Compositional and environmental factors role on compression index

International Journal of CivilJOURNAL OF CIVIL (IJCIET), ISSN 0976 – AND
INTERNATIONAL Engineering and Technology ENGINEERING 6308
(Print), ISSN 0976 – 6316(Online) Volume 3, Issue 2, July- December (2012), © IAEME
TECHNOLOGY (IJCIET)
ISSN 0976 – 6308 (Print)
ISSN 0976 – 6316(Online)
Volume 3, Issue 2, July- December (2012), pp. 392-403 IJCIET
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COMPOSITIONAL AND ENVIRONMENTAL FACTORS
ROLE ON COMPRESSION INDEX
Ch. Sudha Rani1, K Mallikarjuna Rao 2
1
(Associate Professor, Dept of Civil Engineering, Sri Venkateswara University, Tirupati,
India-517502. E-mail: sudhajawahar@gmail.com)
2
(Professor, Dept of Civil Engineering, Sri Venkateswara University, Tirupati, India-517502,
E-mail: kmr_svuce@yahoo.com)

ABSTRACT

Empirical correlations developed by several investigators for prediction of
Compression Index either in terms of Liquid Limit/Plasticity Index, represent composition
and Dry Density/initial Moisture Content/ initial Void Ratio reflect the state/environment of
the soil. In this investigation an attempt has been made to find the influence of each of the
compositional and environmental factors on Compression Index through experimental
investigations. Fifteen regression models were developed after carrying out linear regression
analyses for prediction of Compression Index (Cc) in terms of the environmental factors
alone, compositional factors alone and combined environmental and compositional factors.
The degree of influence of each of the variables on the dependant variable was found by
estimating partial correlation coefficient. Plasticity Index (IP), Initial Dry Density (γd), Initial
Moisture Content (mc) and Liquid limit (wL) were found to have influence on Compression
Index (Cc) in that order. Comparison of predicted and observed Compression Index of
seventy soils collected from literature indicate that the models developed using all the four
influencing parameters or atleast one compositional factor but both the environmental factors
have more general applicability than other models.

KEY WORDS: Consolidation, Compression Index, Regression coefficient, Compositional
factors, Environmental factors, Partial correlation coefficient.

1. INTRODUCTION

Compression Index is widely used in Geotechnical Engineering practice for
evaluation of settlement of structures resting on clayey soils. Compressibility of soils is
represented by Compression Index (Cc), the slope of virgin part of the compression curve
392

International Journal of Civil Engineering and Technology (IJCIET), ISSN 0976 – 6308
obtained from One-Dimensional Consolidation test on undisturbed samples. However,
collection of undisturbed samples and conduct of consolidation test involves considerable
time and money apart from the services of the domain experts and trained technicians. Hence
several attempts have been made in the past to develop simple correlations for prediction of
Compression Index using properties which can be easily determined. Ever since Casagrande
found that the Atterberg limits provide more reliable indication of engineering properties,
several investigators developed correlations for prediction of Compression Index in terms of
Liquid Limit (Skempton 1944, Terzaghi&Peck 1967, and Bowles 1979), Plasticity Index
(Jian-Hua Yin 1999, AmithNath and DeDalal 2004) or Shrinkage Index (Sridharan and
Nagraj 2001) based on tests conducted on a limited number of soils pertaining to certain
region. Another group of investigators expressed Compression Index in terms of in-situ Void
Ratio (Nishida 1956, Hough 1957, and Bowles 1979) or in-situ Moisture Content (Bowles
1979, and Koppula 1981) or in-situ Dry Density (Oswald 1980) presuming that the
compressibility is mainly a function of state of soil. However, the engineering properties of
soils are now said to be dependent on the composite effect of compositional and
environmental factors (Mitchel, 1993). None of the currently used correlations or models
account for both compositional and environmental factors in their development.
Conventionally, Atterberg limits or indices derived from it are used as indicators of soil
composition as direct determination of mineralogical composition is both difficult and not
routinely carried out in any soil investigation. Liquid Limit and Plasticity Index are known to
reflect compositional factors while in-situ Dry Density and natural Moisture Content are the
important environmental factors that influence the engineering properties significantly. The
objective of this investigation is to assess the degree of association between Compression
Index and each of the influencing parameters namely Liquid limit (wL),Plasticity Index (IP),
Initial Dry Density (γd) and Initial Moisture Content (mc) and to develop a model accounting
for all the influencing parameters. Such a model is expected to have a more general
applicability.

2. EXPERIMENTAL INVESTIGATION

Undisturbed and Representative but disturbed clayey soil samples from different parts
of India are collected from open trial pits at depths ranging from 2.0m to 2.5m depths after
thorough saturation. Undisturbed samples are obtained using 100mm diameter thin walled
sampling tubes essentially satisfying the specifications laid in IS: 2132, 1986. One
Dimensional Consolidation tests and identification and classification tests are conducted on
all these 15 samples as per the specifications given in special publication (SP 36 Part I, 1987)
published by Bureau of Indian Standards (BIS). The loading sequence followed in
consolidation test is 5, 10,20,40,80,160, and 320 kPa, the load increment ratio being one and
nominal surcharge being 5 kPa. Each load is sustained for at least 24 hours before applying
next load increment. The index properties of soils used, placement conditions and
compression Index of all soils tested are presented in Table 1. From Table 1 it can be
observed that for the soil samples tested, the Liquid Limit is ranging from 30% to 140%, Dry
Density is varying from 14kN/m3 to 21kN/m3, Moisture Content is ranging from 10% to 32%
and Plasticity Index is ranging from 15% to 105%. The range of each of the parameters
considered is so wide that it covers practically most of the soils that are likely to be
encountered in general practice. The fifteen soils used in the series of tests were designated
as CS1, CS2, CS3, CS4, CS5, CS6, CS7, CS8, CS9, CS10, CS11, CS12, CS13, CS14 and
CS15 for convenience.
393


Table 1. Results of the Tested Soils
Placement
Atterberg Limits
Conditions
Compressi
Soil In-
S.No In on Index
Designation wL(% wP IP situ
-situ (Cc )
) (%) (%) γd kN/
3 mc (%)
m

22.0 41.0 10.5
1. CS1 63.00 20.80 0.290
0 0 0

2 C 8 3 4 1 2 0
3 C 6 2 4 1 1 0
4 C 5 2 1 2 1 0
5 C 7 2 3 1 2 0
6 C 3 1 1 2 1 0
7 C 3 2 1 1 1 0
8 C 1 3 9 1 3 0
9 C 9 1 7 1 2 0
1 C 1 3 1 1 2 0
1 CS11 5 2 2 1 2 0
1 C 6 3 3 1 3 0
1 C 4 1 3 1 2 0
1 C 5 3 2 1 2 0
1 CS15 5 3 2 1 2 0

3. RESULTS AND DISCUSSIONS

Typical e-log p plots obtained from One-Dimensional Consolidation tests are shown
in Fig 1. The initial portion of these plots is observed to be fairly flat upto a stress of about 50
kPa. This is owing to the fact that the soil samples are collected at depths ranging from 2.0m
to 2.5m, the insitu overburden pressure being about 50 kPa. Compression Index values
denoted by Cc are obtained by taking the slope of the virgin portion of e-log p plots (slope of
the average straight line beyond 50 kPa) of all the soils tested and are summarized in Table
1. The Compression Index of the soils is ranging from as low as 0.10 to as high as 0.50. The
Compression Index of the soils may be expressed as given below in terms of compositional
factors (liquid limit, plasticity index) and environmental factors (dry density and moisture
content):

Cc = f ((wL, mc, γd, IP)) … (1)

The Compression Index may also bear relationship with any one or combination of the
above said four parameters provided there is some interaction amongst the parameters
themselves. However, such interactions may or may not be unique for all soils. Consequently

394

all the correlations may or may not be valid for all the soils in general. Hence, linear regression
analyses were carried out to develop correlations for prediction of Compression Index (Cc) in terms of
each of the compositional factors namely, wL and IP and the environmental factors namely, mc and γd.
Further, multiple linear regression analyses were carried out to correlate Cc with all possible
combinations of environmental factors alone, compositional factors alone and combined
environmental and compositional factors. The details of multiple linear regression analysis correlating
dependent variable with more than one independent variable may be found in Applied Statistics for
Engineers by Montgomery and Runger (1999) or in any standard text book on Applied Statistics. .
Statistical software like SPSS or Data Analysis tool Pack of Microsoft excel supports a function or
subroutine for carrying out multiple linear regression analysis. Data Analysis tool pack of Microsoft
excel is used In this investigation .The regression models so developed along with correlation
coefficients are presented in Table 2. These correlations are designated as E1 to E15 for convenience.
Regression models E1, E2, and E3 consider only compositional factors whereas the models E4, E5,
and E6 accounts for only environmental factors in the development of models. Rest of the models
from E7 to E15 considers all the possible combinations of both compositional and environmental
factors. The correlation coefficient (R2) values of models E1, E2, and E3 indicate that Cc has very
good correlation with any of the compositional factors wL or IP and also with the combination of wL
and IP. The three models namely E4, E5, and E6 which are developed considering the environmental
factors alone are found to yield very low correlation coefficient. Regression model E4 relates Cc with
environmental factor ‘mc’ and the correlation coefficient R2 is 0.11 and model E5 relates Cc with
environmental factor γd and the R2 = 0 .125 whereas model E6 relates Cc with combination of these
two environmental factors namely mc and γd and the R2 value is 0.136. Hence, it may be concluded
that the correlations involving environmental factors alone (models E4, E5, and E6) are not
satisfactory. All the models E7 to E15 which relate Cc with all possible combination of
environmental and compositional factors are observed to yield good correlation coefficients. In other
words, when the environmental factors are combined with any one of the compositional factors
namely wL and IP there is a considerable improvement in the correlation coefficient. In fact the
standard deviation of residuals is lowest for two models E11 and E15 which involve both the
environmental factors apart from compositional factors. Further, correlations involving compositional
factors alone (models E1, E2, and E3) are also good. This clearly brings out that even though the
compositional factors play dominant role in determining the compressibility of clayey soils, inclusion
of environmental factors improve the model efficiency and possibly the general applicability too
which needs to be ascertained by comparing with others data.

1.5
CS12
CS11
CS9

1.25
Void Ratio

1

0.75
1 10 100 1000
Pressure(kPa)

Fig. 1 Typical e-log p plots

395

4. DEGREE OF INFLUENCE OF COMPOSITIONAL AND ENVIRONMENTAL
FACTORS ON COMPRESSION INDEX

From Table 2, it can be observed that the correlation coefficient is good for all models
except for the models relating Cc with environmental factors (mc or γd or mc & γd) alone (i.e.
models E4,E5, & E6). The fact that compression index bears good correlation with any of
the compositional factors (models E1, E2, and E3) and any combination of compositional and
environmental factors (models E7 to E15) indicate that there is some interaction among the
factors themselves. However, such interactions may or may not be unique for all soils. The
model which accounts for all the influencing parameters is expected to have a more general
applicability. Hence there is a need to identify the degree of association between compression
index and each of the compositional and environmental factors in order to arrive at the best
amongst the remaining 13 models from the view point of general applicability. Regression
model E15 correlates dependent variable Cc with all the independent variables namely, wL,
mc, γd and IP. Multiple correlation coefficient (R2) of this regression model is 0.991 which is
highest among all the fifteen models developed. The regression coefficients of wL, mc, γd
and IP are 0.0027, 0.007, 0.031, and 0.002 respectively. The regression coefficient is highest
for γd followed by mc, wL, and IP in that order. The degree of influence of each of the
independent variables (wL, mc, γd, and IP) on dependant variable Cc cannot be estimated based
on either regression coefficients or multiple correlation coefficients alone (Yevjevich 1972).
In other words, neither the multiple correlation coefficients nor the regression coefficients are
a measure of association between dependant and independent variables. However, the degree
of influence of each of the variables on the dependant variable can be found statistically by
estimating partial correlation coefficient ( r1−i ). The partial correlation coefficients measure
the association of each independent variable with the dependent one, after the influence of
certain related variables has been accounted for (Chandra Sekhar et.al. 2005, Yevjevich
1972). The influence of the parameters considered are found out by keeping aside only one of
these parameters at a time and finding the multiple correlation coefficient, thereby partial
correlation coefficient. Estimation of partial correlation coefficient ( r1−i ) involves the
determination of:
(a) The multiple correlation coefficient R12 between dependant variable Cc and all the
independent variables wL, mc, γd and IP.
Multiple correlation coefficients R 12− i between dependant variable Cc and all the
independent variables except the chosen independent variable xi (choosing one among wL,
mc, γd and IP at a time for xi) whose association with the dependant variable is to be assessed.
The variable xi is referred as influencing parameter.
The partial correlation coefficient r1-i is determined by

r1−i = (1 – ((1 – R12 ) / (1 – R12−i ))) … (2)

The partial correlation coefficients estimated using the above equation choosing wL, mc,
γd and IP as influencing parameters in that order are given in Table 3. The partial correlation
coefficients are 0.809, 0.554, 0.725 and 0.632, respectively. Since all the partial correlation
coefficients are significant, it may be concluded that all the four parameters have significant
influence on compression index. It is also supported by the fact that the standard deviation of
residuals is low for all the models. However, the partial correlation coefficient is highest and

396

standard deviation of residuals is lowest when IP is chosen as the influencing parameter. In
other words, the IP may be expected to have highest influence on Compression Index. The
observation made by Sridharan and Nagraj (2001) indicates that soils having same wL but
different IP have different Cc values, serves as an evidence for this. Further, it may be
observed that environmental factors mc, and γd have more association with Cc than wL
indicated by the partial correlation coefficients. Hence the models E11, E12 and E15 are
expected to have more general applicability than other models as they account for either all
or most of the influencing parameters in the development of these models.

Table 2. Regression Models Developed for Prediction of Compression Index
Multiple Standard
Model Parameters Correlation Deviation
S.No. Regression Model
No. Used Coefficient of
(R2) Residuals
Compositional Factors alone
1. E1 wL 0.934 0.54 (0.046+ (0.003* wL))
2. E2 IP 0.959 1.14 (0.130+(0.0347* IP))
3. E3 wL, IP 0.968 0.54 (0.090 + (0.001* wL) + (0.002* IP))
Environmental Factors alone
4. E4 mc 0.11 0.42 (0.168+(.0048* mc))
5. E5 γd 0.125 0.66 (0.556- (0.016* γd)
6. E6 mc, γd 0.136 1.14 (1.250 - (0.009* mc) - (0.045* γd))

Combined Compositional and Environmental Factors

5. E7 wL, mc 0.945 0.63 (0.070 + (0.003* wL) – (0.002* mc))

6. E8 wL, γd 0.951 0.55 (-0.087 + (0.003* wL) + (0.007* γd)))

9. E9 mc, IP 0.971 0.65 (0.160 - (0.002* mc) + (0.004* IP))

10. E10 γd, IP 0.973 0.74 (0.014+ (0.006* γd)+ (0.0001* IP))

11. E11 wL, mc, γd 0.968 0.17 (-1.020 + (0.003* wL) + (0.012* mc) +
(0.040* γd))

12. E12 mc, γd, IP 0.974 0.42 (-0.200 + (0.003* mc) + (0.010* γd) +
(0.003* IP))

13. E13 wL, mc, IP 0.981 0.79 (1.270 - (0.001* wL) - (0.002* mc) +
(0.002* IP))

14. E14 wL, γd, IP 0.985 0.79 (-0.038 + (0.001* wL) + (0.007* γd) -
(0.002* IP))

15. E15 wL, mc, γd, IP 0.991 0.19 (-0.629 + (0.0027* wL) + (0.007* mc) +
(0.031* γd ) + (0.002* IP))

397


Table 3. Partial Correlation Coefficients for Different Influencing Parameters

Influenci
Multiple Partial Std
ng
Model No. Correlation correlation Deviation of
Paramete
coefficient coefficient Residuals
r

E15 - 0.991 - 0.190

E11 IP 0.968 0.809 0.174

E12 wL 0.974 0.554 0.417

E13 γd 0.981 0.725 0.791

E14 mc 0.985 0.632 0.790

5. VERFICATION WITH THE REPORTED DATA

The statistical analysis of the test results presented in this investigation reveal that the
Compression Index is significantly influenced by the parameters IP, γd, mc, and wL in that
order. Hence regression models E15, E11 and E12 are expected to have more general
applicability than the other models. In order to verify the same the test data reported by
Oswald (1980) is used. Oswald (1980) reported about 100 soils consolidation test data,
obtained from United States Army Corps of Engineers (USACE) records covering the offices
throughout the Continental United States. Amongst them only seventy one soils test data
were used for verification purpose, as either liquid limit or in-situ void ratio was not reported
for remaining soils. The details of these seventy one soils test data are summarized in Table
4. The compression index of all the seventy one soils test data is predicted using the
regression models E1 to E3 and E7 to E15. The observed Cc values are plotted against Cc
values for all twelve models and the typical plots are shown in Figs 2 to 5. The solid line in
the plots is the line of equality. Careful observation of these plots indicate that the
predictability of 3 models namely E11, E12 and M15 appear to be fair to good since most of
the points are falling close to the line of equality. All other models are found to either under
predicting or over predicting the Compression Index. This indicates that environmental
factors mc, and γd have more association with Cc and the models E11, E12, and E15 which
involve both the environmental factors apart from compositional factors have more general
applicability.

398

Table 4. Data Base Used for Verifying the Compression Index Models Developed

S.No WP WL γd
mc % IP Cc
. % % kN/m3
1 31.00 87.00 32.70 13.86 56.0 0.13
2 26.00 51.00 26.80 14.80 0 25.0 0.31
3 23.00 92.00 45.60 11.93 0 69.0 0.39
4 28.00 55.00 30.30 14.32 0 27.0 0.14
5 30.00 65.00 28.70 14.27 0 35.0 0.09
6 27.00 60.00 41.70 12.54 0 33.0 0.34
7 28.00 81.00 44.00 12.34 0 53.0 0.37
8 24.00 55.00 37.30 13.33 0 31.0 0.21
9 27.00 83.00 48.30 11.82 0 56.0 0.38
10 31.00 84.00 45.60 11.91 0 53.0 0.45
11 22.00 67.00 35.20 13.94 0 45.0 0.26
12 25.00 64.00 34.70 13.85 0 39.0 0.34
13 24.00 57.00 40.00 12.76 0 33.0 0.29
14 37.00 92.00 30.90 13.96 0 55.0 0.27
15 25.00 80.00 26.90 14.57 0 55.0 0.22
16 22.00 54.00 21.60 16.63 0 32.0 0.09
17 23.00 85.00 38.70 13.12 0 62.0 0.18
18 22.00 53.00 26.10 15.45 0 31.0 0.20
19 28.00 52.00 51.80 11.00 0 24.0 0.46
20 27.00 91.00 39.70 12.27 0 64.0 0.44
21 24.00 77.00 39.30 12.91 0 53.0 0.30
22 27.00 60.00 44.30 11.89 0 33.0 0.22
23 22.00 58.00 28.50 14.79 0 36.0 0.17
24 24.00 69.00 45.60 11.32 0 45.0 0.36
25 17.00 38.00 21.00 16.77 0 21.0 0.14
0 25.0
26 15.00 40.00 23.20 16.09 0.18
27 25.00 45.00 17.60 17.40 0 20.0 0.08
28 20.00 47.00 31.00 14.27 0 27.0 0.27
29 20.00 45.00 40.50 12.86 0 25.0 0.26
30 18.00 35.00 26.10 15.96 0 17.0 0.07
31 17.00 38.00 22.70 16.61 0 21.0 0.12
32 19.00 45.00 34.40 14.03 0 26.0 0.23
33 19.00 45.00 34.40 13.72 0 26.0 0.23
34 18.00 47.00 26.40 15.58 0 29.0 0.17
35 18.00 42.00 22.20 16.70 0 24.0 0.11
36 18.00 39.00 25.50 15.85 0 21.0 0.10
0

399


γd
S.No.
WP WL mc kN/m3 IP Cc
% % %

37 17.00 38.00 22.70 16.63 21.0 0.12
38 16.00 48.00 24.60 15.77 32.0 0.22
39 22.00 45.00 20.30 16.71 23.0 0.12
40 18.00 46.00 32.00 14.32 28.0 0.26
41 16.00 40.00 25.20 16.06 24.0 0.19
42 22.00 37.00 13.50 19.17 15.0 0.14
43 23.00 36.00 12.40 18.57 13.0 0.14
44 23.00 36.00 14.60 18.73 0 13.0 0.09
45 20.00 37.00 17.50 18.28 0 17.0 0.11
46 22.00 41.00 19.30 17.79 0 19.0 0.11
47 20.00 39.00 21.80 16.60 0 19.0 0.15
48 22.00 43.00 22.20 16.94 0 21.0 0.14
49 21.00 39.00 19.20 17.13 0 18.0 0.13
50 21.00 35.00 16.80 17.91 0 14.0 0.13
51 14.00 33.00 19.20 15.40 0 19.0 0.23
52 16.00 33.00 16.90 16.57 0 17.0 0.18
53 14.00 27.00 20.70 16.09 0 13.0 0.17
54 18.00 34.00 20.80 15.61 0 16.0 0.25
55 18.00 34.00 21.20 15.61 0 16.0 0.19
56 12.00 24.00 20.70 16.14 0 12.0 0.17
57 18.00 32.00 26.50 14.90 0 14.0 0.18
58 20.00 30.00 20.00 15.21 0 10.0 0.08
59 18.00 27.00 13.60 17.10 0 9.00 0.11
60 45.00 112.00 88.10 7.34 67.0 0.87
61 43.00 120.00 101.0 6.69 0 77.0 1.07
62 45.00 122.00 0 108.5 6.85 0 77.0 0.90
63 45.00 130.00 0 111.5 6.40 0 85.0 1.00
64 38.00 96.00 0 65.80 9.35 0 58.0 0.50
65 46.00 104.00 93.60 7.29 0 58.0 0.99
66 70.00 164.00 132.7 5.49 0 94.0 1.43
67 44.00 124.00 0 101.4 7.09 0 80.0 1.02
68 43.00 109.00 0 103.9 6.90 0 66.0 0.82
69 69.00 166.00 0 129.3 5.70 0 97.0 1.42
70 42.00 121.00 0 109.4 6.51 0 79.0 1.13
71 16.00 29.00 0 13.40 17.46 0 13.0 0.14
0

400


2.00 2.00

1.50 1.50
(Cc)predicted

(Cc)Predicted
1.00 1.00

0.50
0.50

0.00
0.00 0.50 1.00 1.50 2.00 0.00
0.00 0.50 1.00 1.50 2.00
(C bserved
c)O (C bserved
c)O

Fig 2 Predicted Vs Observed Cc Fig 3 Predicted Vs Observed Cc
(Model E11) (Model E12)

2.00
2.00

1.50
(Cc)Predicted

1.50
(C c )P r ed ic te d

1.00
1.00

0.50
0.50

0.00
0.00
0.00 0.50 1.00 1.50 2.00
0.00 0.50 1.00 1.50 2.00
(C bserved
c)O
(Cc)Observed

Fig 4 Predicted Vs Observed Cc Fig 5 Predicted Vs Observed Cc
(Model E13) (Model E15)

401

6. CONCLUSIONS

Based on One-Dimensional Consolidation tests on fifteen different soils, fifteen
regression models were developed relating Compression Index with each of the
compositional factors (Liquid Limit and Plasticity Index) and environmental factors (Dry
Density and Initial Moisture Content) alone as well as with all the possible combinations of
these parameters. Compression Index is found to bear good correlation with any of the
compositional factors and any combination of compositional and environmental factors. The
degree of association between compression index and each of the compositional and
environmental factors is assessed statistically by evaluating the partial correlation
coefficients. Statistical evaluation revealed that all the four parameters namely Liquid Limit
and Plasticity Index among the compositional factors and Dry Density and Initial Moisture
Content among the environmental factors are found to have significant influence on
prediction of Compression Index. Hence the model developed using all the four influencing
parameters is expected to have more general applicability than any other model which is
confirmed by verification with the others data . The models developed using atleast one
compositional factor and both the environmental factors were also found to be fair to good.

7. REFERENCES

Journal Papers
1. AMITHNATH and DEDALAL, S.S. (2004). The role of plasticity index in predicting
Compression Index behaviour of clays. Electronic Journal of Geotechnical
Engineering, http://www. ejge.com/2004/Per0466/Ppr0466.htm
2. CHANDRASEKHAR, M., MALLIKARJUNA, P. and PRADIPKUMAR, G.N.
(2005). Empirical modeling and correlation analysis of evapotranspiration: A case
study. ISH Journal of Hydraulic Engineering, Vol. 11, No.2, pp. 1-17.
3. JIAN- HUA YIN (1999). Properties and Behaviour of Hong Kong Marine Deposits
with Different Clay Contents. Canadian Geotechnical Journal, Vol 36, pp. 1085 -
1095.
4. KOPPULA, S. D. (1981). Statistical Estimation of Compression Index. ASTM
Geotechnical Testing Journal, Vol 4, No.2, pp 68 -73.
5. NISHIDA, Y. (1956). A Brief Note on the Compression Index of Soil. Journal of Soil
Mechanics and. Foundation Division, American Society of Civil Engineers, Vol 82,
No.3, pp1-14.
6. OSWALD, R. H. (1980). Universal Compression Index Equation. Journal of
Geotechnical. Engineering Division: American Society of Civil Engineers, Vol 106,
pp.1179-1200.
7. SKEMPTON, A. W. (1944). Notes on the Compressibility of Clays. Quarterly Journal
of Geotechnical Society. London, Vol 100, pp.119-135.
8. SRIDHARAN, A. and NAGARAJ, H.B. (2001). Compressibility behaviour of
remoulded fine-grained soils and correlation with index properties. Canadian
Geotechnical Engineering Journal, No. 38, pp. 1139-1154.
9. N.Ganesan, Bharati Raj, A.P.Shashikala and Nandini S.Nair, “Effect Of Steel Fibres
On The Strength And Behaviour Of Self Compacting Rubberised Concrete”
International Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue2,
2012, pp. 94 - 107, Published by IAEME

402

10. S.R.Debbarma and S.Saha, “An Experimental Study On Growth Of Time-Dependent
Strain In Shape Memory Alloy Reinforced Concrete Beams And Slabs” International
Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue2, 2012,
pp. 108 - 122, Published by IAEME
11. Sadam Hade Hussein, Kamal Nasharuddin Bin Mustapha, Zakaria Che Muda, Salmia
Budde, “Modeling Of Ultimate Load For Lightweight Palm Oil Clinker Reinforced
Concrete Beams With Web Openings Using Response Surface Methodology”
International Journal of Civil Engineering & Technology (IJCIET), Volume3, Issue1,
2012, pp. 33-44, Published by IAEME
12. Sadam H. Hussein, Kamal Nasharuddin Bin Mustapha, and Zakaria Che Muda,
“Modeling Of First Crack For Lightweight Palm Oil Clinker Reinforced Concrete
Beams With Web Openings By Response Surface Methodology” International
Journal of Civil Engineering & Technology (IJCIET), Volume2, Issue2, 2011, pp. 13
- 24, Published by IAEME
13. A.S Jeyabharathy, Dr.S.Robert Ravi, and Dr.G.Prince Arulraj “Finite Element
Modeling Of Reinforced Concrete Beam Column Joints Retrofitted With Gfrp
Wrapping” International Journal of Civil Engineering & Technology (IJCIET),
Volume2, Issue1, 2011, pp. 35-39, Published by IAEME
Books:
14. BOWLES, J. W. (1979). Physical and Geotechnical Properties of Soils. New York:
McGraw Hill.
15. HOUGH, B. K. (1957). Basic Soil Engineering. New York: Ronald.
16. IS: 2132 (1986) (Reaffirmed 1997). Code of Practice for Thin-Walled Tube Sampling
of Soils, New Delhi: Bureau of Indian Standards.
17. SP: 36(Part I) (1987)). Compendium of Indian Standards on Soils for Civil
Engineering Purposes, New Delhi: Bureau of Indian Standards.
18. MITCHELL, J.K. (1993). Fundamentals of Soil Behavior. New York: John Wiley
and Sons.
19. MONTGOMERY, D.C. and RUNGER, G.C. (1999). Applied Statistics and
Probability for Engineers. 2nd Edition, pp 483-560, New York: John Wiley and Sons.
20. TERZAGHI, K. and PECK. R. B. (1967). Soil Mechanics in Engineering Practice.
New York: John Wiley and Sons.
21. YEVJEVICH, V. (1972). Probability and Statistics in Hydrology. Colarado: Water
Resources Publications.

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