dry

density (MDD) given by equations 4 to 7 with the

degree of correlation greater than 0.85.

CBR max= γdmax-9.63 for fine grained soils

15 𝑘𝑁/𝑚3 < 𝛾𝑑𝑚𝑎𝑥 < 20 𝑘𝑁/𝑚3 ---(5)

CBR max= 2.95 γdmax-9.08 for laterite soils

19.5 𝑘𝑁/𝑚3 < 𝛾𝑑𝑚𝑎𝑥 < 21.5 𝑘𝑁/𝑚3---(6)

CBR max= 17.44 γdmax -276.76 for the crushed

rocks

22.2 𝑘𝑁/𝑚3 < 𝛾𝑑𝑚𝑎𝑥 < 22.8 𝑘𝑁/𝑚3---(7)

where γdmaxis expressed in kN/m3

They also reported that, CBR values can be

correlated with maximum CBR values given by

equation- 8 with a high degree of correlation of

0.94.

𝐶𝐵𝑅

𝐶𝐵𝑅𝑚𝑎𝑥= 4.95 −

𝛾𝑑

𝛾𝑑𝑚𝑎𝑥− 3.96 − −(8)

for 90% <γdmax< 100%

where CBRmax is the CBR value

corresponding to the maximum dry unit weight.

Relationship of soils having admixtures with

index properties and compaction

characteristics.

Attempts have been made by several researchers to

establish the relationship between CBR values of

soils with various admixtures with compactions

and index properties of soils.

Patil B.M., Patil K.A., [10,11] studied the effect of

pond ash and RBI Grade 81 on properties of sub-

grade soil and they found that the CBR value of

grade –III base course treated with 20% pond ash

and 4% of RBI Grade 81 is increased by 125.93%

as compared to untreated grade –III base course.

They concluded that, the geotechnical properties of

clayey soil improve significantly due to addition of

pond ash and RBI grade 81.

Haricharan et al [12] studied the effect of RBI

Grade 81 on black cotton soils and they found that

unconfined compression strength of specimens

treated with RBI-81 increased approximately by

250% for curing period of 28 days as compared to

virgin soil. Further the CBR value improved

approximately by 400%.The increase in CBR value

of treated soil increased by 400%.

Najia Nouf and Sureka Nagesh [13] studied the

effect of RBI 81 on properties of black cotton soil

and found that UCS of stabilized sample increases

with increase in period of curing. It was also found

that, increase in strength was more upon lime

addition, compared to addition of RBI.The

stabilizer RBI Grade 81 is effective in reducing the

plasticity characters. The soaked CBR values

increased three folds with addition of 6% stabilizer.

Lekha and Ravi Shankar [14] studied the effect of

RBI Grade 81 stabilized soils for pavements and

they concluded that, evidence for stabilization can

be seen from change in chemical composition of

soils when treated with stabilizer. The percentage

of calcium oxide, alumina and sulphates which are

important by products are increased on

stabilization.

MATERIALS AND METHODOLOGY

Materials

For the present experimental study, six field soils

from Mysuru and Chamarajanagar districts were

selected based on their index properties and clay

mineralogical composition.

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Methodology

In order to understand the soil characteristics, the

following physical tests and index property tests

were conducted on the soil samples which were

oven dried, passing through 425µ, as per BIS

specifications are Free swell ratio test [IS: 2911

(part-3)-1980] [15], Specific gravity test [IS: 2720

(part-3 sec-1)-1980] [16],Grain size analysis [IS:

2720(part-4)-1985] [17], Atterberg limits [IS: 2720

(part-5)-1985] [18], Cone Penetrometer test using

both water and kerosene as pore fluids [IS: 11196

(1985)] [19], Compaction tests-Reduced Standard

Proctor test (RSP)*, Standard Proctor test (SP) [IS:

2720 (part-7)-1980] [20], Reduced Modified

Proctor test (RMP)**, Modified Proctor test[IS:

2720 (part-8)-1983][21]. Table-1 shows

predominant clay mineral present in the soil

Table-1 Soil Vd Vk Vd/Vk Predominant clay

mineral

1 11 10 1.1 KAOLINITE



4 18 13 1.4 MONTMORILLONITE

5 23 13 1.77 MONTMORILLONITE

6 13 10 1.3 KAOLINITE and

MONTMORILLONITE

* RSP energy is 60% of SP energy.

** RMP energy is 60% of MP energy. Where

Vd = Sediment volume of soil in distilled

water.

Vk = Sediment volume of soil in kerosene.

Table-2 shows physical properties of the soils

Table-2 Soil SOIL

1

SOIL2 SOIL

3

SOIL

4

SOIL

5

SOIL

6

Specific

gravity,G

2.63 2.67 2.60 2.63 2.61 2.72

Grain size

distribution

(%) clay

50

60

50

40

25

45

silt 20 15 22 20 40 22

sand 30 25 18 40 35 33

Atterberg

limits(%)

WL

35

30

31

55

61

32

WP 19 14 17 20 28 20

PI 16 16 14 35 33 12

WS 7 4 3.1 8.4 11 11.4

IS

classification

CL CL CL CH CH CL

About 3kg of soil passing 425 µm is mixed with

different water contents thoroughly and kept inside

a separate polythene covers. The samples were let

to achieve the equilibrium moisture for five to ten

days. Compaction tests RSP, SP, RMP & MP were

done using standard Proctor mould. For each of the

soils, minimum of six trials are done to plot

compaction curves.

Figure 1 through 6 shows compaction curves for

the soils under varying compaction energy levels

Fig-1

Fig-2

1.21.31.41.51.61.71.81.9

22.1

0 10 20 30

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.63)

1.3

1.5

1.7

1.9

2.1

2.3

0 5 10 15 20 25

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.67)

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Fig-3

Fig-4

Fig-5

Fig-6

Table-3 shows the values of optimum moisture

content (OMC) and maximum dry density (MDD)

for the soils with varying compaction energy

levels.

Table-3 Soil OMC

( SP)

γdmax

(SP)

OMC

(RSP)

γ(RSP) OMC(M

P)

γ(MP) OMC(R

MP)

γ(RMP)

Soil

1

17.1 16.7 19.1 16.4 13.4 18.6 13.8 18.1

Soil

2

16 16.4 17 16.1 14.4 16.8 14.7 16.8

Soil

3

13.5 18.5 14.2 18.2 10.2 19.8 10.7 19.3

Soil

4

25.2 14.7 28 14 19.2 16 20 15

Soil

5

18.2 16.3 19 15.7 16.2 17.4 16.6 17.1

Soil

6

16.2 17.5 18.2 17.4 14.6 18.6 15.8 17.8

CBR tests are carried out on compacted soils as per

IS:2720 (Part 16;1987)[22].

Figures 7 to 12 shows load-penetration curves

obtained for compacted soils for varying

percentages of RBI Grade 81 with 20% fly ash.

Fig-7

Fig-8

1.4

1.5

1.6

1.7

1.8

1.9

2

0 10 20 30

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.60)

1.3

1.4

1.5

1.6

1.7

1.8

1.9

0 5 10 15 20 25 30

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.63)

1.2

1.3

1.4

1.5

1.6

1.7

0 10 20 30 40

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.61)

1.4

1.5

1.6

1.7

1.8

1.9

2

0 5 10 15 20 25

Dry

den

sity

(g/

cc)

Water content (%)

RSP

SP

RMP

MP

ZAV LINE (G=2.72)

0

100

200

300

400

500

600

700

0 2 4 6 8

Load

(kg

)

Penetartion (mm)

PENETRATION CURVE FOR SOIL 1

0%

2%

4%

6%

0

50

100

150

200

250

300

0 2 4 6 8

Load

(kg

)

Penetration (mm)


0%

2%

4%

6%

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Fig-9

Fig-10

Fig-11

Fig-12

From these figures, it can be observed that CBR

values for soils obtained with 20% fly ash and 4%

RBI Grade 81 admixture is found to be optimum, in

relative comparison to soils without admixture.

Table-4 shows the values of CBR for soils having

different percentage of RBI Grade 81 with 20% fly

ash.

Table-4

Soil

No

Plain

soil

20%fly

ash+2%

RBI

20%fly

ash+4%

RBI

20%fly

ash+6%

RBI

1 1.1 16 18 17

2 1.34 16.7 31.7 16.9

3 1.14 19 30.7 14.9

4 5.1 7.6 12.6 21

5 1.8 9.8 36.5 31

6 7.4 17.4 16 10

Fig-13 shows the variation of CBR values for soils

having different clay mineralogy having 20% fly

ash and varying percentage of RBI Grade 81 with

Liquid limit of soils.

Fig-13

Fig-14shows the variation of CBR values for soils



Plastic limit of soils.

0

100

200

300

400

500

600

700

0 2 4 6 8

Load

(kg

)

Penetration (mm)


0%

2%

4%

6%

0

100

200

300

400

0 2 4 6 8

Load

(kg

)

Penetration (mm)


0%

2%

4%

6%

0

200

400

600

800

1000

0 2 4 6 8

Load

(kg

)

Penetration (mm)


0%

2%

4%

6%

0

100

200

300

400

500

600

700

0 2 4 6 8

Load

(kg

)

Penetration (mm)


0%

2%

4%

6%

0

5

10

15

20

25

0 20 40 60 80

(CB

R)%

WL (%)

VARIATION OF CBR WITH LIQUID LIMIT

0%

2%

4%

6%

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Fig-14

Fig-15 shows the variation of CBR values for soils



Plasticity index of soils.

Fig-15

Table-5 shows the equations with regression

coefficient for CBR values with index properties of

soils with and without admixture.

Table-5

From the figures 13 to 15, it can be observed that

CBR values can be better correlated with plastic

limit in relative comparison to liquid limit and

plasticity index of the soils, without admixture

and with 4% RBI Grade 81 admixture and 20%

fly ash.

Figure 16 to 19 shows the relationship between

the CBR values and OMC of soils obtained with

different compaction energy levels.

Fig-16

Fig-17

0

5

10

15

20

25

0 10 20 30 40

CB

R (

%)

WP (%)

VARIATION OF CBR WITH PLASTIC LIMIT

0%

2%

4%

6%

0

5

10

15

20

25

0 20 40 60 80

CB

R (

%)

PI (%)

VARIATION OF CBR WITH PLASTICITY INDEX

0%

2%

4%

6%

0

5

10

15

20

25

0 10 20 30

CB

R (

%)

OMC-RSP (%)

VARIATION OF CBR WITH OMC(RSP)

0%

2%

4%

6%

0

5

10

15

20

25

0 10 20 30 40

CB

R (

%)

OMC-SP (%)

VARIATION OF CBR WITH OMC(SP)

0%

2%

4%

6%

Relation

ship

between

index

properti

es and

compact

ion

characte

ristics of

soils

with

CBR

soils

0%

2%

4%

6%

Equation

(y)

Regress

ion

Co-

efficien

t

Equation

(y)

Regress

ion

Co-

efficient

Equation

(y)

Regress

ion

Co-

efficien

t

Equation

(y)

Regr

essio

n

Co-

effici

ent

WL vs

CBR

0.094x –

1.916

0.81 -0.063x +

18.65

0.35 0.037x +

16.54

0.30 0.166x +

14.84

0.76

WP vs

CBR

0.302x –

3.882

0.93 -0.084x +

19.29

0.46 -0.175x +

20.69

0.76 0.057x +

16.03

0.32

PI vs

CBR

0.049x +

19.06

0.83 -0.045x +

19.16

0.63 -0.049x +

19.22

0.710 -0.049x +

19.22

0.71

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Fig-18

Fig-19


coefficient for CBR values with OMC of soils

obtained from varying energy levels.

Table-6 Relationship

between

index

properties

and

compaction

characteristi

cs of soils

with CBR

soils

0% 2% 4% 6%

Equatio

n (y)

Regressio

n

Co-

efficient

Equatio

n (y)

Regressio

n

Co-

efficient

Equatio

n (y)

Regressio

n

Co-

efficient

Equatio

n (y)

Regressio

n

Co-

efficient

OMC (RSP)

vs CBR

0.308x

– 3.912

0.94

0.757x

+4.665

0.88

0.825x

+ 3.585

0.91

1.006x

+ 0.788

0.96

OMC (SP)

vs CBR

0.367x

– 4.520

0.95

0.080x

+ 19.21

0.61

-0.057x

+ 18.35

0.63

-0.080x

+ 19.21

0.61

OMC

(RMP) vs

CBR

0.423x

– 4.318

0.85

0.859x

+ 2.053

0.98

0.906x

+ 1.257

0.99

0.906x

+ 1.257

0.99

OMC (MP)

vs CBR

0.424x

– 4.134

0.83

0.709x

+ 5.795

0.89

0.820x

+ 4.076

0.94

0.820x

+ 4.076

0.94

From the figures 16 to 19 it can be observed that,

the CBR values can be better correlated with OMC

irrespective of clay mineralogy of soils and

compactive energy levels, with coefficients ranging

from 0.88 to 0.96 for RSP, 0.61 to 0.95 for SP,

0.86 to 0.99 for RMP and 0.83 to 0.94 for MP.

Figures 20 to 23 show the relationship between the

CBR values and MDD of soils obtained from

different compaction energy levels.

Fig -20

Fig-21

0

5

10

15

20

25

0 5 10 15 20 25

CB

R (

%)

OMC-RMP (%)

VARIATION OF CBR WITH OMC(RMP)

0%

2%

4%

6%

0

5

10

15

20

25

0 5 10 15 20 25

CB

R (

%)

OMC-MP (%)

VARIATION OF CBR WITH OMC (MP)

0%

2%

4%

6%0

5

10

15

20

25

0 10 20 30

CB

R (

%)

γ-RSP (g/cc)

VARIATION OF CBR WITH γRSP

0%

2%

4%

6%

0

5

10

15

20

25

0 5 10 15 20 25 30

CB

R (

%)

γ-SP (g/cc)

VARIATION OF CBR WITH γSP

0%

2%

4%

6%

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Fig-22

Fig-23


coefficient for CBR values with MDD of soils

obtained from varying energy levels.

Table-7

It can also observed that CBR values can also be

correlated with MDD of soils having different clay

mineralogy and compactive energy levels with

regression coefficients ranging from 0.77 to 0.83

for RSP, 0.80 to 0.85 for SP, 0.78 to 0.92 for RMP

and 0.3 to 0.95 for MP respectively.

Figures 24 to 29 show variations of CBR for soils

having different percentages of RBI Grade 81

admixtures.

Fig-24

Fig-25

0

5

10

15

20

25

0 5 10 15 20 25

CB

R (

%)

γ- RMP(g/cc)

VARIATION OF CBR WITH γRMP

0%

2%

4%

6%

0

5

10

15

20

25

0 5 10 15 20 25

CB

R (

%)

γ-MP (g/cc)

VARIATION OF CBR WITH γMP

0%

2%

4%

6%

y = -0.993x2 + 8.447x + 1.595R = 0.99

0

5

10

15

20

25

0 2 4 6 8

CB

R(%

)

RBI(%)

CBR v/s %RBI FOR SOIL 1

y = -1.883x2 + 14.38x - 0.136R = 0.95

-5

0

5

10

15

20

25

30

35

0 2 4 6 8

CB

R(%

)

RBI(%)


Relationshi

p between

index

properties

and

compaction

characterist

ics of soils

with CBR

soils

0% 2% 4% 6%

Equati

on (y)

Regressi

on

Co-

efficient

Equati

on (y)

Regressi

on

Co-

efficient

Equati

on (y)

Regressi

on

Co-

efficient

Equati

on (y)

Regressi

on

Co-

efficient

γ (RSP) vs

CBR

-0.909x

+ 17.23

0.831

-0.171x

+ 20.56

0.765

-0.277x

+ 21.83

0.833

-0.222x

+ 21.81

0.813

γ (SP) vs

CBR

-1.010x

+ 18.79

0.803 -0.274x

+ 22.39

0.847 -0.266

+ 22.39

0.827 -0.212x

+21.02

0.822

γ (RMP) vs

CBR

-0.940x

+ 18.44

0.775 -0.379x

+ 23.25

0.924 -0.379x

+ 23.25

0.924 -0.349x

+ 22.74

0.843

γ (MP) vs

CBR

-0.815x

+ 16.56

0.716 -0.403x

+ 23.27

0.948 -0.403x

+ 23.37

0.298 -0.388x

+ 23.12

0.891

50

th

IGC

50th


17th

– 19th



Fig-26

Fig-27

Fig-28

Fig-29

From the figures 24 to 29 it can be concluded that,

CBR values for soils having different clay

mineralogy with varying energy levels can be

better correlated with percentage of RBI Grade 81

to be mixed with soils with higher regression range

from 0.92 to 0.99.

CONCLUSIONS

From the present experimental study, the following

conclusions can be made

Plastic limit of soils can be better correlated

with CBR of soils with and without

admixture, irrespective of clay mineralogy in

relative comparison to liquid limit and

plasticity index.

CBR of plain soils and soils having 4% RBI

Grade 81 and 20% fly ash admixture can be

better correlated with plastic limit of soils, in

relative comparison to liquid limit and

plasticity index of the soils.

Compaction characteristics, OMC and MDD

can be correlated effectively with soils

having 20% fly ash and varying percentages

of RBI Grade 81 (2% to 6%) irrespective of

compactive energy levels and clay

mineralogy of soils.

y = -2.041x2 + 15.24x + 0.023R= 0.97

0

5

10

15

20

25

30

35

0 2 4 6 8

CB

R(%

)

RBI(%)


y = 0.373x2 + 0.393x + 5.152R = 0.99

0

5

10

15

20

25

0 2 4 6 8

CB

R(%

)

RBI(%)


y = -0.838x2 + 10.75x - 0.738R = 0.92

-5

0

5

10

15

20

25

30

35

40

0 2 4 6 8

CB

R(%

)

RBI(%)


y = -1.002x2 + 6.341x + 7.702R = 0.98

0

5

10

15

20

0 2 4 6 8

CB

R(%

)

RBI(%)


50

th

IGC

50th


17th

– 19th



REFERENCES

1. H.B. Nagaraj, (2000), Prediction of

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50

th

IGC

50th


17th

– 19th



water content-density relation using light

compaction (Second revision), Bureau of




water content- Dry density relation using heavy

compaction (Second revision), Bureau of


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correlation of index properties with the cbr values of soils with and without...

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