Asphalt fatigue damage self-healing evaluation method, device, equipment and medium

Abstract

The invention relates to the technical field of asphalt material performance detection, and discloses an asphalt fatigue damage self-healing evaluation method, device, equipment and medium, and the method comprises the following steps: preparing an asphalt sample; testing by taking the first group of asphalt samples as a reference, determining a test pause point, and determining a first stress coefficient according to the test pause point; testing the second group of asphalt samples, performing a self-healing experiment on the second group of asphalt samples after the test is finished by taking the test pause point as an ending condition, so as to obtain a self-healing asphalt sample, and continuously testing the self-healing asphalt sample by taking the test pause point as a starting condition, so as to determine a second stress coefficient; testing the third group of asphalt samples by taking the test pause point as a starting condition, and determining a third stress coefficient; and determining the self-healing coefficient of the asphalt sample by utilizing a preset self-healing coefficient model based on the first stress coefficient, the second stress coefficient and the third stress coefficient. The method has the beneficial effect that the self-healing condition of the asphalt is more comprehensively analyzed.

Description

Technical Field

[0001] The present application relates to the technical field of asphalt material performance detection, and in particular to an asphalt fatigue damage self-healing assessment method, device, equipment and medium. Background Art

[0002] Asphalt is a complex organic material composed of multiple molecules, and its microscopic motion (such as vibration, rotation, etc.) has a significant impact on adsorption and molecular diffusion capabilities, providing the basis for its self-healing ability. The self-healing ability enables asphalt to repair itself after fatigue damage.

[0003] The current fatigue damage and self-healing performance evaluation is mainly carried out through time sweep (TS) test and linear amplitude sweep (LAS) test, but due to the lack of a unified standard for calculating the self-healing coefficient, there are differences in the results of different experiments. In addition, the existing methods mainly focus on mechanical properties and do not fully consider the stress-strain changes, making it difficult to intuitively reflect the actual effect and mechanism of asphalt self-healing. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for evaluating asphalt fatigue damage self-healing, which solves the technical problem of not fully considering stress-strain changes and difficulty in fully reflecting asphalt self-healing, and achieves the technical effect of more comprehensively analyzing the self-healing of asphalt.

[0005] In order to achieve the above objectives, the main technical solutions adopted in this application include: In a first aspect, an embodiment of the present application provides an asphalt fatigue damage self-healing assessment method, the method comprising: preparing asphalt samples, and dividing the asphalt samples into three groups, obtaining a first group of asphalt samples, a second group of asphalt samples, and a third group of asphalt samples; Conducting a test based on the first group of asphalt samples, determining a test pause point, and determining a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point; The second group of asphalt samples are tested, with reaching the test suspension point as the end condition, a self-healing experiment is performed on the second group of asphalt samples that have finished the test to obtain self-healing asphalt samples, and the self-healing asphalt samples are continuously tested with the test suspension point as the start condition to determine the second stress coefficient corresponding to the second group of asphalt samples; Conducting a test on the third group of asphalt samples with the test pause point as a starting condition to determine a third stress coefficient corresponding to the third group of asphalt samples; Based on the first stress coefficient, the second stress coefficient and the third stress coefficient, the self-healing coefficient of the asphalt sample is determined using a preset self-healing coefficient model.

[0006] The present embodiment provides a method for evaluating the self-healing of fatigue damage of asphalt. The asphalt samples are divided into three groups to ensure that the initial state of each group of samples is consistent, providing a reliable basis for subsequent comparative tests. By conducting a benchmark test on the first group of samples, the test pause point is determined and the first stress coefficient is calculated, providing a performance benchmark for the self-healing test. Then, a self-healing experiment is carried out on the second group of samples, and the second stress coefficient is calculated to quantify the recovery of material properties during the self-healing process. In addition, through the test of the third group of samples, the comparative analysis of the self-healing ability of different samples is further strengthened. Finally, by combining the stress coefficients of the three groups of samples and using the preset self-healing coefficient model, the overall self-healing ability of the asphalt material is accurately determined. It can provide a scientific basis for the optimization and engineering application of asphalt samples, not only supporting the performance comparison of asphalt samples of different batches or formulas, but also providing performance prediction and quality control basis for applications in actual road engineering.

[0007] Optionally, the test is conducted based on the first group of asphalt samples as a benchmark to determine the test pause point, including: performing a linear amplitude scan on the first group of asphalt samples to determine the stored pseudo-strain energy and damage modulus of the first group of asphalt samples; constructing a correlation curve with the stored pseudo-strain energy and the damage modulus as the vertical coordinates and the sine wave load amplitude as the horizontal coordinate; determining the peak point of the stored pseudo-strain energy in the correlation curve as the asphalt fatigue failure point, and determining the loss modulus corresponding to the asphalt fatigue failure point as the critical loss modulus; According to the critical loss modulus, the test pause point of the sinusoidal wave load amplitude in the correlation curve is determined according to a preset selection threshold.

[0008] This embodiment can measure the stored pseudo-strain energy and damage modulus of asphalt materials through linear amplitude scanning, and then evaluate its elastic energy storage capacity and degree of damage under external loads. Subsequently, a correlation curve is constructed with the stored pseudo-strain energy and damage modulus as the vertical coordinates and the sine wave load amplitude as the horizontal coordinate to reveal the fatigue evolution law of asphalt materials under periodic loads. By locating the peak point of the stored pseudo-strain energy in the curve, the fatigue failure point of the asphalt can be determined, and the value corresponding to the loss modulus is determined as the critical loss modulus, which is an important indicator for evaluating the fatigue life of asphalt. Finally, according to the set threshold, the test pause point is determined to accurately identify the maximum bearing capacity of asphalt under a specific fatigue state.

[0009] Optionally, determining the first stress coefficient corresponding to the first group of asphalt samples according to the test pause point includes: Obtaining a first peak shear stress obtained by testing the first group of asphalt samples; determining a first stress corresponding to the first group of asphalt samples at the test pause point; The first stress coefficient is determined based on the first peak shear stress and the first stress.

[0010] In this embodiment, by testing the first group of asphalt samples, the first peak shear stress can be obtained, which reflects the maximum shear stress of asphalt under external load, and further reflects the strength and durability of the asphalt sample. Furthermore, the first stress value of the asphalt sample at this point is determined according to the test pause point. This data helps to evaluate the degree of damage and durability of the material under fatigue state. Finally, by combining the first peak shear stress and the first stress, the first stress coefficient is calculated, which provides a quantitative index for analyzing the fatigue performance and damage evolution of asphalt materials under different stress conditions.

[0011] Optionally, continuing to test the self-healing asphalt sample with the test pause point as the starting condition to determine the second stress coefficient corresponding to the second group of asphalt samples includes: Continue to test the self-healing asphalt sample with the test pause point as the starting condition to obtain a second peak shear stress; determine the stress of the self-healing asphalt sample at the test pause point as the second stress; The second stress coefficient is determined based on the second peak shear stress and the second stress.

[0012] This embodiment can capture the dynamic changes of the asphalt material during the self-healing process by continuously evaluating the stress changes of the asphalt material. By testing the second peak shear stress, the mechanical properties of the material after recovery are reflected. By determining the stress value at the test pause point, namely the second stress, as the benchmark in the self-healing process of the material, the stress state is further quantified. Finally, by calculating the second stress coefficient, a quantitative indicator is obtained that can reflect the degree of stress recovery in the self-healing process of the material. A higher coefficient indicates that the material has a stronger self-healing ability. Overall, the entire scheme accurately evaluates the recovery ability of self-healing asphalt materials through stress testing and quantitative evaluation, providing a scientific basis for its development, optimization and long-term durability prediction.

[0013] Optionally, the testing the third group of asphalt samples with the test pause point as the starting condition to determine the third stress coefficient corresponding to the third group of asphalt samples includes: Obtaining a reference complex modulus obtained by testing the first group of asphalt samples and a first stress corresponding to a pause point of the test; Conducting a test on the third group of asphalt samples with the test suspension point as a starting condition to obtain a third peak shear stress and a reference complex modulus corresponding to the test suspension point; Determining a third stress corresponding to the third group of asphalt samples at the test pause point based on the baseline complex modulus, the reference complex modulus and the first stress; The third stress coefficient is determined based on the third peak shear stress and the third stress.

[0014] This embodiment obtains the benchmark complex modulus and the first stress through experiments, providing an initial mechanical performance benchmark for the asphalt sample. Then, the reference complex modulus and the third peak shear stress are obtained, reflecting the performance changes of the material after a certain self-healing. Based on these data, the third stress is determined, and the stress recovery during the self-healing process is quantified. Finally, by calculating the third stress coefficient, the self-healing ability of the asphalt material is accurately evaluated. A higher coefficient indicates that the material has a stronger recovery force.

[0015] Optionally, the method of determining the third stress corresponding to the third group of asphalt samples at the test pause point is: Among them, τ INTIAL is the third stress, τ LAS is the first stress, |G * | LVE-INTI is the reference complex modulus, |G * | LVE-LAS is the base complex modulus.

[0016] Optionally, the self-healing coefficient model is: HI=(INDEX AFTER -INDEX LAS ) / (INDEX INITIAL -INDEX LAS ) Among them, INDEX LAS is the first stress coefficient, INDEX AFTER is the second stress coefficient, INDEX INITIAL is the third stress coefficient.

[0017] In a second aspect, an embodiment of the present application provides an asphalt fatigue damage self-healing assessment device, the device comprising: a sample preparation unit, used to prepare asphalt samples, and divide the asphalt samples into three groups, to obtain a first group of asphalt samples, a second group of asphalt samples and a third group of asphalt samples; a first stress coefficient determining unit, configured to perform a test based on the first group of asphalt samples, determine a test pause point, and determine a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point; A second stress coefficient determination unit is used to test the second group of asphalt samples, take reaching the test suspension point as an end condition, perform a self-healing experiment on the second group of asphalt samples that have finished the test to obtain self-healing asphalt samples, and continue to test the self-healing asphalt samples with the test suspension point as a start condition to determine a second stress coefficient corresponding to the second group of asphalt samples; A third stress coefficient determining unit, configured to test the third group of asphalt samples with the test pause point as a starting condition, and determine a third stress coefficient corresponding to the third group of asphalt samples; The self-healing coefficient determination unit is used to determine the self-healing coefficient of the asphalt sample based on the first stress coefficient, the second stress coefficient and the third stress coefficient using a preset self-healing coefficient model.

[0018] In a third aspect, an embodiment of the present application provides a computer device, including: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned asphalt fatigue damage self-healing assessment method by executing the computer instructions.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to enable a computer to execute the asphalt fatigue damage self-healing assessment method described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 A flow chart of a method for evaluating self-healing of fatigue damage in asphalt provided in an embodiment of the present application; Figure 2 A flowchart for determining a test pause point provided in an embodiment of the present application; Figure 3 A schematic diagram of a correlation curve provided in an embodiment of the present application; Figure 4 A schematic diagram of selecting a test pause point provided in an embodiment of the present application; Figure 5 A flow chart for determining a first stress coefficient provided in an embodiment of the present application; Figure 6A flow chart for determining a second stress coefficient provided in an embodiment of the present application; Figure 7 A flow chart for determining a third stress coefficient provided in an embodiment of the present application; Figure 8 It is a schematic diagram of a change curve of a specific application embodiment; Fig. 9 A block diagram of an asphalt fatigue damage self-healing assessment device provided in an embodiment of the present application; Fig.10 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0023] Asphalt is a complex organic mixed material composed of a variety of molecules that exhibit different microscopic movements at different temperatures, such as vibration, rotation, sliding, stretching and contraction. These microscopic movements play an important role in the adsorption and molecular diffusion of asphalt, which in turn provides the basis for its self-healing ability. Self-healing ability is a property of asphalt that can self-repair after fatigue damage. Current research shows that asphalt can self-heal under certain conditions.

[0024] In order to evaluate the fatigue damage and self-healing performance of asphalt, the time sweep (TS) test is currently introduced, defining the fatigue life as the number of cyclic loads required for the composite modulus to drop to 50% of the initial value. On this basis, the linear amplitude sweep (LAS) test was subsequently developed and combined with the viscoelastic continuous damage (VECD) model, which can predict the fatigue resistance of asphalt through a 5-minute LAS program. The commonly used asphalt self-healing test method combines the frequency sweep test and the linear amplitude sweep (LAS) test.

[0025] However, in asphalt self-healing tests, the calculation of the self-healing coefficient depends on test data, but there is a lack of a unified and clear calculation method or value standard, which makes the results different between different experiments and affects the comprehensive evaluation of asphalt self-healing behavior. In addition, the existing methods only start from the mechanical properties of the material, but fail to fully consider the changes in asphalt stress and strain, and fail to intuitively reflect the actual effect and mechanism of asphalt self-healing.

[0026] In order to solve the above technical problems, according to an embodiment of the present application, an embodiment of an asphalt fatigue damage self-healing assessment method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] In this embodiment, a method for evaluating self-healing of asphalt fatigue damage is provided. Figure 1 A flowchart of a method for evaluating self-healing of fatigue damage of asphalt provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the process includes the following steps: Step S1, preparing asphalt samples, and dividing the asphalt samples into three groups, obtaining a first group of asphalt samples, a second group of asphalt samples, and a third group of asphalt samples.

[0028] Specifically, the asphalt samples were properly prepared according to the test requirements to ensure the consistency and repeatability of the samples. The samples were divided into three groups, which were marked as the first group of asphalt samples, the second group of asphalt samples and the third group of asphalt samples.

[0029] Step S3, conducting a test based on the first group of asphalt samples, determining a test pause point, and determining a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point.

[0030] Specifically, the linear amplitude sweep LAS test is performed based on the first group of asphalt samples. The test pause point is determined, which is the point where the material damage is still in the reversible stage and the self-healing effect is the best. According to the test pause point, the first stress coefficient corresponding to the first group of asphalt samples is determined.

[0031] Step S5, testing the second group of asphalt samples, taking reaching the test pause point as the end condition, conducting a self-healing experiment on the second group of asphalt samples that have finished the test to obtain self-healing asphalt samples, and continuing to test the self-healing asphalt samples with the test pause point as the starting condition to determine the second stress coefficient corresponding to the second group of asphalt samples.

[0032] Specifically, the second group of asphalt samples were subjected to the LAS test, with reaching the test pause point as the end condition. The second group of asphalt samples that had completed the test were subjected to a self-healing experiment. Within the preset 2 minutes and 30 seconds, the second group of asphalt samples that had completed the test were placed in an insulation bin, and the temperature was raised to the set healing temperature. This step is to simulate the self-healing conditions that asphalt may encounter in an actual environment, such as the effect of temperature changes on self-healing behavior. After completing the preset healing temperature and time, the dynamic shear rheometer DSR restores the sample and the insulation bin to the standard test temperature. This step is to ensure that the sample can self-heal under controlled conditions and that the self-healing effect can be evaluated. After the sample returns to the standard test temperature, let it stand for 10 minutes to ensure that the temperature inside the sample is balanced. This step is critical to ensuring uniform temperature distribution inside the sample and evaluating the self-healing effect.

[0033] Then, taking the test pause point as the starting condition, the LAS test is continued on the self-healed asphalt samples to determine the second stress coefficient corresponding to the second group of asphalt samples.

[0034] Step S7, testing the third group of asphalt samples with the test pause point as the starting condition, and determining the third stress coefficient corresponding to the third group of asphalt samples.

[0035] Specifically, taking the test suspension point as the starting condition, the LAS test is performed on the third group of asphalt samples to determine the third stress coefficient corresponding to the third group of asphalt samples.

[0036] Step S9, based on the first stress coefficient, the second stress coefficient and the third stress coefficient, the self-healing coefficient of the asphalt sample is determined using a preset self-healing coefficient model.

[0037] In some preferred embodiments, the self-healing coefficient model is: HI=(INDEX AFTER -INDEX LAS ) / (INDEX INITIAL -INDEX LAS ) Among them, INDEX LAS is the first stress coefficient, INDEX AFTER is the second stress coefficient, INDEX INITIAL is the third stress coefficient.

[0038] Specifically, through the preset self-healing coefficient model, the performance changes of asphalt samples during the self-healing process can be evaluated and quantified, providing a data basis for the subsequent optimization of the asphalt mixture formula and improving the durability and maintenance efficiency of the road.

[0039] It should be noted that the embodiment of the present application can use Python language to implement data processing, reducing possible errors in manual processing.

[0040] The present embodiment provides a method for evaluating the self-healing of fatigue damage of asphalt. The asphalt samples are divided into three groups to ensure that the initial state of each group of samples is consistent, providing a reliable basis for subsequent comparative tests. By conducting a benchmark test on the first group of samples, the test pause point is determined and the first stress coefficient is calculated, providing a performance benchmark for the self-healing test. Then, a self-healing experiment is carried out on the second group of samples, and the second stress coefficient is calculated to quantify the recovery of material properties during the self-healing process. In addition, through the test of the third group of samples, the comparative analysis of the self-healing ability of different samples is further strengthened. Finally, by combining the stress coefficients of the three groups of samples and using the preset self-healing coefficient model, the overall self-healing ability of the asphalt material is accurately determined. It can provide a scientific basis for the optimization and engineering application of asphalt samples, not only supporting the performance comparison of asphalt samples of different batches or formulas, but also providing performance prediction and quality control basis for applications in actual road engineering.

[0041] Figure 2 A flowchart for determining a test pause point is provided in an embodiment of the present application, and the flowchart may include the following steps: Step S311, performing a linear amplitude scan on a first group of asphalt samples to determine the stored pseudo-strain energy and damage modulus of the first group of asphalt samples.

[0042] Specifically, the first group of asphalt samples were subjected to a linear amplitude sweep (LAS) test, following the American AASHTO TP101 standard, using an 8mm diameter parallel plate rotor and a 2mm gap to prepare the samples. Under a constant strain of 0.1%, a frequency sweep test was performed to determine the sensitive parameter α of the asphalt. The frequency range is from 0.2Hz to 30Hz, with a total of 12 frequency points, each frequency is executed for 30 seconds, and 12 data points are recorded. The storage modulus G'(ω) at different loading frequencies ω is obtained by frequency scanning; the storage modulus data is linearly fitted using formula 1 to obtain the slope m and intercept b; the sensitive parameter α of the asphalt sample in the damaged state is obtained using formula 2.

[0043] Formula 1: logG'(ω)=m(logω)+b Formula 2: Strain sweep: At a constant frequency of 10 Hz, the strain is increased by 0.1% per second, and the strain is increased from 0.1% to 30% within 300 seconds, with a total of 300 data points.

[0044] The fatigue damage evolution process parameters are calculated and the damage evolution rate is quantified. The basic performance data relationship is as follows: Where C is pseudo-stiffness; DMR is dynamic modulus ratio (ratio of initial modulus to dynamic modulus, which can be 0.9-1.1); |G * | LVE is the initial modulus of the asphalt sample test, obtained in the strain sweep; |G * | INIT is the dynamic modulus of the asphalt sample test, obtained in the frequency sweep; τ P is the peak shear stress during the loading cycle and the stress change within 300 seconds; γ P is the peak shear strain during the loading cycle, and the strain change within 300 seconds; G R is a reference modulus in the calculation, taken as 1 mPa; is the peak virtual strain during the loading cycle.

[0045] Fatigue damage usually occurs when the pseudo-stiffness C decreases. In the S-VECD fatigue model, the stored pseudo-strain energy at any time in the LAS test is The calculation is as follows: The damage modulus is an evaluation method that calculates the performance change between each data point of the asphalt sample during the test. It is used to characterize the degree of damage of the sample during the test. The calculation method is: Wherein, i is the number of cycles; DMR is the dynamic modulus ratio (the ratio of the initial modulus to the dynamic modulus, which can be 0.9-1.1); is the peak virtual strain during the loading cycle; C i is the pseudo-stiffness of the ith time; t i is the time; α is the sensitive parameter of the asphalt sample under the damaged state.

[0046] Step S313, constructing a correlation curve with the stored pseudo strain energy and damage modulus as the ordinate and the sine wave load amplitude as the abscissa.

[0047] Specifically, the sinusoidal load amplitude is used as the horizontal coordinate, and the pseudo strain energy and damage modulus are stored as the vertical coordinate to construct a correlation curve. This correlation curve is called a damage curve or fatigue curve, which is used to describe the damage response of asphalt samples under different load amplitudes.

[0048] Step S315: determine the pseudo strain energy peak point stored in the correlation curve as the asphalt fatigue failure point, and determine the loss modulus corresponding to the asphalt fatigue failure point as the critical loss modulus.

[0049] Specifically, the stored pseudo-strain energy varies with the amplitude of the sinusoidal load, see Figure 3The schematic diagram of the correlation curve provided in the embodiment of the present application shows that the stored pseudo-strain energy shows a trend of first increasing and then decreasing. This trend indicates that as the load amplitude increases, the pseudo-strain energy that the asphalt sample can store increases until it reaches a peak value. Then, as more pseudo-strain energy is released, the stored pseudo-strain energy of the asphalt sample begins to decrease. The decrease in stored pseudo-strain energy indicates that more and more pseudo-strain energy is released, which means that fatigue damage gradually occurs in the asphalt sample. This peak value is considered to be the maximum external energy that the asphalt sample can store, and the loss modulus corresponding to this point is determined as the critical loss modulus S f , critical loss modulus S f It refers to the point where the damage modulus S begins to increase rapidly, indicating that the material is about to undergo fatigue failure.

[0050] Step S317, determining a test pause point of the sinusoidal wave load amplitude in the correlation curve according to the critical loss modulus and a preset selection threshold.

[0051] Specifically, the test pause point is generally selected as the critical loss modulus S in the damage modulus S curve of the correlation curve. f This is because the peak of the stored pseudo strain energy is reached After that, the damage modulus S of the asphalt sample began to increase rapidly, indicating that the asphalt sample was about to suffer fatigue failure. f Previously, the damage of asphalt samples was still in the reversible stage, and the self-healing effect was better. The threshold is usually selected as 0.1-0.9, preferably 0.5, that is, S f0.5 The corresponding sine wave load amplitude is used as the test pause point, see Figure 4 A schematic diagram of selecting a test pause point provided in an embodiment of the present application.

[0052] and Figure 1 Compared with the embodiment shown, this embodiment can measure the stored pseudo-strain energy and damage modulus of the asphalt material through linear amplitude scanning, and then evaluate its elastic energy storage capacity and degree of damage under external loads. Subsequently, a correlation curve is constructed with the stored pseudo-strain energy and damage modulus as the ordinate and the sine wave load amplitude as the abscissa to reveal the fatigue evolution law of the asphalt material under periodic loads. By locating the peak point of the stored pseudo-strain energy in the curve, the fatigue failure point of the asphalt can be determined, and the value corresponding to the loss modulus is determined as the critical loss modulus, which is an important indicator for evaluating the fatigue life of asphalt. Finally, according to the set threshold, the test pause point is determined to accurately identify the maximum bearing capacity of the asphalt under a specific fatigue state.

[0053] Figure 5A flow chart for determining a first stress coefficient is provided in an embodiment of the present application, and the flow chart may include the following steps: Step S331, obtaining a first peak shear stress obtained by testing a first group of asphalt samples.

[0054] Specifically, in the linear amplitude sweep LAS test, the shear stress response of the asphalt material has a peak value, which is the first peak shear stress τ in the loading cycle. PLAS , stress change within 300 seconds.

[0055] Step S333, determining the first stress corresponding to the first group of asphalt samples at the test pause point.

[0056] Specifically, in the linear amplitude sweep LAS test, the first stress corresponding to the test pause point is recorded.

[0057] Step S335, determining a first stress coefficient according to the first peak shear stress and the first stress.

[0058] Specifically, through the formula INDEX LAS =τ PLAS / τ LAS Calculate the first stress coefficient INDEX LAS , where τ LAS is the first stress. And the first stress coefficient is used as a reference value for subsequent comparison and analysis.

[0059] and Figure 1 Compared with the embodiment shown in the figure, the first peak shear stress of the first group of asphalt samples can be obtained by testing the first group of asphalt samples. This value reflects the maximum shear stress of the asphalt under the external load, and further reflects the strength and durability of the asphalt sample. Furthermore, the first stress value of the asphalt sample at this point is determined according to the test pause point. This data is helpful to evaluate the degree of damage and durability of the material under fatigue state. Finally, by combining the first peak shear stress and the first stress, the first stress coefficient is calculated, which provides a quantitative index for analyzing the fatigue performance and damage evolution of asphalt materials under different stress conditions.

[0060] Figure 6 A flow chart for determining the second stress coefficient is provided in an embodiment of the present application, and the flow chart may include the following steps: Step S51, continuing the test on the self-healing asphalt sample with the test pause point as the starting condition to obtain the second peak shear stress.

[0061] Specifically, the test pause point is to select the critical loss modulus S in the correlation curve damage modulus S curve fA point before. This point is when the material damage is still in the reversible stage and the self-healing effect is the best. After the determined test pause point, the second group of asphalt samples that have completed the test are subjected to a self-healing experiment to obtain self-healing asphalt samples, and the linear amplitude scanning LAS test is continued. This means that after the strain level at the pause point, the strain amplitude continues to be increased to conduct a dynamic shear rheometer DSR test. In the continued LAS test, the first peak shear stress that occurs after the test pause point is the starting condition is recorded, which will be used as the second peak shear stress. This peak shear stress is the maximum shear stress value reached when the material is loaded again after self-healing.

[0062] Step S53, determining the stress of the self-healing asphalt sample at the test pause point as the second stress.

[0063] Specifically, the stress at the test pause point is determined as the second stress and recorded as τ HEALING .

[0064] Step S55, determining a second stress coefficient according to the second peak shear stress and the second stress.

[0065] Specifically, through the formula INDEX AFTER =τ PAFTER / τ HEALING Calculate the second stress coefficient INDEX AFTER .

[0066] and Figure 1 Compared with the embodiment shown, this embodiment can capture the dynamic changes of the asphalt material during the self-healing process by continuously evaluating the stress changes of the asphalt material. By testing the second peak shear stress, the mechanical properties of the material after recovery are reflected. By determining the stress value at the test pause point, namely the second stress, as the benchmark in the self-healing process of the material, the stress state is further quantified. Finally, by calculating the second stress coefficient, a quantitative indicator is obtained, which can reflect the degree of stress recovery in the self-healing process of the material. A higher coefficient indicates that the material has a stronger self-healing ability. Overall, the entire scheme accurately evaluates the recovery ability of self-healing asphalt materials through stress testing and quantitative evaluation, providing a scientific basis for its development, optimization and long-term durability prediction.

[0067] Figure 7 A flowchart for determining the third stress coefficient is provided in an embodiment of the present application, and the flowchart may include the following steps: Step S71, obtaining a reference complex modulus obtained by testing a first group of asphalt samples and a first stress corresponding to a test pause point.

[0068] Specifically, in the linear amplitude sweep LAS test of the first group of asphalt samples, the frequency sweep data were obtained at 0.1% strain and 10 Hz, and the benchmark complex modulus |G*| LVE-LAS, and the shear stress value corresponding to the test pause point in the test is taken as the first stress τ LAS .

[0069] Step S73, testing the third group of asphalt samples with the test suspension point as the starting condition, to obtain the third peak shear stress and the reference complex modulus corresponding to the test suspension point.

[0070] Specifically, after the determined test pause point, the LAS test is continued on the third group of asphalt samples, and the first peak shear stress that occurs during the continued test is recorded, which will be used as the third peak shear stress τ PINITIAL , and obtain the reference complex modulus |G*| at 0.1% strain and 10 Hz for the frequency sweep data. LVE-INTI .

[0071] Step S75, determining the third stress corresponding to the third group of asphalt samples at the test pause point based on the baseline complex modulus, the reference complex modulus and the first stress.

[0072] In some preferred embodiments, the third stress corresponding to the third group of asphalt samples at the test pause point is determined as follows: Among them, τ INTIAL is the third stress, τ LAS is the first stress, |G*| LVE-INTI is the reference complex modulus, |G*| LVE-LAS is the base complex modulus.

[0073] Specifically, since the third group of asphalt samples were directly subjected to strain sweep tests at the test pause point as the starting condition, without previous cumulative damage, their stress-strain curves are relatively independent, which means that the test data may not be directly associated with the LAS test data after cumulative damage. To solve this problem, the frequency sweep data of the benchmark LAS test can be used to calculate the virtual reference stress τ at the test pause point. INTIAL , i.e., the third stress. This calculation method makes it possible to relate the data from the unstarted condition test at the test pause point to the benchmark LAS test data, thereby evaluating the stress response of the third group of asphalt samples in the absence of accumulated damage. This approach allows to compare the behavior of asphalt under different test conditions, even if they have different damage histories.

[0074] Step S77, determining a third stress coefficient according to the third peak shear stress and the third stress.

[0075] Specifically, through the formula INDEX INITIAL =τ PINITIAL / τ INITIAL Calculate the first stress coefficient INDEXINITIAL .

[0076] and Figure 1 Compared with the embodiment shown in the figure, this embodiment obtains the benchmark complex modulus and the first stress through experiments, providing an initial mechanical performance benchmark for the asphalt sample. Then, the reference complex modulus and the third peak shear stress are obtained, reflecting the performance changes of the material after a certain self-healing. Based on these data, the third stress is determined, and the stress recovery during the self-healing process is quantified. Finally, by calculating the third stress coefficient, the self-healing ability of the asphalt material is accurately evaluated. A higher coefficient indicates that the material has a stronger recovery force.

[0077] The specific implementation of the present invention is described below in conjunction with a specific application embodiment.

[0078] (1) Preparation of asphalt samples Seven different asphalts were prepared, the base asphalt was sieved into four components (asphaltene, colloid, saturated fraction and aromatic fraction), and then seven blended asphalts (#1 to #7) were reconfigured by adjusting the ratio of the four components. As shown in Table 1, the asphalt sample status table.

[0079] Table 1 Asphalt sample status Four components 1# 2# 3# 4# 5# 6# 7# Asphaltene (wt%) 15.5 1 6.9 18.3 19.7 21.1 22.5 24 Aromatic content (wt%) 52 49 46 43 40 37 34 Gel (wt%) 15.5 17.42 19.33 21.26 23.18 25.1 27 Saturated content (wt%) 17 16.68 16.37 16.04 15.72 15.4 15 (2) Test process The parameters of the dynamic shear rheometer (DSR) were set to use a rotor with a diameter of 8 mm and a gap of 2 mm, and the standard LAS test temperature was set to 30°C.

[0080] The self-healing coefficient of each asphalt sample is determined according to the above embodiment. Taking the 1# asphalt sample as an example, the LAS test results are shown in Table 2: Table 21# Asphalt Sample Standard LAS Frequency Scanning Test Results Schematic Table Step S1001, perform a linear amplitude sweep (LAS) test to determine the test pause point, where S is taken. f0.5 The corresponding sine wave load amplitude is taken as the test pause point γ S0.5 .

[0081] Step S1002, setting the self-healing temperature to 40°C and the self-healing time to 5 minutes.

[0082] Step S1003, directly from γ S0.5 The strain sweep was started and the test was terminated when the strain reached 30%.

[0083] The result is generated, and the self-healing coefficient of the asphalt sample is determined using the preset self-healing coefficient model. Finally, the self-healing coefficient obtained by calculating all asphalt samples is plotted into a variation curve, such as Figure 8 shown.

[0084] Accordingly, please refer to Fig. 9 A block diagram of an asphalt fatigue damage self-healing assessment device provided in an embodiment of the present application, the device comprising: The sample preparation unit 101 is used to prepare asphalt samples and divide the asphalt samples into three groups to obtain a first group of asphalt samples, a second group of asphalt samples and a third group of asphalt samples; A first stress coefficient determination unit 103 is used to perform a test based on the first group of asphalt samples, determine a test pause point, and determine a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point; The second stress coefficient determination unit 105 is used to test the second group of asphalt samples, take the test suspension point as the end condition, perform a self-healing experiment on the second group of asphalt samples that have finished the test, obtain self-healing asphalt samples, and continue to test the self-healing asphalt samples with the test suspension point as the start condition to determine the second stress coefficient corresponding to the second group of asphalt samples; A third stress coefficient determination unit 107, used to test the third group of asphalt samples with the test pause point as the starting condition, and determine the third stress coefficient corresponding to the third group of asphalt samples; The self-healing coefficient determination unit 109 is used to determine the self-healing coefficient of the asphalt sample based on the first stress coefficient, the second stress coefficient and the third stress coefficient using a preset self-healing coefficient model.

[0085] In some optional embodiments, the test is performed based on the first group of asphalt samples as a benchmark to determine the test suspension point, including: Performing linear amplitude scanning on the first group of asphalt samples to determine the stored pseudo-strain energy and damage modulus of the first group of asphalt samples; constructing a correlation curve with the stored pseudo-strain energy and damage modulus as the ordinate and the sine wave load amplitude as the abscissa; The peak point of the pseudo strain energy stored in the correlation curve is determined as the asphalt fatigue failure point, and the loss modulus corresponding to the asphalt fatigue failure point is determined as the critical loss modulus; According to the critical loss modulus, the test pause point of the sinusoidal wave load amplitude in the correlation curve is determined according to a preset selection threshold.

[0086] In some optional embodiments, determining the first stress coefficient corresponding to the first group of asphalt samples according to the test pause point includes: Obtaining a first peak shear stress obtained by testing a first group of asphalt samples; Determine the first stress corresponding to the first group of asphalt samples at the test pause point; A first stress coefficient is determined based on the first peak shear stress and the first stress.

[0087] In some optional embodiments, the self-healing asphalt sample is continuously tested with the test pause point as the starting condition to determine the second stress coefficient corresponding to the second group of asphalt samples, including: The self-healing asphalt sample is tested continuously with the test suspension point as the starting condition to obtain the second peak shear stress; The stress of the self-healing asphalt sample at the test pause point is determined as the second stress; A second stress coefficient is determined based on the second peak shear stress and the second stress.

[0088] In some optional implementations, the third group of asphalt samples are tested with the test pause point as the starting condition to determine the third stress coefficient corresponding to the third group of asphalt samples, including: Obtaining the reference complex modulus obtained by testing the first group of asphalt samples and the first stress corresponding to the test pause point; testing the third group of asphalt samples with the test pause point as the starting condition to obtain the third peak shear stress and the reference complex modulus corresponding to the test pause point; Determining a third stress corresponding to the third group of asphalt samples at a test pause point based on the baseline complex modulus, the reference complex modulus and the first stress; A third stress coefficient is determined based on the third peak shear stress and the third stress.

[0089] In some optional embodiments, the third stress corresponding to the third group of asphalt samples at the test pause point is determined as follows: Among them, τ INTIAL is the third stress, τ LAS is the first stress, |G * | LVE-INTI is the reference complex modulus, |G * | LVE-LAS is the base complex modulus.

[0090] In some optional embodiments, the self-healing coefficient model is: HI=(INDEX AFTER -INDEX LAS ) / (INDEX INITIAL -INDEX LAS ) Among them, INDEX LAS is the first stress coefficient, INDEX AFTER is the second stress coefficient, INDEX INITIALis the third stress coefficient.

[0091] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0092] In this embodiment, an asphalt fatigue damage self-healing assessment device is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0093] See also Fig.10 , Fig.10 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown in FIG. Fig.10 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig.10 A processor 10 is taken as an example.

[0094] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0095] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0096] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0097] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0098] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0099] The embodiment of the present application also provides a computer-readable storage medium. The above method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0100] The devices and units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0101] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0102] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods and devices. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and apparatuses according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0106] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0107] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0108] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

[0109] Although the embodiments of the present application have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present application, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for evaluating self-healing of asphalt fatigue damage, characterized in that: The method comprises: preparing asphalt samples, and dividing the asphalt samples into three groups, obtaining a first group of asphalt samples, a second group of asphalt samples, and a third group of asphalt samples; Conducting a test based on the first group of asphalt samples, determining a test pause point, and determining a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point; The second group of asphalt samples are tested, with reaching the test suspension point as the end condition, a self-healing experiment is performed on the second group of asphalt samples that have finished the test to obtain self-healing asphalt samples, and the self-healing asphalt samples are continuously tested with the test suspension point as the start condition to determine the second stress coefficient corresponding to the second group of asphalt samples; Conducting a test on the third group of asphalt samples with the test pause point as a starting condition to determine a third stress coefficient corresponding to the third group of asphalt samples; Based on the first stress coefficient, the second stress coefficient and the third stress coefficient, the self-healing coefficient of the asphalt sample is determined using a preset self-healing coefficient model.

2. The method according to claim 1, characterized in that The step of conducting a test based on the first group of asphalt samples and determining a test suspension point includes: Performing a linear amplitude scan on the first group of asphalt samples to determine the stored pseudo-strain energy and damage modulus of the first group of asphalt samples; constructing a correlation curve with the stored pseudo-strain energy and the damage modulus as the ordinate and the sine wave load amplitude as the abscissa; determining the peak point of the stored pseudo-strain energy in the correlation curve as the asphalt fatigue failure point, and determining the loss modulus corresponding to the asphalt fatigue failure point as the critical loss modulus; According to the critical loss modulus, the test pause point of the sinusoidal wave load amplitude in the correlation curve is determined according to a preset selection threshold.

3. The method according to claim 1, characterized in that The determining, according to the test pause point, a first stress coefficient corresponding to the first group of asphalt samples comprises: Obtaining a first peak shear stress obtained by testing the first group of asphalt samples; determining a first stress corresponding to the first group of asphalt samples at the test pause point; The first stress coefficient is determined based on the first peak shear stress and the first stress.

4. The method according to claim 1, characterized in that: The step of continuing to test the self-healing asphalt sample with the test pause point as the starting condition to determine the second stress coefficient corresponding to the second group of asphalt samples includes: Continue to test the self-healing asphalt sample with the test pause point as the starting condition to obtain a second peak shear stress; determine the stress of the self-healing asphalt sample at the test pause point as the second stress; The second stress coefficient is determined based on the second peak shear stress and the second stress.

5. The method according to claim 1, characterized in that The step of testing the third group of asphalt samples with the test pause point as the starting condition to determine the third stress coefficient corresponding to the third group of asphalt samples includes: Obtaining a reference complex modulus obtained by testing the first group of asphalt samples and a first stress corresponding to a pause point of the test; Conducting a test on the third group of asphalt samples with the test suspension point as a starting condition to obtain a third peak shear stress and a reference complex modulus corresponding to the test suspension point; Determining a third stress corresponding to the third group of asphalt samples at the test pause point based on the baseline complex modulus, the reference complex modulus and the first stress; The third stress coefficient is determined based on the third peak shear stress and the third stress.

6. The method according to claim 5, characterized in that The method of determining the third stress corresponding to the third group of asphalt samples at the test pause point is: Among them, τ INTIAL is the third stress, τ LAS is the first stress, |G * | LVE-INTI is the reference complex modulus, |G * | LVE-LAS is the base complex modulus.

7. The method according to claim 1, characterized in that The self-healing coefficient model is: HI=(INDEX AFTER -INDEX LAS ) / (INDEX INITIAL -INDEX LAS ) Among them, INDEX LAS is the first stress coefficient, INDEX AFTER is the second stress coefficient, INDEX INITIAL is the third stress coefficient.

8. An asphalt fatigue damage self-healing assessment device, characterized in that: The device comprises: A sample preparation unit, used for preparing asphalt samples, and dividing the asphalt samples into three groups, to obtain a first group of asphalt samples, a second group of asphalt samples and a third group of asphalt samples; a first stress coefficient determining unit, configured to perform a test based on the first group of asphalt samples, determine a test pause point, and determine a first stress coefficient corresponding to the first group of asphalt samples according to the test pause point; A second stress coefficient determination unit is used to test the second group of asphalt samples, take reaching the test suspension point as an end condition, perform a self-healing experiment on the second group of asphalt samples that have finished the test to obtain self-healing asphalt samples, and continue to test the self-healing asphalt samples with the test suspension point as a start condition to determine a second stress coefficient corresponding to the second group of asphalt samples; A third stress coefficient determining unit, configured to test the third group of asphalt samples with the test pause point as a starting condition, and determine a third stress coefficient corresponding to the third group of asphalt samples; The self-healing coefficient determination unit is used to determine the self-healing coefficient of the asphalt sample based on the first stress coefficient, the second stress coefficient and the third stress coefficient using a preset self-healing coefficient model.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the asphalt fatigue damage self-healing assessment method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the asphalt fatigue damage self-healing assessment method according to any one of claims 1 to 7.

Images