Research Journal of Environmental and Earth Sciences 4(5): 553-559, 2012
ISSN: 2041-0492
© Maxwell Scientific Organization, 2012
Submitted: March 23, 2012
Accepted: April 14, 2012
Published: May 15, 2012
Predicting Compaction Characteristics of Lateritic Soil of Western Niger
Delta, Nigeria
Felix C. Ugbe
Department of Geology, Delta State University, Abraka
Abstract: The study was undertaken on the lateritic soils of Western Niger Delta. These soils are generally
sandy clays and clayey sands. A total of 152 samples were collected and tested for fines percent, liquid limit,
specific gravity, maximum dry density, optimum moisture content using BS procedures. Predictive equations
were developed to relate fines percent, liquid limit and specific gravity to compaction characteristics. Following
the development of the predictive equations a new set of 47 soil samples were collected, tested and their results
were used to validate the predictive equation. Validating the predicting equations using new set of field data
yielded prediction of 80 and 90% for MDD and OMC, respectively.
Keywords: Compaction, Niger delta, predictive equations
fines percent significantly influence compaction
characteristics of lateritic soil within Niger Delta. Ugbe
(2011) estimated compaction characteristics fines in A-2
type lateritic soil from western Niger Delta. Omar et al.
(2003) found that compaction characteristics are also
influenced by specific gravity and liquid limit in addition
to fines.
No study has been attempted to predict compaction
characteristics from fines, specific gravity and liquid limit
in lateritic soils of Niger Delta. The study is therefore
aimed at predicting compaction characteristics from three
geotechnical properties instead of only one parameter. It
is expected that these three properties will best explain
compaction characteristics of lateritic soils of Niger Delta
rather than depending on only one parameter.
INTRODUCTION
Lateritic soils are the product of tropical weathering
with red, reddish brown or dark brown color with or
without concretion found below hardened ferruginous
crusts (Ola, 1978). These soils are found in the dry flat
lands of eastern and western Niger Delta. (Fig. 1)
The general geology of the study area have been
described by various researchers as comprising of various
types of Quaternary and Tertiary deposits (Allen, 1970;
Ejedawe, 1981; Petters, 1984; Merki, 1972; Nwachukwu
and Chukwura, 1986; Weber and Daukoru, 1975;
Statcher, 1995; Reijers et al., 1996). Short and Stauble
(1967) recognized three main subsurface lithostratigraphic
unit ranging from the oldest to the youngest Akata,
Agbada and Benin Formations.
In western Niger Delta, lateritic soils have been
immensely utilized as road construction materials.
However, these lateritic soil require improvement before
utilization. Soil improvement is generally defined as the
alteration of any property of the soil to improve its
engineering performance (Lambe and Whiteman, 1969).
The most common method of soil improvement is
densification using compaction test. Appreciably huge
quantity of soil samples is usually required for effective
compaction test. However because of the inaccessibility
of some terrains in Niger Delta due to high precipitation
during the wet season, such bulk samples may not be
easily obtainable. One way to tackle this challenge is to
deduce compaction characteristics from simpler and less
laborious test which require smaller quantity of samples
(Johnson and Shallberg, 1960; Winterkorn, 1967).
Kofiatis and Manifopoulous (1982) have previously
predicted compaction characteristics from other simpler
geotechnical test. Akpokodje (1987) emphasized that
MATERIALS AND METHODS
A total of 152 bulk samples were collected in the
study area which covers parts of Edo and Delta State of
Nigeria. The area is accessible from Benin, Warri and
Asaba (Fig. 2). Ugbe (2011) had earlier investigated the
basic index properties (Table 1, Fig. 3, 4, 5 and 6).
The investigation shows that the soil is fine to
medium grained soil and mainly clayey sands and sandy
clays. The results from the geotechnical tests were in
January 2012, subjected to statistical and qualitative
analyses, Analysis of Variance (ANOVA). Correlation
and regression analysis were also carried out to:
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553
Evaluate the difference between the means of
independent variables so as to test for statistical
significance of the data set.
Detect and measure correlation among and between
each other index properties and compaction
Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
Fig. 1: The major geomorphic units of the Niger delta (Allen, 1970)
Fig. 2: Map of study area
Table 1: Statistical summary of test results
Variables
Range of values
Fines (%)
14-56
Liquid (%)
22.20-48.30
Maximum dry density (%)
1700-2140
Optimum mixture content (%) 7.7-18.00
Specific gravity
2.50-2.83
N = 152
C
A multiple regression analysis (step-wise regression)
was performed to select the variables that best accounted
for prediction of compaction characteristics in the
presence of other variables. The output gave a summary
of all variables that entered the equation and their
respective contributions at the point of entry.
Compaction characteristics (maximum dry density
and optimum moisture content) were used as dependent
variables while fine percent, specific gravity of solids and
liquid limit were used as independent variables.
The computer software programme statistical
package for social sciences (SPSS/PC, 1999) was used to
develop regression model for the data. To arrive at the
Mean
33.54
36.39
1961.81
11.30
2.62
characteristics both singly and on a group in order to
predict compaction characteristics.
Model compaction characteristics as a function of
significant data set in order to access the variables
that contribute significantly to the prediction of
compaction characteristics.
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Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
Fig. 3: Particle size distribution of soils of study area (A-2 type) (Ugbe, 2011)
Fig. 4: Particle size distribution curve of soils of study area (A-6 type) (Ugbe, 2011)
best predictive model, four factors were considered
namely:
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ranges from 0 to 1. Values of zero are obtained when all
of the regression is zero (i.e., no relationship exist
between the independent and dependent variables) and a
value of 1 indicate a perfect fit (i.e., all observed data are
exactly predicted by the model).
Model goodness-of-fit
Model predicted accuracy
Model selection
Model validation
Model prediction accuracy: The prediction accuracy of
the regression model is commonly assessed by the
Standard Error of Estimate (SEE). A model with good
prediction capability will have low value of SEE.
Model goodness-of-fit: The coefficient of model
correlation, denoted by R2, is usually employed in a
goodness of fit measure of a regression model. This
coefficient measures, the reduction in the model error
associated with the use of the independent variable. R2
Model selection: To arrive at a reliable prediction model,
the model should:
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Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
Fig. 5: Particle size distribution curve of soils of study area (A-7 type) (Ugbe, 2011b)
Fig. 6: Casagrande plot of soils from study area (Ugbe, 2011b)
C
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compute the compaction characteristics of samples. These
were compared with the laboratory results obtained for
MDD and OMC values to validate the model developed.
Pass the F and t-test with a preselected x-significance
value (usually 0.05)
Possess a high value of R2
Have a low value of SEE
RESULTS AND DISCUSSION
Model validation: Model validation was conducted to
access the performance of the model in predicting
compaction characteristics. Forty seven fresh samples
within the region were obtained. Laboratory testing was
conducted to obtain geotechnical properties such as
specific gravity of solids, fines percent, liquid limit,
Maximum Dry Density (MDD), Optimum Moisture
Content (OMC). The values for liquid limit, specific
gravity, fines percent, maximum dry density and moisture
content was determined (Table 2). The values of liquid
limit, fine percent, and specific gravity were utilized to
Table 3 presents the bivariate relationship between
the geotechnical properties of the soil. Positive relations
(R>0.8) exist between OMC, fine and liquid limit. On the
other hand negative relations exist between fines, MDD
and specific gravity. The underlying reasons for both
positive and negative relations are presented in Table 4.
Predictive models: Table 5 and 6 show stepwise
regression of the compaction characteristics with other
geotechnical properties of the soil, the percentage of fines
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Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
Table 2:Some geotechnical test result of soils from other areas to test
the predictive model
Sample
FINES
L.L
M.D.D
OMC
No
(%)
(%)
Kg/m3
(%)
S.G
1
19
20.8
2050
9.1
2.59
2
17
25.3
2040
8.6
2.62
3
19
23.8
2070
9.4
2.61
4
19
23.0
2080
9.0
2.60
5
25
29.7
2060
10.3
2.63
6
20
26.7
2050
9.2
2.62
7
22
27.1
2050
9.8
2.62
8
21
29.6
2080
9.7
2.65
9
22
29.3
2080
9.6
2.58
10
25
30.0
2100
10.3
2.60
11
28
38.3
2020
10.2
2.56
12
40
33.1
2000
11.9
2.57
13
27
29.8
2070
10.2
2.60
14
31
37.0
2060
11.1
2.61
15
33
39.7
1980
11.3
2.56
16
30
38.0
2010
11.0
2.57
17
12
29.8
2140
7.8
2.63
18
37
40.2
1950
11.8
2.60
19
39
45.8
2010
12.1
2.60
20
41
50.0
2030
12.0
2.61
21
30
31.3
2070
10.8
2.59
22
32
34.8
2030
11.2
2.59
23
26
47.0
2070
10.0
2.61
24
16
21.8
2040
8.8
2.62
25
24
27.5
2050
10.4
2.61
26
25
27.1
2080
9.8
2.64
27
17
20.7
2080
9.1
2.62
28
18
25.3
2040
8.6
2.54
29
19
25.9
2020
9.4
2.62
30
19
22.3
2080
9.2
2.62
31
23
27.2
2070
9.5
2.60
32
20
23.2
2060
9.6
2.62
33
17
20.7
2070
8.5
2.61
34
18
22.2
2070
8.9
2.62
35
18
25.1
2070
8.7
2.63
36
22
24.1
2090
9.7
2.57
37
24
22.8
2080
10.1
2.59
38
27
22.1
2060
10.7
2.63
39
34
25.6
2030
11.4
2.61
40
36
36.3
2050
11.7
2.61
41
33
31.2
1980
10.9
2.60
42
18
22.2
2080
8.9
2.61
43
17
23.4
2070
8.8
2.61
44
16
25.3
2060
8.7
2.60
45
21
25.0
2020
10
2.73
46
22
27.1
2010
9.9
2.71
47
24
28.0
1990
10.3
2.65
Table 3: Pearson correlation matrix of soil properties
Fines
L.L
MDD
OMC
Fines
1.000
L.L
+0.835
1.000
MDD
-0.810
+0.598
1.000
OMC
0.868
+0.825
-0.496
1.000
S.G
-0.805
-0.658
0.758
-0.800
Table 4:Underlying reasons behind closely related test results positive
relationship (R$+0.8)
Properties
Underlying reasons
OMC and fines
Increase fine result in increase water requirement,
because of the water affinity.
OMC, liquid
Consistency limit test is carried out on
limit fines
soil finer than 425 um. This fine group requires
more water because of greater specific surface.
Negative relationships (R$-0.8)
MDD and fines
More fine require more water and greater porosity
both of which reduces density.
SG and fines
More fines imply less solid grains and more
minerals which reduce the specific gravity.
OMC and SG
Higher SG due to more clay minerals and oxides of
iron in the soil tends to have affinity for water.
Table 5: Stepwise regression of all test data
Significance
$-value
level
Variable
Maximum
dry density
One variable model
Intercept
1680.373
Specific gravity
18.518
0.040
Two variable model
Intercept
1856.906
Specific gravity
14.650
0.035
Liquid limit
1.295
0.050
Three variable model
Intercept
2011.960
Specific gravity
15.665
0.04
Liquid limit
1.526
0.05
Fines
-4.313
0.001
Table 6: Stepwise regression of all test data
Significance
$-value
level
Variable
Optimum moisture
content
One variable model
Intercept
7.028
Fine
0.128
0.000
Two variable model
Intercept
7.635
L.L
-2.789E-02
0.005
Fine
0.140
0.000
Three variable model
Intercept
11.399
S.G.
-1.423
0.042
L.L.
-1.960E-02
0.045
Fines
0.129
0.000
F
R2
5.950
0.752
4.418
0.780
3.930
0.795
F
R2
52.381
0.795
52.381
26.384
0.890
17.677
0.895
variance in OMC. When liquid limit values are added R2
increases to 0.890, this is so because both percent fines
and liquid limit are closely related in terms of water
affinity. However when the values of SG are added, the
R2 marginally increase to 0.895. This implies that the
three variables can explain about 90% of the variance in
OMC.
In case of MDD when the specific gravity as
independent variables is regressed against variables
MDD, R2 is 0.752. This implies that SG can explain about
75% of variance in MDD. This is so because specific
gravity is mass dependent. When the liquid limit values
are added, R2 becomes 0.780 and when fines values are
S.G
1.000
plays a major role in the predicting of OMC values while
the specific gravity of soil solids plays a significant role
in predicting the MDD value.
When fine percent as an independent variable is
regressed against OMC, R2 of 0.795 is obtained. This
implies that fine percent can explain about 80% of
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Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
(X 10)
214
213
212
211
210
209
208
207
206
205
204
203
202
201
200
199
198
197
196
195
2
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
Measured MDD
R = 0.8341
(X 10)
Predicted MDD
Fig. 7: Plot of measured MDD against predicted MDD
(X 10)
15
Measured OMC
14
13
12
2
R = 0.931
11
10
9
8
8
9
10
11
12
13
Predicted OMC
14
15 (X 10)
Fig. 8: Plot of measured OMC against predicted OMC
added, R2 becomes 0.795. This implies that these three
variables can explain about 80% of variance in MDD.
There are apparently low contributions of liquid limit
and specific gravity to the total variance explanation in
OMC. Also liquid limit and percent fines have apparently
low contributions to the total variance explanation in
MDD. These are due to masking effects of these
properties on one another when interacting together in the
soil mass.
Figure 7 and 8 show the plot of measured MDD
against predicted MDD and measured OMC against
predicted OMC. The R2 values are 0.80 and 0.90
respectively. This indicates that the predictive equations
can give an estimate of MDD and OMC from simpler
geotechnical test with about 80 and 90% accuracy
respectively.
Compaction characteristics for the lateritic soil can be
estimated by the following equations.
MDD
OMC
= 15.665SG + 1.526LL-4.313F + 2011.960
= 0.129F-0.0196LL-1.4233SG + 11.399
where,
F
SG
L.L
OMC
MDD
= Fine percent
= Specific Gravity of Solids
= Liquid Limit
= Optimum Moisture Content
= Maximum Dry Density
CONCLUSION
Apart from fines, other soil properties such as liquid
limit and specific gravity have been found to significantly
influence the compaction characteristics of lateritic soils
in western Niger Delta. Three soil properties namely;
fines percent, liquid limit and specific gravity have been
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Res. J. Environ. Earth. Sci., 4(5): 553-559, 2012
employed to develop predictive models for obtaining
compaction characteristics. The validation of these
models employing field data within the region yielded
correct prediction of 80 and 90% for MDD and OMC
respectively. The advantage of these models over that of
Ugbe (2011) models is that it is not limited to only A-2
type soils. The models are applicable to all the other
lateritic soil types in Niger Delta, not just A-2 type. Also
these models incorporate other soil properties that interact
together for the determination of compaction
characteristics.
Finally, road engineers will find these models quite
useful in quickly estimating compaction characteristics of
soils from several sites within a short period of time.
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AKNOWLEDGMENT
Mr. Omonigho Emmanuel is highly acknowledged
for typing the manuscript.
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