A method and system for predicting shear strength parameter values of drained asphalt mixtures

Abstract

The application provides a method and system for predicting shear strength parameter values of drainage asphalt mixture, comprising the following steps: S1, obtaining drainage asphalt mixture to be predicted, the drainage asphalt mixture comprising asphalt mortar and granular skeleton; S2, performing tests on the granular skeleton at room temperature to obtain a corresponding internal friction angle, and performing tests on the asphalt mortar at a preset temperature to obtain complex modulus and cohesive work of the asphalt mortar at the preset temperature; and S3, inputting the complex modulus, cohesive work and internal friction angle into a pre-constructed parameter value prediction model to obtain shear strength parameter values corresponding to the drainage asphalt mixture. The beneficial effect is that the application can quickly predict shear strength parameter values at multiple temperatures based on material properties at the microscopic level of the drainage asphalt mixture, which can be used for theoretical calculation or finite element simulation to quickly predict the anti-rutting performance of asphalt pavement at multiple temperatures and guide the optimization of the anti-rutting performance at the microscopic level.

Description

Technical Field

[0001] This invention relates to the technical field of asphalt mixture testing, and more specifically, to a method and system for predicting the shear strength parameters of drainage asphalt mixtures. Background Technology

[0002] Drainage asphalt pavement is a very common type of pavement, and rutting is one of the main modes of pavement failure. The rutting resistance of pavement materials is closely related to the parameters of their shear strength. Cohesion and friction angle are common parameters used to characterize the shear strength of materials and can be used for theoretical analysis and finite element simulation analysis to predict the rutting development law or rutting depth of roads.

[0003] However, it is very inconvenient to prepare samples of drainage asphalt mixtures, and it is necessary to carry out a series of macroscopic experiments to determine the cohesion and friction angle of drainage asphalt mixtures. In addition, both of them will change with temperature, and it is very inconvenient and time-consuming to conduct macroscopic experiments to determine their values. Summary of the Invention

[0004] The problem to be solved by the present invention is to provide a method and system for predicting the shear strength parameter value of drainage asphalt mixture, which can quickly predict the shear strength parameter value.

[0005] To address the above problems, this invention provides a method for predicting the shear strength parameter value of drainage asphalt mixtures, comprising the following steps:

[0006] Step S1: Obtain the drainage asphalt mixture to be predicted, wherein the drainage asphalt mixture includes asphalt mortar and particle skeleton;

[0007] Step S2: At room temperature, test the particle skeleton to obtain the internal friction angle corresponding to the particle skeleton, and at a preset temperature, test the asphalt mortar to obtain the complex modulus and cohesive work of the asphalt mortar at the preset temperature.

[0008] Step S3: Input the complex modulus, the cohesive work, and the internal friction angle into the pre-constructed parameter prediction model to obtain the shear strength parameter value corresponding to the drainage asphalt mixture.

[0009] In this scheme, considering that the drainage asphalt mixture is a multiphase material, it can be regarded as being composed of asphalt mortar and particle skeleton at the micro level. Compared with the drainage asphalt mixture, asphalt mortar is easier to prepare, and its shear strength parameters at different temperatures can be quickly determined. The particle skeleton itself is not affected by temperature, and its shear strength parameters are easier to obtain than those of the drainage asphalt mixture. Therefore, separating the drainage asphalt mixture into asphalt mortar and particle skeleton for separate testing can accelerate the prediction speed of shear strength parameters and achieve rapid prediction of shear strength parameters.

[0010] Preferably, in step S2, the internal friction angle is measured by performing a triaxial test or a direct shear test on the particle skeleton.

[0011] Preferably, in step S2, the complex modulus is measured by performing a rheological test on the asphalt mortar.

[0012] Preferably, in step S2, the cohesive work of the asphalt mortar is measured by contact angle test, atomic force microscopy test, or reverse gas chromatography test.

[0013] Preferably, step S3 includes:

[0014] Step S31: Input the internal friction angle and the complex modulus into the parameter value prediction model. The parameter value prediction model calls the pre-configured first prediction formula to process the internal friction angle and the complex modulus to obtain the predicted friction angle corresponding to the preset temperature and includes it in the shear strength parameter value.

[0015] Step S32: Input the complex modulus, the cohesive work, and the predicted friction angle into the parameter value prediction model. The parameter value prediction model calls the pre-configured second prediction model to process the complex modulus, the cohesive work, and the predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature, which is included in the shear strength parameter value.

[0016] Preferably, the first prediction formula is as follows:

[0017]

[0018] in,

[0019] This represents the predicted friction angle at the preset temperature;

[0020] A represents a preset constant;

[0021] B represents a preset constant;

[0022] This represents the complex modulus at the preset temperature;

[0023] This represents the internal friction angle.

[0024] Preferably, the second prediction formula is as follows:

[0025]

[0026] in,

[0027] C PAM,T This represents the predicted cohesive force at the preset temperature;

[0028] This represents the predicted friction angle at the preset temperature;

[0029] B represents a preset constant;

[0030] D represents a preset constant;

[0031] This represents the complex modulus at the preset temperature;

[0032] W c,T This represents the cohesive work at the preset temperature.

[0033] This invention also provides a system for predicting the shear strength parameters of drainage asphalt mixtures, applied to the above-mentioned method for predicting shear strength parameters, comprising:

[0034] A first data acquisition module is used to acquire the internal friction angle of the particle skeleton obtained by testing the particle skeleton at room temperature from an external input.

[0035] A second data acquisition module is used to acquire the complex modulus and cohesive work of the asphalt mortar obtained by testing the asphalt mortar at a preset temperature from external input.

[0036] A parameter value prediction module, connected to the first data acquisition module and the second data acquisition module respectively, is used to input the complex modulus, the cohesive work and the internal friction angle into the pre-constructed parameter value prediction model to obtain the shear strength parameter value corresponding to the drainage asphalt mixture.

[0037] Preferably, the parameter value prediction module includes:

[0038] A first prediction unit is used to input the internal friction angle and the complex modulus into the parameter value prediction model, and to make the parameter value prediction model call a pre-configured first prediction formula to process the internal friction angle and the complex modulus to obtain the predicted friction angle corresponding to the preset temperature and include it in the shear strength parameter value;

[0039] A second prediction unit, connected to the first prediction unit, is used to input the complex modulus, the cohesive work, and the predicted friction angle into the parameter value prediction model, and to cause the parameter value prediction model to call a pre-configured second prediction model to process the complex modulus, cohesive work, and predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature, which is included in the shear strength parameter value. Attached Figure Description

[0040] Figure 1 This is a flowchart of the steps of the present invention;

[0041] Figure 2 This is a flowchart illustrating step S3 of the present invention.

[0042] Figure 3 This is a schematic diagram of the system structure of the present invention;

[0043] Figure 4 This is a comparison chart of the predicted and actual friction angles of No. 70 ordinary asphalt at different preset temperatures in Example 1.

[0044] Figure 5 This is a comparison chart of the predicted and actual friction angles of SBS modified asphalt at different preset temperatures in Example 1.

[0045] Figure 6 This is a comparison chart of the predicted cohesive force and the actual cohesive force of No. 70 ordinary asphalt at different preset temperatures in Example 1.

[0046] Figure 7 This is a comparison chart of the predicted cohesive force and the actual cohesive force of SBS modified asphalt at different preset temperatures in Example 1. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] In a preferred embodiment of the present invention, based on the above-mentioned problems existing in the prior art, a method for predicting the shear strength parameter value of drainage asphalt mixture is provided, such as... Figure 1 As shown, it includes the following steps:

[0049] Step S1: Obtain the drainage asphalt mixture to be predicted. The drainage asphalt mixture includes asphalt mortar and particle skeleton.

[0050] Step S2: At room temperature, test the particle skeleton to obtain the internal friction angle corresponding to the particle skeleton, and at a preset temperature, test the asphalt mortar to obtain the complex modulus and cohesive work of the asphalt mortar at the preset temperature.

[0051] Step S3: Input the complex modulus, cohesive work, and internal friction angle into the pre-constructed parameter prediction model to obtain the shear strength parameter values ​​corresponding to the drainage asphalt mixture.

[0052] Specifically, in this embodiment, considering that the drainage asphalt mixture is a multiphase material, it can be regarded as being composed of asphalt mortar and particle skeleton at the microscopic level. Compared with the drainage asphalt mixture, asphalt mortar is easier to prepare, and its shear strength parameters at different temperatures can be quickly determined. The particle skeleton itself is not affected by temperature, and its shear strength parameters are easier to obtain than those of the drainage asphalt mixture. Therefore, separating the drainage asphalt mixture into asphalt mortar and particle skeleton for separate testing can accelerate the prediction speed of shear strength parameters and achieve rapid prediction of shear strength parameters.

[0053] Preferably, if a predictive formula for the shear strength parameters of the drainage asphalt mixture can be established based on the material properties at the microscopic level, it will be of key significance for rapidly predicting the rutting resistance of asphalt pavement and guiding the optimization of the rutting resistance at the microscopic level of the material.

[0054] Preferably, the shear strength of drainage asphalt is related to the mechanical properties of its microstructure. Compared with macroscopic experiments on drainage asphalt, the determination of the mechanical properties of its microstructure has advantages such as simple sample preparation and convenient experimental operation. Therefore, predicting the macroscopic shear strength of drainage asphalt based on the mechanical property indicators of its microstructure is of key significance.

[0055] In a preferred embodiment of the present invention, in step S2, the internal friction angle is measured by a triaxial test or a direct shear test on the particle skeleton.

[0056] Specifically, in this embodiment, the specific procedures for conducting triaxial or direct shear tests on the particle skeleton are existing technologies and will not be described here.

[0057] In a preferred embodiment of the present invention, in step S2, the complex modulus of the asphalt mortar is measured by a rheological test.

[0058] Specifically, in this embodiment, when preparing for the rheological test, the asphalt mortar needs to be heated to a sufficiently fluid state for casting the specimen. The heating temperature of the original asphalt mortar should not exceed 135°C, and the heating temperature of the modified asphalt should not exceed 163°C. During the heating process, the sample is covered and stirred appropriately to ensure the uniformity of the sample and remove air bubbles.

[0059] Preferably, after mixing, the selected test plate is fixed on the testing machine. At the test temperature, the test plate is leveled with zero gap. The top plate is moved upward so that the gap between the plates is 1mm ± 0.05mm or 2mm ± 0.05mm. Then, the surface of the test plate is carefully cleaned so that the asphalt mortar is evenly and firmly adhered to the test plate. When using 8mm specimens, the ambient temperature is raised to about 45°C. When using 25mm specimens, the ambient temperature is raised to the test temperature or the initial temperature of the test temperature range.

[0060] Preferably, after the ambient temperature is adjusted, the test plate is taken out and asphalt mortar is poured into the center of the test plate so that the asphalt mortar basically covers the entire test plate. After the asphalt hardens, the test plate is put back into the rheometer. Then, the test plate is moved to press the specimen between the two test plates, the specimen trimmer is heated, and the excess asphalt mortar around the perimeter is trimmed. After trimming, the gap is adjusted to the test gap.

[0061] Preferably, after adjusting the gap between the test plates, the temperature of the specimen is raised to the test temperature ±0.1℃, and then the test is carried out under stress or strain control mode to obtain the complex modulus.

[0062] In a preferred embodiment of the present invention, in step S2, the cohesive work of the asphalt mortar is measured by contact angle test, atomic force microscopy test, or reverse gas chromatography test.

[0063] Specifically, in this embodiment, the specific procedure for conducting contact angle tests on asphalt mortar is existing technology and will not be described here.

[0064] In a preferred embodiment of the present invention, such as Figure 2 As shown, step S3 includes:

[0065] Step S31: Input the internal friction angle and complex modulus into the parameter value prediction model. The parameter value prediction model calls the pre-configured first prediction formula to process the internal friction angle and complex modulus to obtain the predicted friction angle corresponding to the preset temperature and includes it in the shear strength parameter value.

[0066] Step S32: Input the complex modulus, cohesive work, and predicted friction angle into the parameter value prediction model. The parameter value prediction model calls the pre-configured second prediction model to process the complex modulus, cohesive work, and predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature, which is included in the shear strength parameter value.

[0067] In a preferred embodiment of the present invention, the first prediction formula is as follows:

[0068]

[0069] in,

[0070] This represents the predicted friction angle at the preset temperature;

[0071] A represents a preset constant;

[0072] B represents a preset constant;

[0073] Represents the complex modulus at a preset temperature;

[0074] This represents the angle of internal friction.

[0075] Specifically, in this embodiment, A is a constant whose value is related to the type of asphalt mastic and the type of particle skeleton. For ordinary No. 70 asphalt, A can be -0.12 and B can be 0, while for SBS modified asphalt, A can be -0.1 and B can be 0.

[0076] In a preferred embodiment of the present invention, the second prediction formula is as follows:

[0077]

[0078] in,

[0079] C PAM,T This represents the predicted cohesive force at the preset temperature;

[0080] This represents the predicted friction angle at the preset temperature;

[0081] B represents a preset constant;

[0082] D represents a preset constant;

[0083] Represents the complex modulus at a preset temperature;

[0084] W c,T This represents the cohesive work at the preset temperature.

[0085] Specifically, in this embodiment, B and D are both preset constants. The values ​​of B and D are related to the type of asphalt mastic and the type of particle skeleton. In actual operation, B can take the value 0 and D can take the value 3.8.

[0086] In a preferred embodiment of the present invention, a system for predicting the shear strength parameter value of drainage asphalt mixture is also provided, which is applied to the above-mentioned method for predicting the shear strength parameter value, such as... Figure 3 As shown, it includes:

[0087] The first data acquisition module 1 is used to acquire the internal friction angle of the particle skeleton obtained by testing the particle skeleton at room temperature from an external input.

[0088] The second data acquisition module 2 is used to acquire the complex modulus and cohesive work of the asphalt mortar obtained by testing the asphalt mortar at a preset temperature from external input.

[0089] A parameter value prediction module 3 is connected to the first data acquisition module 1 and the second data acquisition module 2 respectively. It is used to input the complex modulus, cohesive work and internal friction angle into the pre-constructed parameter value prediction model to obtain the shear strength parameter value corresponding to the drainage asphalt mixture.

[0090] In a preferred embodiment of the present invention, the parameter value prediction module 3 includes:

[0091] A first prediction unit 31 is used to input the internal friction angle and complex modulus into the parameter value prediction model, and to make the parameter value prediction model call the pre-configured first prediction formula to process the internal friction angle and complex modulus to obtain the predicted friction angle corresponding to the preset temperature and include it in the shear strength parameter value.

[0092] A second prediction unit 32, connected to the first prediction unit 31, is used to input the complex modulus, cohesive work and predicted friction angle into the parameter value prediction model, and to make the parameter value prediction model call the pre-configured second prediction model to process the complex modulus, cohesive work and predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature and include it in the shear strength parameter value.

[0093] Example 1:

[0094] To determine the effectiveness of this method, taking No. 70 ordinary asphalt and SBS modified asphalt as examples, triaxial tests were conducted on asphalt mixtures at different preset temperatures at the macroscopic level to obtain the corresponding actual cohesion and actual friction angle, which were then compared with the predicted cohesion and predicted friction angle. Figures 4 to 7 As shown, Figures 4 to 7 The right bar in the figure represents the predicted cohesion or predicted friction angle, and the left bar represents the actual cohesion or actual friction angle. As can be seen from the figure, the predicted cohesion and the actual cohesion are well correlated, and the predicted friction angle and the actual friction angle are also well correlated.

[0095] While the disclosure is as stated above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of this disclosure, and all such changes and modifications will fall within the protection scope of this invention.

Claims

1. A method for predicting the shear strength parameter value of drainage asphalt mixture, characterized in that, Includes the following steps: Step S1: Obtain the drainage asphalt mixture to be predicted, wherein the drainage asphalt mixture includes asphalt mortar and particle skeleton; Step S2: At room temperature, test the particle skeleton to obtain the internal friction angle corresponding to the particle skeleton, and at a preset temperature, test the asphalt mortar to obtain the complex modulus and cohesive work of the asphalt mortar at the preset temperature. Step S3: Input the complex modulus, the cohesive work, and the internal friction angle into the pre-constructed parameter value prediction model to obtain the shear strength parameter value corresponding to the drainage asphalt mixture; Step S3 includes: Step S31: Input the internal friction angle and the complex modulus into the parameter value prediction model. The parameter value prediction model calls the pre-configured first prediction formula to process the internal friction angle and the complex modulus to obtain the predicted friction angle corresponding to the preset temperature and includes it in the shear strength parameter value. Step S32: Input the complex modulus, the cohesive work, and the predicted friction angle into the parameter value prediction model. The parameter value prediction model calls the pre-configured second prediction formula to process the complex modulus, the cohesive work, and the predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature, which is included in the shear strength parameter value. The first prediction formula is as follows: ； in, This represents the predicted friction angle at the preset temperature; Indicates a preset constant; Indicates a preset constant; This represents the complex modulus at the preset temperature; Indicates the internal friction angle; The second prediction formula is shown below: ； This represents the predicted cohesive force at the preset temperature; This represents the predicted friction angle at the preset temperature; Indicates a preset constant; Indicates a preset constant; This represents the complex modulus at the preset temperature; This represents the cohesive work at the preset temperature.

2. The method for predicting shear strength parameters according to claim 1, characterized in that, In step S2, the internal friction angle is measured by performing a triaxial test or a direct shear test on the particle skeleton.

3. The method for predicting shear strength parameters according to claim 1, characterized in that, In step S2, the complex modulus is measured by conducting a rheological test on the asphalt mortar.

4. The method for predicting shear strength parameters according to claim 1, characterized in that, In step S2, the cohesive work of the asphalt mortar is measured by contact angle test, atomic force microscopy test, or reverse gas chromatography test.

5. A system for predicting the shear strength parameters of drainage asphalt mixtures, characterized in that, The method for predicting shear strength parameters as described in any one of claims 1-4 includes: A first data acquisition module is used to acquire the internal friction angle of the particle skeleton obtained by testing the particle skeleton at room temperature from an external input. A second data acquisition module is used to acquire the complex modulus and cohesive work of the asphalt mortar obtained by testing the asphalt mortar at a preset temperature from external input. A parameter value prediction module, connected to the first data acquisition module and the second data acquisition module respectively, is used to input the complex modulus, the cohesive work and the internal friction angle into the pre-constructed parameter value prediction model to obtain the shear strength parameter value corresponding to the drainage asphalt mixture.

6. The shear strength parameter prediction system according to claim 5, characterized in that, The parameter value prediction module includes: A first prediction unit is used to input the internal friction angle and the complex modulus into the parameter value prediction model, and to make the parameter value prediction model call a pre-configured first prediction formula to process the internal friction angle and the complex modulus to obtain the predicted friction angle corresponding to the preset temperature and include it in the shear strength parameter value; A second prediction unit, connected to the first prediction unit, is used to input the complex modulus, the cohesive work, and the predicted friction angle into the parameter value prediction model, and to make the parameter value prediction model call a pre-configured second prediction formula to process the complex modulus, the cohesive work, and the predicted friction angle to obtain the predicted cohesive force corresponding to the preset temperature, which is included in the shear strength parameter value.

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