Statistical analysis of the effective factors on the 28 days compressive strength and setting time of the concrete

CCS by the
predictive equations for each experiment.


704

B. Abolpour et al.

Fig. 5 The calculated Error of the predicted IST by the
predictive equations for each experiment.

Variation in Fe2O3 causes to vary CCS as a curve with a
minimum at zero level when other factors are stabilized at
low level and have a descending nonlinear curve when other
factors are stabilized at high level. Increasing of Fe2O3

decreases IST linearly in both cases, i.e. other factors are stabilized in their high or low level. This variation has been shown
in Fig. 12.
Increasing of CaO causes a nonlinear decrease in the CCS
when other factors are in their low level. The CCS varies as
a curve with a maximum at level 0.6 of the CaO, when other
factors are in their high level. Increasing of CaO causes a negligible linear increase in the IST in both cases when other factors are in their high or low level. This behavior of the concrete
has been shown in Fig. 13.
Fig. 14 shows that increasing of SO3 causes an increase or
decrease in the CCS linearly when other factors are in their
high or low level, respectively. This increment has a more complex effect on the IST. Increasing of this factor causes a nonlinear decrease in the IST when other factors are in their
high level. This Figure shows that variation in the SO3 value
has no important effect on the IST when other factors are in
their low level.
As can be observed from Fig. 15 variation in Blaine has no
significant effect on the CCS and IST when the concrete composition is stabilized at their low level. When composition of

Fig. 6

The effects of SiO2 on the CCS and IST when other factors are in their low or high level.

Fig. 7

The effects of Al2O3 on the CCS and IST when other factors are in their low or high level.


Statistical analysis of the effective factors on the main properties of the concrete

Fig. 8

Fig. 9

Fig. 10

The effects of Na2O on the CCS and IST when other factors are in their low or high level.

The effects of Cl on the CCS and IST when other factors are in their low or high level.

The effects of MgO on the CCS and IST when other factors are in their low or high level.

705


706

B. Abolpour et al.

Fig. 11

Fig. 12

Fig. 13

The effects of K2O on the CCS and IST when other factors are in their low or high level.

The effects of Fe2O3 on the CCS and IST when other factors are in their low or high level.

The effect of CaO on the CCS and IST when other factors are in their low or high level.


Statistical analysis of the effective factors on the main properties of the concrete

Fig. 14

Fig. 15

Table 3

xSiO2

xCaO
xMgO
xNa2 O
xK2 O
xSO3
xCl

xBlaine

The effects of Blaine on the CCS and IST when other factors are in their low or high level.

Level of other fixed factors

xSiO2

xFe2 O3

The effects of SO3 on the CCS and IST when other factors are in their low or high level.

The effect of factors on the CCS and IST.

Considered factor

xAl2 O3

707

+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À

xAl2 O3

xFe2 O3

xCaO

xMgO

xNa2 O

xK2 O

xSO3

xCl

xBlaine

+
À

+
À
+
À

+
À
+
À
+
À

+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+

À

+
À
+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+
À
+
À
+

À
+
À
+
À

+
À
+
À
+
À
+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+

À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+
À
+
À

+
À
+
À
+
À
+
À
+

À

+
À
+
À
+
À
+
À

+
À
+
À
+
À

+
À
+
À

+
À

Effect on the CCS

Effect on the IST

Decrease
Decrease
Increase
Decrease
Decrease
Complex
Complex
Decrease
Decrease
Decrease
Increase
Decrease
Increase
Increase
Increase
Decrease
Increase
Decrease
Increase
Complex

Complex
Complex
Decrease
Decrease
Decrease
Decrease
Increase
Increase
Complex

Complex
Decrease
Decrease
Decrease
Decrease
Decrease
Complex
Decrease
Decrease
Complex
Complex


708
the concrete is stabilized at high level, increasing of Blaine will
increase CCS by an ascending curve and changes IST through
a curve with a maximum at about level 0.2.
The setting and hardening of cement are the result of chemical reactions between cement and water (i.e. hydration). The
hydration reactions starts directly after adding water to cement
and in the first 30 min a part of C3A and sulfate carrier is dissolved and results more strength in concrete. This very fast
process produces heat during the initial period of hydration.
C3A phase sets quickly with evolution of heat and enhances
strength of the silicates. Coarse cements with low specific surface area usually take longer times to set due to the sluggish
hydration kinetics. On the other hand, high content of C3A
speeds up the reactions resulting in relatively short setting
times. Increasing the amount of C3A causes a significant
increase in the CCS and also decreases the IST as Eqs. (11)
and (12).
Conclusions
In this study, the effects of various factors on the concrete

compressive strength and also initial setting time have been
investigated. The effective factors are weight percent of the
SiO2, Al2O3, Fe2O3, Na2O, K2O, CaO, MgO, Cl, SO3 of the
raw materials and the Blaine of cement particles. Interactions
of these factors with probability of a 97.5% confidence have
been obtained using analysis of variance. Then the equations
have been obtained through regression to predict the concrete
compressive strength and initial setting time as function of the
mentioned factors. The mean of the calculated absolute Error
for predicted values of CCS and IST was 1.92% and 4.3%,
respectively for regression equations. Attention to the
coefficients of these regression equations shows that the
quadruplet combinations of xSiO2 Á xMgO Á xSO3 Á xBlaine and
xSiO2 Á xSO3 Á xK2 O Á xBlaine have the most positive and negative
effect on the CCS, respectively. Also the quadruplet combinations of xSiO2 Á xMgO Á xNa2 O Á xK2 O and xSiO2 Á xNa2 O Á x2K2 O have
the most positive (increasing) and negative (reducing) effect
on the IST of concrete, respectively. Also, simple and applicable formulas have been developed using the genetic algorithm
to predict these parameters. The accuracy of these predictive
equations is completely acceptable. They have a less than
6% absolute mean error. Finally the effect of each factor has
been investigated when other factors are in their low or high
level and summary of the results has been presented in Table 3.
Conflict of interest
The authors have declared no conflict of interest.
Compliance with Ethics Requirements
This article does not contain any studies with human or animal
subjects.
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