CN113990414B - Method for predicting complex modulus of modified asphalt - Google Patents
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Abstract
The invention discloses a method for predicting complex modulus of modified asphalt, which relates to the field of road engineering and comprises the following steps: carrying out a temperature slope scanning experiment on the modified asphalt to obtain a characteristic value data set of actually measured complex modulus, a characteristic value data set of glass state modulus and a characteristic value data set of rubber state modulus of the modified asphalt; solving the measured data set of the glass state-rubber state conversion coefficient and the measured data set of the rubber state-viscous state conversion coefficient; calibrating a fitting parameter of a glassy state Arrhenius equation, a glassy state-rubbery state transition activation energy parameter, a rubbery state Arrhenius equation fitting parameter and a rubbery state-viscous state transition activation energy parameter; and predicting the complex modulus of the modified asphalt. According to the method, parameter fitting and calculation are carried out through temperature slope scanning data of the modified asphalt, so that the complex modulus of the modified asphalt is predicted, the complex modulus data at different temperatures can be accurately fitted, and the accuracy can be guaranteed in a wider temperature domain range.
Description
Technical Field
The invention relates to the field of road engineering, in particular to a method for predicting complex modulus of modified asphalt.
Background
Asphalt is a typical non-crystalline viscoelastic material, and the viscoelastic properties of asphalt material refer to the properties of both solid (hooke's elastomer) and liquid (newtonian fluid) properties under load. The viscoelastic properties of asphalt are closely related to its road properties.
The asphalt shows extremely strong temperature sensitivity under the influence of viscoelastic properties. Taking the most representative complex modulus index as an example, asphalt molecules are frozen at low temperature, have extremely high modulus, present glass state and are easy to crack; at high temperature, asphalt molecules move freely to generate relative slippage, the modulus is extremely low, and the viscous flow state is easy to deform. The hard, brittle, high temperature softening characteristics of base asphalt are highly detrimental to its road-use properties. The modulus sensitivity of SBS (Styrene-Butadiene-Styrene) modified asphalt is obviously smaller than that of matrix asphalt, so that the low-temperature and high-temperature performances are obviously better than that of the matrix asphalt. In order to study the viscoelastic properties and temperature sensitivity of different kinds of asphalt, the asphalt is generally studied by using a temperature scanning test.
However, at present, no method or model is available to establish a direct relationship between complex modulus and temperature of modified asphalt. Many studies have discussed the relationship between temperature and asphalt viscosity using either the WLF model, andrade model or VFT model, but the complex modulus contains not only the viscous flow portion but also the elastic portion. The viscosity-temperature model is not directly equivalent to the modulus-temperature model. There are also many researchers to study the direct relationship between temperature and modulus by using the time-temperature equivalent principle and the main curve method. The main curve method, however, achieves a correlation between modulus and time/frequency, not a direct correlation between modulus and temperature. Furthermore, the main curve method is very effective for base asphalt, but is not necessarily applicable to modified asphalt. On one hand, common modified asphalt (such as SBS modified asphalt) is not a thermal rheological simple material, and phase separation can occur at high temperature, so that the main curve structure fails and the curve is not smooth; on the other hand, the construction of the main curve is very dependent on the fitting model chosen. The viscoelastic property of the modified asphalt is obviously different from that of the matrix asphalt, so that the same main curve model is not suitable for fitting, and the test complexity is increased.
Disclosure of Invention
Aiming at the defects in the prior art, the method for predicting the complex modulus of the modified asphalt provided by the invention solves the problems that the existing method cannot establish the relation between the complex modulus of the modified asphalt and the temperature and cannot predict the complex modulus of the asphalt by testing the temperature.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a method for predicting the complex modulus of modified asphalt comprises the following steps:
s1, performing a temperature slope scanning experiment on modified asphalt to obtain a characteristic value data set of actually measured complex modulus, a characteristic value data set of glass state modulus and a characteristic value data set of rubber state modulus of the modified asphalt;
s2, solving an actual measurement data set of the glassy state-rubbery state conversion coefficient and an actual measurement data set of the rubbery state-viscous state conversion coefficient according to an actual measurement complex modulus characteristic value data set, a glassy state modulus characteristic value data set and a rubbery state modulus characteristic value data set of the modified asphalt;
s3, according to the actually measured data set of the glass state-rubber state conversion coefficient and the actually measured data set of the rubber state-viscous state conversion coefficient, calibrating glass state Arrhenius equation fitting parameters, glass state-rubber state conversion activation energy parameters, rubber state Arrhenius equation fitting parameters and rubber state-viscous state conversion activation energy parameters;
and S4, predicting the complex modulus of the modified asphalt according to the fitting parameter of the glassy state Arrhenius equation, the glassy state-rubber state transition activation energy parameter, the fitting parameter of the rubbery state Arrhenius equation, the rubbery state-viscous state transition activation energy parameter, the characteristic value data set of the glassy state modulus and the characteristic value data set of the rubber state modulus.
The invention has the beneficial effects that: according to the method, parameter fitting and calculation are carried out through the temperature slope scanning data of the modified asphalt, so that the complex modulus of the modified asphalt is predicted, the complex modulus data at different temperatures can be accurately fitted, and the accuracy can be guaranteed in a wider temperature domain range.
Further, the characteristic value data set of the actually measured complex modulus, the characteristic value data set of the glass state modulus, the characteristic value data set of the rubber state modulus, the actually measured data set of the glass state-rubber state conversion coefficient and the actually measured data set of the rubber state-viscous state conversion coefficient of the modified asphalt are all data sets taking the temperature involved in the temperature ramp scanning experiment as an independent variable.
Further, the step S2 includes the following sub-steps:
s21, solving the actually measured data set of the glassy state-rubbery state conversion coefficient according to the actually measured characteristic value data set of the complex modulus, the characteristic value sequence of the glassy state modulus and the characteristic value data set of the rubbery state modulus of the modified asphalt by the following formula:
wherein T is a test temperature value, G g (T) is the characteristic value of the glass state modulus corresponding to the T temperature, G r (T) is a characteristic value of rubbery modulus corresponding to the T temperature, G m (T) is the measured complex modulus characteristic value of the modified asphalt corresponding to the temperature T,
is the measured value of the glass state-rubber state conversion coefficient corresponding to the T temperature;
s22, according to the characteristic value data set of the actually measured complex modulus of the modified asphalt and the characteristic value data set of the rubbery modulus, solving the actually measured data set of the rubbery-viscous flow state conversion coefficient through the following formula:
wherein the content of the first and second substances,
is the measured value of the conversion coefficient of rubber state-viscous state corresponding to the T temperature.
The beneficial effects of the above further scheme are: the method provides a characteristic value data set of a plurality of moduli actually measured by the modified asphalt, a characteristic value data set of the glass state modulus and a characteristic value data set of the rubber state modulus, solves the expression of the actually measured data set of the glass state-rubber state conversion coefficient and the actually measured data set of the rubber state-viscous flow state conversion coefficient, and lays a foundation for the subsequent estimation of the glass state-rubber state conversion coefficient value and the rubber state-viscous flow state conversion coefficient value.
Further, the step S3 includes the following sub-steps:
s31, according to the actually measured data set of the glass state-rubber state conversion coefficient, the glass state-rubber state conversion activation energy parameter is determined through the following formula:
wherein gamma is a bias constant, R is a universal gas constant, E g The activation energy parameter of the glass state-rubber state transition is ln (·) is a natural logarithmic function;
s32, solving a glassy state Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the actually measured data set of the glassy state-rubbery state conversion coefficient and the glassy state-rubbery state conversion activation energy parameter by the following formula:
wherein beta is the rate of temperature rise, A g (T) is a fitting parameter value of a glassy state Arrhenius equation corresponding to the T temperature, and exp (·) is an exponential function with a natural constant e as a base;
s33, averaging the fitting parameter values of the glassy state Arrhenius equation corresponding to the temperature involved in the temperature ramp scanning experiment to obtain the fitting parameters of the glassy state Arrhenius equation;
s34, according to the measured data set of the rubber state-viscous state conversion coefficient, calibrating the rubber state-viscous state conversion activation energy parameter through the following formula:
wherein, E r Is a rubber state-viscous state transition activation energy parameter;
s35, solving the rubber Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the measured data set of the rubber state-viscous flow state conversion coefficient and the rubber state-viscous flow state conversion activation energy parameter by the following formula:
wherein A is r (T) is a rubbery Arrhenius equation fitting parameter value corresponding to the T temperature;
s36, averaging the fitting parameter values of the rubbery Arrhenius equation corresponding to the temperature involved in the temperature slope scanning experiment to obtain the fitting parameters of the rubbery Arrhenius equation.
The beneficial effects of the above further scheme are: based on the Arrhenius model, each expression which takes the temperature as independent variable and is convenient to fit and solve is designed for solving intermediate parameters required by predicting the complex modulus of the asphalt.
Further, the step S4 includes the following sub-steps:
s41, calculating the value of the glassy state-rubbery state conversion coefficient according to the fitting parameter of the glassy state Arrhenius equation and the glassy state-rubbery state conversion activation energy parameter by the following formula:
wherein alpha is g (T) is the value of the glass state-rubber state transformation coefficient corresponding to the temperature T;
s42, according to the rubber Arrhenius equation fitting parameter and the rubber-viscous state conversion activation energy parameter, calculating to obtain a rubber-viscous state conversion coefficient value through the following formula:
wherein alpha is r (T) is the rubber state-viscous state conversion coefficient value corresponding to the temperature T;
s43, predicting the complex modulus of the modified asphalt according to the following formula according to the glassy state-to-rubbery state conversion coefficient value, the rubbery state-to-viscous state conversion coefficient value, the characteristic value data set of the glassy modulus and the characteristic value data set of the rubbery modulus:
G p (T)＝(1-α g (T))·G g (T)+α g (T)·(1-α r (T))·G r (T)
wherein, G p And (T) is the characteristic value of the complex modulus of the modified asphalt corresponding to the temperature T.
The beneficial effects of the above further scheme are: by using the above formulas, the complex modulus of the modified asphalt at the corresponding temperature can be directly predicted according to the fitting parameter of the glassy state Arrhenius equation, the glassy state-rubbery state transition activation energy parameter, the rubbery state-viscous state transition activation energy parameter, the characteristic values of glassy state moduli at different temperatures and the characteristic values of rubbery state moduli at different temperatures, so that the prediction result is accurate, and the temperature application range is wide.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting complex modulus of modified asphalt according to an embodiment of the present invention;
FIG. 2 is a plot of the characteristic values of the glassy modulus and the rubbery modulus at-40 to 140 degrees Celsius;
FIG. 3 is a graph showing the measured glass state-to-rubber state conversion coefficient and the measured rubber state-to-viscous state conversion coefficient at-40 to 140 ℃;
FIG. 4 shows the activation energy parameter E for the glass-rubber transition g Fitting a parameter calibration graph by using the straight line;
FIG. 5 is a calculated value of the glassy state-to-rubbery state conversion coefficient and the rubbery state-to-viscous state conversion coefficient;
FIG. 6 shows the predicted complex modulus of the modified asphalt.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, in an embodiment of the present invention, a method for predicting complex modulus of modified asphalt comprises the following steps:
s1, performing a temperature slope scanning experiment on the modified asphalt to obtain a characteristic value data set of actually measured complex modulus, a characteristic value data set of glass state modulus and a characteristic value data set of rubber state modulus of the modified asphalt.
In this embodiment, a temperature ramp scanning experiment is performed by using a TA instruments DHR-3 dynamic shear rheometer, the testing temperature range is-40 ℃ to 140 ℃, and in order to avoid the influence caused by different frequencies, the detection frequency of 10rad/s recommended by PG classification detection is used. And recording the characteristic value of the actually measured complex modulus of the modified asphalt at each test temperature to form a data set, and recording phase angle data. The characteristic value data set of the glassy modulus and the characteristic value data set of the rubbery modulus are determined by a phase angle minimum value method. Specifically, when a phase angle minimum appears in the temperature sweep curve, a value corresponding to the phase angle minimum is selected as a characteristic value of the glassy modulus or a characteristic value of the rubbery modulus. The characteristic value of the glassy modulus or the characteristic value of the rubbery modulus can also be determined empirically when no phase angle minima occur in the temperature sweep curve. For bituminous materials, at-40 ℃ the bitumen has not yet fully entered the glassy state, usually 10 9 Pa is a characteristic value of the glass modulus. The test results of the temperature ramp sweep experiment are shown in fig. 2.
S2, solving an actually measured data set of the glassy state-rubbery state conversion coefficient and an actually measured data set of the rubbery state-viscous state conversion coefficient according to the actually measured characteristic value data set of the complex modulus, the actually measured characteristic value data set of the glassy state modulus and the characteristic value data set of the rubbery state modulus of the modified asphalt.
The characteristic value data set of the actually measured complex modulus, the characteristic value data set of the glass state modulus, the characteristic value data set of the rubber state modulus, the actually measured data set of the glass state-rubber state conversion coefficient and the actually measured data set of the rubber state-viscous state conversion coefficient of the modified asphalt are all data sets taking the temperature related to the temperature ramp scanning experiment as an independent variable.
Step S2 includes the following substeps:
s21, solving the actually measured data set of the glassy state-rubbery state conversion coefficient according to the actually measured characteristic value data set of the complex modulus, the characteristic value sequence of the glassy state modulus and the characteristic value data set of the rubbery state modulus of the modified asphalt by the following formula:
wherein T is a test temperature value, G g (T) is the characteristic value of the glass state modulus corresponding to the T temperature, G r (T) is a characteristic value of rubbery modulus corresponding to the T temperature, G m (T) is the measured complex modulus characteristic value of the modified asphalt corresponding to the temperature T,
is the measured value of the glass state-rubber state conversion coefficient corresponding to the T temperature.
S22, according to the characteristic value data set of the actually measured complex modulus of the modified asphalt and the characteristic value data set of the rubbery modulus, solving the actually measured data set of the rubbery-viscous flow state conversion coefficient through the following formula:
wherein the content of the first and second substances,
is at T temperatureThe rubber state-viscous state conversion coefficient corresponding to the degree is measured.
The above contents provide a characteristic value data set of a plurality of actually measured moduli of the modified asphalt, a characteristic value data set of a glass state modulus and a characteristic value data set of a rubber state modulus, and an expression of the actually measured data set of the glass state-rubber state conversion coefficient and the actually measured data set of the rubber state-viscous flow state conversion coefficient is solved, so that a foundation is laid for the subsequent estimation of the glass state-rubber state conversion coefficient value and the rubber state-viscous flow state conversion coefficient value.
The measured data set of the glassy state-to-rubbery state conversion coefficient and the measured data set of the rubbery state-to-viscous state conversion coefficient are shown in fig. 3.
And S3, calibrating a glass state Arrhenius equation fitting parameter, a glass state-rubber state transition activation energy parameter, a rubber state Arrhenius equation fitting parameter and a rubber state-viscous state transition activation energy parameter according to the measured data set of the glass state-rubber state conversion coefficient and the measured data set of the rubber state-viscous state conversion coefficient.
Step S3 comprises the following substeps:
s31, according to the actually measured data set of the glass state-rubber state conversion coefficient, the glass state-rubber state conversion activation energy parameter is determined through the following formula:
wherein gamma is an offset constant, R is a universal gas constant, and has a value of 8.314J/mol.K, E g The activation energy parameter for the glass-rubber transition is ln (-) as a natural logarithmic function.
This formula was derived by the Coats-Redfern method according to the Arrhenius model. The Arrhenius model provides the following formula:
integrating the two sides, we can get:
by the Coats-Redfern method, the formula can be expanded to:
wherein n is the reaction order, and n =1 for the phase transition reaction of the modified asphalt. Thus, the left side of the equal sign of the above formula is just-ln (1-alpha) g ) By a taylor expansion, whereby:
further derivation yields:
due to the fact that
Is substantially constant, so the present invention marks this term with a bias constant γ. For ln (-ln (1-alpha) g )/T 2 ) And/or>
By performing linear correlation analysis, the activation energy parameter E of the glass-rubber transition can be determined g . The effect of the fit is shown in figure 4.
As can be seen from FIG. 4, ln (-ln (1-. Alpha.)) g )/T 2 ) And
has good linear fitting effect, and the fitting effect index is the correlation coefficient R 2 Was 0.9537.
S32, solving a glassy state Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the actually measured data set of the glassy state-rubbery state conversion coefficient and the glassy state-rubbery state conversion activation energy parameter by the following formula:
wherein beta is the rate of temperature rise, A g (T) is a fitting parameter value of a glassy state Arrhenius equation corresponding to the T temperature, and exp (·) is an exponential function with a natural constant e as a base.
S33, averaging the fitting parameter values of the glassy state Arrhenius equation corresponding to the temperature involved in the temperature ramp scanning experiment to obtain the fitting parameters of the glassy state Arrhenius equation.
In this example, the results of the activation energy for glass-rubber transition parameter and the fitting parameter of the glass Arrhenius equation are shown in the following table:
TABLE 1 solving results of glass state-rubber state transition activation energy parameter and glass state Arrhenius equation fitting parameter
The activation energy parameter of the glass state-rubber state transition is 12117.6J/mol, and the fitting parameter of the glass state Arrhenius equation is 134.5670811min -1 。
S34, according to the measured data set of the rubber state-viscous flow state conversion coefficient, the rubber state-viscous flow state conversion activation energy parameter is calibrated through the following formula:
wherein, E r Is a rubber state-viscous state transition activation energy parameter.
S35, solving the rubber Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the measured data set of the rubber state-viscous flow state conversion coefficient and the rubber state-viscous flow state conversion activation energy parameter by the following formula:
wherein A is r And (T) is a rubbery Arrhenius equation fitting parameter value corresponding to the T temperature.
And S36, averaging the fitting parameter values of the rubber Arrhenius equation corresponding to the temperature involved in the temperature slope scanning experiment to obtain the fitting parameters of the rubber Arrhenius equation.
The steps are based on an Arrhenius model, and each expression which takes the temperature as an independent variable and is convenient to fit and solve is designed to solve the intermediate parameters required by predicting the complex modulus of the asphalt.
In this example, the rubbery-viscous state transition activation energy parameter is 74367.7J/mol, and the rubbery Arrhenius equation fitting parameter is 523.0330494min -1 。
And S4, predicting the complex modulus of the modified asphalt according to the fitting parameter of the glassy state Arrhenius equation, the glassy state-rubbery state transition activation energy parameter, the rubbery state-viscous state transition activation energy parameter, the characteristic value data set of the glassy state modulus and the characteristic value data set of the rubbery state modulus.
Step S4 includes the following substeps:
s41, calculating the value of the glassy state-rubbery state transformation coefficient according to the glassy state Arrhenius equation fitting parameter and the glassy state-rubbery state transformation activation energy parameter by the following formula:
wherein alpha is g (T) is the value of the glass-to-rubber transition coefficient corresponding to the T temperature.
S42, according to the rubber Arrhenius equation fitting parameter and the rubber-viscous state conversion activation energy parameter, calculating to obtain a rubber-viscous state conversion coefficient value through the following formula:
wherein alpha is r (T) is the value of the rubber state-viscous state conversion coefficient corresponding to the temperature T.
Example α g (T) and alpha r (T) theoretical calculation result values and measured values
And &>
As shown in fig. 5.
S43, predicting the complex modulus of the modified asphalt according to the following formula according to the glassy state-to-rubbery state conversion coefficient value, the rubbery state-to-viscous state conversion coefficient value, the characteristic value data set of the glassy modulus and the characteristic value data set of the rubbery modulus:
G p (T)＝(1-α g (T))·G g (T)+α g (T)·(1-α r (T))·G r (T)
wherein G is p And (T) is the characteristic value of the complex modulus of the modified asphalt corresponding to the T temperature.
By using the above formulas, the complex modulus of the modified asphalt at the corresponding temperature can be directly predicted according to the fitting parameter of the glassy state Arrhenius equation, the glassy state-rubbery state transition activation energy parameter, the rubbery state-viscous state transition activation energy parameter, the characteristic values of glassy state moduli at different temperatures and the characteristic values of rubbery state moduli at different temperatures, so that the prediction result is accurate, and the temperature application range is wide.
The results of the modified asphalt complex modulus prediction of this example are shown in FIG. 6.
In conclusion, the method carries out parameter fitting and calculation through the temperature slope scanning data of the modified asphalt so as to predict the complex modulus of the modified asphalt, can accurately fit the complex modulus data at different temperatures, and can ensure the accuracy in a wider temperature domain range.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (5)
1. The method for predicting the complex modulus of the modified asphalt is characterized by comprising the following steps of:
s1, performing a temperature slope scanning experiment on modified asphalt to obtain a characteristic value data set of actually measured complex modulus, a characteristic value data set of glass state modulus and a characteristic value data set of rubber state modulus of the modified asphalt;
s2, solving an actual measurement data set of the glassy state-rubbery state conversion coefficient and an actual measurement data set of the rubbery state-viscous state conversion coefficient according to an actual measurement complex modulus characteristic value data set, a glassy state modulus characteristic value data set and a rubbery state modulus characteristic value data set of the modified asphalt;
s3, calibrating a glassy state Arrhenius equation fitting parameter, a glassy state-rubbery state transition activation energy parameter, a rubbery state Arrhenius equation fitting parameter and a rubbery state-viscous state transition activation energy parameter according to the measured data set of the glassy state-rubbery state conversion coefficient and the measured data set of the rubbery state-viscous state conversion coefficient;
and S4, predicting the complex modulus of the modified asphalt according to the fitting parameter of the glassy state Arrhenius equation, the glassy state-rubber state transition activation energy parameter, the fitting parameter of the rubbery state Arrhenius equation, the rubbery state-viscous state transition activation energy parameter, the characteristic value data set of the glassy state modulus and the characteristic value data set of the rubber state modulus.
2. The method for predicting the complex modulus of the modified asphalt according to claim 1, wherein the data set of the characteristic value of the actually measured complex modulus, the data set of the characteristic value of the glassy modulus, the data set of the characteristic value of the rubbery modulus, the data set of the actually measured glassy-to-rubbery transformation coefficient, and the data set of the actually measured rubbery-to-viscous transformation coefficient of the modified asphalt are data sets using the temperature involved in the temperature ramp scan experiment as an independent variable.
3. The method for predicting the complex modulus of modified asphalt according to claim 2, wherein the step S2 comprises the following substeps:
s21, according to the characteristic value data set of the actually measured complex modulus, the characteristic value sequence of the glass state modulus and the characteristic value data set of the rubber state modulus of the modified asphalt, solving the actually measured data set of the glass state-rubber state conversion coefficient through the following formula:
wherein T is a test temperature value, G g (T) is the characteristic value of the glass state modulus corresponding to the T temperature, G r (T) is a characteristic value of rubbery modulus corresponding to the T temperature, G m (T) is the measured complex modulus characteristic value of the modified asphalt corresponding to the temperature T,
is the measured value of the glass state-rubber state conversion coefficient corresponding to the T temperature;
s22, according to the characteristic value data set of the actually measured complex modulus of the modified asphalt and the characteristic value data set of the rubbery modulus, solving the actually measured data set of the rubbery-viscous flow state conversion coefficient through the following formula:
wherein, the first and the second end of the pipe are connected with each other,
is the measured value of the conversion coefficient of the rubbery state to the viscous state corresponding to the temperature T.
4. The method for predicting the complex modulus of modified asphalt according to claim 3, wherein the step S3 comprises the following substeps:
s31, according to the actually measured data set of the glass state-rubber state conversion coefficient, determining the glass state-rubber state conversion activation energy parameter through the following formula:
wherein gamma is a bias constant, R is a universal gas constant, E g The activation energy parameter of the glass state-rubber state transition is ln (·) is a natural logarithmic function;
s32, solving a glassy state Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the actually measured data set of the glassy state-rubbery state conversion coefficient and the glassy state-rubbery state conversion activation energy parameter by the following formula:
wherein beta is the rate of temperature rise, A g (T) is a fitting parameter value of a glassy state Arrhenius equation corresponding to the T temperature, and exp (·) is an exponential function with a natural constant e as a base;
s33, averaging the fitting parameter values of the glassy state Arrhenius equation corresponding to the temperature involved in the temperature ramp scanning experiment to obtain the fitting parameters of the glassy state Arrhenius equation;
s34, according to the measured data set of the rubber state-viscous flow state conversion coefficient, the rubber state-viscous flow state conversion activation energy parameter is calibrated through the following formula:
wherein E is r Is a rubber state-viscous state transition activation energy parameter;
s35, solving the rubber Arrhenius equation fitting parameter value corresponding to the temperature involved in the temperature ramp scanning experiment according to the measured data set of the rubber state-viscous flow state conversion coefficient and the rubber state-viscous flow state conversion activation energy parameter by the following formula:
wherein A is r (T) is a rubbery Arrhenius equation fitting parameter value corresponding to the T temperature;
and S36, averaging the fitting parameter values of the rubber Arrhenius equation corresponding to the temperature involved in the temperature slope scanning experiment to obtain the fitting parameters of the rubber Arrhenius equation.
5. The method for predicting the complex modulus of modified asphalt according to claim 4, wherein the step S4 comprises the following substeps:
s41, calculating the value of the glassy state-rubbery state conversion coefficient according to the fitting parameter of the glassy state Arrhenius equation and the glassy state-rubbery state conversion activation energy parameter by the following formula:
wherein alpha is g (T) is the value of the glass state-rubber state transformation coefficient corresponding to the temperature T;
s42, calculating according to the rubber Arrhenius equation fitting parameter and the rubber-viscous state transformation activation energy parameter by the following formula to obtain a rubber-viscous state transformation coefficient value:
wherein alpha is r (T) is the value of the rubber state-viscous state conversion coefficient corresponding to the temperature T;
s43, predicting the complex modulus of the modified asphalt according to the glassy state-rubbery state conversion coefficient value, the rubbery state-viscous state conversion coefficient value, the characteristic value data set of glassy modulus and the characteristic value data set of rubbery modulus by the following formula:
G p (T)＝(1-α g (T))·G g (T)+α g (T)·(1-α r (T))·G r (T)
wherein, G p And (T) is the characteristic value of the complex modulus of the modified asphalt corresponding to the temperature T.
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