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http://www.iaeme.com/IJARET/index.asp 86 [email protected]
International Journal of Advanced Research in Engineering and Technology
(IJARET) Volume 6, Issue 12, Dec 2015, pp. 86-103, Article ID: IJARET_06_12_009
Available online at
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=12
ISSN Print: 0976-6480 and ISSN Online: 0976-6499
© IAEME Publication
MATHEMATICAL MODEL TO PREDICT
COMPRESSION INDEX OF UNIFORM
LOOSE SAND IN COASTAL AREA OF
DEGEMA, RIVERS STATE OF NIGERIA
Eluozo. S. N
Subaka Nigeria Limited Port Harcourt Rivers State of Nigeria
Director and Principal Consultant Civil and Environmental Engineering,
Research and Development
Ode T
Department of Civil Engineering, faculty of Engineering
Rivers State University of Science and Technology Port Harcourt
ABSTRACT
Predicting the compression index applying mathematical model for loose
dense sand has been thoroughly developed, this is to monitor the rate of
compression during settlement of loose dense sand, the model were generated
to monitor the compression index of uniform loose sand in coastal area of
Degema, the study express compression index at various depth within the
specified range, the generated model produced simulation values compared
with the measured values, both parameters developed faviourable fits, the
compression index expressed linear increase to the optimum level at different
depth, the study has also express the rate of homogeneity of the strata in
various formations, these developed model will definitely be applied to predict
the compression index for uniform loose sand under the influences of
settlement in caring any impose load .
Key words: Mathematical Model, Compression Index and Uniform Loose
Sand
Cite this Article: Eluozo. S. N and Ode T, Mathematical Model To Predict
Compression Index of Uniform Loose Sand In Coastal Area of Degema,
Rivers State of Nigeria. International Journal of Advanced Research in
Engineering and Technology, 6(12), 2015, pp. 86-103.
http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=6&IType=12
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 87 [email protected]
1. INTRODUCTION
Compression of soil structural setting basic aspect of soil deformation,, compression
of such behaviour normally forms the base of modelling thus the stress-strain
relationships of soils (e.g., Pestana and Whittle, 1995; Hong and Onitsuka, 1998;
Potts and Zdravkovic 1999; Baudet and Stallebrass, 2001; Liu et al, 2011 Martin et
al 2013). The perceptive of compression behaviour has consequently in normal
setting been imperative to geological and geotechnical engineering practice, the
research for such soil condition has been on course for many decades (e.g., Skempton,
1944; Bowles, 1989; Butterfield and Baligh, 1996; Desai, 2001; and Chai et al, 2004).
Stress is one of the common conditions which soils have been subjected; the
surroundings in which they are shaped thus the time that has lapsed on the
geotechnical time scale over numerous steps of their formation have been recognized
as prospective factors in their compressibility. It has been observed that soils age and
creep over time; hence, bonds build up at particle contacts in natural clay, which can
also be considered as “structured clay” (Leonards, 1972; Leroueil et al., 1979;
Michell, 1996; and Shibuya, 2000; etc.). The resistances of soil formation are known
to be responsible for the various conditions in the engineering behaviour of natural
soils; these are between the structured and the destructured (reconstituted) states
(Leroueil et al., 1979 and 1983; Hanzawa and Adachi, 1983; Leroueil and Vaughan,
1990; Mitchell, 1996; and Shibuya, 2000 Martin et al 2013). reconstituted clay is
intrinsic base on compression curve such as (devoid of soil structure) and is normally
applied as a frame of reference for the behaviour of naturally and artificially soils
formation (Burland, 1990; Nagaraj and Miura, 2001; Nagaraj et al., 1990 and 1998)
these are some conditions that couild be applied as a basis for modelling the
behaviour of soils in the structured state (e.g., Liu et al, 2000; Masín, 2007;
Hinchberger and Qu, 2009; Horpibulsuk et al, 2007, 2010; 2013; Suebsuk et al., 2010
and 2011). Moreover, reconstituted clay including its characteristic are applied as a
liner for landfill and a fill for reclaimed area, and the compressibility of the
reconstituted clay is one of the required design parameters. In his fortieth Rankine
lecture, Burland (1990) introduced the concept of void index and performed a
systematic study on the compression behaviour of clays via the void index. The
current research is carried out based on Burland’s original work and subsequent
research by 136 others (e.g., Amorosi and Rampello, 2007; Bobet et al, 2011; Hong et
al, 2012).
2. GOVERNING EQUATION
02
2
dx
dck
dx
dcV
dx
cdVt o
(1)
Nomenclature
V = Velocity of fluid
k = Permeability
Vo = Void Ratio
Cc = Compression index
Z = Depth
002
2
dx
dcKV
dx
cdVt (2)
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 88 [email protected]
Let
0n
n
n xaC
1
11
n
n
n xnaC
2
211 1n
n
n xannC
011
1
0
2
2
n
n
n
n
n
n xnaVxannVt (3)
Replace n in the 1st term by n+2 and in the 2
nd term by n+1, so that we have;
01120
1
0
2
n
n
no
n
n
n xanVxannVt (4)
i.e. 102 112 nn anKVannVt
(5)
12
1 102
nnVt
anKVa n
n
(6)
2
102
nVt
akVa n
n
(7)
for
Vt
aKVan
2,0 10
2
(8)
(9)
Subject equation (16) to the following boundary condition
HoCandoC 10
xVt
kV
aaxC
0
10
010 aaoC
i.e. 010 aa (10)
x
Vt
KV
aVt
VxC
0
101
!2
Ha
Vt
KVoC
1
01
!2
xVt
kV
aaxC
0
10
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 89 [email protected]
KV
HVta
0
1
(11)
Substitute (10) into equation (11)
01 aa
KV
HVta
0
0 (12)
Hence, the particular solution of equation (16) is of the form:
xVt
kV
KV
HVt
KV
HVtxC
0
00
10
0
xVt
KV
KV
HVtxC (13)
3. MATERIALS AND METHOD
Standard laboratory experiment where performed to monitor compression index of
loose dense sand at different formation, the soil deposition of the strata were collected
in sequences base on the structural deposition at different locations, this samples
collected at different location generated variations at different depth producing
deposition of stiff clay compression at different strata, the experimental result are
applied to be compared with the theoretical values to determined the validation of the
model.
4. RESULT AND DISCUSSION
Results and discussion are presented in tables including graphical representation of
compression index of loose dense sand
Table 1 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.002
0.4 0.004
0.6 0.0066
0.8 0.0088
1 0.011
1.2 0.0132
1.4 0.0154
1.6 0.0176
1.8 0.0198
2 0.022
2.2 0.0242
2.4 0.0264
2.6 0.0286
2.8 0.0308
3 0.033
3.2 0.0352
3.4 0.0376
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 90 [email protected]
Depth [M] Predictive of loose sand Cc
3.6 0.0396
3.8 0.0418
4 0.044
4.2 0.0462
4.4 0.0484
4.6 0.0506
4.8 0.0528
5 0.055
Table 2 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.002 0.00211
0.4 0.004 0.00431
0.6 0.0066 0.00651
0.8 0.0088 0.00871
1 0.011 0.0109
1.2 0.0132 0.0131
1.4 0.0154 0.0153
1.6 0.0176 0.0175
1.8 0.0198 0.0197
2 0.022 0.0219
2.2 0.0242 0.0241
2.4 0.0264 0.0263
2.6 0.0286 0.0285
2.8 0.0308 0.0307
3 0.033 0.0329
3.2 0.0352 0.03511
3.4 0.0376 0.0373
3.6 0.0396 0.03951
3.8 0.0418 0.0417
4 0.044 0.0439
4.2 0.0462 0.04611
4.4 0.0484 0.04831
4.6 0.0506 0.0505
4.8 0.0528 0.05271
5 0.055 0.0549
Table 3 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.00289
0.4 0.0056
0.6 0.0084
0.8 0.0112
1 0.014
1.2 0.0168
1.4 0.0196
1.6 0.0224
1.8 0.0252
2 0.026
2.2 0.03
2.4 0.0336
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 91 [email protected]
Depth [M] Predictive of loose sand Cc
2.6 0.0364
2.8 0.0392
3 0.042
3.2 0.048
3.4 0.0476
3.6 0.0504
3.8 0.0532
4 0.056
Table 4 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.00289 0.0028
0.4 0.0056 0.0056
0.6 0.0084 0.0084
0.8 0.0112 0.0112
1 0.014 0.014
1.2 0.0168 0.0167
1.4 0.0196 0.0188
1.6 0.0224 0.0221
1.8 0.0252 0.0262
2 0.026 0.024
2.2 0.03 0.034
2.4 0.0336 0.035
2.6 0.0364 0.038
2.8 0.0392 0.041
3 0.042 0.045
3.2 0.048 0.049
3.4 0.0476 0.051
3.6 0.0504 0.052
3.8 0.0532 0.054
4 0.056 0.058
Table 5 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.013
0.4 0.024
0.6 0.036
0.8 0.052
1 0.066
Table 6 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.013 0.0127
0.4 0.024 0.0237
0.6 0.036 0.0361
0.8 0.052 0.04988
1 0.066 0.07
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 92 [email protected]
Table 7 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.0038
0.4 0.0076
0.6 0.011
0.8 0.015
1 0.019
1.2 0.0228
1.4 0.0266
1.6 0.0304
1.8 0.0342
2 0.038
2.2 0.0418
2.4 0.046
2.6 0.0495
2.8 0.052
3 0.057
Figure 8 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.0038 0.00378
0.4 0.0076 0.00758
0.6 0.011 0.0114
0.8 0.015 0.0152
1 0.019 0.0189
1.2 0.0228 0.02278
1.4 0.0266 0.0266
1.6 0.0304 0.0304
1.8 0.0342 0.03418
2 0.038 0.03798
2.2 0.0418 0.04178
2.4 0.046 0.04558
2.6 0.0495 0.04938
2.8 0.052 0.05318
3 0.057 0.05698
Table 9 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.0088
0.4 0.017
0.6 0.0264
0.8 0.0352
1 0.044
1.2 0.052
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 93 [email protected]
Figure 10 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.0088 0.0059
0.4 0.017 0.0172
0.6 0.0264 0.02579
0.8 0.0352 0.03439
1 0.044 0.04299
1.2 0.052 0.05159
Table 11 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.0024
0.4 0.004
0.6 0.006
0.8 0.008
1 0.01
1.2 0.012
1.4 0.014
1.6 0.016
1.8 0.018
2 0.02
2.2 0.022
2.4 0.024
2.6 0.026
2.8 0.028
3 0.03
3.2 0.032
3.4 0.034
3.6 0.036
3.8 0.038
4 0.04
4.2 0.042
4.4 0.044
4.6 0.046
4.8 0.048
5 0.05
Figure 12 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.0024 0.00206
0.4 0.004 0.00406
0.6 0.006 0.00606
0.8 0.008 0.00803
1 0.01 0.01
1.2 0.012 0.012
1.4 0.014 0.016
1.6 0.016 0.018
1.8 0.018 0.024
2 0.02 0.026
2.2 0.022 0.03
2.4 0.024 0.032
Eluozo. S. N and Ode T
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2.6 0.026 0.034
2.8 0.028 0.036
3 0.03 0.038
3.2 0.032 0.039
3.4 0.034 0.041
3.6 0.036 0.044
3.8 0.038 0.046
4 0.04 0.047
4.2 0.042 0.048
4.4 0.044 0.049
4.6 0.046 0.05
4.8 0.048 0.051
5 0.05 0.053
Table 13 Predictive Values of loose sand compression index at Different Depth
Depth [M] Predictive of loose sand Cc
0.2 0.014
0.4 0.0208
0.6 0.0312
0.8 0.0416
1 0.052
Table 14 Predicted and Measured of compression index for loose sand at Different Depth
Depth [M] Predictive of loose sand Cc Measured Values of loose sand Cc
0.2 0.014 0.01308
0.4 0.0208 0.02112
0.6 0.0312 0.03012
0.8 0.0416 0.04008
1 0.052 0.0576
Figure 1 Predictive Values of loose sand compression index at Different Depth
y = 0.011x - 9E-05 R² = 1
0
0.01
0.02
0.03
0.04
0.05
0.06
0 2 4 6
pre
dic
tive
val
ue
s fo
r lo
ose
de
nse
sa
nd
Depth [M ]
Predictive of loose sand Cc
Linear (Predictive of loose sand Cc)
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 95 [email protected]
Figure 2 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 3 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0 2 4 6
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
den
se
san
d o
n c
om
pre
ssio
n in
dex
Depth [ m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.0141x - 0.0002 R² = 0.9973
0
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4 5
pre
dic
tive
val
ues
fo
r lo
ose
den
se s
and
Depth [m]
Predictive of loose sand Cc
Linear (Predictive of loose sand Cc)
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 96 [email protected]
Figure 4 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 5 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 1 2 3 4 5
pre
dic
tive
an
d m
easu
red
val
ue
s fo
r lo
ose
de
nse
san
d
on
co
mp
ress
ion
ind
ex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.0179x2 + 0.0456x + 0.003 R² = 0.9989
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0 0.2 0.4 0.6 0.8 1 1.2
pre
dic
tive
val
ues
fo
r lo
ose
den
se s
and
Depth [m]
Predictive of loose sand Cc
Poly. (Predictive of loose sand Cc)
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 97 [email protected]
Figure 6 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 7 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0 0.5 1 1.5
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
den
se
san
d o
n c
om
pre
ssio
n in
dex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.019x - 2E-05 R² = 0.9996
0
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4
pre
dic
tive
val
ues
of
loo
se d
ense
san
d
Depth[m]
Predictive of loose sand Cc
Linear (Predictive of loose sand Cc)
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 98 [email protected]
Figure: 8 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 9 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
den
se
san
d o
n c
om
pre
ssio
n in
dex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.0437x - 1E-05 R² = 0.9995
0
0.01
0.02
0.03
0.04
0.05
0.06
0 0.5 1 1.5
pre
dic
tive
val
ues
fo
r lo
ose
den
se s
and
Depth [m]
Predictive of loose sand Cc
Linear (Predictive of loose sand Cc)
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 99 [email protected]
Figure 10 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 11 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0 0.5 1 1.5
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
d
en
se s
and
on
co
mp
ress
ion
ind
ex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.01x + 6E-05 R² = 1
0
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4 5 6
pre
dic
tive
val
ues
fo
r lo
ose
den
se s
and
Depth [m]
Predictive of loose sand Cc
Linear (Predictive of loose sand Cc)
Eluozo. S. N and Ode T
http://www.iaeme.com/IJARET/index.asp 100 [email protected]
Figure 12 Predicted and Measured of compression index for loose sand at Different
Depth
Figure 13 Predictive Values of loose sand compression index at Different Depth
0
0.01
0.02
0.03
0.04
0.05
0.06
0 2 4 6
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
d
en
se s
and
on
cm
pre
ssio
n in
dex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
y = 0.0129x2 + 0.033x + 0.0065 R² = 0.9984
0
0.01
0.02
0.03
0.04
0.05
0.06
0 0.2 0.4 0.6 0.8 1 1.2
pre
dic
tive
val
ues
fo
r lo
ose
den
se s
and
Depth [m]
Predictive of loose sand Cc
Poly. (Predictive of loose sand Cc)
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 101 [email protected]
Figure 14 Predicted and Measured of compression index for loose sand at Different
Depth
The figure presented express the generated express the deposition of loose dense
sand in various depositions, figure one and two shows the uniformity of the deposition
structured in homogeneous condition, linear increase were observed in the
compression of the strata at different formation to the optimum depth porosity were
more experienced within the [0.2-1.4] these implies that the permeability in terms of
fluid flow experienced low deposition more, but the compression index of the
formation observed linear deposition to five metres, comparative analysis between
predictive and measured developed best fits as stated in figure two observing the same
linear increase between both parameters. Figure three and four observed different
depositions on compression index of the soil in different depth to optimum level, low
permeability were observed between [0.2-0.6]. while porosity express higher
deposition, thus pressure the deposition of compression in loose dense sand,
fluctuation in compression were observed on the exponential deposition of
compression index for loose dense sand to the optimum level. Comparative process
between predictive and measured values express similar fluctuation thus developed
best fits. While five and six were ex press slight different deposition compare to
previous figures, the prediction of the compression were monitored between the
specified ranged thus from [0.2-1.0M],gradual increase were experienced from the
lowest to the optimum level, the measured compared with predictive maintained
faviourable fits, figure seven and eight experienced slight higher porosity but
maintained linear homogeneity in compression to the optimum depth of prediction to
three metres, comparing these predictive values with experimental data best fits were
observed in figure eight, while figure nine and ten obtained exponential state to the
optimum level of loose dense soil at [1.0M], the formation observed homogeneous
setting as it was structured in the formation, compression of the loose dense sand were
pressured by these characteristics, the predictive were compared with the measured
values, both parameter express the best fits. Figure eleven and twelve express linear
increase of compression index, but the measured developed fluctuation from [2-6M],
these can be attributed to the depositional variation of some formation characteristic at
various depth thus predicted within the specified standard of loose dense sand.. Figure
thirteen and fourteen developed its compression between the specified ranged for
loose dense sand but at more shallow depth, these implies that the developed model
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0 0.5 1
pre
dic
tive
an
d m
easu
red
val
ues
fo
r lo
ose
den
se s
and
on
co
mp
ress
ion
in
dex
Depth [m]
Predictive of loose sand Cc
Measured Values of loose sand Cc
Eluozo. S. N and Ode T
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can be applied at any depth for the determination of compression index, linear
increase were experienced between the predicted depth thus within the specified for
such type of formation, comparing predictive and measured both parameters express
faviourable fits validating the developed model for the study.
5. CONCLUSION
The loose dense sand compression in the developed model has been thoroughly
expressed, the compression index of the soil were to monitored at various depth to
predict within the specified range, several experts has been applying the experimental
approach to monitor, thus empirical solution has been applied to predict compression
index of the soil, but these conceptual framework using analytical method has not
been applied by other experts, this concepts developed the generated model that
produces predictive values for loose dense sand specified within the range for loose
dense sand , several prediction at different depth producing the specified compression
within the range for loose dense sand , the predictive values were compared with
measured valued , both parameters generated faviourable fit validating the developed
model for loose dense sand.
REFERENCES
[1] Amorosi, A and Rampello, S 2007, ‘An experimental investigation into the
mechanical behaviour of a structured stiff clay’, Géotechnique, vol.57,
pp.153-166.
[2] Baudet, BA and Stallebrass, SE 2001, ‘Modelling the destructuring of soft
natural clays’ Computer Methods and Advances in Geomechanics, vol.1,
pp.297-302.
[3] Bobet, A, Hwang, J, Johnston, CT and Santagata, M 2011, ‘One-dimensional
consolidation behavior of cement-treated organic soil’, Canadian
Geotechnical Journal, Vol.48 (7), pp.1100-1115.
[4] Bowles, JE 1989, Physical and Geotechnical Properties of Soils, McGraw-
Hill, New York
[5] Burland JB 1990, ‘On the compressibility and shear strength of natural soils’,
Géotechnique, vol.40, pp.329-378
[6] Chai, JC, Miura, N, and Zhu, HH 2004, ‘Compression and consolidation
characteristics of structured natural clays’, Canadian Geotechnical Journal,
vol.41, no.6, pp.1250-1258.
[7] Desai, CS and Toth, J 1996, ‘Disturbed state constitutive modelling based on
stress-strain and 505 non-destructive behaviour’, Int. J. of Solids and
Structures, vol.33, pp.1619-1650
[8] Hong, Z and Onitsuka, K 1998, ‘A method of correcting yield stress and
compression index 519 of Ariake clays for sample disturbance’, Soils and
Foundations, vol.38 (2), pp.211-222.
[9] Hanzawa, H and Adachi, K 1983, ‘Overconsolidation of alluvial clays’, Soils
and 513 Foundations, vol.23 (4), pp.106-118.
[10] Horpibulsuk, S, Liu MD, Liyanapathirana, S and Suebsuk, J 2010,
‘Behaviour of cemented clay simulated via the theoretical framework of the
SCC model’, Computers and Geotechnics, vol.37 (1), pp.1-9.
[11] Horpibulsuk, S, Yangsukaseam, N, Chinkulkijniwat, A 525, and Du, YJ
2011, ‘Compressibility and permeability of Bangkok clay compared with
kaolinite and bentonite’, Applied Clay Science, Vol.52, pp.150-159.
Mathematical Model To Predict Compression Index of Uniform Loose Sand In Coastal Area
of Degema, Rivers State of Nigeria
http://www.iaeme.com/IJARET/index.asp 103 [email protected]
[12] Horpibulsuk, S, Rachan, R, Suddeepong, A, Liu, MD and Du, YJ 2013,
‘Compressibility of lightweight cemented clays’, Engineering Geology,
Vol.159, pp.59-66.
[13] Lancellotta, R and Pepe, C 1990, “Mechanical behaviour of upper Pisa clay”,
Internal report, Technical University of Torino
[14] Liu MD and Carter JP 1999, ‘Virgin compression of structured soils’,
Géotechnique, vol.49 (1), pp.43-57.
[15] Liu, MD, Carter, JP, Desai, CS and Xu, KJ 2000, ‘Analysis of the
compression of structured soils using the disturbed state concept’, Int. J. for
Numerical and Analytical Methods in Geomechanics, vol.24, pp.723-735.
[16] Leonard, GA 1972, Discussion of “Shallow foundation”, Proceedings of
ASCE Spec. Conf. on Perf. of Earth and Earth supported Struct., ASCE, New
York, vol.3, pp.169-173.
[17] Leroueil, S and Vaughan, PR 1990, ‘The general and congruent effects of
structure in natural soils and week rock’, Geotechique, 40, pp.467-488.
[18] Leroueil, S, Tavenas, F, Brucy, F, La Rochelle, P, and Roy, M 1979,
‘Behavior of destructured natural clays’. Journal of Geotechnical
Engineering, ASCE, vol.105 (6), pp.759-778.
[19] Leroueil, S, Tavenas, F, Samson, L, and Morin, P 1983, ‘Preconsolidation
pressure of Champlain clay. Part II: Laboratory determination’, Canadian
Geotechnical Journal, vol.20 (4), pp.803-816.
[20] Masín, D 2007, ‘A hypoplastic constitutive model for clays with meta-stable
structure’ Canadian geotechnical journal, vol.44 (3), pp.363-375.
[21] Mitchell, JK 1996, Fundamentals of soil behavior, John Willey&Sons Inc.,
New York, 437p
[22] Pestana, JM and Whittle, AJ 1995, ‘Compression model for cohesionless
soils’, Géotechnique, vol.45, pp.621-631.
[23] Potts, DM and Zdravkovic, L 1999, Finite Element Analysis in Geotechnical
Engineering: Theory, Thomas Telford, London
[24] Nagaraj, TS, Srinivasa Murthy, BR, Vatsala, A and Joshi, RC 1990,
‘Analysis of compressibility of sensitive clays’, Journal of Geotechnical
Engineering, ASCE, vol.116 (GT1), pp.105-118.
[25] Nagaraj, TS, Pandian, NS, and Narasimha Raju, PSR 1998, ‘Compressibility
behavior of soft cemented soils’, Geotechnique, vol.48(2), pp.281-287
[26] Shibuya, S 2000, ‘Assessing structure of aged natural sedimentary clays’,
Soils and Foundations, vol.40 (3), pp.1-16.
[27] Skempton, AW 1944, ‘Notes on compressibility of clays’, Qaurterly Journal
of the Geological Society, London, vol. 100(2), pp.119-135.
[28] Suebsuk, J, Horpibulsuk, S, and Liu, MD 2010, ‘Modified Structured Cam
Clay: A constitutive model for destructured, naturally structured and
artificially structured clays’, Computers and Geotechnics, vol.37 (7-8),
pp.956-968.
[29] Suebsuk, J, Horpibulsuk, S and Liu, MD 2011, 'A critical state model for
overconsolidated structured clays’, Computers and Geotechnics, vol.38,
pp.648-658
[30] Martin D. liu and Ziling Zhuang Suksun H 2013 Estimation of the
compression behaviour of Reconstituted clays Geology, 167 (December), 84-
94.
[31] S.O. Nkakini and Ndor .M.Vurasi, Ergonomic Evaluation of Lawn Mower
Operation for Comfort in Rivers State, Nigeria. International Journal of
Advanced Research in Engineering and Technology, 6(7), 2015, pp. 43-51