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