Method for predicting dynamic modulus of asphalt mixture based on self-consistent micromechanical model

Abstract

The invention is suitable for the technical field of road engineering, and particularly relates to a method for predicting the dynamic modulus of an asphalt mixture based on a self-consistent micromechanical model, and the method comprises the following steps: obtaining an aggregate sample, screening the aggregate sample, and recording the passing rate of aggregate on each sieve pore size; the method comprises the following steps: acquiring an asphalt sample, testing asphalt, and establishing a shear dynamic modulus main curve of asphalt; preparing a corresponding asphalt mixture, testing the asphalt mixture, and establishing a dynamic modulus main curve corresponding to the asphalt mixture; calculating an aggregate modulus based on the reverse model, and calibrating parameters of a Poisson's ratio equation; and acquiring the aggregate gradation of the to-be-predicted mixture, and predicting the dynamic modulus main curve of the to-be-predicted mixture based on the forward model. According to the method, closed-loop application of forward prediction and reverse back calculation is achieved, the anisotropic characteristic of the strength of the asphalt mixture and the influence of aggregates with different particle sizes on the overall modulus of the mixture are fully considered, and therefore the accuracy and applicability of the model are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of road engineering, and particularly relates to a method for predicting the dynamic modulus of asphalt mixtures based on a self-consistent micromechanical model. Background Art

[0002] As a typical viscoelastic material, the dynamic modulus of asphalt mixtures is a key material parameter characterizing their viscoelastic properties and is also an important performance index clearly required to be tested in current specifications. The dynamic modulus can not only reflect the variation law of the strength of asphalt mixtures with temperature and frequency but also serve as the basic basis for evaluating their anti-fatigue cracking and anti-permanent deformation properties. However, the dynamic modulus test needs to be carried out under multi-temperature and multi-frequency conditions, and at the same time, there are relatively high requirements for the flatness of specimens. In addition, as a stress-controlled test, the precise control of its test parameters also has a certain degree of difficulty. Therefore, it is not efficient to directly test the dynamic modulus for each asphalt mixture.

[0003] Asphalt and aggregates, as key raw materials for the mix design of asphalt mixtures, their material properties must pass relevant tests and meet the specified standard thresholds before preparing asphalt mixtures. Especially for asphalt materials, testing the master curve of their shear modulus using a dynamic shear rheometer has become the standard process in current material testing. At present, existing methods mainly rely on the Witczak formula to predict the dynamic modulus of asphalt mixtures. However, this model is only based on empirical regression analysis and lacks mechanical and theoretical support. When the data of the asphalt mixtures used are inconsistent with the calibration database, the prediction accuracy of the model will decrease significantly. Therefore, if the dynamic modulus of asphalt mixtures can be predicted by micromechanical methods based on the moduli of asphalt and aggregates, it will greatly save the material design time and improve the design efficiency.

[0004] When using micromechanical models to predict the properties of asphalt mixtures, the self-consistency of the models is crucial, that is, the models should be able to invert the component properties through the overall properties and predict the overall properties through the component properties. However, existing models generally lack the consideration of the Poisson's ratio of asphalt mixtures and ignore the characteristics of asphalt mixtures as anisotropic materials. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for predicting the dynamic modulus of asphalt mixtures based on a self-consistent micromechanical model, aiming to solve the problem that existing models generally lack the consideration of the Poisson's ratio of asphalt mixtures and ignore the characteristics of asphalt mixtures as anisotropic materials.

[0006] The present invention is implemented as follows. A method for predicting the dynamic modulus of asphalt mixtures based on a self-consistent micromechanical model, the method comprising:

[0007] Obtain aggregate samples, screen the aggregate samples, and record the passing rate of aggregates at each sieve hole size;

[0008] Obtain an asphalt sample, conduct a shear dynamic modulus test on the asphalt, and establish a master curve of the shear dynamic modulus of the asphalt;

[0009] Conduct a compressive dynamic modulus test on the asphalt mixture, and establish a master curve of the compressive dynamic modulus of the asphalt mixture;

[0010] Calculate the aggregate modulus based on the inverse model of the self - consistent micromechanics model, and calibrate the parameters of the Poisson's ratio equation;

[0011] Obtain the aggregate gradation of the mixture to be predicted, and predict the master curve of the dynamic modulus of the mixture to be predicted based on the forward model of the self - consistent micromechanics model.

[0012] Preferably, before testing the compressive dynamic modulus of the asphalt mixture, test the volume ratios of the aggregate, asphalt, and voids.

[0013] Preferably, in the step of testing the asphalt, use a dynamic shear rheometer to test the shear dynamic modulus of the asphalt.

[0014] Preferably, in the step of testing the asphalt mixture, use a universal testing machine to test the compressive dynamic modulus of the asphalt mixture.

[0015] Preferably, the Poisson's ratio equation is expressed as:

[0016]

[0017] where: z1, z2, and z3 are calibration parameters, and |E * | is the compressive dynamic modulus of the asphalt mixture.

[0018] Preferably, the self - consistent micromechanics model is expressed as:

[0019]

[0020]

[0021] where: 1, 2, 3, and * represent the aggregate, asphalt, air, and asphalt mixture respectively, || represents the modulus amplitude, c1, c2, and c3 are the volume ratios of the aggregate, asphalt, and air respectively, K is the bulk modulus, and G is the shear modulus.

[0022] Preferably, in the inverse model of the self - consistent micromechanics model, the aggregate modulus is calculated based on the asphalt modulus and the asphalt mixture modulus, and in the forward model of the self - consistent micromechanics model, the asphalt mixture modulus is calculated based on the aggregate modulus and the asphalt modulus.

[0023] Preferably, the compressive dynamic modulus of the asphalt mixture is expressed as:

[0024]

[0025] Preferably, in the step of obtaining the aggregate gradation of the mixture to be predicted and predicting the master curve of the dynamic modulus of the mixture to be predicted based on the forward model of the self-consistent micromechanics model, after calculating the modulus of the asphalt mixture for each sieve hole size through the forward model of the self-consistent micromechanics model, the overall effective modulus of the mixture to be predicted is expressed as:

[0026]

[0027] Where: |E * eff | is the overall effective modulus of the asphalt mixture, a min为 is the radius of the aggregate with the minimum sieve hole size, a max is the radius of the aggregate with the maximum sieve hole size, f(a) is the gradation function, M is the total number of sieve hole sizes, a i is the radius of each gradation of aggregate, P i+1 and P i are the passing rates of two adjacent gradations of aggregate respectively.

[0028] Preferably, in the step of calibrating the parameters of the Poisson's ratio equation, the reverse model is used to predict the aggregate modulus. When the change rate of its bulk modulus with frequency is lower than the preset value, the parameters of the corresponding Poisson's ratio equation are the calibrated model parameters.

[0029] The method for predicting the dynamic modulus of asphalt mixture based on the self-consistent micromechanics model provided by the present invention comprehensively considers the viscoelastic properties of the modulus and Poisson's ratio of the asphalt mixture by introducing the Poisson's ratio equation into the three-phase composite model. On the basis of comprehensively considering the aggregate gradation, the dynamic modulus of the asphalt mixture is predicted by using the asphalt modulus and the aggregate modulus. Compared with the existing models, the self-consistent micromechanics model proposed by the present invention not only realizes the closed-loop application of forward prediction and reverse calculation, but also fully considers the anisotropic characteristics of the strength of the asphalt mixture and the influence of aggregates with different particle sizes on the overall modulus of the mixture, thereby improving the accuracy and applicability of the model. The present invention can directly predict the master curve of the dynamic modulus after mixing based on the strength test data of the raw materials of the asphalt mixture, providing efficient and reliable theoretical support for the material design of the asphalt mixture and having wide engineering application value. Brief Description of the Drawings

[0030] Figure 1 is a flow chart for predicting the dynamic modulus of asphalt mixture based on the self-consistent micromechanics model provided by an embodiment of the present invention;

[0031] Figure 2 is a schematic diagram of the self-consistent micromechanics model provided by an embodiment of the present invention;

[0032] Figure 3The master curve of the asphalt shear dynamic modulus provided by the embodiments of the present invention;

[0033] Figure 4 The master curve of the dynamic modulus of the asphalt mixture provided by the embodiments of the present invention;

[0034] Figure 5 The schematic diagram showing that the aggregate bulk modulus provided by the embodiments of the present invention does not change significantly with frequency;

[0035] Figure 6 The comparison chart of the predicted value and the measured value of the master curve of the dynamic modulus of the asphalt mixture provided by the embodiments of the present invention (using the original asphalt);

[0036] Figure 7 The comparison chart of the predicted value and the measured value of the master curve of the dynamic modulus of the asphalt mixture provided by the embodiments of the present invention (using other asphalt). Detailed implementation manners

[0037] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0038] As Figure 1 shown, it is the flow chart for predicting the dynamic modulus of the asphalt mixture based on the self-consistent micromechanics model provided by the embodiments of the present invention, and the method includes:

[0039] Obtain asphalt and aggregate samples, screen the aggregate samples, and further prepare the corresponding asphalt mixture, and perform volume index tests on the asphalt mixture to obtain test results.

[0040] In this step, select asphalt and aggregate test samples, screen the aggregate, record the passing rate of the aggregate on each sieve hole size, and prepare the corresponding asphalt mixture, and test the volume ratios of the aggregate, asphalt and voids in the asphalt mixture.

[0041] Test the asphalt, establish the master curve of the shear dynamic modulus of the asphalt, and test the asphalt mixture to establish the corresponding master curve of the dynamic modulus of the asphalt mixture.

[0042] In this step, use a dynamic shear rheometer to test the shear dynamic modulus of the asphalt and draw the master curve of the shear dynamic modulus of the asphalt; use a universal testing machine to test the compressive dynamic modulus of the asphalt mixture and draw the master curve of the dynamic modulus of the asphalt mixture according to the test results.

[0043] Calculate the aggregate modulus based on the inverse model of the self-consistent micromechanics model and calibrate the parameters of the Poisson's ratio equation.

[0044] Obtain the aggregate gradation of the mixture to be predicted, and predict the master curve of the dynamic modulus of the mixture to be predicted based on the forward model of the self-consistent micromechanics model.

[0045] In a specific embodiment of the present invention, as Figure 2 shown, it is a schematic diagram of the self-consistent micromechanics model in the embodiment of the present invention. The specific method includes:

[0046] Select 70# base asphalt and limestone aggregate samples, screen the limestone aggregate, record the passing rate of the aggregate on each sieve hole size, and prepare the corresponding asphalt mixture.

[0047] Test the volume ratios of the aggregate, asphalt, and voids in the asphalt mixture. In this embodiment, they are 0.8657, 0.0915, and 0.0428 respectively;

[0048] Use a dynamic shear rheometer to test the shear dynamic modulus of the asphalt, and draw the master curve of the shear dynamic modulus of the asphalt, as Figure 3 shown;

[0049] Use a universal testing machine to test the compressive dynamic modulus of the asphalt mixture, and draw the master curve of the dynamic modulus of the asphalt mixture according to the test results, as Figure 4 shown;

[0050] The self-consistent micromechanics model is shown as follows. When using the inverse model, the aggregate moduli K1 and G1 should be calculated based on the asphalt moduli K2 and G2, and the asphalt mixture moduli |K * | and |G * Calculate the aggregate moduli K1 and G1; when using the forward model, the asphalt mixture moduli |K * | and |G * .

[0051]

[0052]

[0053] Among them: 1, 2, 3, and * represent the aggregate, asphalt, air, and asphalt mixture respectively, || represents the modulus amplitude, c1, c2, and c3 are the volume ratios of the aggregate, asphalt, and air respectively, K is the bulk modulus, and G is the shear modulus.

[0054] Based on the asphalt mixture modulus and the asphalt modulus, calculate the Poisson's ratio of the asphalt mixture using the equation shown as follows, indirectly calculate the aggregate modulus using the inverse model of the self-consistent micromechanics model, and calibrate the parameters of the Poisson's ratio equation. As Figure 5As shown, when the bulk modulus of the aggregate does not change significantly with frequency, the parameters corresponding to the Poisson's ratio equation are the appropriate model parameters.

[0055]

[0056] Where: z1, z2, and z3 are calibration parameters, and |E * | is the compressive dynamic modulus of the asphalt mixture.

[0057] After obtaining the aggregate modulus, based on the aggregate gradation, the master curve of the dynamic modulus of the corresponding mixture after mixing the aggregate with other asphalt at each sieve hole size can be predicted through the forward model of the self-consistent micromechanics model. Further, by obtaining the bulk modulus K and shear modulus G of the material, its compressive dynamic modulus can be calculated using the following formula.

[0058]

[0059] After obtaining the modulus of the asphalt mixture corresponding to each sieve hole size using the forward model, its overall effective modulus should be calculated using the following formula.

[0060]

[0061] Where: |E * eff | is the overall effective modulus of the asphalt mixture, a min is the radius of the aggregate with the smallest sieve hole size, a max is the radius of the aggregate with the largest sieve hole size, f(a) is the gradation function, M is the total number of sieve hole sizes, a i is the radius of each gradation of aggregate, P i+1 and P i are the passing rates of two adjacent gradations of aggregate, respectively.

[0062] In this embodiment, taking 70# asphalt and limestone aggregate as an example, Figure 6 the comparison results between the predicted values and measured values of the master curve of the dynamic modulus of the asphalt mixture by the self-consistent micromechanics model are shown, and compared with the predicted results by the Witczak formula used in the existing method. When using another kind of asphalt (SBS modified asphalt in this embodiment), Figure 7 the comparison of the predicted values, measured values, and predicted values by the Witczak formula of the master curve of the dynamic modulus of the corresponding asphalt mixture is further shown. The results show that the model not only realizes the self-consistency of the forward and inverse models, but also can still accurately predict the master curve of the dynamic modulus of the mixture after changing the asphalt type. This characteristic significantly reduces the time required for laboratory testing of the dynamic modulus, improves the efficiency of material design, and has higher accuracy than the existing standard method.

[0063] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0064] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent for the present invention should be subject to the appended claims.

[0065] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should all be included within the protection scope of the present invention.

Claims

1. A method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model, characterized in that: The method comprises: Obtain aggregate samples, sieve the aggregate samples, and record the passing rate of aggregate on each sieve size; Obtain asphalt samples, perform shear dynamic modulus test on the asphalt, and establish a shear dynamic modulus master curve of the asphalt; Conduct compression dynamic modulus test on asphalt mixture and establish compression dynamic modulus master curve of asphalt mixture; The inverse model based on the self-consistent micromechanical model is used to calculate the aggregate modulus and calibrate the parameters of the Poisson's ratio equation; The aggregate gradation of the mixture to be predicted is obtained, and the dynamic modulus master curve of the mixture to be predicted is predicted based on the forward model of the self-consistent micromechanics model.

2. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1 is characterized in that: Before testing the compression dynamic modulus of asphalt mixture, the volume ratio of aggregate, asphalt and voids is tested.

3. The method for predicting the dynamic modulus of asphalt mixture based on the self-consistent micromechanics model according to claim 1 is characterized in that: In the step of testing the asphalt, a dynamic shear rheometer is used to test the shear dynamic modulus of the asphalt.

4. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1, characterized in that: In the step of testing the asphalt mixture, a universal testing machine is used to test the compression dynamic modulus of the asphalt mixture.

5. The method for predicting the dynamic modulus of asphalt mixture based on the self-consistent micromechanics model according to claim 1 is characterized in that: The Poisson's ratio equation is expressed as: Among them: z1, z2 and z3 are calibration parameters, |E * | is the compression dynamic modulus of asphalt mixture.

6. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1, characterized in that: The self-consistent micromechanical model is expressed as: Where: 1, 2, 3 and * represent aggregate, asphalt, air and asphalt mixture respectively, || represents the modulus amplitude, c1, c2 and c3 are the volume proportions of aggregate, asphalt and air respectively, K is the bulk modulus, and G is the shear modulus.

7. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1, characterized in that: In the inverse model of the self-consistent micromechanics model, the aggregate modulus is calculated based on the asphalt modulus and the asphalt mixture modulus, and in the forward model of the self-consistent micromechanics model, the asphalt mixture modulus is calculated based on the aggregate modulus and the asphalt modulus.

8. The method for predicting the dynamic modulus of asphalt mixture based on the self-consistent micromechanics model according to claim 3, characterized in that: The compression dynamic modulus of the mixture is expressed as:

9. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1, characterized in that: In the step of obtaining the aggregate gradation of the mixture to be predicted and predicting the dynamic modulus master curve of the mixture to be predicted based on the forward model of the self-consistent micromechanics model, after calculating the modulus of the asphalt mixture of each sieve size by the forward model of the self-consistent micromechanics model, the overall effective modulus of the mixture to be predicted is expressed as: Where: |E * eff | is the overall effective modulus amplitude of asphalt mixture, a min is the aggregate radius of the minimum sieve size, a max is the aggregate radius of the largest sieve hole particle size, f(a) is the gradation function, M is the total number of sieve hole particle sizes, a i is the radius of each aggregate, P i+1 and P i are the passing rates of two adjacent aggregate grades respectively.

10. The method for predicting the dynamic modulus of asphalt mixture based on a self-consistent micromechanics model according to claim 1, characterized in that: In the step of calibrating the parameters of the Poisson's ratio equation, an inverse model is used to predict the aggregate modulus. When the rate of change of its bulk modulus with frequency is lower than a preset value, the corresponding parameters of the Poisson's ratio equation are the calibrated model parameters.

Images