concrete application, Ravindrauses attenuation of
splitting strength of concrete under fatigue loading
to define damage variable, Lu et al.(2002)[12], Zhao
et al.(1999)[13] proposed static compressive residual
strength to define the damage variable, but the
control index of the mechanical properties of the
cement concrete pavement is the flexural tensile
strength. the results of the previous tests also
confirmed that the residual flexural tensile strength
of cement concrete pavement is decreasing under the
three factors of loading, high temperature and
wetting-drying cycle. Therefore, the residual flexural
strength of concrete is introduced as the damage
variable, and It is defined as the ratio of the concrete
flexural strength attenuation after a certain time to
the initial flexural strength of concrete. The formula
(1) is as follows:
0
0
f
ff
D
σ
−
=
(1)
σ f0: Flexural strength of concrete specimens in
initial state
σ f :Residual flexural strength of concrete
specimens subjected to damage
4.2 Residual Flexural Tensile Strength
Damage Model of Concrete Under
Loading ,High Temperature and
Wetting-Drying Cycle
4.2.1 Variation of Residual Flexural
Strength of Concrete Under Loading,
High Temperature and wetting-Drying
Cycle
Scattered points distribution in Figure 1(a)-(d)
shows the variation law of the residual flexural
strength of C1 and C2 concrete under loading, high
temperature and wetting-drying cycle. Under this
condition, the flexural tensile strength of concrete
decreases with the increase of time, 80% stress level
significantly accelerated damage to concrete, the
lower the strength grade of concrete performance is
more significant. Under 50% and 80% stress level,
C1 concrete flexural strength decrease by 11.6% and
2.5% respectively, C2 concrete flexural strength by
10.4% and 4.8% respectively, compared with the
condition under loading of single factor. C1 concrete
flexural strength decrease by 33.4% and 3.4% lower
respectively, C2 concrete flexural strength by 31.9%
and 4.9% respectively, compared with the conditions
under loading and high temperature of double factor.
Compare the effects of the three factors, the order of
influence degree of concrete flexural tensile strength
is loading<high temperature<wetting-drying cycle.
When evaluating the mechanical properties value of
cement concrete pavement changing with time under
the condition of high temperature and wetting-
drying cycle,only consider the loading or consider
the effect of loading and temperature is not enough.
So it is necessary to introduce an influencing factor
to consider the humidity factor, and in the existing
two factors superimposed wetting-dryingcycle, will
produce a doubling damage effect.
4.2.2 Decreasing Model of Residual Flexural
Strength of Concrete Subjected to
Loading, High Temperature and
Wetting-Drying Cycle
Three variables of loading, high temperature and dry
wet cycle are needed in this model. Due to the
humidity and temperature have the characteristics of
adaptability and simultaneity,therefore, the residual
flexural tensile strength damage model of cement
concrete pavement is designed as two function
combinations: δ=F[f(nh), f(t)],n represents the
number of fatigue loading, t represents the time of
high temperature wetting-drying cycle.
The modeling idea is as follows: Query existing
literature, the concrete strength and the fatigue
loading approximate relation of power function.
Preliminary test indicates that there is a linear
relationship between the strength temperature,
humidity and the power exponent of time. Fatigue
damage formula under loading, high temperature
and wetting-drying cycle is as follows:(2)
(2)
δ/δf: The ratio of residual tensile strength to
maximum flexural tensile strength
nh/Nh: The ratio of loading times to fatigue life
t/tma: The ratio of the operating time and the
maximum test period under high temperature
wetting-drying cycle
Multiple regression analysis (Formula 3-6) was
used to calculate the results, the complex correlation
coefficient is above 0.88. Table 2 shows the
comparison between the experimental values and the
predicted values using regression models, the results
show that the maximum prediction error is below
cb
h
h
f
t
t
N
n
aD )()1(1
max
•−•=−=
σ
σ