The number of people using generated energy

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31 August 2024
Computation mated
Table of Contents
Introduction..........................................................................................................................2
Literature Review................................................................................................................2
Results..................................................................................................................................3
Descriptive statistics............................................................................................................3
Inferential statistics..............................................................................................................8
Regression analysis............................................................................................................24
Discussion..........................................................................................................................24
Conclusion.........................................................................................................................25

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Introduction
There has been a rise in the consumption of concretes over the past years, [7]. The
number of people using generated energy has been approximated to be more than 2.5
billion with the use of virgin materials being considered to have negative effect on the
environment [17]. The properties of a good concrete are determined by factors such as
compressive strength, bulk density, water absorption and initial modulus of elasticity [1].
Optimizing the above-mentioned properties is expected to result in high quality of
concrete that are durable and of great strength, [8].
A total of 380 concrete samples were tested for properties including bulk density,
compressive strength, initial modulus of elasticity, and water absorption. Following
parameters were used for the study:
 Curing Type: The samples were either dry-cured in the open air or wet-cured by
placing them in a tank of water.
 Plasticizer: Plasticizer was used in the concrete mix for preparing some of the
samples, [12].
 Fine Aggregate Type: There were a total of 5 types of fine aggregate. These
types have been named A, B, C, D, and E.
 Coarse Aggregate Type: There were a total of 3 types of coarse aggregates.
These types have been named A, B, and C.
 Water/binder Ratio: The water/binder ratio ranged between 0.3 and 0.75.
 Curing Age: Curing age refers to the number of days after casting when the tests
were performed. The samples were tested 7, 14, or 28 days after casting.
The following research hypothesis was developed and tested;
1. There is no statistically significant difference between curing type and the
comprehensive strength
2. There is no statistically significant difference between Uses Plasticizer and the
Water/binder Ratio
3. There is no statistically significant difference between curing age and water
absorption
4. There is no mean difference of the bulk density for the sand types
5. There is no mean difference of the Modulus of Elasticity for the Coarse
Aggregate Types
6. There is no statistically significant difference between curing type and the sand
type
7. There is no statistically significant difference between curing age and the Coarse
Aggregate Type
Literature Review
Generally, in order to enhance the comprehensive strength of any building, wet curing
strategies must be adopted. The authors [11] stated that there is a relationship between
comprehensive strength and wet curing strategies. In addition, several studies have
established that there is no significant difference between using Plasticizer and the
Water/binder Ratios [16].

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Furthermore, some studies investigating the relationship between curing age and water
absorption have shown that the more time is taken for the curing process, the more likely
water absorption is required. For example, in a study by [9], the authors established that
for curing processes that go for several weeks like 1 month requires more water for
absorption.
In addition, some authors have established that there is no mean difference in the bulk
density for the sand types. For example, in a study by [18] show that different sand types
do not influence the soil bulkiness. In fact, the authors found out that despite different
types of sand that may be used, in one way or the other they do not influence the bulk
densities.
Finally, literature shows that there is a mean difference in the Modulus of Elasticity for
the Coarse Aggregate Types. For example, [2], in their studies established that the
Modulus of Elasticity has a mean difference for the Coarse Aggregate Types.
Results
In this section, both the descriptive statistics and inferential statistics have been
presented. Generally, descriptive statistics include the presentation of the results by the
use of means, percentages and frequency distributions in either tabular formats or
graphical presentations. Usually, descriptive statistics are useful when it comes to a
description of the results, summarizing the study findings and presentation of the results
in such a way that it can easily be understood and interpreted.
In addition, the use of inferential statistics has been adopted as part of the analysis. Well,
inferential statistics help in making conclusions about the entire population. On this note,
some of the inferential statistics conducted include but not limited to the use of
independent sample t-tests, chi-square tests, ANOVA, and logistic regression tests. The
advantage of inferential statistics to descriptive statistics is that inferential statistics helps
in identifying some forms of associations between variables that can be used to inform
decision-making processes.
All statistical interpretations that have been computed at alpha are equals to 0.05. this
means that the calculated p-values of less than 0.05 imply that there is a statistically
significant association between variables hence we reject the null hypothesis while the p-
values greater 0.05 implies that there is o statistical significant association between
variables thus we fail to reject the null hypothesis. In other words, p-values are
probability statistics that help in identifying relevant predictors of any given outcome of
interest in the study.
Finally, the statistical package for social sciences (SPSS version 24) has been used to
conduct the analysis of the provided dataset. Again, all the descriptive statistics have
been performed first after which, inferential statistics have been presented. In addition,
the results have been presented by the use of tables and charts to enhance data
visualization in a way that it can be understood and interpreted to enhance decision-
making processes.

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Descriptive statistics
The results indicate that the data was collected from a total subject of 380 as shown by
the value of N. The following results indicate the descriptive statistics.
Table 1: Curing Type
Frequency Percent Valid
Percent
Cumulative
Percent
Valid
Dry
Curing 189 49.7 49.7 49.7
Wet
Curing 191 50.3 50.3 100.0
Total 380 100.0 100.0
The findings in table 1 show that the leading curing type is wet curing; 191 (50.3%)
which is slightly followed by dry curing at 189 (49.7%). From the findings, there is not
much difference in the curing types and that the ratio of wet and dry curing types is
almost 1:1.
In addition, the results in table 2 show that the majority of the data points indicate no
Uses of Plasticizer as shown by 197 (51.8%) while only 183 (48.2%) Uses Plasticizer.
Table 2: Uses Plasticizer
Frequency Percent Valid
Percent
Cumulative
Percent
Valid
No 197 51.8 51.8 51.8
Yes 183 48.2 48.2 100.0
Total 380 100.0 100.0
Furthermore, the results of the sand type shown in Table 3 below indicate that A is the
leading sand type at 84 (22.1%). This is closely followed by sand types C and D which
accounts for 78 (20.5%) and 76 (20.0%) respectively. On the other hand, sand types B
and E account for 74 (19.5%) and 68 (17.9%) respectively. Looking at the findings on the
sand types, it has been observed that there is not much gap between the sand type
preferences.
Table 3: Sand Type
Frequency Percent Valid
Percent
Cumulative
Percent
Valid
A 84 22.1 22.1 22.1
B 74 19.5 19.5 41.6
C 78 20.5 20.5 62.1
D 76 20.0 20.0 82.1
E 68 17.9 17.9 100.0
Total 380 100.0 100.0

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Moreover, the results in table 4 below show that the Coarse Aggregate Type B and C are
among the leading Coarse Aggregate Types accounting for 138 (36.3%) and 130 (34.2%)
respectively. On the other hand, the Coarse Aggregate Type A is the least at 112 (29.5%).
Basically, this finding confirms that people have different preferences to Coarse
Aggregate Types.
Table 4: Coarse Aggregate Type
Frequency Percent Valid
Percent
Cumulative
Percent
Valid
A 112 29.5 29.5 29.5
B 138 36.3 36.3 65.8
C 130 34.2 34.2 100.0
Total 380 100.0 100.0
The above findings have also been visualized by the use of the bar chart below showing
only the frequency counts of the Coarse Aggregate Types.
In addition, the descriptive statistics of the numerical variables have been presented in
table 5 below.
Table 5: Descriptive Statistics
N Minimu
m
Maximu
m
Mean Std.
Deviation
Water/binder Ratio 380 0 1 .53 .139

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Bulk Density 380 1775 2725 2202.65 216.549
Compressive
Strength 380 18 72 43.33 11.649
Modulus of
Elasticity 380 19 39 28.53 5.291
Water Absorption 380 1 12 6.43 2.207
Valid N (listwise) 380
From the findings in table 5, the mean of the Water/binder Ratio is calculated to be 0.53
with a standard deviation of 0.139. in addition, the Water/binder Ratio has a minimum
and a maximum value of 0 to 1 respectively. For the Bulk Density, it has a mean of
2202.65 and a standard deviation of 216.549 while its minimum and maximum values are
calculated to be 1775 and 2725 respectively. Moreover, the results indicate that the mean
Compressive Strength is 43.33 while its standard deviation is calculated to be 11.649. the
minimum and maximum values of the Compressive Strength are 18 and 72 respectively.
Additionally, the mean and standard deviation of the Modulus of Elasticity are 28.53 and
5.291 respectively while the minimum and maximum values of the Modulus of Elasticity
are given as 19 and 39 respectively. Finally, Water Absorption has a mean of 6.43 with a
standard deviation of 2.207. the minimum and maximum values of the Water Absorption
are given as 1 and 12 respectively.
Finally, the results in table 6 indicate that the highest curing age is 14 days and 7 days
accounting for 134 (35.3%) each respectively while 28 days curing age is the lowest at
112 (29.5%).
Table 6: Curing Age
Frequency Percent Valid
Percent
Cumulative
Percent
Valid
14 days 134 35.3 35.3 35.3
28 days 112 29.5 29.5 64.7
7 days 134 35.3 35.3 100.0
Total 380 100.0 100.0
The results have been visualized in the figure 2 below shows the frequency percentages
of the curing ages.

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Inferential statistics
For the purposes of conducting the inferential statistics, the data was checked in general
and see if there are any outliers depending on the study hypothesis as indicated above.
This was made possible by drawing skewness graphs as well as boxplots as a means of
performing the relevant tests to check the data validity, skewness, bias, etc.
Looking at the boxplot below, there are no outliers for the data showing the relationship
between curing type and the comprehensive strength hence independent sample t-test is
conducted with the data the way it is.
Similarly, the boxplot below indicates that there are no outliers for the data showing the
relationship between Uses Plasticizer and the Water/binder Ratio thus the independent
sample t-test is conducted with the data the way it is.

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However, the results in the boxplot showing the difference between curing age and water
absorption indicate some form of outliers as shown below. For the 7 days curing age, the
water absorption value of 1 is an outlier. Hence, the variable of water absorption was
standardized first before performing independent sample t-test to find out the significant
difference between curing age and water absorption.

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Furthermore, the boxplot below indicates that there are no outliers for the data showing
the relationship between sand type and bulk density thus the ANOVA test is conducted
with the data the way it is.

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Finally, the boxplot below indicates that there are no outliers for the data showing the
mean difference of the Modulus of Elasticity for the Coarse Aggregate Types thus the
ANOVA test is conducted with the data the way it is.

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The following graphs indicate the normal distribution of the data before performing the
inferential statistics. From the histograms, there is evidence that the data is normally
distributed which is one of the assumptions of the inferential statistics.

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This section shows the results of the inferential statistics based on the study
hypothesis.
An independent t-test has been used to test the first null hypothesis, “There is no
statistically significant difference between curing type and the comprehensive strength”.
An independent sample t-test was done to compare the curing type in the comprehensive
strength. According to the results in table 7, the findings show that wet curing had a
higher mean of comprehensive strength score of 44.33, with a standard deviation of
11.289 and standard error of 0.817 than the dry curing with a mean of 42.32, a standard
deviation of 11.948 and standard error of 0.869, [10].
From the Levene's Test for Equality of Variances, the results are not significant (p
=.751>.05), the equal variances are assumed, hence, the variances were not significantly
different; as a result, there is evidence that the homogeneity of variances assumptions was
not violated. Because of the consistency, there is no need to read test statistics from the
row labeled Equal variance not assumed, which also indicates a p-value>.05. Therefore,
the findings of the study indicate that on average the wet curing in the comprehensive
strength score (M=44.33, SE=0.817), was not significantly higher than the dry curing in
comprehensive strength score (M= 42.32, SE=0.869), t (378) = 1.689, p = .751 as shown
in table 7a.

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Based on the study findings, it is prudent to conclude that curing type has no significant
influence on the comprehensive strength.
Table 7: Group Statistics
Curing
Type
N Mean Std.
Deviation
Std. Error
Mean
Compressive
Strength
Wet Curing 191 44.33 11.289 .817
Dry Curing 189 42.32 11.948 .869
Table 7a: Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig.
(2-
tailed)
Mean
Differe
nce
Std.
Error
Differe
nce
95%
Confidence
Interval of the
Difference
Lower Upper
Compressi
ve Strength
Equal
variances
assumed
.101 .751 1.68
9 378 .092 2.014 1.192 -.330 4.358
Equal
variances not
assumed
1.68
9
376.
301 .092 2.014 1.193 -.331 4.359
In addition, an independent t-test has been used to test the second null hypothesis, “There
is no statistically significant difference between Uses Plasticizer and the Water/binder
Ratio”.
According to the results in table 8, the findings show that those who use plasticizer had a
higher mean of Water/binder Ratio score of 0.53, with a standard deviation of 0.137 and
standard error of 0.010 than those who do not use plasticizer with a mean of 0.52, a
standard deviation of 0.141 and standard error of 0.010, [5].
From the Levene's Test for Equality of Variances, the results are not significant (p
=.573>.05), the equal variances are assumed, hence, the variances were not significantly
different; as a result, there is evidence that the homogeneity of variances assumptions was
not violated. Because of the consistency, there is no need to read test statistics from the
row labeled Equal variance not assumed, which also indicates a p-value>.05. Therefore,
the findings of the study indicate that on average those who use plasticizer (M=0.53,
SE=0.010), was not significantly higher than those who do not use plasticizer (M= 0.52,
SE=0.010), t (378) = 0.204 p = .571 as shown in table 8a, [6].
Based on the study findings, it is prudent to conclude that using plasticizer has no
significant influence on the water/binder ratio.
Table 8: Group Statistics

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Uses
Plasticizer
N Mean Std.
Deviation
Std. Error
Mean
Water/binder
Ratio
Yes 183 .53 .137 .010
No 197 .52 .141 .010
Table 8a: Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig.
(2-
tailed)
Mean
Differe
nce
Std.
Error
Differe
nce
95% Confidence
Interval of the
Difference
Lower Upper
Water/
binder
Ratio
Equal
variances
assumed
.318 .573 .204 378 .838 .003 .014 -.025 .031
Equal
variances not
assumed
.204 377.
376 .838 .003 .014 -.025 .031
Moreover, a one-way ANOVA test has been used to test the third null hypothesis, “There
is no statistically significant difference between curing age and water absorption”. This
was done by investigating whether the standardized water absorption variable has a mean
difference for the curing ages.
In this analysis, the scores on standardized water absorption were used as the dependent
variable (explanatory) and curing age which had several different levels (7 days, 14 days
and 28 days) was used as the independent variable (a factor). By using the analysis of
variance, it is easy to compare the variance (variability in scores) between the different
age curing Categories (believed to be due to the independent variable) with the variability
within each of the age curing (believed to be due to chance).
From the summary of the descriptive shown in the SSPS output, the results show that the
curing age of 28 days had the highest score (M=.4963, and SE= 0.1) followed by the
curing age of 14 days with a mean of 0.2333 and a standard error of 0.0792. However,
the curing age of 7 days had the least score (M=-0.6481, and SE =0.0531), (refer to table
9).
From the ANOVA table 9a, the results indicate that there was significant difference (Sig.
<.05) somewhere in the standardized water absorption scores for the three curing ages
confirming that the curing age of 28 days is more likely to have a maximized water
absorption compared to curing ages of 7 days and 14 days respectively, [3].
Table 9: Descriptives
Zscore: Water Absorption
N Mean Std.
Error
95% Confidence
Interval for Mean
Minim
um
Maxi
mum
Between-
Compone

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Std.
Deviatio
n
nt
Variance
Lower
Bound
Upper
Bound
14 days 134 .23331
73
.9163704
0
.07916
235 .0767372 .3898973
-
1.7097
9
2.1099
1
28 days 112 .49631
37
1.065298
41
.10066
124 .2968467 .6957807
-
1.8123
7
2.6166
0
7 days 134 -.6481
466
.6141803
9
.05305
710
-.753091
5
-.543201
7
-
2.4378
4
.86891
Total 380 0E-7 1.000000
00
.05129
892
-.100866
1 .1008661
-
2.4378
4
2.6166
0
Mod
el
Fixed
Effects
.8737616
3
.04482
303
-.088134
5 .0881345
Random
Effects
.34807
799
-
1.497658
7
1.497658
7
.3550673
2
Table 9a: ANOVA
Zscore: Water Absorption
Sum of
Squares
df Mean
Square
F Sig.
Between
Groups 91.176 2 45.588 59.712 .000
Within Groups 287.824 377 .763
Total 379.000 379

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In addition, a one-way ANOVA test has been used to test the fourth null hypothesis,
“There is no mean difference of the bulk density for the sand types”. This was done by
investigating whether the bulk density variable has a mean difference for the sand types.
In this analysis, the scores on the bulk density were used as the dependent variable
(explanatory) and the sand types which had several different levels (A, B, C, D, E) were
used as the independent variable (a factor). By using the analysis of variance, it is easy to
compare the variance (variability in scores) between the different sand types (believed to
be due to the independent variable) with the variability within each of the sand type
(believed to be due to chance).
From the summary of the descriptive shown in the SSPS output, the results show that the
sand type E had the highest score (M=2424.75, and SE= 19.853) followed by the sand
type D with a mean of 2308.58 and a standard error of 18.412. However, the sand type A
had the least score (M=2014.26, and SE =17.6651), (refer to table 10).
From the ANOVA table 10a, the results indicate that there was a significant difference
(Sig. <.05) somewhere in the bulk density scores for the five sand types confirming that
the sand type E and D are more likely to have a maximized bulk density compared to the
sand type A, [14].
Table 10: Descriptives
Bulk Density
N Mean Std.
Deviatio
n
Std.
Error
95% Confidence
Interval for Mean
Mini
mum
Maxi
mum
Between
-
Compon
ent
Variance
Lower
Bound
Upper
Bound
A 84 2014.
26 161.899 17.66
5 1979.13 2049.40 1775 2279
B 74 2123.
64 163.043 18.95
3 2085.86 2161.41 1862 2385
C 78 2183.
67 170.945 19.35
6 2145.12 2222.21 1957 2502
D 76 2308.
58 160.509 18.41
2 2271.90 2345.26 2053 2581
E 68 2424.
75 163.716 19.85
3 2385.12 2464.38 2137 2725
Total 380 2202.
65 216.549 11.10
9 2180.81 2224.50 1775 2725
Mo
del
Fixed
Effects 164.067 8.416 2186.10 2219.20
Random
Effects
71.28
1 2004.75 2400.56 24933.0
84
Table 10a: ANOVA

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Bulk Density
Sum of
Squares
df Mean
Square
F Sig.
Between
Groups 7678406.151 4 1919601.538 71.313 .000
Within Groups 10094245.99
6 375 26917.989
Total 17772652.14
7 379
Moreover, a one-way ANOVA test has been used to test the fifth null hypothesis, “There
is no mean difference of the Modulus of Elasticity for the Coarse Aggregate Types”. This
was done by investigating whether the Modulus of Elasticity variable has a mean
difference for the Coarse Aggregate Types.
In this analysis, the scores on the Modulus of Elasticity were used as the dependent
variable (explanatory) and the Coarse Aggregate Types which had several different levels
(A, B, C) was used as the independent variable (a factor). By using the analysis of
variance, it is easy to compare the variance (variability in scores) between the different
Coarse Aggregate Types (believed to be due to the independent variable) with the
variability within each of the Coarse Aggregate Types (believed to be due to chance).
From the summary of the descriptive shown in the SSPS output, the results show that the
Coarse Aggregate Type A had the highest score (M=29.17, and SE= 0.504) followed by
the Coarse Aggregate Type C with a mean of 28.98 and a standard error of 0.485.
However, the Coarse Aggregate Type B had the least score (M=27.59, and SE =0.419),
(refer to table 11).
From the ANOVA table 11a, the results indicate that there was a significant difference
(Sig. <.05) somewhere in the Modulus of Elasticity scores for the three Coarse Aggregate
Types confirming that the Coarse Aggregate Type A is likely to have a maximized
Modulus of Elasticity scores compared to the Coarse Aggregate Type B.
Table 11: Descriptives
Modulus of Elasticity
N Mea
n
Std.
Deviati
on
Std.
Error
95% Confidence
Interval for Mean
Mini
mum
Maxi
mum
Betwee
n-
Compo
nent
Varianc
e
Lower
Bound
Upper
Bound
A 112 29.1
7 5.334 .504 28.17 30.17 20 39
B 138 27.5
9 4.921 .419 26.76 28.41 19 36

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C 130 28.9
8 5.526 .485 28.02 29.93 20 38
Total 380 28.5
3 5.291 .271 27.99 29.06 19 39
Mo
del
Fixed
Effects 5.256 .270 28.00 29.06
Random
Effects .508 26.34 30.71 .552
Table 11a: ANOVA
Modulus of Elasticity
Sum of
Squares
df Mean
Square
F Sig.
Between
Groups 194.496 2 97.248 3.520 .031
Within Groups 10415.588 377 27.628
Total 10610.084 379
Again, a chi-square test has been used to test the sixth null hypothesis, “There is no
statistically significant difference between curing type and the sand type”.
According to the results in table 12, the findings show that there is no statistically
significant difference between curing type and the sand type, p-value= 0.282>0.05, chi-
square value = 5.050, [15].
Table 12: Curing Type * Sand Type Crosstabulation
Sand Type Total
A B C D E
Curing
Type
Wet
Curing
Count 40 39 47 34 31 191
% within Curing
Type 20.9% 20.4% 24.6% 17.8% 16.2% 100.0%
% within Sand
Type 47.6% 52.7% 60.3% 44.7% 45.6% 50.3%
% of Total 10.5% 10.3% 12.4% 8.9% 8.2% 50.3%
Dry
Curing
Count 44 35 31 42 37 189
% within Curing
Type 23.3% 18.5% 16.4% 22.2% 19.6% 100.0%
% within Sand
Type 52.4% 47.3% 39.7% 55.3% 54.4% 49.7%
% of Total 11.6% 9.2% 8.2% 11.1% 9.7% 49.7%
Total Count 84 74 78 76 68 380

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% within Curing
Type 22.1% 19.5% 20.5% 20.0% 17.9% 100.0%
% within Sand
Type 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
% of Total 22.1% 19.5% 20.5% 20.0% 17.9% 100.0%
Table 12a: Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 5.050a 4 .282
Likelihood Ratio 5.076 4 .280
Linear-by-Linear
Association .331 1 .565
N of Valid Cases 380
a. 0 cells (0.0%) have expected count less than 5. The minimum
expected count is 33.82.
Finally, a chi-square test has been used to test the seventh null hypothesis, “There is no
statistically significant difference between curing age and the Coarse Aggregate Type”.
According to the results in table 13, the findings show that there is no statistically
significant difference between curing age and the Coarse Aggregate Type, p-value=
0.144>0.05, chi-square value = 6.853, [13].
Table 13: Curing Age * Coarse Aggregate Type Crosstabulation
Coarse Aggregate Type Total
A B C
Curing
Age
14 days
Count 41 58 35 134
% within Curing Age 30.6% 43.3% 26.1% 100.0%
% within Coarse
Aggregate Type 36.6% 42.0% 26.9% 35.3%
% of Total 10.8% 15.3% 9.2% 35.3%
28 days
Count 33 36 43 112
% within Curing Age 29.5% 32.1% 38.4% 100.0%
% within Coarse
Aggregate Type 29.5% 26.1% 33.1% 29.5%
% of Total 8.7% 9.5% 11.3% 29.5%
7 days
Count 38 44 52 134
% within Curing Age 28.4% 32.8% 38.8% 100.0%
% within Coarse
Aggregate Type 33.9% 31.9% 40.0% 35.3%
% of Total 10.0% 11.6% 13.7% 35.3%

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Total
Count 112 138 130 380
% within Curing Age 29.5% 36.3% 34.2% 100.0%
% within Coarse
Aggregate Type 100.0% 100.0% 100.0% 100.0%
% of Total 29.5% 36.3% 34.2% 100.0%
Table 13a: Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 6.853a 4 .144
Likelihood Ratio 6.959 4 .138
Linear-by-Linear
Association 2.346 1 .126
N of Valid Cases 380
a. 0 cells (0.0%) have expected count less than 5. The
minimum expected count is 33.01.
Regression analysis
Assuming the comprehensive strength as the dependent variable and other variables as
independent variables, the results show that only Modulus of Elasticity and Water
Absorption are relevant predictors, [4].
Coefficients
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) -8.419 9.826 -.857 .392
Curing Type -.037 .482 -.002 -.077 .939
Uses Plasticizer .753 .487 .032 1.547 .123
Sand Type .199 .653 .024 .305 .761
Coarse Aggregate Type 1.080 1.303 .074 .829 .408
Water/binder Ratio -2.138 1.759 -.026 -1.216 .225
Curing Age -.187 .400 -.013 -.466 .641
Bulk Density -.003 .006 -.065 -.553 .581
Modulus of Elasticity 2.000 .061 .908 32.922 .000
Zscore: Water
Absorption -5.120 .259 -.439 -19.756 .000
a. Dependent Variable: Compressive Strength
Discussion
For the first null hypothesis, “There is no statistically significant difference between
curing type and the comprehensive strength”. The results indicate that on average the wet
curing in the comprehensive strength score was not significantly higher than the dry

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curing in comprehensive strength score. This finding is contrary to the study by [11] who
stated that there is a relationship between comprehensive strength and wet curing
strategies.
Moreover, the study finding on the null hypothesis, “There is no statistically significant
difference between Uses Plasticizer and the Water/binder Ratio” is consistent to other
studies by [16] whose studies indicate lack of statistically significant association between
Uses Plasticizer and the Water/binder Ratio.
Again, the results for the hypothesis, “There is no statistically significant difference
between curing age and water absorption” established that there was significant
difference (Sig. <.05) somewhere in the standardized water absorption scores for the
three curing ages confirming that the curing age of 28 days is more likely to have a
maximized water absorption compared to curing ages of 7 days and 14 days respectively.
The results are consistent with other studies like [9], where the authors established that
for curing processes that go for several weeks like 1 month requires more water for
absorption.
Moreover, for the hypothesis, “There is no mean difference of the bulk density for the
sand types”, the findings show that there was significant difference (Sig. <.05)
somewhere in the bulk density scores for the five sand types confirming that the sand
type E and D are more likely to have a maximized bulk density compared to the sand
type A. this finding is in contrary to the findings of other researchers. For example, in a
study by [18] show that different sand types do not influence the soil bulkiness.
For the null hypothesis, “There is no mean difference of the Modulus of Elasticity for the
Coarse Aggregate Types”, the results have confirmed that there was significant difference
(Sig. <.05) somewhere in the Modulus of Elasticity scores for the three Coarse Aggregate
Types confirming that the Coarse Aggregate Type A is likely to have a maximized
Modulus of Elasticity scores compared to the Coarse Aggregate Type B which is
consistent to other kinds of literature like [2].
Conclusion
In conclusion, on average the wet curing in the comprehensive strength score was not
significantly higher than the dry curing in comprehensive strength score. Again, there is
no statistically significant difference between Uses Plasticizer and the Water/binder
Ratio. Furthermore, there was a significant difference (Sig. <.05) somewhere in the
standardized water absorption scores for the three curing ages confirming that the curing
age of 28 days is more likely to have a maximized water absorption compared to curing
ages of 7 days and 14 days respectively. Similarly, there was significant difference (Sig.
<.05) somewhere in the bulk density scores for the five sand types confirming that the
sand type E and D are more likely to have a maximized bulk density compared to the
sand type A. Finally, there was significant difference (Sig. <.05) somewhere in the
Modulus of Elasticity scores for the three Coarse Aggregate Types confirming that the
Coarse Aggregate Type A is likely to have a maximized Modulus of Elasticity scores
compared to the Coarse Aggregate Type B.

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