CN117929171B - Asphalt mixture performance evaluation method based on immersed hamburger rut data - Google Patents
Asphalt mixture performance evaluation method based on immersed hamburger rut data Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 67
- 239000010426 asphalt Substances 0.000 title claims abstract description 63
- 239000000203 mixture Substances 0.000 title claims abstract description 61
- 235000015220 hamburgers Nutrition 0.000 title claims abstract description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 45
- 238000012360 testing method Methods 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000010606 normalization Methods 0.000 claims abstract description 13
- 238000002791 soaking Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 238000012512 characterization method Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 9
- 239000012492 regenerant Substances 0.000 description 8
- 238000012545 processing Methods 0.000 description 7
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- 239000002699 waste material Substances 0.000 description 6
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- VZSRBBMJRBPUNF-UHFFFAOYSA-N 2-(2,3-dihydro-1H-inden-2-ylamino)-N-[3-oxo-3-(2,4,6,7-tetrahydrotriazolo[4,5-c]pyridin-5-yl)propyl]pyrimidine-5-carboxamide Chemical compound C1C(CC2=CC=CC=C12)NC1=NC=C(C=N1)C(=O)NCCC(N1CC2=C(CC1)NN=N2)=O VZSRBBMJRBPUNF-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
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- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
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- 230000002159 abnormal effect Effects 0.000 description 1
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- 230000001808 coupling effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
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- 238000011158 quantitative evaluation Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
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- 238000010257 thawing Methods 0.000 description 1
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Abstract
The invention provides an asphalt mixture performance evaluation method based on immersed hamburger rut data, which relates to the technical field of asphalt mixture test characterization and comprises the following steps: testing the asphalt mixture through a immersed hamburg rutting test to obtain a fitting curve; analyzing the fitted curve, removing t loads causing errors, and obtaining an updated fitted curve; obtaining a normalized strain level curve by using a normalization function, and calculating to obtain a total strain level; obtaining an inflection point, and dividing a normalized strain level curve into a first curve segment and a second curve segment; fitting the first curve segment by using a second fitting function to obtain a first strain level ɛ v; calculating to obtain a second strain level ɛ m; the asphalt mix was evaluated for rut resistance and water damage resistance for the first strain level ɛ v and the second strain level ɛ m. According to the invention, the anti-rutting performance and the water damage resistance of the asphalt mixture are comprehensively evaluated by a method combining a hamburg rutting test and mathematical modeling.
Description
Technical Field
The invention relates to the technical field of asphalt mixture test characterization, in particular to an asphalt mixture performance evaluation method based on immersed hamburger rut data.
Background
Asphalt pavement ruts and water damage reduce their service performance and service life, thereby increasing maintenance and repair costs. The asphalt mixture can be evaluated for the high-temperature permanent deformation resistance by a rutting test and a hamburg rutting test under a drying condition, and the water-resistant damage resistance of the asphalt mixture can be evaluated by a water-immersion Marshall test and a freeze thawing splitting test. Compared with the single test method, the immersed hamburg rutting test can evaluate rutting resistance and water damage resistance of the asphalt mixture under the coupling action of load and moisture, and can remarkably reduce workload.
However, in the immersed hamburger rutting test, the confounding effect of distinguishing load from moisture is a key issue in characterizing the mix using this test. The deformation caused by the water and the load at the same time can be obtained under the soaking condition, but the water damage deformation and the viscoplastic flow deformation caused by the water and the load respectively cannot be accurately distinguished. Therefore, there is a need to develop a new analytical method for accurately separating viscoplastic deformation and water damage deformation in immersed hamburger rut data.
Disclosure of Invention
In view of the above, the invention provides an asphalt mixture performance evaluation method based on immersed hamburger rut data, which solves the problem that rut resistance and water damage resistance are not distinguished and mixed in the prior art.
The technical purpose of the invention is realized as follows:
the invention provides an asphalt mixture performance evaluation method based on immersed hamburger rut data, which comprises the following steps:
s1, testing asphalt mixture through a immersed hamburg rutting test to obtain rutting depth H and loading times n, and obtaining a relation curve of an actual strain level and loading times;
S2, fitting the relation curve according to a first fitting function to obtain a fitting curve, and displaying a corresponding fitting strain level according to increment of the loading times in the fitting curve;
S3, analyzing the fitted curve, acquiring t times of loading causing errors in the times of loading n, and removing the fitted strain level corresponding to the t times of loading from the fitted curve to obtain an updated fitted curve;
S4, normalizing the data of the fitting strain level in the updated fitting curve by using a normalization function to obtain a normalized strain level curve, and calculating to obtain a total strain level;
s5, obtaining an inflection point in the normalized strain level curve, and dividing the normalized strain level curve into a first curve segment and a second curve segment according to the inflection point;
S6, fitting the first curve segment by adopting a second fitting function to obtain a first strain level ɛ v;
S7, calculating a second strain level ɛ m according to the total strain level and the first strain level ɛ v;
s8, evaluating the first strain level ɛ v and the second strain level ɛ m to obtain a first evaluation result and a second evaluation result, wherein the first evaluation result is used for representing the rutting resistance of the asphalt mixture, and the second evaluation result is used for representing the water damage resistance of the asphalt mixture.
Based on the above technical solution, preferably, the first fitting function is:
；
Wherein ɛ (N) is a fitting strain level, N is a loading frequency, alpha, beta and N are fitting parameters, wherein alpha is used for controlling the shape of a fitting curve, beta is used for controlling the amplitude of the fitting curve, and N is a constant and used for controlling the position of the fitting curve.
Based on the above technical solution, preferably, the normalization function is:
；
Where t is the t-times load times causing the error, n ' is the normalized load times, t+n ' =n, ɛ (n ') is the normalized strain level, ɛ (t) is the fitted strain level for the t-times load times.
Based on the above technical solution, preferably, the second fitting function is:
；
Wherein ɛ v is a first level of strain, a, b, and k are fitting parameters, where a is used to control the slope of the first curve segment, b is used to control the curvature of the second curve segment, and k is a constant used to control the magnitude of the first curve segment.
Based on the above technical solution, preferably, the inflection point is a point where the curvature changes from negative to positive, and the inflection point is obtained by calculating the second derivative of the normalized strain level curve and finding the position where the first reaches the zero point.
Based on the above technical solution, preferably, the calculation formula of the second strain level is:
；
Wherein ɛ m is a second strain level, N' is a normalized number of loads, α, β, N, a, b, and k are fitting parameters, where α is used to control the shape of the fitted curve, β is used to control the magnitude of the fitted curve, N is a constant, used to control the position of the fitted curve, a is used to control the slope of the first curve segment, b is used to control the curvature of the second curve segment, and k is a constant, used to control the magnitude of the first curve segment.
Based on the above technical solution, preferably, step S8 includes:
obtaining the value of the fitting parameter b when the first strain level is obtained through calculation, evaluating the first strain level ɛ v by using the fitting parameter b, and determining a first evaluation result according to the value of the fitting parameter b;
Evaluating a second strain level ɛ m by using the water damage evaluation factor, constructing a calculation formula of the water damage evaluation factor according to the first strain level, the second strain level and the fitting parameters a, b and k, and determining a second evaluation result according to the value of the water damage evaluation factor;
Quantifying the rut resistance of the asphalt mixture according to the first evaluation result to obtain a rut resistance value; and quantifying the water damage resistance of the asphalt mixture according to the second evaluation result to obtain a water damage resistance value.
On the basis of the technical scheme, preferably, the calculation formula of the water damage evaluation factor is as follows:
；
Wherein T is a water damage evaluation factor, ɛ m is a second strain level, n' is a normalized loading number, a, b and k are fitting parameters, wherein a is used for controlling the slope of the first curve segment, b is used for controlling the curvature of the second curve segment, and k is a constant used for controlling the amplitude of the first curve segment.
Based on the above technical solution, preferably, step S1 includes:
placing an asphalt mixture sample in a hamburg rutting tester, loading the test sample under a soaking condition, and recording rutting depths H under different loading times;
In the test process, measuring each time of loading, recording data points of the rut depth H and the loading times n, and establishing a data set of the rut depth H and the loading times n;
And according to the data set of the measured rut depth H and the loading times n, calculating to obtain the relation between the actual strain level and the loading times, and drawing to obtain a relation curve.
Based on the above technical solution, preferably, step S3 includes:
Taking the loading times n as n data points, and calculating residual error of each data point, wherein the residual error is the difference value between the actual strain level corresponding to the data point and the fitting strain level;
sequencing n data points according to the sequence from the largest residual error to the smallest residual error to obtain a first sequence;
determining a t value according to an error calculation formula, taking the first t data points in the first sequence as t loading times causing errors, and removing a fitting strain level corresponding to the t loading times from a fitting curve to obtain an updated fitting curve;
the error calculation formula is as follows:
；
Where E is the residual, max (E) is the maximum value of the residual in the first sequence, δ is the standard deviation of the residual, and μ is the error coefficient.
Compared with the prior art, the method has the following beneficial effects:
(1) According to the invention, the anti-rutting performance and the water damage resistance of the asphalt mixture are comprehensively evaluated by a method combining a hamburg rutting test and mathematical modeling. Through the steps of curve fitting, normalization processing, sectional analysis and the like, error factors can be effectively eliminated, and the accuracy and precision of data analysis are improved. The obtained evaluation result can effectively guide the selection and design of asphalt mixture in engineering practice, and improve the durability and performance stability of the pavement;
(2) The first fitting function provided by the invention can accurately fit the hamburg rutting test under the soaking condition to obtain a relation curve of the strain level and the loading times;
(3) According to the invention, the influence on the immersed hamburg rutting test result after the void and compaction of the sample is effectively eliminated by removing the fitting strain level of t loads causing errors;
(4) According to the method, the deviation situation between the fitting curve and the actual data is more intuitively known by calculating the residual error of each data point. Reducing the influence of abnormal data points on the fitting result according to an error correction method, and improving the accuracy and stability of the fitting curve;
(5) According to the invention, the first strain level is evaluated according to the fitting parameter b, the second strain level is evaluated by utilizing the water damage evaluation factor, the water damage resistance of the asphalt mixture can be quantitatively evaluated by a quantitative evaluation method, and the objectivity and comparability of an evaluation result are improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of normalization processing according to an embodiment of the present invention;
FIG. 3 is a diagram showing the fitting result of a second fitting function according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a first evaluation result and a second evaluation result according to an embodiment of the present invention;
FIG. 5 is a normalized result chart of an embodiment of the present invention;
FIG. 6 is a schematic diagram of the evaluation of example 1-1 of the present invention;
FIG. 7 is a schematic diagram of the evaluation of the embodiment 1-2 of the present invention;
FIG. 8 is a schematic diagram showing the evaluation of examples 1 to 3 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will clearly and fully describe the technical aspects of the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
As shown in fig. 1, the invention provides an asphalt mixture performance evaluation method based on immersed hamburger rut data, which comprises the following steps:
s1, testing asphalt mixture through a immersed hamburg rutting test to obtain rutting depth H and loading times n, and obtaining a relation curve of an actual strain level and loading times;
S2, fitting the relation curve according to a first fitting function to obtain a fitting curve, and displaying a corresponding fitting strain level according to increment of the loading times in the fitting curve;
S3, analyzing the fitted curve, acquiring t times of loading causing errors in the times of loading n, and removing the fitted strain level corresponding to the t times of loading from the fitted curve to obtain an updated fitted curve;
S4, normalizing the data of the fitting strain level in the updated fitting curve by using a normalization function to obtain a normalized strain level curve, and calculating to obtain a total strain level;
s5, obtaining an inflection point in the normalized strain level curve, and dividing the normalized strain level curve into a first curve segment and a second curve segment according to the inflection point;
S6, fitting the first curve segment by adopting a second fitting function to obtain a first strain level ɛ v;
S7, calculating a second strain level ɛ m according to the total strain level and the first strain level ɛ v;
s8, evaluating the first strain level ɛ v and the second strain level ɛ m to obtain a first evaluation result and a second evaluation result, wherein the first evaluation result is used for representing the rutting resistance of the asphalt mixture, and the second evaluation result is used for representing the water damage resistance of the asphalt mixture.
Specifically, in an embodiment of the present invention, step S1 includes:
placing an asphalt mixture sample in a hamburg rutting tester, loading the test sample under a soaking condition, and recording rutting depths H under different loading times;
In the test process, measuring each time of loading, recording data points of the rut depth H and the loading times n, and establishing a data set of the rut depth H and the loading times n;
And according to the data set of the measured rut depth H and the loading times n, calculating to obtain the relation between the actual strain level and the loading times, and drawing to obtain a relation curve.
First, a sample of asphalt mix to be evaluated is prepared and placed in a hamburg rutting test apparatus. Ensuring that the test equipment can simulate the rutting effect and record the rutting depth H under different loading times. During the test, each loading is measured and the data points of the rut depth H and the number of loading n are recorded. According to the data set of the measured rut depth H and the loading times n, data analysis and processing can be performed by utilizing data processing software. From the measurement of the rut depth H, the actual strain level can be calculated. The actual strain level can be calculated from the relationship between rut depth and specimen thickness. And fitting the data of the actual strain level and the loading times by utilizing a fitting function in the data processing software to obtain a fitting curve. And connecting the data points into a relation curve according to a relation equation obtained by fitting the curve, and displaying the relation between the rut depth and the actual strain level under different loading times.
Specifically, in an embodiment of the present invention, step S2 includes:
Setting a first fitting function, wherein the expression is as follows:
the first fitting function is:
；
Wherein ɛ (N) is a fitting strain level, N is a loading frequency, alpha, beta and N are fitting parameters, wherein alpha is used for controlling the shape of a fitting curve, beta is used for controlling the amplitude of the fitting curve, and N is a constant and used for controlling the position of the fitting curve.
Substituting the loading times into the first fitting function, and calculating to obtain the fitting strain level corresponding to each loading time.
In this embodiment, the first fitting function describes the relationship between the actual strain level and the number of loads. The parameters alpha, beta and N in the formula control the shape, amplitude and position of the fitting curve respectively.
Alpha parameter: for controlling the shape of the fitted curve. When α takes different values, the curvature and shape of the fitted curve will be affected. A larger alpha value will cause the curve to drop rapidly with a smaller number of loads, while a smaller alpha value will cause the curve to drop slowly with a smaller number of loads.
Beta parameters: for controlling the amplitude of the fitted curve. The magnitude of β determines the height of the fitted curve, i.e. the maximum of the actual strain level. A larger beta value will result in an increase in the magnitude of the fitted curve, while a smaller beta value will result in a decrease in the magnitude of the fitted curve.
N parameter: for controlling the position of the fitted curve. The value of N affects the position of the fitted curve on the load minor axis, i.e. where to start or end. A larger value of N will move the fitted curve to the right, while a smaller value of N will move the fitted curve to the left.
By controlling the parameters alpha, beta and N, the shape, amplitude and position of the fitting curve can be flexibly controlled, so that the relation between the actual strain level and the loading times can be better described.
The strain level in this example refers to the deformation of the asphalt mixture test piece at a thickness of 61 mm. The relation curve of the strain level and the loading times can be obtained by accurately fitting the hamburg rutting test under the water immersion condition through the first fitting function.
Specifically, in an embodiment of the present invention, step S3 includes:
Taking the loading times n as n data points, and calculating the residual error of each data point, wherein the residual error is the difference between the actual strain level corresponding to the data point and the fitting strain level.
The n data points are ordered in order of the residuals from big to small, resulting in a first sequence.
Determining a t value according to an error calculation formula, taking the first t data points in the first sequence as t loading times causing errors, and removing a fitting strain level corresponding to the t loading times from a fitting curve to obtain an updated fitting curve;
the error calculation formula is as follows:
；
Where E is the residual, max (E) is the maximum value of the residual in the first sequence, δ is the standard deviation of the residual, and μ is the error coefficient.
The error calculation formula in this embodiment is used to determine the loading times causing errors, and correct the fitting curve according to the error coefficient, so as to improve the accuracy and reliability of the fitting. When the maximum value of the residual error E is larger, the t value is increased, so that the number of loading times required to be corrected is larger, and the fitted curve can be more accurately adapted to the fluctuation of actual data. When the standard deviation delta of the residual error is smaller, the t value is increased, so that the sensitivity of the fitting curve to the residual error is higher, and the change of actual data can be reflected better. The error coefficient mu is used for controlling the error correction force, and when the error coefficient is larger, the t value is reduced, so that the correction force on the residual error is smaller; conversely, when the error coefficient is smaller, the value of t is increased, which means that the correction force on the residual error is larger.
In particular, the error coefficient μ is typically determined empirically or experimentally, with the particular value being dependent on the particular data and experimental conditions. The selection of the error coefficient needs to comprehensively consider factors such as the fluctuation of actual data, the noise level, the sensitivity of a fitting curve and the like so as to achieve better fitting effect and accuracy. In this embodiment, the error coefficient is set to 1.5 times the residual.
Specifically, in an embodiment of the present invention, step S4 includes:
And processing the fitting strain level data in the updated fitting curve through a normalization function, wherein the normalization function is as follows:
；
Where t is the t-times load times causing the error, n ' is the normalized load times, t+n ' =n, ɛ (n ') is the normalized strain level, ɛ (t) is the fitted strain level for the t-times load times.
After such processing, the fitted strain level data in the updated fitted curve will be mapped into the range of [0,1 ].
After the normalized strain level curve is obtained, the total strain level is obtained by calculation on the normalized data.
In the embodiment, the data of the fitting strain level in the updated fitting curve is normalized, and the total strain level is calculated, so that the characteristics and the performance of the fitting curve are better understood and analyzed.
Specifically, in an embodiment of the present invention, step S5 includes:
calculating the second derivative of the normalized strain level curve: and obtaining the second derivative of the curve by conducting twice derivative on the normalized strain level curve. Performing a second order derivative helps to find the inflection point in the curve.
Finding the position where the second derivative reaches zero for the first time: after the second derivative is calculated, the position where the second derivative reaches zero for the first time is found. The positions corresponding to the zero points are inflection points of the curve, namely points at which the curvature changes from negative to positive.
Dividing curve segments: once the inflection point is found, the normalized strain level curve is divided into two curve segments according to the location of the inflection point. The first curve segment is a portion from the curve start point to the inflection point, and the second curve segment is a portion from the inflection point to the curve end point.
In this embodiment, the normalization processing is performed on the measured data, and the fitting of the measured data and the normalized data of the immersed hamburg rutting test are shown in fig. 2, so that the fitting strain level corresponding to the t-time loading times causing errors is eliminated, and the influence on the immersed hamburg rutting test result after the sample gap and compaction can be effectively eliminated.
Specifically, in an embodiment of the present invention, step S6 includes:
setting a second fitting function, wherein the expression is as follows:
；
Wherein ɛ v is a first level of strain, a, b, and k are fitting parameters, where a is used to control the slope of the first curve segment, b is used to control the curvature of the second curve segment, and k is a constant used to control the magnitude of the first curve segment.
In this embodiment, the first strain level refers to the deformation degree of the asphalt mixture caused under the dry condition, and the deformation degree of the material caused by viscoplasticity can be better understood by fitting the first curve segment through the second fitting function. The use of this fitting function can help analyze and predict the deformation behavior of the material.
Specifically, in the second fitting function, the change of the parameter a affects the slope of the fitted curve at the smaller n', so as to control the degree of slope change of the first curve segment. The change in parameter b affects the curvature of the fitted curve where n' is greater, thereby controlling the degree of curvature change of the second curve segment. The variation of the constant k affects the overall amplitude of the fitted curve, thereby controlling the amplitude magnitude of the first curve segment.
In this embodiment, a relationship curve of strain level and loading times of the hamburg rutting test under the dry condition is fitted by using a second fitting function, and the curve is shown in fig. 3.
Specifically, n p in fig. 3, 4, and 6-8 refers to the number of loads n' required to normalize the strain level to 0.205. According to the standard strain value of the asphalt mixture, the strain level threshold is set to be 0.205, namely, the measured loading times n 'when the normalized strain level reaches 0.205 is recorded as n p, and the n' is substituted into a formula to calculate the first strain level and the second strain level.
Specifically, in an embodiment of the present invention, step S7 includes:
The total strain level is quantified according to a first fitting function, and a second strain level is calculated by the total strain level and the first strain level, wherein the calculation formula is as follows:
；
Wherein ɛ m is a second strain level, N' is a normalized number of loads, α, β, N, a, b, and k are fitting parameters, where α is used to control the shape of the fitted curve, β is used to control the magnitude of the fitted curve, N is a constant, used to control the position of the fitted curve, a is used to control the slope of the first curve segment, b is used to control the curvature of the second curve segment, and k is a constant, used to control the magnitude of the first curve segment.
In this embodiment, the second strain level refers to the degree of deformation of the asphalt mixture based on moisture, and the embodiment can determine the deformation caused by viscoplasticity and the deformation caused by moisture by simple separation.
Specifically, in an embodiment of the present invention, step S8 includes:
obtaining the value of the fitting parameter b when the first strain level is obtained through calculation, evaluating the first strain level ɛ v by using the fitting parameter b, and determining a first evaluation result according to the value of the fitting parameter b;
Evaluating a second strain level ɛ m by using the water damage evaluation factor, constructing a calculation formula of the water damage evaluation factor according to the first strain level, the second strain level and the fitting parameters a, b and k, and determining a second evaluation result according to the value of the water damage evaluation factor;
Quantifying the rut resistance of the asphalt mixture according to the first evaluation result to obtain a rut resistance value; and quantifying the water damage resistance of the asphalt mixture according to the second evaluation result to obtain a water damage resistance value.
The calculation formula of the water damage evaluation factor is as follows:
；
Wherein T is a water damage evaluation factor, ɛ m is a second strain level, n' is a normalized loading number, a, b and k are fitting parameters, wherein a is used for controlling the slope of the first curve segment, b is used for controlling the curvature of the second curve segment, and k is a constant used for controlling the amplitude of the first curve segment.
In this example, the evaluation of rut resistance and water damage resistance of the asphalt mixture is schematically shown in fig. 4. And taking the fitting parameter b in the first fitting function as an evaluation index of the first strain level, wherein the value of the fitting parameter b can influence the curvature of the fitting curve at the larger position of n', so as to reflect the characteristics of the asphalt mixture in the region. In particular, a larger value of the fitting parameter b means that the fitting curve changes more severely in the strain range, and the curvature is larger, indicating that the viscoplastic deformation of the asphalt mixture in the strain range is more pronounced. By observing the magnitude of the fitting parameter b, the characteristics of the asphalt mixture at the first level of strain can be evaluated. A larger value of the fitting parameter b indicates that the asphalt mixture has a greater viscoplastic deformation at the first level of strain, i.e. exhibits poorer rut resistance. On the contrary, a smaller value of the fitting parameter b indicates that the asphalt mixture has smaller viscoplastic deformation at the first strain level and better rut resistance. Thus, from the value of the fitting parameter b, a first evaluation result, i.e. an evaluation of the rut resistance, can be determined.
In this embodiment, a calculation formula of the water damage evaluation factor is constructed according to the first strain level, the second strain level, and the fitting parameters a, b, and k, and the second evaluation result is determined according to the value of the water damage evaluation factor. This factor can be used to evaluate the water damage resistance of the material, with a larger value indicating a poorer water damage resistance of the material.
According to the invention, the performance of the asphalt mixture can be comprehensively and quantitatively evaluated by respectively evaluating the rutting resistance and the water damage resistance of the asphalt mixture, and the asphalt mixture is distinguished in loss, which part is caused by viscoplastic deformation and which part is caused by moisture.
Specifically, 50% of waste asphalt mixture is selected, normalization processing is carried out on measured data, the actual measured data fitting and normalization data schematic diagram of the immersed hamburg rutting test are shown in fig. 5, a normalization program is adopted to eliminate the fitting strain level of t loads causing errors, the influence of sample gaps and compaction on the immersed hamburg rutting test result is effectively eliminated, and the t value calculated by an error formula is 1000 during testing.
In addition, the method provided by the invention can also distinguish the effects of different regenerants, and can know the specific effects of different regenerants on rut resistance and water damage resistance according to the first evaluation result and the second evaluation result, and experiments are carried out aiming at the application, wherein the specific embodiments are as follows:
Example 1-1, 50% waste asphalt mix +5% regenerant a samples were selected and the strain level versus load times for the hamburg rutting test under dry conditions was calculated by fitting using the method of the present invention as shown in fig. 6.
Examples 1-2, 30% waste asphalt mix +3% regenerant a samples were selected and the strain level versus load times for the hamburg rutting test under dry conditions was calculated by fitting using the method of the present invention as shown in fig. 7.
Examples 1-3, 50% waste asphalt mix +5% regenerant B samples were selected and the strain level versus load times for the hamburg rutting test under dry conditions was calculated by fitting using the method of the present invention as shown in fig. 8.
As can be seen from a comparison of fig. 6 and 7, when the content of the waste asphalt mixture is increased, the total strain level remains unchanged, that is, the repairing effect of the regenerant a on the old asphalt pavement is limited, and as the content of the waste asphalt mixture is increased, the second strain level is decreased, which means that the resistance effect of the regenerant a on the damage caused by moisture is slightly stronger than the resistance effect on the damage caused by viscoplasticity.
As can be seen from a comparison of fig. 6 and 8, when the amount of the used asphalt is the same, the total strain level of the regenerant a and the regenerant B is maintained, but the regenerant B has a remarkable resistance effect against the damage caused by viscoplasticity.
While embodiments of the present invention have been shown, those of ordinary skill in the art will appreciate that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
The foregoing embodiments are illustrative of the present invention, and the embodiments of the present invention are not limited to the embodiments, and any person having ordinary skill in the art can make partial changes or modifications by using the technical disclosure without departing from the technical features of the present invention, and still fall within the scope of the technical features of the present invention.
Claims (4)
1. The asphalt mixture performance evaluation method based on the immersed hamburger rut data is characterized by comprising the following steps of:
s1, testing asphalt mixture through a immersed hamburg rutting test to obtain rutting depth H and loading times n, and obtaining a relation curve of an actual strain level and loading times;
S2, fitting the relation curve according to a first fitting function to obtain a fitting curve, and displaying a corresponding fitting strain level according to increment of the loading times in the fitting curve;
the first fitting function is:
；
wherein ɛ (N) is a fitting strain level, N is a loading frequency, alpha, beta and N are fitting parameters, wherein alpha is used for controlling the shape of a fitting curve, beta is used for controlling the amplitude of the fitting curve, and N is a constant and used for controlling the position of the fitting curve;
S3, analyzing the fitted curve, acquiring t times of loading causing errors in the times of loading n, and removing the fitted strain level corresponding to the t times of loading from the fitted curve to obtain an updated fitted curve;
S4, normalizing the data of the fitting strain level in the updated fitting curve by using a normalization function to obtain a normalized strain level curve, and calculating to obtain a total strain level;
The normalization function is:
；
Wherein t is the t times of loading causing an error, n ' is the normalized times of loading, t+n ' =n, ɛ (n ') is the normalized strain level, ɛ (t) is the fitted strain level of the t times of loading;
s5, obtaining an inflection point in the normalized strain level curve, and dividing the normalized strain level curve into a first curve segment and a second curve segment according to the inflection point;
S6, fitting the first curve segment by adopting a second fitting function to obtain a first strain level ɛ v;
The second fitting function is:
；
Wherein ɛ v is a first strain level, a, b and k are fitting parameters, wherein a is used for controlling the slope of the first curve segment, b is used for controlling the curvature of the second curve segment, and k is a constant used for controlling the amplitude of the first curve segment;
S7, calculating a second strain level ɛ m according to the total strain level and the first strain level ɛ v;
The second strain level is calculated as:
；
Wherein ɛ m is a second strain level, N' is a normalized loading number, α, β, N, a, b and k are fitting parameters, where α is used to control the shape of the fitting curve, β is used to control the magnitude of the fitting curve, N is a constant, used to control the position of the fitting curve, a is used to control the slope of the first curve segment, b is used to control the curvature of the second curve segment, and k is a constant, used to control the magnitude of the first curve segment;
S8, evaluating the first strain level ɛ v and the second strain level ɛ m to obtain a first evaluation result and a second evaluation result, wherein the first evaluation result is used for representing the rut resistance of the asphalt mixture, and the second evaluation result is used for representing the water damage resistance of the asphalt mixture;
step S8 includes:
obtaining the value of the fitting parameter b when the first strain level is obtained through calculation, evaluating the first strain level ɛ v by using the fitting parameter b, and determining a first evaluation result according to the value of the fitting parameter b;
Evaluating a second strain level ɛ m by using the water damage evaluation factor, constructing a calculation formula of the water damage evaluation factor according to the first strain level, the second strain level and the fitting parameters a, b and k, and determining a second evaluation result according to the value of the water damage evaluation factor;
quantifying the rut resistance of the asphalt mixture according to the first evaluation result to obtain a rut resistance value; quantifying the water damage resistance of the asphalt mixture according to the second evaluation result to obtain a water damage resistance value;
the calculation formula of the water damage evaluation factor is as follows:
；
Wherein T is a water damage evaluation factor, ɛ m is a second strain level, n' is a normalized loading number, a, b and k are fitting parameters, wherein a is used for controlling the slope of the first curve segment, b is used for controlling the curvature of the second curve segment, and k is a constant used for controlling the amplitude of the first curve segment.
2. The asphalt mixture performance evaluation method based on immersed hamburger rut data according to claim 1, wherein the inflection point is a point where curvature changes from negative to positive, and the inflection point is obtained by calculating a second derivative of a normalized strain level curve and finding a position where the first reaches a zero point.
3. The asphalt mixture performance evaluation method based on immersed hamburger rut data according to claim 1, wherein step S1 comprises:
placing an asphalt mixture sample in a hamburg rutting tester, loading the test sample under a soaking condition, and recording rutting depths H under different loading times;
In the test process, measuring each time of loading, recording data points of the rut depth H and the loading times n, and establishing a data set of the rut depth H and the loading times n;
And according to the data set of the measured rut depth H and the loading times n, calculating to obtain the relation between the actual strain level and the loading times, and drawing to obtain a relation curve.
4. The asphalt mixture performance evaluation method based on immersed hamburger rut data according to claim 1, wherein step S3 comprises:
Taking the loading times n as n data points, and calculating residual error of each data point, wherein the residual error is the difference value between the actual strain level corresponding to the data point and the fitting strain level;
sequencing n data points according to the sequence from the largest residual error to the smallest residual error to obtain a first sequence;
determining a t value according to an error calculation formula, taking the first t data points in the first sequence as t loading times causing errors, and removing a fitting strain level corresponding to the t loading times from a fitting curve to obtain an updated fitting curve;
the error calculation formula is as follows:
；
Where E is the residual, max (E) is the maximum value of the residual in the first sequence, δ is the standard deviation of the residual, and μ is the error coefficient.
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